N95 respirators became scarce to the general public in mid-to-late March of 2020 due to the SARS-CoV-2 epidemic. By mid-April of 2020, most states in the United States were requiring face coverings to be worn while in public enclosed places and in busy outdoor areas where groups of people were in close proximity. Many resorted to cloth masks, homemade masks, procedure masks obtained through online purchases, and other ad-hoc means. Thus, there was and still is a need to determine the aerosol filtration efficacy of commonly available materials that can be used for homemade mask construction. This study focused on non- woven polymeric fabrics that are readily available for homemade mask construction. The conclusion of this study is that non-woven materials that carry a high electric charge or those that can easily acquire charge had the highest aerosol filtration efficiency per unit of pressure drop. Future work should examine a wider variety of these materials and determine the maximum pressure drop that a nominal homemade mask can withstand before a significant portion of airflow is diverted around the mask. More broadly, a better understanding of the charge state on non-woven materials and impact of that charge state on filtration efficiency is needed.
Many languages such as Lua, Haskell and MATLAB support the ability to create nested comments. My proposal is to add a nested comment pragma to BNFC which would allow it to support the parsing of code. In this proposal I will provide sample implementations of nested comments for each of the lexer generators used by BNFC and outline the extension points by which I will add the functionality to the codebase.
Thermo-electric coolers (TECs) provide an essential function in many high-powered applications by diverting heat away from a temperature-sensitive load. This summer I have worked with Sandia National Laboratories in exploring the failure mechanisms behind said devices. I have been tasked with determining a plan of action for two TECs manufactured by two different companies labeled A and B respectively. Prior to my involvement in the project, the former has displayed failures during normal operation within its packaging. The latter was subsequently chosen to resolve these issues. Thermal cycling between the extreme expected operating temperatures (-40°C to 80°C) was applied to 5 unmounted TECAs over a period of 5 hours with 1-hour soaks at each extreme. The unmounted TECBs are currently undergoing the same process, and the task is expected to be completed over the next few weeks. The results of the TECA characterization have indicated no failure has occurred, which indicates that failure will need to be induced through either higher temperature extremes or additional mechanical stress.
This report summarizes the results of a literature survey on coatings and surface treatments that are used to provide corrosion protection for exposed metal surfaces. The coatings are discussed in the context of being used on stainless steel spent nuclear fuel (SNF) dry storage canisters for potential prevention or repair of corrosion and stress corrosion cracking. The report summarizes the properties of different coating classes, including the mechanisms of protection, their physical properties, and modes of degradation (thermal, chemical, radiological). Also discussed are the current standard technologies for application of the coatings, including necessary surface pretreatments (degreasing, rust removal, grinding) and their effects on coating adhesion and performance. The coatings are also classified according their possible use for in situ repair; ex situ repair, requiring removal from the overpack; and ex situ prevention, or application prior to fuel loading to provide corrosion protection over the lifetime of the canister.
The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA's enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.
Dislocations play a vital role in the mechanical behavior of crystalline materials during deformation. To capture dislocation phenomena across all relevant scales, a multiscale modeling framework of plasticity has emerged, with the goal of reaching a quantitative understanding of microstructure-property relations, for instance, to predict the strength and toughness of metals and alloys for engineering applications. This review describes the state of the art of the major dislocation modeling techniques, and then discusses how recent progress can be leveraged to advance the frontiers in simulations of dislocations. The frontiers of dislocation modeling include opportunities to establish quantitative connections between the scales, validate models against experiments, and use data science methods (e.g., machine learning) to gain an understanding of and enhance the current predictive capabilities.
The high-pressure response of titanium dioxide (TiO2) is of interest because of its numerous industrial applications and its structural similarities to silica (SiO2). We used three platforms - Sandia's Z machine, Omega Laser Facility, and density-functional theory-based quantum molecular dynamics (QMD) simulations - to study the equation of state (EOS) of TiO2 at extreme conditions. We used magnetically accelerated flyer plates at Sandia to measure Hugoniot of TiO2 up to pressures of 855 GPa. We used a laser-driven shock wave at Omega to measure the shock temperature in TiO2. Our Z data show that rutile TiO2 reaches 2.2-fold compression at a pressure of 855 GPa and Omega data show that TiO2 is a reflecting liquid above 230 GPa. The QMD simulations are in excellent agreement with the experimental Hugoniot in both pressure and temperature. A melt curve for TiO2 is also proposed based on the QMD simulations. The combined experimental results show TiO2 is in a liquid at these explored pressure ranges and is not highly incompressible as suggested by a previous study.
By using a transformer with multiple secondary coils, a single unipolar power source can drive multiple loads that have different steady-state operating-voltage requirements. We show that, during initial turn-on, a transient voltage that is opposite in sign to the operating voltage can be induced in one or more of the secondary coils. This is because the surging currents in the coils during turn-on produce a strong inductive interaction between all the coils. In a particular secondary coil, the voltage induced by a neighboring secondary can be larger, and opposite in sign to, the voltage directly induced by the primary coil. The effect is transient because, when the secondary circuits reach their steady-state operating currents, they no longer couple inductively to each other. We also show that, during the turn-on period, the voltage induced in a secondary coil can be significantly larger than its steady-state voltage. These transient effects are controlled by the values of the "coil-coupling" parameters, which are functions of the transformer geometry and of the magnetic permeability and electrical resistivity of the materials used. The results are derived from the circuit equations, and verified using PSpice simulations.
The continued operation of Land Ports of Entry (LPOE), managed by the Customs and Border Protection (CBP) and General Services Administration% is vital to the U.S. economy and security. Border faculties are included in the Department of Homeland Security (DHS) Government Facilities Sector2, one of the 16 critical infrastructures "whose assets, systems, and networks, whether physical or virtual, are considered so vital to the United States that their incapacitation or destruction would have a debilitating effect on security, national economic security, national public health or safety, or any combination thereof.'" Specifically, disruptions to the flow of border crossing traffic, in the form of closures or increased border crossing wait times, impact the economy and security of all countries involved. This paper describes a process for analyzing and improving the resilience of U.S. Land Ports of Entry. For LPOE, the team believes that energy resilience is the primary objective due to the complete reliance on the e-manifest system and the increasing use of Multi-Energy Portals (MEPs). Emanifests are part of CPB's Automated Commercial Environment (ACE). They document several key pieces of information about cargo vehicles wishing to cross the border into the United States and are submitted before arriving at the port. Vehicles can be flagged for more invasive inspection based on the content of the e-manifest. MEPs are a non-intrusive inspection (NII) technology used to scan the contents of the cargo. Together MEPs and ACE serve an important role in aiding CBP with their mission to protect "the public from dangerous people and materials", and "enabling legitimate trade and travel.'" To analyze resilience of a port, the team would need to understand the port's current energy usage, which systems depend on energy and what backup systems exist, and any emergency operation plans that dictate how systems are operated in the event of a power outage. The team would also need to determine the design basis threats (DBTs) for the LPOE which could include natural disasters, manmade events, and accidents. The magnitudes of the DBTs are calculated and are then translated to expected impacts on the infrastructure and systems at the port. With this information gathered, existing LPOE models developed here at Sandia National Laboratories could be extended to support decisions about resilience. Current models are implemented in FlexSim, a 3rd party discrete event simulator. FlexSim provides 3-D visuals of physical layout that can reveal valuable insights, allows input to be variable (e.g. time it takes to interact with the CBP officer at primary inspection can vary) so that a whole range of possibilities can be captured in the results, and can be used to collect user-defined output metrics. Current LPOE models focus on cargo vehicle traffic, and process changes caused by the installation of new drive-through MEPs. Extending them to address resilience questions would require the addition of key pieces of information learned during the resilience analysis including critical systems, failure rates, and process changes for when failures occur. The primary output metric for current models is border crossing wait time. Additional metrics would also be added to the model to gain a more complete understanding of impacts related to resilience, for example, MEP scan rate. Once complete, the model could be used to analyze the effectiveness of mitigation strategies representing some future state.
The interaction of energetic ions with the electronic and ionic system of target materials is an interesting but challenging multiscale problem, and understanding of the early stages after impact of heavy, initially charged ions is particularly poor. At the same time, energy deposition during these early stages determines later formation of damage cascades. We address the multiscale character by combining real-time time-dependent density functional theory for electron dynamics with molecular dynamics simulations of damage cascades. Our first-principles simulations prove that core electrons affect electronic stopping and have an unexpected influence on the charge state of the projectile. We show that this effect is absent for light projectiles, but dominates the stopping physics for heavy projectiles. By parametrizing an inelastic energy loss friction term in the molecular dynamics simulations using our first-principles results, we also show a qualitative influence of electronic stopping physics on radiation-damage cascades.
The Federal Radiological Monitoring and Assessment Center (FRMAC) Assessment Manual is the tool used to organize and guide activities of the FRMAC Assessment Division. The mission of the FRMAC Assessment Division in a radiological emergency is to interpret radiological data and predict worker and public doses. This information is used by Decision Makers to recommend protective actions in accordance with Protection Action Guides (PAGs) issued by government agencies. This manual integrates many health physics tools and techniques used to make these assessments.
