For computational physics simulations, code verification plays a major role in establishing the credibility of the results by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, surface integral equations, such as the method-of-moments implementation of the magnetic-field integral equation, are frequently used to solve Maxwell's equations on the surfaces of electromagnetic scatterers. These electromagnetic surface integral equations yield many code-verification challenges due to the various sources of numerical error and their possible interactions. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources. We demonstrate the effectiveness of these approaches for cases with and without coding errors.
This report describes the structure and content of an open dataset created for the purpose of testing and validating PV module temperature prediction models and their parameters. The dataset contains the main environmental parameters that affect temperature: irradiance, ambient temperature, wind speed and down-welling infrared radiation, as well as measured back-of-module temperature.
In this study, oils from various sources were subjected to pyrolysis conditions; that is, without oxidizer, as the samples were heated to 500 °C, and held at that temperature. The oils studied included: (1) heavy oil from Grassy Creek, Missouri; (2) oil from tar sands of Asphalt Ridge in Utah; (3) mid-continent oil shales of three formations (two of Chattanooga formation, Pennsylvanian (age) formation, and Woodford formation); and (4) a Colorado Piceance Basin shale. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) with either gas chromatography (GC) or mass spectrometry (MS) were used to quantify the produced gases evolved in the tests. Purge gases of helium, argon, and humid carbon dioxide were utilized. Larger scale pyrolysis tests were conducted in a tube furnace coupled to a MS and a GC. The results consistently showed that pyrolysis occurred between 300 °C and 500 °C, with the majority of gases being mainly hydrogen and light alkanes. This behavior was essentially consistent, regardless of the oil source.
The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics-informed deep-learning framework for the discovery of hyperelastic constitutive model parameterizations given full-field surface displacement data and global force-displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non-linear elastic behaviour, elastomer behaviour with finite strain lock-up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full-field experimental data are utilized to calibrate constitutive models under finite deformations.
We report ion trapping in crystalline domains of electrochemical transistors can be used to create a device capable of both volatile and non-volatile operation.
The marine energy (ME) industry historically lacked a standardized data processing toolkit for common tasks such as data ingestion, quality control, and visualization. The marine and hydrokinetic toolkit (MHKiT) solved this issue by providing a public software deployment (open-source and free) toolkit for the ME industry to store and maintain commonly used functionality for wave, tidal, and river energy. This paper demonstrates an initial model verification study in MHKiT. Using Delft3D, a numerical model of the Tanana River Test Site (TRTS) at Nenana, Alaska was created. Field data from the site was collected using an Acoustic Doppler Current Profiler (ADCP) at the proposed Current Energy Converter (CEC) locations. MHKiT is used to process model simulations from Delft3D and compare them to the transect data from the ADCP measurements at TRTS. The ability to use a single tool to process simulation and field data demonstrates the ease at which the ME industry can obtain results and collaborate across specialties, reducing errors and increasing efficiency.
Simulation of radar returns, full-duplex systems, and signal repeaters require hundreds of ns of programmable broadband radio frequency (RF) delay in the signal path to simulate large distances in the case of radar returns, for signal cancellation in full-duplex, and for isolation from reflections in signal repeaters. However, programmable broadband RF delay has been limited to ones of ns due to challenges in miniaturization with low loss and low power consumption. In this work, we present a 0.2–2 GHz digitally programmable RF delay element based on a time-interleaved multistage switched-capacitor (TIMS-SC) approach. The proposed approach enables hundreds of ns of broadband RF delay by employing sample time expansion in multiple stages of switched-capacitor storage elements. Further, the delay element was implemented in a 45 nm SOI CMOS process and achieves a 2.55–448.6 ns programmable delay range with < 0.12% delay variation across 1.8 GHz of bandwidth at maximum delay, 2.42 ns programmable delay steps, and 330 ns/mm 2 area efficiency. Through the proposed approach, the device shows minimal delay change across a -40 °C to 85 °C temperature range and < 0.25 dB gain variation across delay settings. The device achieves 26 dB gain, 7.4 dB noise figure, and consumes 74 mW from a 1 V supply with an active area of 1.36 mm 2.
