A neutron fluence map and a total ionizing dose map of the Los Alamos National Laboratory Godiva IV fast burst critical assembly was generated using passive reactor dosimetry, comprised of sulfur pellets and thermoluminescent dosimeters. Godiva IV is an unmoderated, fast burst, critical assembly constructed of approximately 65 kg of highly enriched uranium fuel alloyed with 1.5 % molybdenum for strength. [1] The mapping was performed during a single 75.6 ºC temperature rise burst operation, with the top and sides of the cylindrical Godiva-IV Top Hat covered in passive dosimetry. Dosimetry was placed in a symmetric pattern around the Top Hat, with higher concentrations near the control rods and burst rod. A specific portion of the lower quadrant of the burst rod was mapped to confirm a testing region where the neutron fluence varied by no more than ± 5%. The results will be used to assess the neutron, gamma, and total ionizing dose environment in three-dimensional space around the assembly for higher fidelity experiment placement, active dosimetry positioning, and radiation field characterization.
The characterization of the neutron, prompt gamma-ray, and delayed gamma-ray radiation fields for the White Sands Missile Range (WSMR) Fast Burst Reactor, also known as molybdenum-alloy Godiva (Molly-G) has been assessed at the 6-inch irradiation location. The neutron energy spectra, uncertainties, and common radiation metrics are presented. Code-dependent recommended constants are given to facilitate the conversion of various dosimetry readings into radiation metrics desired by experimenters. The Molly-G core was designed and configured similarly to Godiva II, as an unreflected, unmoderated, cylindrical annulus of uranium-molybdenum-alloy fuel with molybdenum loading of 10%. At the 6-inch position, the axial fluence maximum is about 2.4×1013 n/cm2 per MJ of reactor energy; about 0.1% of the neutron fluence is below 1 keV and 96% is above 100 keV. The 1-MeV Damage-Equivalent Silicon (DES) fluence is estimated at 2.2×1013 n/cm2 per MJ of reactor energy. The prompt gamma-ray dose is roughly 2.5E+03 rad(Si) per MJ and the delayed gamma-ray dose is about 1.3E+03 rad(Si) per MJ.
The characterization of the neutron, prompt gamma-ray, and delayed gamma-ray radiation fields in the University of Texas at Austin Nuclear Engineering Teaching Laboratory (NETL) TRIGA reactor for the beam port (BP) 1/5 free-field environment at the 128-inch location adjacent to the core centerline has been accomplished. NETL is being explored as an auxiliary neutron test facility for the Sandia National Laboratories radiation effects sciences research and development campaigns. The NETL reactor is a TRIGA Mark-II pulse and steady-state, above-ground pool-type reactor. NETL is intended as a university research reactor typically used to perform irradiation experiments for students and customers, radioisotope production, as well as a training reactor. Initial criticality of the NETL TRIGA reactor was achieved on March 12, 1992, making it one of the newest test reactor facilities in the US. The neutron energy spectra, uncertainties, and covariance matrices are presented as well as a neutron fluence map of the experiment area of the cavity. For an unmoderated condition, the neutron fluence at the center of BP 1/5, at the adjacent core axial centerline, is about 8.2×1012 n/cm2 per MJ of reactor energy. About 67% of the neutron fluence is below 1 keV and 22% above 100 keV. The 1-MeV Damage-Equivalent Silicon (DES) fluence is roughly 1.6×1012 n/cm2 per MJ of reactor energy.
High-entropy materials (HEMs) emerged as promising candidates for a diverse array of chemical transformations, including CO2 utilization. However, traditional HEMs catalysts are nonporous, limiting their activity to surface sites. Designing HEMs with intrinsic porosity can open the door toward enhanced reactivity while maintaining the many benefits of high configurational entropy. Here, a synergistic experimental, analytical, and theoretical approach to design the first high-entropy metal-organic frameworks (HEMOFs) derived from polynuclear metal clusters is implemented, a novel class of porous HEMs that is highly active for CO2 fixation under mild conditions and short reaction times, outperforming existing heterogeneous catalysts. HEMOFs with up to 15 distinct metals are synthesized (the highest number of metals ever incorporated into a single MOF) and, for the first time, homogenous metal mixing within individual clusters is directly observed via high-resolution scanning transmission electron microscopy. Importantly, density functional theory studies provide unprecedented insight into the electronic structures of HEMOFs, demonstrating that the density of states in heterometallic clusters is highly sensitive to metal composition. This work dramatically advances HEMOF materials design, paving the way for further exploration of HEMs and offers new avenues for the development of multifunctional materials with tailored properties for a wide range of applications.
The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.
Thermal spray processes can benefit from cooling to maintain substrate temper, reduce processing times, and manage thermally induced residual stresses. “Plume quenching” is a plume-targeted cooling technique which has been shown to reduce substrate temperatures by redirection of hot plume gases using a lateral argon curtain injected into the plume, while limiting interaction with the substrate or affecting coating properties. Here, this study explores the use of this technique for residual stress management by reducing the thermally driven component in nickel and tantalum coatings on titanium and aluminum substrates. The in-situ residual stress profiles were measured for all substrate and coating pairings during spraying and cooling, and the deposition and thermal stresses recorded. For substrate and coating pairings where the predominant component of residual stress was thermal (driven by a large difference in coefficient of thermal expansion, Δα, between coating and substrate), plume quenching reduced both the thermal stress and the final stress state of the coating. This was seen primarily in tantalum on aluminum coatings where the Δα was -17 × 10-6 /°C, and thermal stress was reduced by 7.5% and 22.4% for the plume quenching rates of 50 and 100 slpm, respectively.