This assessment reviewed the Center 600 assessment process; gathered knowledge from 17 assessment points of contact across the Center; piloted an annual assessment planning process; and compared Center 600 Administrative Operating Procedure (AOP) 04-04, Assessments, to current practices and corporate requirements. The assessment identified two observations, three noteworthy practices, and multiple opportunities for improvement beyond the scope of the assessment.
Department 00635, Performance Assurance, assessments reveal risks and opportunities for improvement Labs-wide. This assessment was conducted to identify opportunities for improving the Department 00635 Assessment Program. This evaluation was conducted to identify opportunities for improving the Center 00600 assessment process by reviewing the quality of assessments completed by Environment Safety and Health (ES&H) personnel in fiscal year (FY) 2018. Approximately 20 percent of the Center 00600 assessments completed in FY18 were reviewed. One assessment was selected from each Center 00600 department contingent on availability in the Assurance Information System (AIS).
In order to support the machine learning co-design needs of ECP applications in current and future DOE HPC hardware, we have developed a generative adversarial network (GAN) proxy application, miniGAN, that has been released through the ECP proxy application suite. The proxy application is representative of the needs of ExaLearn's target applications, specifically the Cosmoflow and ExaGAN cosmology applications and the ExaWind energy application. The proxy application also demonstrates the first use of performance portable kernels within widely-used machine learning frameworks: PyTorch (Facebook) and Horovod (Uber). We provide performance scaling results for similar workloads to ExaGAN and a profile of individual GAN training components.
Advances in sensor technology have increased our ability to monitor a wide range of environments. However, even as the cost of sensors decline, only a limited number of sensors can be installed at any given site. The physical placement of sensors, along with the sensor technology and operating conditions, can have a large impact on our ability to adequately monitor environmental change. This paper introduces a new open-source Python package, called Chama, that determines optimal sensor placement and technology to improve a sensor network's detection capabilities. The methods are demonstrated using site-specific methane emission scenarios that capture uncertainty in wind conditions and emission characteristics. Mixed-integer linear programming formulations are used to determine sensor locations and detection thresholds that maximize detection of the emission scenarios. The optimized sensor networks consistently increase the ability to detect leaks, as compared to sensors placed near each potential emission source or along the perimeter of the site.
In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.
This report updates the estimated values of the baseline available drawdowns for the caverns at the Big Hill storage facility, and an updated table listing the available drawdowns. An updated finite element numerical analysis model, which included a fault in the caprock layers, was constructed and the daily data of actual wellhead pressures and oil-brine interfaces was used. The number of available drawdowns for each of the Big Hill SPR caverns is estimated using the new model. All caverns are predicted to have five available drawdowns remaining from a geomechanical perspective. BH-101 and 105 have a region of concern at the floor edge and/or sloping floor, where tensile and dilatant stresses are predicted to occur during each workover. The tensile state is predicted to occur because of the geometries of the edge and floor. Therefore, geomechanical examination for two caverns would be recommended after a drawdown leach. The well integrity of each cavern is not investigated in this report. The estimates for the number of baseline available drawdowns are subject to change in the future as the knowledge of physical phenomena at the sites, and the further development of the models of geomechanical behavior at the sites, evolve over time.
Smallwood, Chuck R.; Boiteau, Rene M.; Kukkadapu, Ravi; Cliff, John B.; Kovarik, Libor; Wirth, Mark G.; Engelhard, Mark H.; Varga, Tamas; Perea, Daniel E.; Wietsma, Thomas; Moran, James J.; Hofmockel, Kirsten S.
Although most studies of organic matter (OM) stabilization in soils have focused on adsorption to aluminosilicate and iron-oxide minerals due to their strong interactions with organic nucleophiles, stabilization within alkaline soils has been empirically correlated with exchangeable Ca. Yet the extent of competing processes within natural soils remains unclear because of inadequate characterization of soil mineralogy and OM distribution within the soil in relation to minerals, particularly in C poor alkaline soils. In this study, we employed bulk and surface-sensitive spectroscopic methods including X-ray diffraction, 57Fe-Mössbauer, and X-ray photoemission spectroscopy (XPS), and transmission electron microscopy (TEM) methods to investigate the minerology and soil organic C and N distribution on individual fine particles within an alkaline soil. Microscopy and XPS analyses demonstrated preferential sorption of Ca-containing OM onto surfaces of Fe-oxides and calcite. This result was unexpected given that the bulk combined amounts of quartz and Fe-containing feldspars of the soil constitute ~90% of total minerals and the surface atomic composition was largely Fe and Al (>10% combined) compared to Ca (4.2%). Soil sorption experiments were conducted with two siderophores, pyoverdine and enterobactin, to evaluate the adsorption of organic molecules with functional groups that strongly and preferentially bind Fe. A greater fraction of pyoverdine was adsorbed compared to enterobactin, which is smaller, less polar, and has a lower aqueous solubility. Using NanoSIMS to map the distribution of isotopically-labeled siderophores, we observed correlations with Ca and Fe, along with strong isotopic dilution with native C, indicating associations with OM coatings rather than with bare mineral surfaces. We propose a mechanism of adsorption by which organics aggregate within alkaline soils via cation bridging, favoring the stabilization of larger molecules with a greater number of nucleophilic functional groups.
Germanium antimony telluride has been the most used and studied phase-change material for electronic memory due to its suitable crystallization temperature, amorphous to crystalline resistance contrast, and stability of the amorphous phase. In this paper, the segregation of Ge in a Ge2Sb2Te5 film of 30 nm thickness during heating inside the transmission electron microscope was observed and characterized. Furthermore, Ge2Sb2Te5 film was deposited using sputtering on a Protochips Fusion holder and left uncapped in atmosphere for about four months. Oxygen incorporated within the film played a significant role in the chemical segregation observed which resulted in amorphous Ge-O island boundaries and Sb and Te rich crystalline domains. Such composition changes can occur when the phase-change material interfaces insulating oxide layers in an integrated device and can significantly impact its electrical and thermal properties.
The ability to accurately locate seismic events is necessary for treaty monitoring. When using techniques that rely on the comparison of observed and predicted travel times to obtain these locations, it is important that the estimated travel times and their estimated uncertainties are also accurate. The methodology of Ballard et al. (2016a) has been used in the past to generate an accurate 3D tomographic global model of compressional wave slowness (the SAndia LoS Alamos 3D tomography model, i.e. SALSA3D). To re-establish functionality and to broaden the capabilities of the method to local distances, we have applied the methodology of Ballard et al. (2016a) to local data in Utah. This report details the results of the initial model generated, including relocations performed using analyst picked mining events at West Ridge Mine and three ground-truth events at Bingham Mine. We were successfully able to generate a feasible tomography model that resulted in reasonable relocations of the mining events.
Sandia National Laboratories is a Federally Funded Research and Development Center (FFRDC) founded in 1949 with the mission of developing and testing the non-nuclear components of nuclear weapons. Sandia has since been involved with numerous projects to support the Department of Defense’s (DOE) National Nuclear Security Administration (NNSA). One such set of projects has been implementing over-the-road transportation security enhancements. Under this program, Sandia National Laboratories (SNL) has worked to develop interface compatibility modifications for existing shipping configurations. This summer I will be working with a line of trailers that have been used to transport high asset cargo. These vehicles have successfully traveled millions of miles without any accidents over the course of 15 years. The primary motivation for the work that I will complete this summer is to provide support for existing electronic communication technologies implemented at SNL. As part of an ongoing project to implement modifications to a trailer system, I will focus primarily on the characterization and testing of thermal electric coolers (TEC). Within the scope of the trailer project, these devices provide temperature control for lasers used on optical communication boards (OCB).
Kaehr, Bryan J.; Chou, Stanley S.; Giri, Ashutosh; Drury, Daniel E.; Tomko, Kathleen Q.; Olson, David; Gaskins, John T.; Hopkins, Patrick E.
Herein we report on the thermal conductivities of alkyl- and indene-group functionalized fullerene derivative thin films as measured via time domain thermoreflectance and steady state thermoreflectance. The thermal conductivities vary from W for [6,6]-phenyl -butyric acid methyl ester (PCBM) to W for indene- bisadduct at room temperature and do not exhibit significant temperature dependence from 300 to 375 K. In comparison to the thermal conductivity of PCBM, increasing the length of the alkyl chain, as in the case of [6,6]-phenyl -butyric acid butyl ester, and [6,6]-phenyl -butyric acid octyl ester leads to higher thermal conductivities. Likewise, increasing the number of alkyl chains attached to the fullerenes as in the case of bisadduct PCBM leads to a higher thermal conductivity compared to that of PCBM. We present atomistic insights into the role of chemical functionalization on the overall heat transfer in these fullerene derivatives by conducting molecular dynamics simulations and lattice dynamics calculations. The thermal conductivities predicted via our atomistic simulations qualitatively agree with the experimental trends for our fullerene derivatives. Lattice dynamics calculations reveal that one of the main factors dictating the ultralow thermal conductivities in fullerene derivatives is the large reduction in modal diffusivities in the molecular crystals as calculated from the Allen-Feldman model, thus providing an explanation for their largely reduced thermal conductivities as compared to that of crystals. The low diffusivities result from high degrees of localization of Einstein-like vibrations in fullerene derivatives due to the molecular side chains, providing the ability to dial-in the properties of these low thermal conductivity solids via molecular engineering.