X-ray imaging offers unique possibilities for Digital Image Correlation (DIC), opening the door for full-field deformation measurements of a test article in complex environments where optical DIC suffers severe biases or is impossible. While X-ray DIC has been performed in the past with standard DIC codes designed for optical images, the path-integrated nature of X-ray images places constraints on the experimental setup, predominantly that only a single surface of interest moves/deforms. These requirements are difficult to realize for many practical situations and limit the amount of information that can be garnered in a single test. Other X-ray based diagnostics such as Digital Volume Correlation (DVC) and Projection DVC (P-DVC) overcome these obstacles, but DVC is limited to quasi-static tests, and both DVC and P-DVC necessitate high-resolution computed tomography (CT) scan(s) and often require a potentially invasive pattern throughout the volume of the specimen. Here this work presents a novel approach to measure time-resolved displacements and strains on multiple surfaces from a single series of 2D, path-integrated (PI) X-ray images, called PI-DIC. The principle of optical flow or conservation of intensity—the foundation of DIC—was reframed for path-integrated images, for an exemplar setup comprised of two plates moving and deforming independently. Synthetic images were generated for rigid translations, rigid rotations, and uniform stretches, where each plate underwent a unique motion/deformation. Experimental specimens were fabricated (either an aluminum plate with tantalum features or a plastic plate with steel features) and the two specimens were independently translated. PI-DIC was successfully demonstrated with the synthetic images and validated with the experimental images. Prescribed displacements were recovered for each plate from the single set of path-integrated, deformed images. Errors were approximately 0.02 px for the synthetic images with 1.5% image noise, and 0.05 px for the experimental images. These results provide the foundation for PI-DIC to measure motion and deformation of multiple, independent surfaces with subpixel accuracy from a single series of path-integrated X-ray images.
We investigated the electronic interactions between transition metal phthalocyanine (TMPc's) on a refractory transition metal nitride support, specifically copper phthalocyanine (CuPc) on titanium nitride (TiN). X-ray Photoelectron Spectroscopy (XPS) results suggest a presence of a few nanometer native oxide layer on the surface of the TiN nanoparticles, which consists of TiN, TiO2, and Titanium oxynitrides (TixOyNz). A TiNCuPc nanocomposite was synthesized via a simple mixing method due to the strong binding between CuPc and TiN confirmed by density functional theory (DFT) calculations. Both XPS data and DFT calculations revealed an electron transfer from TiN substrate to CuPc molecule. The nature of charge transfer is not influenced by the presence of an oxide layer on the surface of TiN. Substantial deviations are however found between photoelectron emission microscopy (PEEM) measured work function for TiN (4.68 eV) and theoretically calculated work function for pristine stoichiometric TiN (2.63 eV). This behavior is attributed to the presence of an oxide layer on the TiN surface. TiNCuPc composite system has a work function value between those of TiN and CuPc. Our studies open up an opportunity to apply a new class of materials based on transition metal phthalocyanine/transition metal nitride composites to catalysis and optoelectronic devices.
Atomic-precision advanced manufacturing enables unique silicon quantum electronics built on quantum dots fabricated from small numbers of phosphorus dopants. The number of dopant atoms comprising a dot plays a central role in determining the behavior of charge and spin confined to the dots and thus overall device performance. In this work, we use both theoretical and experimental techniques to explore the combined impact of lithographic variation and stochastic kinetics on the number of P incorporations in quantum dots made using these techniques and how this variation changes as a function of the size of the dot. Using a kinetic model of PH3 dissociation augmented with novel reaction barriers, we demonstrate that for a 2 × 3 silicon dimer window the probability that no donor incorporates goes to zero, allowing for certainty in the placement of at least one donor. However, this still comes with some uncertainty in the precise number of incorporated donors (either one or two), and this variability may still impact certain applications. We also examine the impact of the size of the initial lithographic window, finding that the incorporation fraction saturates to δ-layer-like coverage as the circumference-to-area ratio decreases. We predict that this incorporation fraction depends strongly on the dosage of the precursor and that the standard deviation of the number of incorporations scales as ∼√n, as would be expected for a sequence of largely independent incorporation events. Finally, we characterize an array of 36 experimentally prepared multidonor 3 × 3 nm lithographic windows with scanning tunneling microscopy, measuring the fidelity of the lithography to the desired array and the final location of PHx fragments within these lithographic windows. We use our kinetic model to examine the expected variability due to the observed lithographic error, predicting a negligible impact on incorporation statistics. We find good agreement between our model and the inferred incorporation locations in these windows from scanning tunneling microscope measurements.
Here, this Letter reports the growth, fabrication, and characterization of molecular beam epitaxy (MBE)-grown quaternary InAlGaAs/GaAs quantum dot (QD) lasers emitting at sub-900 nm. The presence of Al in QD-based active regions acts as the origin of defects and non-radiative recombination centers. Applying optimized thermal annealing annihilates the defects in p-i-n diodes, thus lowering the reverse leakage current by six orders of magnitude compared to as-grown devices. A systematic improvement in the optical properties of the devices is also observed in the laser devices with increasing annealing time. At an annealing temperature of 700°C for 180 s, Fabry–Pérot lasers exhibit a lower pulsed threshold current density at infinite length of 570 A/cm2.