Pozzolans rich in silica and alumina react with lime to form cementing compounds and are incorporated into portland cement as supplementary cementitious materials (SCMs). However, pozzolanic reactions progress slower than portland cement hydration, limiting their use in modern construction due to insufficient early-age strength. Hence, alternative SCMs that enable faster pozzolanic reactions are necessary including synthetic zeolites, which have high surface areas and compositional purity that indicate the possibility of rapid pozzolanic reactivity. Synthetic zeolites with varying cation composition (Na-zeolite, H-zeolite), SiO2/Al2O3 ratio, and framework type were evaluated for pozzolanic reactivity via Ca(OH)2 consumption using ion exchange and in-situ X-ray diffraction experiments. Na-zeolites exhibited limited exchange reactions with KOH and Ca(OH)2 due to the occupancy of acid sites by Na+ and hydroxyl groups. Meanwhile, H-zeolites readily adsorbed K+ and Ca2+ from a hydroxide solution by exchanging cations with H+ at Brønsted acid sites or cation adsorption at vacant acid sites. By adsorbing cations, the H-zeolite reduced the pH and increased Ca2+ solubility to promote pozzolanic reactions in a system where Ca(OH)2 dissolution/diffusion was a rate limiting factor. High H-zeolite reactivity resulted in 0.8 g of Ca(OH)2 consumed per 1 g of zeolites after 16 h of reaction versus 0.4 g of Ca(OH)2 consumed per 1 g of Na-zeolite. The H-zeolite modulated the pore fluid alkalinity and created a low-density amorphous silicate phase via mechanisms analogous to two-step C-S-H nucleation experiments. Controlling these reaction mechanisms is key to developing next generation pozzolanic cementitious systems with comparable hydration rates to portland cement.
Biaxial stress is identified to play an important role in the polar orthorhombic phase stability in hafnium oxide-based ferroelectric thin films. However, the stress state during various stages of wake-up has not yet been quantified. In this work, the stress evolution with field cycling in hafnium zirconium oxide capacitors is evaluated. The remanent polarization of a 20 nm thick hafnium zirconium oxide thin film increases from 9.80 to 15.0 µC cm−2 following 106 field cycles. This increase in remanent polarization is accompanied by a decrease in relative permittivity that indicates that a phase transformation has occurred. The presence of a phase transformation is supported by nano-Fourier transform infrared spectroscopy measurements and scanning transmission electron microscopy that show an increase in ferroelectric phase content following wake-up. The stress of individual devices field cycled between pristine and 106 cycles is quantified using the sin2(ψ) technique, and the biaxial stress is observed to decrease from 4.3 ± 0.2 to 3.2 ± 0.3 GPa. The decrease in stress is attributed, in part, to a phase transformation from the antipolar Pbca phase to the ferroelectric Pca21 phase. This work provides new insight into the mechanisms controlling and/or accompanying polarization wake-up in hafnium oxide-based ferroelectrics.
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints, which also allows them to generalize well to unseen data. In this work, we extend a polyconvex hyperelastic neural network framework to (isotropic) thermo-hyperelasticity by specifying the thermodynamic and material theoretic requirements for an expansion of the Helmholtz free energy expressed in terms of deformation invariants and temperature. Different formulations which a priori ensure polyconvexity with respect to deformation and concavity with respect to temperature are proposed and discussed. The physics-augmented neural networks are furthermore calibrated with a recently proposed sparsification algorithm that not only aims to fit the training data but also penalizes the number of active parameters, which prevents overfitting in the low data regime and promotes generalization. The performance of the proposed framework is demonstrated on synthetic data, which illustrate the expected thermomechanical phenomena, and existing temperature-dependent uniaxial tension and tension-torsion experimental datasets.
In this paper, we present a Riemannian geometric derivation of the governing equations of motion of nonholonomic dynamic systems. A geometric form of the work-energy principle is first derived. The geometric form can be realized in appropriate generalized quantities, and the independent equations of motion can be obtained if the subspace of generalized speeds allowable by nonholonomic constraints can be determined. We provide a geometric perspective of the governing equations of motion and demonstrate its effectiveness in studying dynamic systems subjected to nonholonomic constraints.
See, Judi E.; Handley, Holly A.H.; Savage-Knepshield, Pamela A.
The Human Readiness Level (HRL) scale is a simple nine-level scale that brings structure and consistency to the real-world application of user-centered design. It enables multidisciplinary consideration of human-focused elements during the system development process. Use of the standardized set of questions comprising the HRL scale results in a single human readiness number that communicates system readiness for human use. The Human Views (HVs) are part of an architecture framework that provides a repository for human-focused system information that can be used during system development to support the evaluation of HRL levels. This paper illustrates how HRLs and HVs can be used in combination to support user-centered design processes. A real-world example for a U.S. Army software modernization program is described to demonstrate application of HRLs and HVs in the context of user-centered design.
Distributed Acoustic Sensing (DAS) can record acoustic wavefields at high sampling rates and with dense spatial resolution difficult to achieve with seismometers. Using optical scattering induced by cable deformation, DAS can record strain fields with spatial resolution of a few meters. However, many experiments utilizing DAS have relied on unused, dark telecommunication fibers. As a result, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. This limits our understanding of guiding principles in array design to deploy DAS effectively and efficiently in the field. A better quantitative understanding of DAS array behavior can improve the quality of the data recorded by guiding the DAS array design. Here we use steered response functions, which account for DAS fiber’s directional sensitivity, as well as beamforming and back-projection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A regular heptagon DAS array demonstrated improved capabilities for recording regional sources over other polygonal arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry and can guide future DAS deployments.
Seelinger, Linus; Reinarz, Anne; Lykkegaard, Mikkel B.; Alghamdi, Amal M.A.; Aristoff, David; Bangerth, Wolfgang; Benezech, Jean; Diez, Matteo; Frey, Kurt; Jakeman, John D.; Jorgensen, Jakob S.; Kim, Ki-Tae; Martinelli, Massimiliano; Parno, Matthew; Pellegrini, Riccardo; Petra, Noemi; Riis, Nicolai A.B.; Rosenfeld, Katherine; Serani, Andrea; Tamellini, Lorenzo; Villa, Umberto; Dodwell, Tim J.; Scheichl, Robert
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.