Here, for the first time, we demonstrate the use of an in situ spectroelectrochemical Raman technique to explore simulated atmospheric corrosion scenarios with a variable boundary layer thickness (δ). The effects of solution flow rate on oxygen concentration and δ were explored. It was found solution regeneration is necessary to prevent oxygen depletion in the Raman cell. It was further shown that by increasing the solution flow rate, the effective δ decreases and allows for the investigation of atmospheric corrosion scenarios. Finally, the technique developed was utilized to explore the effect of precipitation on the cathodic behavior of SS304L in dilute MgCl2. During cathodic polarization, evidence supports previous observations that magnesium hydroxide species are kinetically favored over the thermodynamically predicted magnesium carbonate.
This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.
Gonzalez, Rafael I.; Rojas-Nunez, Javier; Valencia, Felipe J.; Munoz, Francisco; Baltazar, Samuel E.; Allende, Sebastian; Rogan, Jose; Valdivia, Juan A.; Kiwi, Miguel; Ramírez, Ricardo; Greathouse, Jeffery A.
Imogolite is a fascinating inorganic nanotube that is found in nature or synthesized in a laboratory. The synthesis process is carried out in liquid media, and leads to the formation of almost monodisperse diameter nanotubes. Here we investigate, employing classical molecular dynamics simulations, the interaction of water and imogolite for nanotubes of several radii. We established that water penetrates the pores of N = 9 and larger nanotubes, and adopts a coaxial arrangement in it. Also, while water molecules can diffuse along the center of the nanotube, the molecules next to the inner imogolite walls have very low mobility. At the outer nanotube wall, an increase of water density is observed, this effect extends up to 1 nm, beyond which water properties are bulk-like. Both phenomena are affected by the imogolite curvature.
Computer Methods in Applied Mechanics and Engineering
Fleeter, Casey M.; Geraci, Gianluca G.; Schiavazzi, Daniele E.; Kahn, Andrew M.; Marsden, Alison L.
Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.
Theocharides, Spyros; Makrides, George; Livera, Andreas; Theristis, Marios; Kaimakis, Paris; Georghiou, George E.
A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. The validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.
Multilayers composed of aluminum (Al) and platinum (Pt) exhibit a nonmonotonic trend in thermal resistance with bilayer thickness as measured by time domain thermoreflectance. The thermal resistance initially increases with reduced bilayer thickness only to reach a maximum and then decrease with further shrinking of the multilayer period. These observations are attributed to the evolving impact of an intermixed amorphous complexion approximately 10 nm in thickness, which forms at each boundary between Al- and Pt-rich layers. Scanning transmission electron microscopy combined with energy dispersive x-ray spectroscopy find that the elemental composition of the complexion varies based on bilayer periodicity as does the fraction of the multilayer composed of this interlayer. These variations in complexion mitigate boundary scattering within the multilayers as shown by electronic transport calculations employing density-functional theory and nonequilibrium Green's functions on amorphous structures obtained via finite temperature molecular dynamics. The lessening of boundary scattering reduces the total resistance to thermal transport leading to the observed nonmonotonic trend thereby highlighting the central role of complexion on thermal transport within reactive metal multilayers.
Curwen, Christopher A.; Reno, J.L.; Williams, Benjamin S.
We report a terahertz quantum-cascade vertical-external-cavity surface-emitting laser (QC-VECSEL) based upon a metasurface consisting of an array of gain-loaded resonant patch antennas. Compared with the typical ridge-based metasurfaces previously used for QC-VECSELs, the patch antenna surface can be designed with a much sparser fill factor of gain material, which allows for reduced heat dissipation and improved thermal performance. It also exhibits larger amplification thanks to enhanced interaction between the incident radiation and the QC-gain material. We demonstrate devices that produce several milliwatts of continuous-wave power in a single mode at ∼4.6 THz and dissipate less than 1 W of pump power. Use of different output couplers demonstrates the ability to optimize device performance for either high power or high operating temperature. Maximum demonstrated power is 6.7 mW at 4 K (0.67% wall-plug efficiency, WPE) and 0.8 mW at 77 K (0.06% WPE). Directive output beams are measured throughout with divergence angles of ∼5°.
An extended/generalized finite element method (XFEM/GFEM) for simulating quasistatic crack growth based on a phase-field method is presented. The method relies on approximations to solutions associated with two different scales: a global scale, that is, structural and discretized with a coarse mesh, and a local scale encapsulating the fractured region, that is, discretized with a fine mesh. A stable XFEM/GFEM is employed to embed the displacement and damage fields at the global scale. The proposed method accommodates approximation spaces that evolve between load steps, while preserving a fixed background mesh for the structural problem. In addition, a prediction-correction algorithm is employed to facilitate the dynamic evolution of the confined crack regions within a load step. Several numerical examples of benchmark problems in two- and three-dimensional quasistatic fracture are provided to demonstrate the approach.
Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.
POur experiments indicated that upon a post-processing anneal, an additively manufactured 316L stainless steel exhibits cubic grains rather than the conventional equiaxed grains. Here, we have used kinetic Monte Carlo simulations to explore the origin of these cubic grains. First, we implemented a new kinetic Monte Carlo model in parallel code SPPARKS to simulate grain growth and recrystallization under a residual energy distribution. Our model incorporates physical properties and real-time, as opposed to generic properties and relative time. We further validated that our SPPARKS simulations reproduced the expected kinetic behavior of single-grain evolution. We then used the validated approach to simulate the anneal of an additively manufactured material under the same conditions used in our experiments. We found that the cubic grains can origin from a periodically varying residual energy that may be present in additively manufactured materials.
Computer Methods in Applied Mechanics and Engineering
Parish, Eric J.; Wentland, Christopher R.; Duraisamy, Karthik
We formulate a new projection-based reduced-order modeling technique for non-linear dynamical systems. The proposed technique, which we refer to as the Adjoint Petrov–Galerkin (APG) method, is derived by decomposing the generalized coordinates of a dynamical system into a resolved coarse-scale set and an unresolved fine-scale set. A Markovian finite memory assumption within the Mori–Zwanzig formalism is then used to develop a reduced-order representation of the coarse scales. This procedure leads to a closed reduced-order model that displays commonalities with the adjoint stabilization method used in finite elements. The formulation is shown to be equivalent to a Petrov–Galerkin method with a non-linear, time-varying test basis, thus sharing some similarities with the Least-Squares Petrov–Galerkin method. Theoretical analysis examining a priori error bounds and computational cost is presented. Numerical experiments on the compressible Navier–Stokes equations demonstrate that the proposed method can lead to improvements in numerical accuracy, robustness, and computational efficiency over the Galerkin method on problems of practical interest. Improvements in numerical accuracy and computational efficiency over the Least-Squares Petrov–Galerkin method are observed in most cases.
Given an input stream of size N, a †-heavy hitter is an item that occurs at least † N times in S. The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = † N-th occurrence (and hence becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams, and with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (ω(N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable trade-off between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device's random I/O throughput, i.e., ∼100K observations per second.
Photovoltaic (PV) system certifications and codes have been modified to allow 1,500 V products onto the market which facilitate the plant engineering, procurement, and construction; however, the codes inadequately address the increased hazards to people and equipment in a high-voltage, photovoltaic plant that emanate from the rapid release of thermal energy, pressure waves, and electromagnetic interference of an arc-fault event. Existing calculations can contradict one another and are rooted in theory, not in physical testing. For this investigation, a localized arc-plasma model for a cylindrical geometry arc was developed from coupled electrodynamic, thermodynamic, and fluid mechanics equations, that were convolved together based on previous arc-discharge models [1]. The model was developed to assess incident energy, used for determining appropriate personal protective equipment (PPE), as a function of spark-gap current. To validate the model, preliminary experiments were performed at Sandia National Laboratories (SNL) with voltage levels as high as 1,500 V. Further utility-scale PV experiments were also conducted with current levels as high as 1,607 A to provide further data. Arc-stability, plasma column spectral features and radiative temperature rise were all evaluated during each respective test to provide radiated power values for validation. Overall preliminary results suggest a logarithmic increase in radiative power between 250 and 2800 W/cm for a current increase from 100 to 300 A.