The characterization of the uncertainty in radiation damage metrics presents many challenges. This paper examines the current approaches to characterizing radiation damage metrics such as hydrogen and helium gas production, material heating, trapped charge in microelectronics, and lattice displacement damage. Critical uncertainty aspects go beyond just the material cross sections and involve the consideration of energy-dependent cross reaction correlations, the recoil ion energy spectrum, and models used for the partitioning of the recoil ion energy into various forms of energy deposition. This paper starts with a review of terminology and then examines the current approaches in the characterization of uncertainty in radiation damage metrics for several applications. The major deficiencies in the uncertainty of the damage metric characterization are also identified.
Oxidative instability of the liquid electrolyte at or near battery cathode oxide surfaces has significant detrimental effects on batteries. Organic solvent molecules are often the fuel and precursors of such degradation processes, releasing electrons and protons that react with cathode oxides and electrolyte anions. These reactions contribute to cathode-electrolyte interphase (CEI) film formation, transition-metal ion dissolution, and phase transformation of the surface regions of the cathode. Here we apply density functional theory calculations to examine four criteria of oxidative stability (oxidation potential, hydrogen removal energies, and initial reactivity on two types of oxide facets) using four different solvent/additive molecules (ethylene carbonate, fluoroethylene carbonate, 1,3-dioxolane, and dimethyl ether). The ranking of molecular stability differs with each criterion. Surprisingly, the all-oxygen-terminated basal planes of layered oxides exhibit lower reaction barriers than spinel surface facets with exposed transition-metal cations, especially for ether solvents; the calculations also suggest basal planes contribute to the dissolution of transition-metal ions. The structure-degradation relation complexity underscores the challenge of understanding the function of the CEI but also offers a guide to future degradation-mitigation strategies including facet engineering. Our predictions and models help establish a framework for future studies relevant to high-voltage conditions.
Kim, Sanghyeon; Hahn, Nathan H.; Fister, Timothy T.; Leon, Noel J.; Lin, Xiao M.; Park, Haesun; Zapol, Peter; Lapidus, Saul H.; Liao, Chen; Vaughey, John T.
Calcium-ion batteries (CIBs) are a promising next-generation energy storage system given the low redox potential of calcium metal and high abundance of calcium compounds. For continued CIB development, the discovery of high energy density calcium ion cathodes is needed to achieve practical energy density values. Here, we report on the use of elemental Se as a promising candidate for a high-capacity cathode material for CIBs that operates via a conversion mechanism in a Ca metal battery at room temperature. The Se electrodes demonstrate a reversible specific capacity of 180 mA h g-1 with a discharge plateau near 2.0 V (vs Ca2+/Ca) at 100 mA g-1 using an electrolyte based on the salt calcium tetrakis(hexafluoroisopropyloxy)borate (Ca(B(hfip)4)2) in 1,2-dimethoxyethane (DME) and Ca metal. The reversible electrochemical reaction between calcium and selenium is investigated using operando synchrotron-based techniques and the possible reaction mechanism discussed.
Practical Mg batteries require electrolytes that are stable both toward reduction by Mg metal and oxidation by high voltage cathodes. State-of-the-art Mg electrolytes based on weakly coordinating Mg salts utilize standard ether-type solvents (usually glymes) due to their reductive stability. However, the oxidative stabilities of these solvents are less than ideal, leading to difficulties in realizing the high oxidative stabilities of recently developed salts. On the other hand, alternative solvents with greater oxidative stability are typically unable to support Mg cycling. In this work, we report a selective solvation approach involving the combination of glyme and hydrofluoroether solvents. Selective solvation of Mg2+ by the glyme solvent component increases the oxidative stability of the glyme while maintaining sufficient reductive stability of the non-coordinating hydrofluoroether. We show that this approach enables the design of electrolytes with greater oxidative stability than glyme-only electrolytes while retaining enough reductive stability to cycle Mg metal. We also relate the influence of various coordination interactions among the solvents and anions with Mg2+ to their electrochemical stabilities to better inform the design of future electrolytes.