Welding processes used in the production of pressure vessels impart residual stresses in the manufactured component. Computational modeling is critical to predicting these residual stress fields and understanding how they interact with notches and flaws to impact pressure vessel durability. Here, in this work, we present a finite element model for a resistance forge weld and validate it using laboratory measurements. Extensive microstructural changes, near-melt temperatures, and large localized deformations along the weld interface pose significant challenges to Lagrangian finite element modeling. The proposed modeling approach overcomes these roadblocks in order to provide a high-fidelity simulation that can predict the residual stress state in the manufactured pressure vessel; a rich microstructural constitutive model accounts for material recrystallization dynamics, a frictional-to-tied contact model is coordinated with the constitutive model to represent interfacial bonding, and adaptive remeshing is employed to alleviate severe mesh distortion. An interrupted-weld approach is applied to the simulation to facilitate comparison to displacement measures. Several techniques are employed for residual stress measurement in order to validate the finite element model: neutron diffraction, the contour method, and the slitting method. Model-measurement comparisons are supplemented with detailed simulations that reflect the configurations of the residual-stress measurement processes themselves. The model results show general agreement with experimental measurements, and we observe some similarities in the features around the weld region. Factors that contribute to model-measurement differences are identified. Finally, we conclude with some discussion of the model development and residual stress measurement strategies, including how to best leverage the efforts put forth here for other weld problems.
Redox flow batteries (RFBs) are an attractive choice for stationary energy storage of renewables such as solar and wind. Non-aqueous redox flow batteries (NARFBs) have garnered broad interest due to their high voltage operation compared to their aqueous counterparts. Further, the utilization of bipolar redox-active molecules (BRMs) is a practical way to alleviate crossover faced by asymmetric RFBs. In this work, ferrocene (Fc) and phthalimide (PI) are covalently linked with various tethering groups which vary in structure and length. The compiled results suggest that the length and steric shielding ability of the linker group can greatly influence the stability and overall performance of Fc-n-PI BRM-based NARFBs. Primary sources of capacity loss are found to be BRM degradation for straight chain spacers <6 carbons and membrane (Nafion) fouling. Fc-hexyl-PI provided the most stable battery cycling and coulombic efficiencies of >98 % over 100 cycles (~13 days). NARFB using Fc-hexyl-PI as an active material exhibited high working voltage (1.93 V) and maximum capacity (1.28 Ah L−1). Additionally, this work highlights rational strategies to improve cycling stability and optimize NARFB performance.
Data-consistent inversion is designed to solve a class of stochastic inverse problems where the solution is a pullback of a probability measure specified on the outputs of a quantities of interest (QoI) map. Here, this work presents stability and convergence results for the case where finite QoI data result in an approximation of the solution as a density. Given their popularity in the literature, separate results are proven for three different approaches to measuring discrepancies between probability measures: f-divergences, integral probability metrics, and Lp metrics. In the context of integral probability metrics, we also introduce a pullback probability metric that is well-suited for data-consistent inversion. This fills a theoretical gap in the convergence and stability results for data-consistent inversion that have mostly focused on convergence of solutions associated with approximate maps. Numerical results are included to illustrate key theoretical results with intuitive and reproducible test problems that include a demonstration of convergence in the measure-theoretic "almost" sense.
The performance and reliability of many structures and components depend on the integrity of interfaces between dissimilar materials. Interfacial toughness Γ is the key material parameter that characterizes resistance to interfacial crack growth, and Γ is known to depend on many factors including temperature. For example, previous work showed that the toughness of an epoxy/aluminum interface decreased 40 % as the test temperature was increased from −60 °C to room temperature (RT). Interfacial integrity at elevated temperatures is of considerable practical importance. Recent measurements show that instead of continuing to decrease with increasing temperature, Γ increases when test temperature is above RT. Cohesive zone finite element calculations of an adhesively bonded, asymmetric double cantilever beam specimen of the type used to measure Γ suggest that this increase in toughness may be a result of R-curve behavior generated by plasticity-enhanced toughening during stable subcritical crack growth with interfacial toughness defined as the critical steady-state limit value. In these calculations, which used an elastic-perfectly plastic epoxy model with a temperature-dependent yield strength, the plasticity-enhanced increase in Γ above its intrinsic value Γo depended on the ratio of interfacial strength σ* to the yield strength σyb of the bond material. There is a nonlinear relationship between Γ/Γo and σ*/σyb with the value Γ/Γo increasing rapidly above a threshold value of σ*/σyb. The predicted increase in toughness can be significant. For example, there is nearly a factor of two predicted increase in Γ/Γo during micrometer-scale crack-growth when σ*/σyb = 2 (a reasonable choice for σ*/σyb). Furthermore, contrary to other reported results, plasticity-enhanced toughening can occur prior to crack advance as the cohesive zone forms and the peak stress at the tip of the original crack tip translates to the tip of the fully formed cohesive zone. These results suggest that plasticity-enhanced toughening should be considered when modeling interfaces at elevated temperatures.
We investigate hydrodynamic fluctuations in the flow past a circular cylinder near the critical Reynolds number Rec for the onset of vortex shedding. Starting from the fluctuating Navier-Stokes equations, we perform a perturbation expansion around Rec to derive analytical expressions for the statistics of the fluctuating lift force. Molecular-level simulations using the direct simulation Monte Carlo method support the theoretical predictions of the lift power spectrum and amplitude distribution. Notably, we have been able to collect sufficient statistics at distances Re/Rec-1=O(10-3) from the instability that confirm the appearance of non-Gaussian fluctuations, and we observe that they are associated with intermittent vortex shedding. These results emphasize how unavoidable thermal-noise-induced fluctuations become dramatically amplified in the vicinity of oscillatory flow instabilities and that their onset is fundamentally stochastic.
A series of drained and undrained water-saturated constant mean-stress tests were performed to investigate the strength, elasticity, and poroelastic response of a water-saturated high porosity nonwelded tuff. Drained strengths are found to increase with increasing effective confining pressures. Elastic moduli increase with increasing mean stress. Undrained strengths are small due to development of high pore pressures that generate low effective confining pressures. Skempton’s values are pressure dependent and appear to reflect the onset of inelastic deformation. Permeabilities decrease after deformation from ∼ 10–14 to ∼ 10–16 m2 and are a function of the applied confining pressure. Deformation is dominated by pore collapse, compaction, and intense microfracturing, with the undrained tests favoring microfracture-dominant deformation and the drained tests favoring compaction-dominant deformation. These property determinations and observations are used to develop/parameterize physics-based models for underground explosives testing.