Field tests of air-copper arcs were completed at a high-voltage, photovoltaic power plant using a simplified, 'arc-in-a-box' geometry to study dc arc-faults. Copper electrodes, 12.7 mm in diameter, were arranged in three configurations and an arc was initiated using < 700 VDCwith applied energy varying from 40-3900 kJ. Constitutive modeling of the arc-discharge predicts arc temperatures much greater than 1000 K. Two diagnostic techniques were fielded to characterize the spectral and thermal emission. Optical emission spectroscopy determined the time-resolved and mean arc temperatures were approximately T_{mean}= 7500 with standard deviations of ± 600 K, and infrared (IR) imaging mapped the mean temperature field, T_{mean}=1500\ \mathrm{K}, of the arc-heated environment.
Field tests of air-copper arcs were completed at a high-voltage, photovoltaic power plant using a simplified, 'arc-in-a-box' geometry to study dc arc-faults. Copper electrodes, 12.7 mm in diameter, were arranged in three configurations and an arc was initiated using < 700 VDCwith applied energy varying from 40-3900 kJ. Constitutive modeling of the arc-discharge predicts arc temperatures much greater than 1000 K. Two diagnostic techniques were fielded to characterize the spectral and thermal emission. Optical emission spectroscopy determined the time-resolved and mean arc temperatures were approximately T_{mean}= 7500 with standard deviations of ± 600 K, and infrared (IR) imaging mapped the mean temperature field, T_{mean}=1500\ \mathrm{K}, of the arc-heated environment.
It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate (R_{D}) may exhibit nonlinear behavior, which neglecting it may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear R_{D}. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the 'real' R_{D} value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.
Principal component analysis (PCA) reduces dimensionality by generating uncorrelated variables and improves the interpretability of the sample space. This analysis focused on assessing the value of PCA for improving the classification accuracy of failures within current-voltage (IV) traces. Our results show that combining PCA with random forests improves classification by only ~1% (bringing the accuracy to >99%), compared to a baseline of only random forests (without PCA) of >98%. The inclusion of PCA, however, does provide an opportunity to study an interesting representation of all of the features on a single, two-dimensional feature space. A visualization of the first two principal components (similar to IV profile but rotated) captures how the inclusion of a current differential feature causes a notable separation between failure modes due to their effect on the slope. This work continues the discussion of generating different ways of extracting information from the IV curve, which can help with failure classification - especially for failures that only exhibit marginal profile changes in IV curves.
Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.
Photovoltaic (PV) system certifications and codes have been modified to allow 1,500 V products onto the market which facilitate the plant engineering, procurement, and construction; however, the codes inadequately address the increased hazards to people and equipment in a high-voltage, photovoltaic plant that emanate from the rapid release of thermal energy, pressure waves, and electromagnetic interference of an arc-fault event. Existing calculations can contradict one another and are rooted in theory, not in physical testing. For this investigation, a localized arc-plasma model for a cylindrical geometry arc was developed from coupled electrodynamic, thermodynamic, and fluid mechanics equations, that were convolved together based on previous arc-discharge models [1]. The model was developed to assess incident energy, used for determining appropriate personal protective equipment (PPE), as a function of spark-gap current. To validate the model, preliminary experiments were performed at Sandia National Laboratories (SNL) with voltage levels as high as 1,500 V. Further utility-scale PV experiments were also conducted with current levels as high as 1,607 A to provide further data. Arc-stability, plasma column spectral features and radiative temperature rise were all evaluated during each respective test to provide radiated power values for validation. Overall preliminary results suggest a logarithmic increase in radiative power between 250 and 2800 W/cm for a current increase from 100 to 300 A.
A finite element model of a four-cell photovoltaic mini-module was developed and compared to experimental results from an accelerated stress test protocol in order to validate that computational models can accurately represent their physical counterparts when subjected to mechanical loading and to assess mini-module representativeness against full scale photovoltaic modules. Deflected shapes across the simulated mini-modules were compared to measured mini-module shapes when subjected to various pressure loads. Displaced mini-module shape results constrained to the experimental protocols of 0.4 mm and 1.1 mm of displacement at the mini-module center were compared to experimental results of full-size modules subjected to module qualification test load levels of 1.0 kPa and 2.4 kPa, to assess if the bending of mini-modules was representative of full-sized modules under the load. Temperature cycling was incorporated into the model to simulate the impacts of stress due to thermal expansion of the backsheet and cells. A preliminary uncertainty analysis was performed to show how variations in material properties and geometric parameters change the simulation results.
During the last decade, utility companies around the world have experienced a significant increase in the occurrences of either planned or unplanned blackouts, and microgrids have emerged as a viable solution to improve grid resiliency and robustness. Recently, power converters with grid-forming capabilities have attracted interest from researchers and utilities as keystone devices enabling modern microgrid architectures. Therefore, proper and thorough testing of Grid-Forming Inverters (GFMIs) is crucial to understand their dynamics and limitations before they are deployed. The use of closed-loop real-time Power Hardware-in-the-Loop (PHIL) simulations will facilitate the testing of GFMIs using a digital twin of the power system under various contingency scenarios within a controlled environment. So far, lower to medium scale commercially available GFMIs are difficult to interface into PHIL simulations because of their lack of a synchronization mechanism that allows a smooth and stable interconnection with a voltage source such as a power amplifier. Under this scenario, the use of the well-known Ideal Transformer Method to create a PHIL setup can lead to catastrophic damages of the GFMI. This paper addresses a simple but novel method to interface commercially available GFMIs into a PHIL testbed. Experimental results showed that the proposed method is stable and accurate under standalone operation with abrupt (step) load-changing dynamics, followed by the corresponding steady state behavior. Such results were validated against the dynamics of the GFMI connected to a linear load bank.
During the last decade, utility companies around the world have experienced a significant increase in the occurrences of either planned or unplanned blackouts, and microgrids have emerged as a viable solution to improve grid resiliency and robustness. Recently, power converters with grid-forming capabilities have attracted interest from researchers and utilities as keystone devices enabling modern microgrid architectures. Therefore, proper and thorough testing of Grid-Forming Inverters (GFMIs) is crucial to understand their dynamics and limitations before they are deployed. The use of closed-loop real-time Power Hardware-in-the-Loop (PHIL) simulations will facilitate the testing of GFMIs using a digital twin of the power system under various contingency scenarios within a controlled environment. So far, lower to medium scale commercially available GFMIs are difficult to interface into PHIL simulations because of their lack of a synchronization mechanism that allows a smooth and stable interconnection with a voltage source such as a power amplifier. Under this scenario, the use of the well-known Ideal Transformer Method to create a PHIL setup can lead to catastrophic damages of the GFMI. This paper addresses a simple but novel method to interface commercially available GFMIs into a PHIL testbed. Experimental results showed that the proposed method is stable and accurate under standalone operation with abrupt (step) load-changing dynamics, followed by the corresponding steady state behavior. Such results were validated against the dynamics of the GFMI connected to a linear load bank.
High-altitude electromagnetic pulses pose an unknown risk to the electric power grid, and the vulnerabilities will continue to arise as the structure and needs of the grid change. This is especially true with the increasing prevalence of renewable energy sources. This work investigates the vulnerability of photovoltaic modules to E1-like radiated environments with maximum field levels exceeding 100 kV/m. State of health checks via I-V curve trace measurements and physical inspection indicated no readily observable damage or degradation of the module behavior after multiple field exposures. Any variation in I-V curve data was attributable to ambient conditions at the time of measurement and was reflected in similar measurements of the experimental control. Follow-up measurements with a calibrated light source showed that all modules aligned with the experimental control and exceeding the manufacturer ratings for fill factor and efficiency, implying that no damage was incurred from field exposure. Coupled current measurements were also performed over the course of testing, showing a damped sine response in common mode and double exponential response in differential mode. The responses were observed to scale with incident field and were dependent on the module orientation.
High-altitude electromagnetic pulses pose an unknown risk to the electric power grid, and the vulnerabilities will continue to arise as the structure and needs of the grid change. This is especially true with the increasing prevalence of renewable energy sources. This work investigates the vulnerability of photovoltaic modules to E1-like radiated environments with maximum field levels exceeding 100 kV/m. State of health checks via I-V curve trace measurements and physical inspection indicated no readily observable damage or degradation of the module behavior after multiple field exposures. Any variation in I-V curve data was attributable to ambient conditions at the time of measurement and was reflected in similar measurements of the experimental control. Follow-up measurements with a calibrated light source showed that all modules aligned with the experimental control and exceeding the manufacturer ratings for fill factor and efficiency, implying that no damage was incurred from field exposure. Coupled current measurements were also performed over the course of testing, showing a damped sine response in common mode and double exponential response in differential mode. The responses were observed to scale with incident field and were dependent on the module orientation.