Mixed-acid vanadium redox flow batteries (VRFBs) are an attractive option to increase energy density and temperature stability relative to conventional VRFBs for grid energy storage applications. However, the inclusion of hydrochloric acid introduces a significant safety risk through chlorine gas (Cl2) evolution. Here, we present the first direct measurements of Cl2 generation in a mixed-acid VRFB. Cl2 is generated through an electrochemical reaction when the system is charged above ∼74% state of charge with concentrations exceeding 3% of the system headspace. We explore how Cl2 evolution is enabled and propose mitigation strategies.
Coupling the mimetic finite difference method with the tensor-train format results in a very effective method for low-rank numerical approximations of the solutions of the time-dependent Maxwell wave propagation equations in three dimensions. To this end, we discretize the curl operators on the primal/dual tensor product grid complex and we couple the space discretization with a staggered-in-time second-order accurate time-marching scheme. The resulting solver is accurate to the second order in time and space, and still compatible, so that the approximation of the magnetic flux field has zero discrete divergence with a discrepancy close to the machine precision level. Our approach is not limited to the second-order of accuracy. We can devise higher-order formulations in space through suitable extensions of the tensor-train stencil to compute the derivatives of the mimetic differential operators. Employing the tensor-train format improves the solver performance by orders of magnitude in terms of CPU time and memory storage. A final set of numerical experiments confirms this expectation.
Numerous experimental investigations indicated that expansive clays such as montmorillonite can intercalate CO2 preferentially into their interlayers and therefore potentially act as a material for CO2 separation, capture, and storage. However, an understanding of the energy-structure relationship during the intercalation of CO2 into clay interlayers remains elusive. Here, we use metadynamics molecular dynamics simulations to elucidate the energy landscape associated with CO2 intercalation. Our free energy calculations indicate that CO2 favorably partitions into nanoconfined water in clay interlayers from a gas phase, leading to an increase in the CO2/H2O ratio in clay interlayers as compared to that in bulk water. CO2 molecules prefer to be located at the centers of charge-neutral hydrophobic siloxane rings, whereas interlayer spaces close to structural charges tend to avoid CO2 intercalation. The structural charge distribution significantly affects the amount of CO2 intercalated in the interlayers. These results provide a mechanistic understanding of CO2 intercalation in clays for CO2 separation, capture, and storage.
Here we present a classical molecular-spin dynamics (MSD) methodology that enables accurate computations of the temperature dependence of the magnetocrystalline anisotropy as well as magnetoelastic properties of magnetic materials. The nonmagnetic interactions are accounted for by a spectral neighbor analysis potential (SNAP) machine-learned interatomic potential, whereas the magnetoelastic contributions are accounted for using a combination of an extended Heisenberg Hamiltonian and a Néel pair interaction model, representing both the exchange interaction and spin-orbit-coupling effects, respectively. All magnetoelastic potential components are parameterized using a combination of first-principles and experimental data. Our framework is applied to the α phase of iron. Initial testing of our MSD model is done using a 0 K parametrization of the Néel interaction model. After this, we examine how individual Néel parameters impact the $B$1 and $B$2 magnetostrictive coefficients using a moment-independent δ sensitivity analysis. The results from this study are then used to initialize a genetic algorithm optimization which explores the Néel parameter phase space and tries to minimize the error in the B1 and B2 magnetostrictive coefficients in the range of 0–1200 K. Our results show that while both the 0 K and genetic algorithm optimized parametrization provide good experimental agreement for $B$1 and $B$2, only the genetic algorithm optimized results can capture the second peak in the $B$1 magnetostrictive coefficient which occurs near approximately 800 K.
Alkyl-substituted oxetanes are cyclic ethers formed via unimolecular reactions of QOOH radicals produced via a six-membered transition state in the preceding isomerization step of organic peroxy radicals, ROO. Owing to radical isomer-specific formation pathways, cyclic ethers are unambiguous proxies for inferring QOOH reaction rates. Therefore, accounting for subsequent oxidation of cyclic ethers is important in order to accurately determine rates for QOOH → products. Cyclic ethers can react via unimolecular reaction (ring-opening) or via bimolecular reaction with O2 to form cyclic ether-peroxy adducts. The computations herein provide reaction mechanisms and theoretical rate coefficients for the former type in order to determine competing pathways for the cyclic ether radicals. Rate coefficients of unimolecular reactions of 2,4-dimethyloxetanyl radicals were computed using master equation modeling from 0.01 to 100 atm and from 300 to 1000 K. Coupled-cluster methods were utilized for stationary-point energy calculations, and uncertainties in the computed rate coefficients were accounted for using variation in barrier heights and in well depths. The potential energy surfaces reveal accessible channels to several species via crossover reactions, such as 2-methyltetrahydrofuran-5-yl and pentanonyl isomers. For the range of temperature over which 2,4-dimethyloxetane forms during n-pentane oxidation, the following are the major channels: 2,4-dimethyloxetan-1-yl → acetaldehyde + allyl, 2,4-dimethyloxetan-2-yl → propene + acetyl, and 2,4-dimethyloxetan-3-yl → 3-butenal + methyl, or, 1-penten-3-yl-4-ol. Well-skipping reactions were significant in a number of channels and also exhibited a markedly different pressure dependence. The calculations show that rate coefficients for ring-opening are approximately an order of magnitude lower for the tertiary 2,4-dimethyloxetanyl radicals than for the primary and secondary 2,4-dimethyloxetanyl radicals. Unlike for reactions of the corresponding ROO radicals, however, unimolecular rate coefficients are independent of the stereochemistry. Moreover, rate coefficients of cyclic ether radical ring-opening are of the same order of magnitude as O2 addition, underscoring the point that a competing network of reactions is necessary to include for accurate chemical kinetics modeling of species profiles for cyclic ethers.