Nematic liquid crystal elastomers (LCEs) are a unique class of network polymers with the potential for enhanced mechanical energy absorption and dissipation capacity over conventional network polymers because they exhibit both conventional viscoelastic behavior and soft-elastic behavior (nematic director changes under shear loading). This additional inelastic mechanism makes them appealing as candidate damping materials in a variety of applications from vibration to impact. The lattice structures made from the LCEs provide further mechanical energy absorption and dissipation capacity associated with packing out the porosity under compressive loading. Understanding the extent of mechanical energy absorption, which is the work per unit mass (or volume) absorbed during loading, versus dissipation, which is the work per unit mass (or volume) dissipated during a loading cycle, requires measurement of both loading and unloading response. In this study, a bench-top linear actuator was employed to characterize the loading-unloading compressive response of polydomain and monodomain LCE polymers and polydomain LCE lattice structures with two different porosities (nominally, 62% and 85%) at both low and intermediate strain rates at room temperature. As a reference material, a bisphenol-A (BPA) polymer with a similar glass transition temperature (9 °C) as the nematic LCE (4 °C) was also characterized at the same conditions for comparing to the LCE polymers. Based on the loading-unloading stress-strain curves, the energy absorption and dissipation for each material at different strain rates (0.001, 0.1, 1, 10 and 90 s-1) were calculated with considerations of maximum stress and material mass/density. The strain-rate effect on the mechanical response and energy absorption and dissipation behaviors was determined. The energy dissipation ratio was also calculated from the resultant loading and unloading stress-strain curves. All five materials showed significant but different strain rate effects on energy dissipation ratio. The solid LCE and BPA materials showed greater energy dissipation capabilities at both low (0.001 s−1) and high (above 1 s−1) strain rates, but not at the strain rates in between. The polydomain LCE lattice structure showed superior energy dissipation performance compared with the solid polymers especially at high strain rates.
A novel algorithm for explicit temporal discretization of the variable-density, low-Mach Navier-Stokes equations is presented here. Recognizing there is a redundancy between the mass conservation equation, the equation of state, and the transport equation(s) for the scalar(s) which characterize the thermochemical state, and that it destabilizes explicit methods, we demonstrate how to analytically eliminate the redundancy and propose an iterative scheme to solve the resulting transformed scalar equations. The method obtains second-order accuracy in time regardless of the number of iterations, so one can terminate this subproblem once stability is achieved. Hence, flows with larger density ratios can be simulated while still retaining the efficiency, low cost, and parallelizability of an explicit scheme. The temporal discretization algorithm is used within a pseudospectral direct numerical simulation which extends the method of Kim, Moin, and Moser for incompressible flow [17] to the variable-density, low-Mach setting, where we demonstrate stability for density ratios up to ∼25.7.
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Fjeldsted, Aaron P.; Morrow, Tyler; Scott, Clayton; Zhu, Yilun; Holland, Darren E.; Hanks, Ephraim M.; Wolfe, Douglas E.
The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including expected pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ratio (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) model are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing a problem space, ranging from 5 cm to 100 cm standoff distances and 5 μCi–100 μCi of 137Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.
We propose a method to couple local and nonlocal diffusion models. By inheriting desirable properties such as patch tests, asymptotic compatibility and unintrusiveness from related splice and optimization-based coupling schemes, it enables the use of weak (or variational) formulations, is computationally efficient and straightforward to implement. We prove well-posedness of the coupling scheme and demonstrate its properties and effectiveness in a variety of numerical examples.
Rattlesnake is a combined-environments, multiple input/multiple output control system for dynamic excitation of structures under test. It provides capabilities to control multiple responses on the part using multiple exciters using various control strategies. Rattlesnake is written in the Python programming language to facilitate multiple input/multiple output vibration research by allowing users to prescribe custom control laws to the controller. Rattlesnake can target multiple hardware devices, or even perform synthetic control to simulate a test virtually. Rattlesnake has been used to execute control problems with up to 200 response channels and 24 shaker drives. This document describes the functionality, architecture, and usage of the Rattlesnake controller to perform combined environments testing.
We numerically investigate the mechanisms that resulted in induced seismicity occurrence associated with CO2 injection at the Illinois Basin–Decatur Project (IBDP). We build a geologi-cally consistent model that honors key stratigraphic horizons and 3D fault surfaces inter-preted using surface seismic data and microseismicity locations. We populate our model with reservoir and geomechanical properties estimated using well-log and core data. We then performed coupled multiphase flow and geomechanics modeling to investigate the impact of CO2 injection on fault stability using the Coulomb failure criteria. We calibrate our flow model using measured reservoir pressure during the CO2 injection phase. Our model results show that pore-pressure diffusion along faults connecting the injection inter-val to the basement is essential to explain the destabilization of the regions where micro-seismicity occurred, and that poroelastic stresses alone would result in stabilization of those regions. Slip tendency analysis indicates that, due to their orientations with respect to the maximum horizontal stress direction, the faults where the microseismicity occurred were very close to failure prior to injection. These model results highlight the importance of accurate subsurface fault characterization for CO2 sequestration operations.
Accident analysis and ensuring power plant safety are pivotal in the nuclear energy sector. Significant strides have been achieved over the past few decades regarding fire protection and safety, primarily centered on design and regulatory compliance. Yet, after the Fukushima accident a decade ago, the imperative to enhance measures against fire, internal flooding, and power loss has intensified. Hence, a comprehensive, multilayered protection strategy against severe accidents is needed. Consequently, gaining a deeper insight into pool fires and their behavior through extensive validated data can greatly aid in improving these measures using advanced validation techniques. A model validation study was performed at Sandia National Laboratories (SNL) in which a 30-cm diameter methanol pool fire was modeled using the SIERRA/Fuego turbulent reacting flow code. This validation study used a standard validation experiment to compare model results against, and conclusions have been published. The fire was modeled with a large eddy simulation (LES) turbulence model with subgrid turbulent kinetic energy closure. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was accounted for with a model utilizing the gray-gas approximation. In this study, additional validation analysis is performed using the area validation metric (AVM). These activities are done on multiple datasets involving different variables and temporal/spatial ranges and intervals. The results provide insight into the use of the area validation metric on such temporally varying datasets and the importance of physics-aware use of the metric for proper analysis.