Modeling and predicting snow-related power loss is important to economic calculations, load management and system optimization for all scales of photovoltaic (PV) power plants. This paper describes a new method for measuring snow shedding from fielded modules and also describes the application of this method to a commercial scale PV power plant in Vermont with two subsystems, one with modules in portrait orientation and the other in landscape. The method relies on time-series images taken at 5 minute intervals to capture the dynamics of module-level snow accumulation and shedding. Module-level images extracted from the full-field view are binarized into snow and clear areas, allowing for the quantification of percentage snow coverage, estimation of resulting module power output, and temporal changes in snow coverage. Preliminary data from the Vermont case study suggests that framed modules in portrait orientation outperform their framed counterparts in landscape orientation by as much as 24% energy yield during a single shedding event. While these data reflect a single event, and do not capture snow shedding behavior across diverse temperature and other climatic conditions, the study nonetheless demonstrates that 1) module orientation and position in the array influence shedding patterns; 2) the start of power production and bypass diode activation differ for portrait and landscape module orientations at similar percentages and orientations of snow coverage; and 3) system design is an important factor in snow mitigation and increased system efficiency in snowy climates.
Modeling and predicting snow-related power loss is important to economic calculations, load management and system optimization for all scales of photovoltaic (PV) power plants. This paper describes a new method for measuring snow shedding from fielded modules and also describes the application of this method to a commercial scale PV power plant in Vermont with two subsystems, one with modules in portrait orientation and the other in landscape. The method relies on time-series images taken at 5 minute intervals to capture the dynamics of module-level snow accumulation and shedding. Module-level images extracted from the full-field view are binarized into snow and clear areas, allowing for the quantification of percentage snow coverage, estimation of resulting module power output, and temporal changes in snow coverage. Preliminary data from the Vermont case study suggests that framed modules in portrait orientation outperform their framed counterparts in landscape orientation by as much as 24% energy yield during a single shedding event. While these data reflect a single event, and do not capture snow shedding behavior across diverse temperature and other climatic conditions, the study nonetheless demonstrates that 1) module orientation and position in the array influence shedding patterns; 2) the start of power production and bypass diode activation differ for portrait and landscape module orientations at similar percentages and orientations of snow coverage; and 3) system design is an important factor in snow mitigation and increased system efficiency in snowy climates.
This paper presents the methodology and preliminary results from a global study on solar over-irradiance events, which are more frequent than previously believed and can negatively impact utility-scale PV operations. Data from five test sites in Florianópolis and Brotas de Macaúbas in Brazil, Bernburg in Germany, Albuquerque, in the USA and Loughborough, in the United Kingdom are presented and analyzed.
This report aims at answering what, how, and when spent nuclear fuel (SNF) or dual-purpose canister (DPC) characteristics could be impacted by disposal events and processes, including decay, corrosion, dissolution, and criticality, such that the potential for criticality initiation or continuation in disposed DPCs becomes permanently significantly diminished. This report uses the term "permanent termination of criticality to denote the significant diminishment of criticality potential, not absolute prevention. The occurrence of disposal processes and events is a direct function of disposal time. For fundamental processes (e.g., decay), time is absolute; however, for other processes (e.g., corrosion), time is relative because it is driven by a combination of DPC characteristics (e.g., fuel conditions, basket composition), geologic parameters (e.g., infiltration rate), engineered barrier design, and other processes and events that impact in-package chemistry.
Electrical responses in the vicinity of energized steel-cased well sources offer significant potential for monitoring induced fractures. However, the high complexity of well-fracture-host models spanning multiple length scales compels analysts to simplify their numerical models due to enormous computational costs. This consequently limits our understanding regarding monitoring capabilities and the limitations of electrical measurements on realistic hydraulically fracturing systems. In this paper, we use the hierarchical finite element approach to construct geoelectric models in which geometrically complex fractures and steel-cased wells are discretely represented in 3D conducting media without sacrificing the model realism and computation efficiency. We have discovered systematic numerical analyses of the electrical responses to evaluate the influences of borehole material conductivity and the source type as well as the effects of well geometry, conductivity contrast, source location, fracture growth, and fracture propagation. Furthermore, the numerical results indicate that the borehole material property has a strong control on the electrical potentials along the production and monitoring wells. The monopole source located at a steel-cased well results in a current density distribution that decays away from the source location throughout the well length, whereas the dipole source produces a current density that dominates mainly along the dipole length. Moreover, the conductivity contrast between the fractures and host does not change the overall pattern of the electrical potentials but varies its amplitude. The fracture models near different well systems indicate that the well geometry controls the entire distribution of potentials, while the characteristics of the voltage difference profiles along the wells before and after fracturing are insensitive to the well geometry and the well in which the source is located. Further, the hydraulic-fracturing models indicate that the voltage differences along the production well before and after fracturing have strong sensitivity to fracture growth and fracture set propagation.
Cordova, Elsa A.; Garayburu-Caruso, Vanessa; Pearce, Carolyn I.; Cantrell, Kirk J.; Morad, Joseph W.; Gillispie, Elizabeth C.; Riley, Brian J.; Colon, Ferdinan C.; Levitskaia, Tatiana G.; Saslow, Sarah A.; Qafoku, Odeta; Resch, Charles T.; Rigali, Mark J.; Szecsody, Jim E.; Heald, Steve M.; Balasubramanian, Mahalingam; Meyers, Peter; Freedman, Vicky L.
Radioiodine (129I) poses a risk to the environment due to its long half-life, toxicity, and mobility. It is found at the U.S. Department of Energy Hanford Site due to legacy releases of nuclear wastes to the subsurface where 129I is predominantly present as iodate (IO3-). To date, a cost-effective and scalable cleanup technology for 129I has not been identified, with hydraulic containment implemented as the remedial approach. Here, novel high-performing sorbents for 129I remediation with the capacity to reduce 129I concentrations to or below the US Environmental Protection Agency (EPA) drinking water standard and procedures to deploy them in an ex-situ pump and treat (P&T) system are introduced. This includes implementation of hybridized polyacrylonitrile (PAN) beads for ex-situ remediation of IO3-contaminated groundwater for the first time. Iron (Fe) oxyhydroxide and bismuth (Bi) oxyhydroxide sorbents were deployed on silica substrates or encapsulated in porous PAN beads. In addition, Fe-, cerium (Ce)-, and Bi-oxyhydroxides were encapsulated with anion-exchange resins. The PAN-bismuth oxyhydroxide and PAN-ferrihydrite composites along with Fe- and Ce-based hybrid anion-exchange resins performed well in batch sorption experiments with distribution coefficients for IO3- of >1000 mL/g and rapid removal kinetics. Of the tested materials, the Ce-based hybrid anion-exchange resin was the most efficient for removal of IO3- from Hanford groundwater in a column system, with 50% breakthrough occurring at 324 pore volumes. The functional amine groups on the parent resin and amount of active sorbent in the resin can be customized to improve the iodine loading capacity. These results highlight the potential for IO3- remediation by hybrid sorbents and represent a benchmark for the implementation of commercially available materials to meet EPA standards for cleanup of 129I in a large-scale P&T system.
Ferroelectric hafnium zirconium oxide holds great promise for a broad spectrum of complementary metal-oxide-semiconductor (CMOS) compatible and scaled microelectronic applications, including memory, low-voltage transistors, and infrared sensors, among others. An outstanding challenge hindering the implementation of this material is polarization instability during field cycling. In this study, the nanoscale phenomena contributing to both polarization fatigue and wake-up are reported. Using synchrotron X-ray diffraction, the conversion of non-polar tetragonal and polar orthorhombic phases to a non-polar monoclinic phase while field cycling devices comprising noble metal contacts is observed. This phase exchange accompanies a diminishing ferroelectric remanent polarization and provides device-scale crystallographic evidence of phase exchange leading to ferroelectric fatigue in these structures. A reduction in the full width at half-maximum of the superimposed tetragonal (101) and orthorhombic (111) diffraction reflections is observed to accompany wake-up in structures comprising tantalum nitride and tungsten electrodes. Combined with polarization and relative permittivity measurements, the observed peak narrowing and a shift in position to lower angles is attributed, in part, to a phase exchange of the non-polar tetragonal to the polar orthorhombic phase during wake-up. These results provide insight into the role of electrodes in the performance of hafnium oxide-based ferroelectrics and mechanisms driving wake-up and fatigue, and demonstrate a non-destructive means to characterize the phase changes accompanying polarization instabilities.
Non-toxic disinfectants composed of readily available commodity chemicals are needed for immediate response to the current COVID-19 pandemic. One such area is the active research field for food-grade sanitization. Combinations of levulinic acid, a five-carbon ketocarboxylic acid, and sodium dodecyl sulfate, have been frequently described for antibacterial use on food contact surfaces. Levulinic acid has been identified as a renewable feedstock but is not presently in commodity production. Other carboxylic acids, such as acetic acid, may be equally usable and food-safe. Acidic and buffered solutions were highly effective, yielding no countable surviving organisms. The high efficacy of each acid may suggest that carboxylic acid sanitizers in general have potential use against viruses.
Proceedings of the National Academy of Sciences of the United States of America
Trahey, Lynn; Brushett, Fikile R.; Balsara, Nitash P.; Ceder, Gerbrand; Cheng, Lei; Chiang, Yet M.; Hahn, Nathan H.; J, Ingrambrian; Minteer, Shelley D.; Moore, Jeffrey S.; Mueller, Karl T.; Nazar, Linda F.; Persson, Kristin A.; Siegel, Donald J.; Xu, Kang; Zavadil, Kevin R.; Srinivasan, Venkat; Crabtree, George W.