Exploding bridgewire detonators (EBWs) containing pentaerythritol tetranitrate (PETN) exposed to high temperatures may not function following discharge of the design electrical firing signal from a charged capacitor. Knowing functionality of these arbitrarily facing EBWs is crucial when making safety assessments of detonators in accidental fires. Orientation effects are only significant when the PETN is partially melted. The melting temperature can be measured with a differential scanning calorimeter. Nonmelting EBWs will be fully functional provided the detonator never exceeds 406 K (133 °C) for at least 1 h. Conversely, EBWs will not be functional once the average input pellet temperature exceeds 414 K (141 °C) for a least 1 min which is long enough to cause the PETN input pellet to completely melt. Functionality of the EBWs at temperatures between 406 and 414 K will depend on orientation and can be predicted using a stratification model for downward facing detonators but is more complex for arbitrary orientations. A conservative rule of thumb would be to assume that the EBWs are fully functional unless the PETN input pellet has completely melted.
This paper details a computational framework to produce automated, graphical workflows, and how this framework can be deployed to support complex modeling problems like those in nuclear engineering. Key benefits of the framework include: automating previously manual workflows; intuitive construction and communication of workflows through a graphical interface; and automated file transfer and handling for workflows deployed across heterogeneous computing resources. This paper demonstrates the framework's application to probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste. However, the framework is a general capability that can help users running a variety of computational studies.
Koper, Keith D.; Burlacu, Relu; Murray, Riley; Baker, Ben; Tibi, Rigobert; Mueen, Abdullah
Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ∼8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2 < ML < 4) recorded at local distances (< 150 km), for which depth estimation is especially challenging. We find that differential magnitudes (Mw= ML–Mc) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡ log10 (M0)+3log10 (fc (,and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. We also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.
A series of reactive-transport models of Enhanced Geothermal Systems (EGS) were constructed using the reactive transport code PFLOTRAN to examine the effect of matrix thermal contraction and mineral dissolution/precipitation on fracture flow in the context of grid cell size and model complexity. It was found that for thermal drawdown at production well, the impact of fracture zone grid cell size is negligible.
Although there are increasing numbers of distributed energy resources (DERs) and microgrids being deployed, current IEEE and utility standards generally strictly limit their interconnection inside secondary networks. Secondary networks are low-voltage meshed (non-radial) distribution systems that create redundancy in the path from the main grid source to each load. This redundancy provides a high level of immunity to disruptions in the distribution system, and thus extremely high reliability of electric power service. There are two main types of secondary networks, called grid and spot secondary networks, both of which are used worldwide. In the future, primary networks in distribution systems that might include looped or meshed distribution systems at the primary-voltage (medium-voltage) level may also become common as a means for improving distribution reliability and resilience.
Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO2 cycloaddition reaction, in metal-organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. We verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.
Tin-lead-antimony (50Sn–47Pb–3Sb wt.%) soldered assemblies were mechanically tested approximately 30 years after initial production and found to have solder joints of reduced strength. The microstructure of this solder alloy exhibits a ternary eutectic structure with Sn-rich, Pb-rich, and SnSb phases. Accelerated aging was performed to evaluate solder microstructural coarsening and associated strength of laboratory solder joints to correlate these properties to the “naturally aged” solder joints. Isothermal aging was conducted at room temperature, 55, 70, 100, and 135 °C and aging times that ranged from 0.1 to 365 days. The coarsening kinetics of the Pb-rich phase were determined through optical microscopy and image analysis methods established in previous studies on binary Sn–Pb solder. A kinetic equation was developed with time exponent n of 0.43 and activation energy of 24000 J/mol, suggesting grain boundary diffusion or other fast diffusion pathways controlling the microstructural evolution. Compression testing and Vickers microhardness showed significant strength loss within the first 20–30 days after soldering; then, the microstructure and mechanical properties changed more slowly over long periods of time. Further, by combining accelerated aging data and the microstructure-based kinetics, strength predictions were made that match well with the properties of the actual soldered assemblies naturally aged for 30 years. However, aging at the highest temperature of 135 °C produced anomalous behavior suggesting that extraneous aging mechanisms are active. Therefore, data obtained at this temperature or higher should not be used. Overall, the combined microstructural and mechanical property methods used in this study confirmed that the observed reduction in strength of ~ 30-year-old solder joints can be accounted for by the microstructural coarsening that takes place during long-term solid-state aging.
Significant vibration amplitudes and cycles can be produced when traffic signal structures with low inherent damping are excited near one of their natural frequencies. For the mitigation of wind-induced vibrations, dynamic vibration absorbers coupled to the structure are often used. Here, this research investigates the performance of a tapered impact damper, consisting of a hanging spring-mass oscillator inside a housing capable of reducing vibration amplitude over a broader frequency range than the conventional tuned mass damper. A nonlinear, two degree-of-freedom model is developed with coordinates representing the traffic structure and the tapered impact damper. This research focuses on the application of the harmonic balance method to approximate the periodic solutions of the nonlinear equations to compute the nonlinear dynamics of the damped traffic signal structure. After designing and manufacturing a tapered impact damper, the traffic signal structure is tested with and without the damper using free vibration snapback tests. The experimental frequency and damping backbone curves are used to validate the analytical model, and the effectiveness of the damper is discussed.
Janicki, Tesia D.; Liu, Rui; Im, Soohyun; Wan, Zhongyi; Butun, Serkan; Lu, Shaoning; Basit, Nasir; Voyles, Paul M.; Evans, Paul G.; Schmidt, J.R.
Strontium titanate (SrTiO3, STO) is a complex metal oxide with a cubic perovskite crystal structure. Due to its easily described and understood crystal structure in the cubic phase, STO is an ideal model system for exploring the mechanistic details of solid-phase epitaxy (SPE) in complex oxides. SPE is a crystallization approach that aims to guide crystal growth at low homologous temperatures to achieve targeted microstructures. Beyond planar thin films, SPE can also exploit the addition of a chemically inert, noncrystallizing, amorphous obstacle in the path of crystallization to generate complex three-dimensional structures. The introduction of this mask fundamentally alters the SPE process, inducing a transition from two- to three-dimensional geometries and from vertical to lateral crystal growth under the influence of the crystal/mask/amorphous boundary. Using a combination of molecular dynamics simulations and experiments, we identify several unique phenomena in the nanoscale growth behaviors in both conventional (unmasked) and masked SPE. Examining conventional SPE of STO, we find that crystallization at the interface is strongly correlated to, and potentially driven by, density fluctuations in the region of the amorphous STO near the crystalline/amorphous interface with a strong facet dependence. In the masked case, we find that the crystalline growth front becomes nonplanar near contact with the mask. We also observe a minimum vertical growth requirement prior to lateral crystallization. Both phenomena depend on the relative bulk and interfacial free energies of the three-phase (crystal/mask/amorphous) system.
Many technologies require stable or metastable surface morphology. In this paper we study the factors that control the metastability of a common feature of rough surfaces: "hillocks."We use low energy electron microscopy to follow the evolution of the individual atomic steps in hillocks on Pd(111). We show that the uppermost island in the stack often adopts a static, metastable configuration. Modeling this result shows that the degree of the metastability depends on the configuration of steps dozens of atomic layers lower. Our model allows us to link surface metastability to the atomic processes of surface evolution.
Polymers are an effective test bed for studying topological constraints in condensed matter due to a wide array of synthetically available chain topologies. When linear and ring polymers are blended together, emergent rheological properties are observed as the blend can be more viscous than either of the individual components. This emergent behavior arises since ring-linear blends can form long-lived topological constraints as the linear polymers thread the ring polymers. Here, we demonstrate how the Gauss linking integral can be used to efficiently evaluate the relaxation of topological constraints in ring-linear polymer blends. For majority-linear blends, the relaxation rate of topological constraints depends primarily on reptation of the linear polymers, resulting in the diffusive time τd,R for rings of length NR blended with linear chains of length Nl to scale as τd,R∼NR2NL3.4.
The transmission interference fringe (TIF) technique was developed to visualize the dynamics of evaporating droplets based on the Reflection Interference Fringe (RIF) technique for micro-sized droplets. The geometric formulation was conducted to determine the contact angle (CA) and height of macro-sized droplets without the need for the prism used in RIF. The TIF characteristics were analyzed through experiments and simulations to demonstrate a wider range of contact angles from 0 to 90°, in contrast to RIF's limited range of 0-30°. TIF was utilized to visualize the dynamic evaporation of droplets in the constant contact radius (CCR) mode, observing the droplet profile change from convex-only to convex-concave at the end of dry-out from the interference fringe formation. The TIF also observed the contact angle increase from the fringe radius increase. This observation is uniquely reported as the interference fringe (IF) technique can detect the formation of interference fringe between the reflection from the center convex profile and the reflection from the edge concave profile on the far-field screen. Unlike general microscopy techniques, TIF can detect far-field interference fringes as it focuses beyond the droplet-substrate interface. The formation of the convex-concave profile during CCR evaporation is believed to be influenced by the non-uniform evaporative flux along the droplet surface.
Caskey, Susan; Keating, Charles B.; Katina, Polinpapilinho F.; Bradley, Joseph M.; Hodge, Richard; Martin, James N.
The purpose of this paper is to explore the concept of ‘enterprise’ in the context of Systems Engineering (SE). The term ‘enterprise’ has been used extensively to generally describe large complex entities that have an extensive scope of operations. However, a deeper examination of ‘enterprise’ significance for SE can provide insights as our challenges continue with increasingly complex, uncertain, ambiguous, and integrated entities struggling to thrive in the future. The paper explores three central topics. First, the concept of enterprise is introduced as a central aspect of the future focus for SE, as recognized in the INCOSE SE Vision 2035. Second, a more detailed examination of the enterprise concept is developed in relationship to SE. The thrust of this examination is to understand the nature and role of ‘enterprise’ across a broad spectrum of literature and knowledge, ultimately providing a more informed perspective of enterprise for SE. As part of this exploration, a bibliometric analysis of the term ‘enterprise’ is performed. This exploration extracts key themes (clusters) in the ‘enterprise’ literature. Third, challenges for further development and inculcation of ‘enterprise’ within the SE discipline and support for realization of the SE 2035 Vision are suggested. These challenges point out the need to ‘think differently’ about ‘enterprise’ within the SE context. ‘Enterprise’ is proposed as a central, albeit different, perspective for the SE discipline. Finally, the paper closes with a first–generation perspective for ‘enterprise’ in pursuit of the SE Vision 2035.
Organizations play a key role in supporting various societal functions, ranging from environmental governance to the manufacturing of goods. Here, the behaviors of organization are impacted by various influences, including information, technology, authority, economic leverage, historical experiences, and external factors, such as regulations. This paper introduces a generalized framework, focused on the relative structure of an organization (tight vs. loose), that can be used to understand how different influence pathways can impact decision-making within differently structured organizations. This generalized framework is then translated into a modeling and simulation platform to support and assess implications of these structural differences in resilience to disinformation (measured by organizational behaviors of timeliness and inclusion of quality information) using a systems dynamics approach Preliminary results indicate that a tightly structured organization may be less timely at processing information but could be more resilient against using poor quality information in organizational decisions compared to a loosely structured organization. Ongoing work is underway to understand the robustness of these findings and to validate current model design activities with empirical insights.
Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.
The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.
A novel approach is presented for parametric analysis of remotely-sensed ground and cloud clutter. A spatial-frequency-domain clutter model is generated from an extensive, one-year database of weather imagery and statistics are given for each spatial frequency. This approach is useful for the analysis and design of spatial and temporal clutter-rejection filters, which can also be analyzed in this domain.
Anomalous behavior poses serious risks to assured performance and reliability of complex, high-consequence systems. For spaceborne assets and their state-of-health (SOH) telemetry, the challenges of high-dimensional data of varying data types are compounded by computational limitations from size, weight, and power (SWaP) constraints as well as data availability. Automated anomaly detection methods tend to perform poorly under these constraints, while current operational approaches can introduce delays in response time due to the manual, retrospective processes for understanding system failures. As a result, presently deployed space systems, and those deployed in the near future, face situations where mission operations might be delayed or only be able to operate under degraded capabilities. Here, we examine a near-term lightweight solution that provides real-time detection capabilities for rare events and assess state-of-the-art anomaly detection techniques against real SOH telemetry from space platforms. This report describes our methodology and research, which could support more automated capabilities for comprehensive space operations as well as for other resource-constrained edge applications.