Energy storage is an integral part of modern society. A contemporary example is the lithium (Li)-ion battery, which enabled the launch of the personal electronics revolution in 1991 and the first commercial electric vehicles in 2010. Most recently, Li-ion batteries have expanded into the electricity grid to firm variable renewable generation, increasing the efficiency and effectiveness of transmission and distribution. Important applications continue to emerge including decarbonization of heavy-duty vehicles, rail, maritime shipping, and aviation and the growth of renewable electricity and storage on the grid. This perspective compares energy storage needs and priorities in 2010 with those now and those emerging over the next few decades. The diversity of demands for energy storage requires a diversity of purpose-built batteries designed to meet disparate applications. Advances in the frontier of battery research to achieve transformative performance spanning energy and power density, capacity, charge/discharge times, cost, lifetime, and safety are highlighted, along with strategic research refinements made by the Joint Center for Energy Storage Research (JCESR) and the broader community to accommodate the changing storage needs and priorities. Innovative experimental tools with higher spatial and temporal resolution, in situ and operando characterization, first-principles simulation, high throughput computation, machine learning, and artificial intelligence work collectively to reveal the origins of the electrochemical phenomena that enable new means of energy storage. This knowledge allows a constructionist approach to materials, chemistries, and architectures, where each atom or molecule plays a prescribed role in realizing batteries with unique performance profiles suitable for emergent demands.
This memo summarizes the results of verification tests on a cone-socket fitting to the ISO 5356-1:2015 standard, annexes B and F. The components were found to meet requirements holding 50N tensile force and more than 35 N-cm of torque for 10 seconds without separating at 35°C with a fresh socket. Humidity control was unavailable; consequently, humidity was significantly lower than the values in the ISO standard. Several repeat tests of the fitting with force, torque, and time excursions give us confidence that the fittings perform suitably to the ISO standard. Force overload tests suggest that the samples will disengage under tension at around 69 N, while torque overload tests under a force of 50 N will fail at 79 N-cm of torque. If repeated tests are performed on the same component, the socket can deform and lead to the connection failing the standard requirements. However, such failures only occur with repeated testing, and the gripping of such units can be modified so that they pass the standard. This observation demonstrates that socket gripping is responsible for those failures and would not be a problem in application.
Schrader, Andrew J.; Schieber, Garrett L.; Ambrosini, Andrea A.; Loutzenhiser, Peter G.
A two-step cycle was considered for solar thermochemical energy storage based on aluminum-doped calcium manganite reduction/oxidation reactions for direct integration into Air Brayton cycles. The two steps encompassed (1) the storage of concentrated solar direct irradiation via the thermal reduction of aluminum-doped calcium manganite and (2) the delivery of heat to an Air-Brayton cycle via re-oxidation of oxygen-deficient aluminum-doped calcium manganite. The re-oxidized aluminum-doped calcium manganite was fed back to the first step to complete the cycle. A 5 kWth solar thermochemical reactor operating under vacuum was fabricated and tested to examine the first cycle reduction step. Reactor operating conditions and high-flux solar simulator control were tuned for continuous reactor operation with particle temperatures >1073 K. Continuous operation was achieved using intermittent, dense granular flows. A maximum absorption efficiency of 64.7% was demonstrated, accounting for both sensible and chemical heat storage.
We calculate the solvation energy of monovalent and divalent ions in various liquids with coarse-grained molecular dynamics simulations. Our theory treats the solvent as a Stockmayer fluid, which accounts for the intrinsic dipole moment of molecules and the rotational dynamics of the dipoles. Despite the simplicity of the model, we obtain qualitative agreement between the simulations and experimental data for the free energy and enthalpy of ion solvation, which indicates that the primary contribution to the solvation energy arises mainly from the first and possibly second solvation shells near the ions. Our results suggest that a Stockmayer fluid can serve as a reference model that enables direct comparison between theory and experiment and may be invoked to scale up electrostatic interactions from the atomic to the molecular length scale.
Silva-Quis, Dhamelyz; He, Chuan; Butera, Robert E.; Wang, George T.; Teplyakov, Andrew V.
The reaction of boron trichloride with the H and Cl-terminated Si(100) surfaces was investigated to understand the interaction of this molecule with the surface for designing wet-chemistry based silicon surface doping processes using a carbon- and oxygen-free precursor. The process was followed with X-ray photoelectron spectroscopy (XPS). Within the reaction conditions investigated, the reaction is highly effective on Cl-Si(100) for temperatures below 70°C, at which point both surfaces react with BCl$_3$. The XPS investigation followed the formation of a B 1s peak at 193.5 eV corresponding to (B-O)$_x$ species. Even the briefest exposure to ambient conditions lead to hydroxylation of surface borochloride species. However, the Si 2p signature at 102 eV allowed for a confirmation of the formation of a direct Si-B bond. Density functional theory was utilized to supplement the analysis and identify possible major surface species resulting from these reactions. This work provides a new pathway to obtain a functionalized silicon surface with a direct Si-B bond that can potentially be exploited as a means of selective, ultra-shallow, and supersaturated doping.
We present SUSPECT, an open source toolkit that symbolically analyzes mixed-integer nonlinear optimization problems formulated using the Python algebraic modeling library Pyomo. We present the data structures and algorithms used to implement SUSPECT. SUSPECT works on a directed acyclic graph representation of the optimization problem to perform: bounds tightening, bound propagation, monotonicity detection, and convexity detection. We show how the tree-walking rules in SUSPECT balance the need for lightweight computation with effective special structure detection. SUSPECT can be used as a standalone tool or as a Python library to be integrated in other tools or solvers. We highlight the easy extensibility of SUSPECT with several recent convexity detection tricks from the literature. We also report experimental results on the MINLPLib 2 dataset.
Tractions exerted by cells on the extracellular matrix (ECM) are critical in many important physiological and pathological processes such as embryonic morphogenesis, wound healing, and cancer metastasis. Three-dimensional Traction Microscopy (3DTM) is a tool to quantify cellular tractions by first measuring the displacement field in the ECM in response to these tractions, and then using this measurement to infer tractions. Most applications of 3DTM have assumed that the ECM has spatially-uniform mechanical properties, but cells secrete enzymes that can locally degrade the ECM. In this work, a novel computational method is developed to quantify both cellular tractions and ECM degradation. In particular, the ECM is modeled as a hyperelastic, Neo-Hookean solid, whose material parameters are corrupted by a single degradation parameter. The feasibility of determining both the traction and the degradation parameter is first demonstrated by showing the existence and uniqueness of the solution. An inverse problem is then formulated to determine the nodal values of the traction vector and the degradation parameter, with the objective of minimizing the difference between a predicted and measured displacement field, under the constraint that the predicted displacement field satisfies the equation of equilibrium. The inverse problem is solved by means of a gradient-based optimization approach, and the gradient is computed efficiently using appropriately derived adjoint fields. The computational method is validated in-silico using a geometrically realistic neuronal cell model and synthetic traction and degradation fields. It is found that the method accurately recovers both the traction and degradation fields. Moreover, it is found that neglecting ECM degradation can yield significant errors in traction measurements. Our method can extend the range of context where tractions can be appropriately measured.
Brigner, Wesley H.; Hu, Xuan; Hassan, Naimul; Jiang-Wei, Lucian; Bennett, Christopher H.; Akinola, Otitoaleke; Pasquale, Massimo; Marinella, Matthew J.; Incorvia, Jean A.C.; Friedman, Joseph S.
Due to their nonvolatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient and the third uses shape variations of the DW track.
In this work, a stabilized continuous Galerkin (CG) method for magnetohydrodynamics (MHD) is presented. Ideal, compressible inviscid MHD equations are discretized in space on unstructured meshes using piecewise linear or bilinear finite element bases to get a semi-discrete scheme. Stabilization is then introduced to the semi-discrete method in a strategy that follows the algebraic flux correction paradigm. This involves adding some artificial diffusion to the high order, semi-discrete method and mass lumping in the time derivative term. The result is a low order method that provides local extremum diminishing properties for hyperbolic systems. The difference between the low order method and the high order method is scaled element-wise using a limiter and added to the low order scheme. The limiter is solution dependent and computed via an iterative linearity preserving nodal variation limiting strategy. The stabilization also involves an optional consistent background high order dissipation that reduces phase errors. The resulting stabilized scheme is a semi-discrete method that can be applied to inviscid shock MHD problems and may be even extended to resistive and viscous MHD problems. To satisfy the divergence free constraint of the MHD equations, we add parabolic divergence cleaning to the system. Various time integration methods can be used to discretize the scheme in time. We demonstrate the robustness of the scheme by solving several shock MHD problems.