We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.
In this study, different approaches in performance assessment (PA) of the long-term safety of a repository for radioactive waste were examined. This investigation was carried out as part of the DECOVALEX-2023 project, an international collaborative effort for research and model comparison. One specific task of the DECOVALEX-2023 project was the Salt Performance Assessment Modelling task (Salt PA), which aimed at comparing various models and methods employed in the performance assessment of deep geological repositories in salt. In the context of the Salt PA task, three distinct teams from SNL (United States), Quintessa Ltd (United Kingdom), and GRS (Germany) examined the consequences of employing different levels of abstractions when modelling the repository's geometry and implementing various features and processes, using the example of a simple hypothetical repository structure in domal salt. Each team applied their own tools: PFLOTRAN (SNL), QPAC (Quintessa) and LOPOS (GRS). These differ essentially regarding numerical concept and degree of detail in the representation of the underlying physical processes. The discussion focused on when simplifications can be appropriately applied and what consequences result from them. Furthermore, it was explored when and if a higher level of fidelity in geometry or physical processes is required.
For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.
U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping costs reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security into the design process can provide robust, cost-effective, and sufficient physical protection systems. The purpose of this report is to capture lessons learned by the Advanced Reactor Safeguards and Security (ARSS) program that may be beneficial for other advanced and small modular reactor (SMR) vendors to use when developing security systems and postures. This report will capture relevant information that can be used in the security-by-design (SeBD) process for SMR and microreactor vendors.
Agafonov, Andrei; Pineda-Romero, Nayely; Witman, Matthew D.; Nassif, Vivian; Vaughan, Gavin B.M.; Lei, Lei; Ling, Sanliang; Grant, David M.; Dornheim, Martin; Allendorf, Mark; Stavila, Vitalie; Zlotea, Claudia
The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb)75Cr25-xFex system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.
This report summarizes the work performed under the author's two-year John von Neumann LDRD project, which involves the non-intrusive surrogate modeling of dynamical systems with remarkable structural properties. After a brief introduction to the topic, technical accomplishments and project metrics are reviewed including peer-reviewed publications, software releases, external presentations and colloquia, as well as organized conference sessions and minisymposia. The report concludes with a summary of ongoing projects and collaborations which utilize the results of this work.
Barnard, James P.; Shen, Jianan; Tsai, Benson K.; Zhang, Yizhi; Chhabra, Max R.; Sarma, Raktim S.; Siddiqui, Aleem; Wang, Haiyan
Magnetic and ferroelectric oxide thin films have long been studied for their applications in electronics, optics, and sensors. The properties of these oxide thin films are highly dependent on the film growth quality and conditions. To maximize the film quality, epitaxial oxide thin films are frequently grown on single-crystal oxide substrates such as strontium titanate (SrTiO3) and lanthanum aluminate (LaAlO3) to satisfy lattice matching and minimize defect formation. However, these single-crystal oxide substrates cannot readily be used in practical applications due to their high cost, limited availability, and small wafer sizes. One leading solution to this challenge is film transfer. In this demonstration, a material from a new class of multiferroic oxides is selected, namely bismuth-based layered oxides, for the transfer. A water-soluble sacrificial layer of Sr3Al2O6 is inserted between the oxide substrate and the film, enabling the release of the film from the original substrate onto a polymer support layer. The films are transferred onto new substrates of silicon and lithium niobate (LiNbO3) and the polymer layer is removed. These substrates allow for the future design of electronic and optical devices as well as sensors using this new group of multiferroic layered oxide films.
In this letter, we present interfacial fracture toughness data for a polymer-metal interface where tests were conducted at various test temperatures T and loading rates δ˙. An adhesively bonded asymmetric double cantilever beam (ADCB) specimen was utilized to measure toughness. ADCB specimens were created by bonding a thinner, upper adherend to a thicker, lower adherend (both 6061 T6 aluminum) using a thin layer of epoxy adhesive, such that the crack propagated along the interface between the thinner adherend and the epoxy layer. The specimens were tested at T from 25 to 65 °C and δ˙ from 0.002 to 0.2 mm/s. The measured interfacial toughness Γ increased as both T and δ˙ increased. For an ADCB specimen loaded at a constant δ˙, the energy release rate G increases as the crack length a increases. For this reason, we defined rate effects in terms of the rate of change in the energy release rate G˙. Although not rigorously correct, a formal application of time–temperature superposition (TTS) analysis to the Γ data provided useful insights on the observed dependencies. In the TTS-shifted data, Γ decreased and then increased for monotonically increasing G˙. Thus, the TTS analysis suggests that there is a minimum value of Γ. This minimum value could be used to define a lower bound in Γ when designing critical engineering applications that are subjected to T and δ˙ excursions.
Hydrogen is known to embrittle austenitic stainless steels, which are widely used in high-pressure hydrogen storage and delivery systems, but the mechanisms that lead to such material degradation are still being elucidated. The current work investigates the deformation behavior of single crystal austenitic stainless steel 316L through combined uniaxial tensile testing, characterization and atomistic simulations. Thermally precharged hydrogen is shown to increase the critical resolved shear stress (CRSS) without previously reported deviations from Schmid's law. Molecular dynamics simulations further expose the statistical nature of the hydrogen and vacancy contributions to the CRSS in the presence of alloying. Slip distribution quantification over large in-plane distances (>1 mm), achieved via atomic force microscopy (AFM), highlights the role of hydrogen increasing the degree of slip localization in both single and multiple slip configurations. The most active slip bands accumulate significantly more deformation in hydrogen precharged specimens, with potential implications for damage nucleation. For 〈110〉 tensile loading, slip localization further enhances the activity of secondary slip, increases the density of geometrically necessary dislocations and leads to a distinct lattice rotation behavior compared to hydrogen-free specimens, as evidenced by electron backscatter diffraction (EBSD) maps. The results of this study provide a more comprehensive picture of the deformation aspect of hydrogen embrittlement in austenitic stainless steels.
Helium-4-based scintillation detector technology is emerging as a strong alternative to pulse-shape discrimination-capable organic scintillators for fast neutron detection and spectroscopy, particularly in extreme gamma-ray environments. The 4He detector is intrinsically insensitive to gamma radiation, as it has a relatively low cross-section for gamma-ray interactions, and the stopping power of electrons in the 4He medium is low compared to that of 4He recoil nuclei. Consequently, gamma rays can be discriminated by simple energy deposition thresholding instead of the more complex pulse shape analysis. The energy resolution of 4He scintillation detectors has not yet been well-characterized over a broad range of energy depositions, which limits the ability to deconvolve the source spectra. In this work, an experiment was performed to characterize the response of an Arktis S670 4He detector to nuclear recoils up to 9 MeV. The 4He detector was positioned in the center of a semicircular array of organic scintillation detectors operated in coincidence. Deuterium–deuterium and deuterium–tritium neutron generators provided monoenergetic neutrons, yielding geometrically constrained nuclear recoils ranging from 0.0925 to 8.87 MeV. The detector response provides evidence for scintillation linearity beyond the previously reported energy range. The measured response was used to develop an energy resolution function applicable to this energy range for use in high-fidelity detector simulations needed by future applications.
Shock invariant relationship, which was conceived for inert shock waves to derive the 4th power relationship between shock pressure and maximum strain rate, is generalized for reactive shock waves such as Chapman-Jouget detonation and shock-induced vaporization. The generalization, based on the first-order reaction models, is a power function relationship between overall dissipated energy ( Δ e d i s ) and reaction time Δ τ such that Δ e d i s Δ τ 1 / α = constant , where the power coefficient α is found to be in the range of 2/3-4. Experimental data, though scarce, are consistent with the generalization. Implication of the generalization for inert shocks is also considered and suggests a broad range of the 4th power coefficient including an inequality equation that constrains the shock and particle velocity relationship.
Shrestha, Shilva; Goswami, Shubhasish; Banerjee, Deepanwita; Garcia, Valentina; Zhou, Elizabeth; Olmsted, Charles N.; Majumder, Erica L.W.; Kumar, Deepak; Awasthi, Deepika; Mukhopadhyay, Aindrila; Singer, Steven W.; Gladden, John M.; Simmons, Blake A.; Choudhary, Hemant
The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.
Morris, Joseph P.; Pyrak-Nolte, Laura J.; Yoon, Hongkyu; Bobet, Antonio; Jiang, Liyang
In this article, We present results from a recent exercise where participating organizations were asked to provide model-based blind predictions of damage evolution in 3D-printed geomaterial analogue test articles. Participants were provided with a range of data characterizing both the undamaged state (e.g., ultrasonic measurements) and damage evolution (e.g., 3-point bending, unconfined compression, and Brazilian testing) of the material. In this paper, we focus on comparisons between the participants’ predictions and the previously secret challenge problem experimental observations. We present valuable lessons learned for the application of numerical methods to deformation and failure in brittle-ductile materials. The exercise also enables us to identify which specific types of calibration data were of most utility to the participants in developing their predictions. Further, we identify additional data that would have been useful for participants to improve the confidence of their predictions. Consequently, this work improves our understanding of how to better characterize a material to enable more accurate prediction of damage and failure propagation in natural and engineered brittle-ductile materials.
This research article presents a robust approach to optimizing the layout of pressure sensors around an airfoil. A genetic algorithm and a sequential quadratic programming algorithm are employed to derive a sensor layout best suited to represent the expected pressure distribution and, thus, the lift force. The fact that both optimization routines converge to almost identical sensor layouts suggests that an optimum exists and is reached. By comparing against a cosine-spaced sensor layout, it is demonstrated that the underlying pressure distribution can be captured more accurately with the presented layout optimization approach. Conversely, a 39 %-55 % reduction in the number of sensors compared to cosine spacing is achievable without loss in lift prediction accuracy. Given these benefits, an optimized sensor layout improves the data quality, reduces unnecessary equipment and saves cost in experimental setups. While the optimization routine is demonstrated based on the generic example of the IEA 15 MW reference wind turbine, it is suitable for a wide range of applications requiring pressure measurements around airfoils.
The list of standards, best practice, and regulations below are intended to give insight into what resources are available for developing a chemical control regime as well as information on what regulations other countries have used to implement such a regime. This list is not intended to be all inclusive and other regulations and standards related to controlling hazardous chemicals exist and should be consulted.
Remote radioactive source applications require frequent transportation of sources from storage locations to remote sites. This introduces risk of theft of a source during the transportation process, with the level of risk proportional to the radioactivity of the source. To that end, theft of smaller sources, such as microcurie-level moisture density gauges, are of minor concern, but larger sources, such as those used for radiography and well logging, present more risk. Radiography sources include 192Ir, 75Se, or 60Co radionuclides with radioactivity amounts at or exceeding IAEA Category 2. Well-logging sources, primarily 241Am/Be, are used for their neutron-emission properties. 137Cs is also used in well-logging at lower activities than in radiography but at levels that still present some risk. The vulnerability for malicious use of such sources to cause contamination and associated economic effects is dependent on the elemental chemical and physical properties, especially melting point and bulk modulus. Theft of radiography sources is somewhat common, well-logging sources less so. Theft of sources commonly occurs in concert with theft of the vehicle, with the source subsequently abandoned. There have been some instances where a source appears to have been specifically targeted. There are a variety of security measures and protocols, available and under development, to mitigate the risk of theft and assist in source recovery.
On Wednesday, March 8th and Thursday, March 9th, 2023, the University of Texas at Austin hosted Sandia National Laboratories (Sandia) for “Sandia Day 2023 at UT Austin” with the intention of reviewing, planning and shaping ongoing and future collaborations in key areas that reflect each organization’s priorities and strengths. The event brought together nearly 100 UT and Sandia participants including executive leadership, researchers, faculty, staff, and students. The primary sessions of Sandia Day consisted of a half-day tour of select J.J. Pickle Research Campus facilities, a networking happy hour, leadership meetings, presentations by both Sandia and UT Austin representatives in areas of research strategic priorities: Grid Resiliency, Examining Climate Change, and Microelectronics, and a research poster session with lunch. The group also discussed growth opportunities in the following research areas: nuclear and radiation engineering, pulsed power and fusion physics, and digital engineering, specifically as it related to materials discovery and advanced manufacturing. Appendix A contains the full Sandia Day agenda.