In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate solutions are accurate; their accuracy depends on the ability of the low-dimensional trial basis to represent the FOM solution. As a result, ROMs can generate inaccurate approximate solutions, e.g., when the FOM solution at the online prediction point is not well represented by training data used to construct the trial basis. To address this fundamental deficiency of standard model-reduction approaches, this work proposes a novel online-adaptive mechanism for efficiently enriching the trial basis in a manner that ensures convergence of the ROM to the FOM, yet does not incur any FOM solves. The mechanism is based on the previously proposed adaptive h-refinement method for ROMs (carlberg, 2015), but improves upon this work in two crucial ways. First, the proposed method enables basis refinement with respect to any orthogonal basis (not just the Kronecker basis), thereby generalizing the refinement mechanism and enabling it to be tailored to the physics characterizing the problem at hand. Second, the proposed method provides a fast online algorithm for periodically compressing the enriched basis via an efficient proper orthogonal decomposition (POD) method, which does not incur any operations that scale with the FOM dimension. These two features allow the proposed method to serve as (1) a failsafe mechanism for ROMs, as the method enables the ROM to satisfy any prescribed error tolerance online (even in the case of inadequate training), and (2) an efficient online basis-adaptation mechanism, as the combination of basis enrichment and compression enables the basis to adapt online while controlling its dimension.
Soft ferromagnetic alloys are often utilized in electromagnetic applications due to their desirable magnetic properties. In support of these applications, the ferromagnetic alloys are also required to bear mechanical load under various loading and environmental conditions. In this study, a Fe–49Co–2V alloy was dynamically characterized in tension with a Kolsky tension bar and a Drop–Hopkinson bar at various strain rates and temperatures. Dynamic tensile stress–strain curves of the Fe–49Co–2V alloy were obtained at strain rates ranging from 40 to 230 s−1 and temperatures from − 100 to 100 °C. All dynamic tensile stress–strain curves exhibited an initial linear elastic response to an upper yield followed by Lüders band response and then a nearly linear work-hardening behavior. The yield strength of this material was found to be sensitive to both strain rate and temperature, whereas the hardening rate was independent of strain rate or temperature. The Fe–49Co–2V alloy exhibited a feature of brittle fracture in tension under dynamic loading with no necking being observed.
Four D flip-flop (DFF) layouts were created from the same schematic in Sandia National Laboratories' CMOS7 silicon-on-insulator (SOI) process. Single-event upset (SEU) modeling and testing showed an improved response with the use of shallow (not fully bottomed) N-type metal-oxide-semiconductor field-effect transistors (NMOSFETs), extending the size of the drain implant and increasing the critical charge of the transmission gates in the circuit design and layout. This research also shows the importance of correctly modeling nodal capacitance, which is a major factor determining SEU critical charge. Accurate SEU models enable the understanding of the SEU vulnerabilities and how to make the design more robust.
Legged humanoid robots promise revolutionary mobility and effectiveness in environments built for humans. However, inefficient use of energy significantly limits their practical adoption. The humanoid biped walking anthropomorphic novelly-driven efficient robot for emergency response (WANDERER) achieves versatile, efficient mobility, and high endurance via novel drive-trains and passive joint mechanisms. Results of a test in which WANDERER walked for more than 4 h and covered 2.8 km on a treadmill, are presented. Results of laboratory experiments showing even more efficient walking are also presented and analyzed in this article. WANDERER's energetic performance and endurance are believed to exceed the prior literature in human-scale humanoid robots. This article describes WANDERER, the analytical methods and innovations that enable its design, and system-level energy efficiency results.
Opportunities and constraints for wave energy conversion technologies and projects are evaluated by identifying and characterizing the dominant wave energy systems for United States (US) coastal waters using marginal and joint distributions of the wave energy in terms of the peak period, wave direction, and month. These distributions are computed using partitioned wave parameters generated from a 30 year WaveWatch III model hindcast, and regionally averaged to identify the dominant wave systems contributing to the total annual available energy (AAE) for eleven distinct US wave energy climate regions. These dominant wave systems are linked to the wind systems driving their generation and propagation. In addition, conditional resource parameters characterizing peak period spread, directional spread, and seasonal variability, which consider dependencies of the peak period, direction, and month, are introduced to augment characterization methods recommended by international standards. These conditional resource parameters reveal information that supports project planning, conceptual design, and operation and maintenance. The present study shows that wave energy resources for the United States are dominated by long-period North Pacific swells (Alaska, West Coast, Hawaii), short-period trade winds and nor'easter swells (East Coast, Puerto Rico), and wind seas (Gulf of Mexico). Seasonality, peak period spread, and directional spread of these dominant wave systems are characterized to assess regional opportunities and constraints for wave energy conversion technologies targeting the dominant wave systems.
Residual strain in electrodeposited Li films may affect safety and performance in Li metal battery anodes, so it is important to understand how to detect residual strain in electrodeposited Li and the conditions under which it arises. To explore this Li films, electrodeposited onto Cu metal substrates, were prepared under an applied pressure of either 10 or 1000 kPa and subsequently tested for the presence or absence of residual strain via sin(ψ) analysis. X-ray diffraction (XRD) analysis of Li films required preparation and examination within an inert environment; hence, a Be-dome sample holder was employed during XRD characterization. Results show that the Li film grown under 1000 kPa displayed a detectable presence of in-plane compressive strain (-0.066%), whereas the Li film grown under 10 kPa displayed no detectable in-plane strain. The underlying Cu substrate revealed an in-plane residual strain near zero. Texture analysis via pole figure determination was also performed for both Li and Cu and revealed a mild fiber texture for Li metal and a strong bi-axial texture of the Cu substrate. Experimental details concerning sample preparation, alignment, and analysis of the particularly air-sensitive Li films have also been detailed. This work shows that Li metal exhibits residual strain when electrodeposited under compressive stress and that XRD can be used to quantify that strain.
2020 IEEE Conference on Communications and Network Security, CNS 2020
Fritz, David J.; Soroush, Hamed; Albanese, Massimiliano; Mehrabadi, Milad A.; Iganibo, Ibifubara; Mosko, Marc; Gao, Jason H.; Rane, Shantanu; Bier, Eric
Addressing security misconfiguration in complex distributed systems, such as networked Industrial Control Systems (ICS) and Internet of Things (IoT) is challenging. Owners and operators must go beyond tuning parameters of individual components and consider the security implications of configuration changes on entire systems. Given the growing scale of cyber systems, this task must be highly automated. Unfortunately, prior work on configuration errors has largely ignored the security impact of configurations of connected components. To address this gap, we present SCIBORG, a framework that improves the security posture of distributed systems by examining the impact of configuration changes across interdependent components using a graph-based model of the system and its vulnerabilities. It formulates a Constraint Satisfaction Problem from the graph-based model and uses an SMT solver to find optimal configuration parameter values that minimize the impact of attacks while preserving system functionality. SCIBORG also provides supporting evidence for the proposed configuration changes. We evaluate SCIBORG on an IoT testbed.
In this study we examine a split-foundry multilevel application specific integrated circuit (ASIC) Si-interposer and die bonded using the direct bond interface (DBI) process, in addition to shortloop vehicles. The designs have been subject to relaxed pattern density rules, and exhibit chemical mechanical planarization (CMP) systematic process issues of varying degrees. We find that the interconnect formation is robust against moderate dielectric thickness variation, as well as a moderate degree of copper corrosion. We discuss and demonstrate various CMP methods which have a clear and repeatable impact. Pattern density effects and defectivity on the bond quality are examined using focused ion beam scanning electron microscope (FIB-SEM) images at the feature scale (sub 100 um) and intra-die scale (few mm). Impact to the CMP performance, including plug recess, and defectivity are discussed.
Radiation-Induced Surface Activation is an inherent phenomenon where surfaces that are exposed to gamma irradiation are observed to undergo an increase in wettability. This increase in wettability as a result of the ionizing radiation exposure has so far been demonstrated to have a pronounced impact on Leidenfrost temperature and two-phase fluid dynamics. Test results from previous experiments have shown that incorporation of this effect on heat transfer equipment design may increase the thermal-hydraulic margin leading to higher thermal efficiency. However, the mechanism behind the increased wettability is not clearly understood. In the present work, three different materials (Zircaloy-4, 316 stainless steel, and copper) were exposed at two different dose rates with use of two different gamma irradiation facilities. A detailed surface characterization on the post-irradiated samples is carried out to understand the changes in surface chemistry, wettability and surface morphology. It is observed from the experiments that the increase in wettability upon irradiation depended on the total dose and not on the dose rate. Moreover, localized oxidation and porosity induced by radiolysis was seen to be the predominant mechanism behind increased wettability which leads to improved Leidenfrost temperature.
Refractory High Entropy Alloys (RHEAs) and Refractory Complex Concentrated Alloys (RCCAs) are high-temperature structural alloys ideally suited for use in harsh environments. While these alloys have shown promising structural properties at high temperatures that exceed the practical limits of conventional alloys, such as Ni-based superalloys, exploration of the complex phase-space of these materials remains a significant challenge. We report on a high-throughput alloy processing and characterization methodology, leveraging laser-based metal additive manufacturing (AM) and mechanical testing techniques, to enable rapid exploration of RHEAs/RCCAs. We utilized in situ alloying and compositional grading, unique to AM processing, to rapidly-produce RHEAs/RCCAs using readily available and inexpensive commercial elemental powders. We demonstrate this approach with the MoNbTaW alloy system, as a model material known for having exceptionally high strength at elevated temperature when processed using conventional methods (e.g., casting). Microstructure analysis, chemical composition, and strain rate dependent hardness of AM-processed material are presented and discussed in the context of understanding the structure-properties relationships of RHEAs/RCCAs.
The current COVID-19 pandemic is stretching both the global supply for face masks and personal protective equipment (PPE). Production capacity is severely limited in many countries. This is a call for the R&D community, particularly to those in the polymer degradation and stability field. We have not only an opportunity but an obligation to engage and collaborate with virology and bio-medical experts. We require comparative R&D for extended, reuse and recyclability options. There is urgent need for large scale institutional approaches and methods that can be quickly applied locally by non-experts with limited resources.
We study a trajectory analysis problem we call the Trajectory Capture Problem (TCP), in which, for a given input set T of trajectories in the plane, and an integer k-2, we seek to compute a set of k points ("portals") to maximize the total weight of all subtrajectories of T between pairs of portals. This problem naturally arises in trajectory analysis and summarization. We show that the TCP is NP-hard (even in very special cases) and give some first approximation results. Our main focus is on attacking the TCP with practical algorithm-engineering approaches, including integer linear programming (to solve instances to provable optimality) and local search methods. We study the integrality gap arising from such approaches. We analyze our methods on different classes of data, including benchmark instances that we generate. Our goal is to understand the best performing heuristics, based on both solution time and solution quality. We demonstrate that we are able to compute provably optimal solutions for real-world instances. 2012 ACM Subject Classification Theory of computation ! Design and analysis of algorithms.
Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homogeneous and inhomogeneous linewidths are comparable. We implemented a machine learning algorithm to analyze 2D spectra to retrieve homogeneous and inhomogeneous linewidths. The algorithm was trained using simulated 2D spectra with known linewidth values. The trained algorithm can analyze both simulated (not used in training) and experimental spectra to extract the homogeneous and inhomogeneous linewidths. This approach can be potentially applied to 2D spectra with more sophisticated spectral features.
From manufacturing plants to power grids, industrial control systems are increasingly controlled and networked digitally. While networking these systems together improves their efficiency and convenience to control, it also opens them up to attacks by malicious actors. When these attacks occur, forensic investigators should be able to determine what was compromised and which corrective actions need to be taken.In this paper, we propose a method to investigate attacks on industrial control systems by simulating the logged inputs of the system over time using a model constructed from the control programs. We detect any attacks that will lead to perturbations of the normal operation of the system by comparing the simulated output to the actual output. We also perform dependency tracing between the inputs and outputs of the system, so that attacks can be traced from the anomaly to their sources and vice-versa. Our method can greatly aid investigators in recovering the complete attack graph used by the attacker using only the input and output logs from an industrial control system. To evaluate our method, we constructed a hybrid testbed with a simulated version of the Simplified Tennessee Eastman process, using a hardware-inthe-loop Allen-Bradley Micrologix 1100 PLC. We were able to accurately detect all attack anomalies with a false positive rate of 0.3% or less.
Standard meteorological balloons can deliver small scientific payloads to the stratosphere for a few tens of minutes, but achieving multihour level flight in this region is more difficult. We have developed a solarpowered hot-air balloon named the heliotrope that can maintain a nearly constant altitude in the upper troposphere–lower stratosphere as long as the sun is above the horizon. It can accommodate scientific payloads ranging from hundreds of grams to several kilograms. The balloon can achieve float altitudes exceeding 24 km and fly for days in the Arctic summer, although sunset provides a convenient flight termination mechanism at lower latitudes. Two people can build an envelope in about 3.5 h, and the materials cost about $30. The low cost and simplicity of the heliotrope enables a class of missions that is generally out of reach of institutions lacking specialized balloon expertise. Here, we discuss the design history, construction techniques, trajectory characteristics, and flight prediction of the heliotrope balloon. We conclude with a discussion of the physics of solar hot-air balloon flight.
Grillot, Frederic; Norman, Justin C.; Duan, Jianan; Zhang, Zeyu; Dong, Bozhang; Huang, Heming; Chow, Weng W.; Bowers, John E.
Photonic integrated circuits (PICs) have enabled numerous high performance, energy efficient, and compact technologies for optical communications, sensing, and metrology. One of the biggest challenges in scaling PICs comes from the parasitic reflections that feed light back into the laser source. These reflections increase noise and may cause laser destabilization. To avoid parasitic reflections, expensive and bulky optical isolators have been placed between the laser and the rest of the PIC leading to large increases in device footprint for on-chip integration schemes and significant increases in packaging complexity and cost for lasers co-packaged with passive PICs. This review article reports new findings on epitaxial quantum dot lasers on silicon and studies both theoretically and experimentally the connection between the material properties and the ultra-low reflection sensitivity that is achieved. Our results show that such quantum dot lasers on silicon exhibit much lower linewidth enhancement factors than any quantum well lasers. Together with the large damping factor, we show that the quantum dot gain medium is fundamentally dependent on dot uniformity, but through careful optimization, even epitaxial lasers on silicon can operate without an optical isolator, which is of paramount importance for the future high-speed silicon photonic systems.
Due to the direct relationship between thermal history and mechanical behavior, in situ thermal monitoring is key in gauging quality of parts produced with additive manufacturing (AM). Accurate monitoring of temperatures in an AM process requires knowledge of environment and object parameters including object emissivity. The emissivity is dependent on several variables, including: wavelength, material composition, temperature, and surface topography. Researchers have been concerned with the thermal emissivity dependence on temperature since large ranges are seen in metal powder bed processes, but there is also an extensive range of surfaces produced by AM. This work focused on discovering what roughness characteristics control thermal emissivity through investigation of prototypic 316 stainless steel AM samples produced with a range of build conditions on a laser powder bed fusion machine. Through experimental measurements of emissivity using hemispherical directional reflectance (HDR), guided by simulations using a finite-difference time-domain (FDTD) Maxwell solver, it was found that combinations of existing roughness parameters describing both height and slope of the surface correlate well with emissivity changes. These parameters work well due to their apt description of surface features encouraging internal reflection, which is the phenomenon that increases emissivity when a surface falls under the geometric optical region conditions.
Power systems with highly flexible architectures (i.e. permitting many configurations) may allow for more economic operation as well as improved reliability and resiliency. The greater number of configurations enable optimization for attaining the former benefit and redundancy for achieving the latter. Flexibility is of great importance in electric ship power systems wherein the system must ensure delivery of power to vital loads. The United States (US) Navy is currently investigating new architectures that enable a greater number of interconnection permutations. Among the new features considered are generators that may supply two buses; this may be done using conventional (single winding set) generators and two rectifiers or a dual wound machine with two rectifiers. In systems supplied by dual-wound machines, buses may not be tied directly but are linked dynamically through the shared generator dynamics. In systems with conventional generation supplying two rectifiers, the two buses are tied through a common AC bus supplying both rectifiers. This paper presents a comparison of these two approaches of supplying two buses from one generator; the evaluation considers issues associated with dynamic coupling through these two candidate architectures, including the coupled response due to faults and systems with pulsed loads. Results are based on analysis, simulation results, and hardware experiment.
Long Range (LoRa) is an emerging low-power wide-area network technology. LoRa messages can be transmitted with a variety of parameters including transmit power, spreading factor, bandwidth, and error coding rates. While adaptive data rate (ADR) capabilities exist in the LoRa wide-area network (LoRaWAN) specification, this work is motivated by a cattle monitoring application where LoRaWAN is not feasible. In this scenario, the mobility of the animal changes the optimal parameter selections, which are the settings that transmit the data with the lowest energy consumption. This work analyzes ADR techniques to most efficiently find the optimal data rate for a firmware update, although the techniques are still valid for any large data exchange. It extends the ADR to use frequency shift keying (FSK) when there is enough signal strength since Semtech LoRa integrated circuits support FSK mode. The work uses dynamic acknowledgements and timeout values to improve the convergence time. The paper experimentally validates an analytical transmit time model and then describes three different methods for accomplishing the adaptive data rate. The methods are modeled analytically for the different convergence settings and two are demonstrated using the Microchip SAMR34 Explained boards.
This application note describes how Release 7.1 of the Xyce circuit simulator can be coupled with external simulators via either a Python-based interface that leverages the Python ctypes foreign function library or via the Verilog Procedural Interface (VPI). It also documents the usage of these interfaces on RHEL7 with Python 2.6 or 2.7. These interfaces are still under development and may change in the future. So, a key purpose of this application note is to solicit feedback on these interfaces from both internal Sandia Xyce users and other performers on the DARPA Posh Open Source Hardware (POSH) program.