Tungsten (W) is a material of choice for the divertor material due to its high melting temperature, thermal conductivity, and sputtering threshold. However, W has a very high brittle-to-ductile transition temperature, and at fusion reactor temperatures (≥1000 K), it may undergo recrystallization and grain growth. Dispersion-strengthening W with zirconium carbide (ZrC) can improve ductility and limit grain growth, but much of the effects of the dispersoids on microstructural evolution and thermomechanical properties at high temperatures are still unknown. We present a machine learned Spectral Neighbor Analysis Potential for W-ZrC that can now be used to study these materials. In order to construct a potential suitable for large-scale atomistic simulations at fusion reactor temperatures, it is necessary to train on ab initio data generated for a diverse set of structures, chemical environments, and temperatures. Further accuracy and stability tests of the potential were achieved using objective functions for both material properties and high temperature stability. Validation of lattice parameters, surface energies, bulk moduli, and thermal expansion is confirmed on the optimized potential. Tensile tests of W/ZrC bicrystals show that although the W(110)-ZrC(111) C-terminated bicrystal has the highest ultimate tensile strength (UTS) at room temperature, observed strength decreases with increasing temperature. At 2500 K, the terminating C layer diffuses into the W, resulting in a weaker W-Zr interface. Meanwhile, the W(110)-ZrC(111) Zr-terminated bicrystal has the highest UTS at 2500 K.
Here in this work, we investigate the applicability of the harmonic balance method (HBM) to predict periodic solutions of a single degree-of-freedom forced Duffing oscillator with freeplay nonlinearity. By studying the route to impact, which refers to a parametric study as the contact stiffness increases from soft to hard, the convergence behavior of the HBM can be understood in terms of the strength of the non-smooth forcing term. HBM results are compared to time-integration results to facilitate an evaluation of the accuracy of nonlinear periodic responses. An additional contribution of this study is to perform convergence and stability analysis specifically for isolas generated by the non-smooth nonlinearity. Residual error analysis is used to determine the approximate number of harmonics required to get results accurate to a given error tolerance. Hill’s method and Floquet theory are employed to compute the stability of periodic solutions and identify the types of bifurcations in the system.
A common approach for the development of partitioned schemes employing different time integrators on different subdomains is to lag the coupling terms in time. This can lead to accuracy issues, especially in multistage methods. In this article, we present a novel framework for partitioned heterogeneous time‐integration methods, which allows the coupling of arbitrary multistage and multistep methods without reducing their order of accuracy. At the core of our approach are accurate estimates of the interface flux obtained from the Schur complement of an auxiliary monolithic system . We use these estimates to construct a polynomial‐in‐time approximation of the interface flux over the current time coupling window. This approximation provides the interface boundary conditions necessary to decouple the subdomain problems at any point within the coupling window. In so doing our framework enables a flexible choice of time‐integrators for the individual subproblems without compromising the time‐accuracy at the coupled problem level. This feature is the main distinction between our framework and other approaches. To demonstrate the framework, we construct a family of partitioned heterogeneous time‐integration methods, combining multistage and multistep methods, for a simplified tracer transport component of the coupled air‐sea system in Earth system models. We report numerical tests evaluating accuracy and flux conservation for different pairs of time‐integrators from the explicit Runge‐Kutta and Adams‐Moulton families.
Calcite (CaCO3) composition and properties are defined by the chemical environment in which CaCO3 forms. However, a complete understanding of the relationship between aqueous chemistry during calcite precipitation and resulting chemical and physical CaCO3 properties remains elusive; therefore, we present an investigation into the coupled effects of divalent cations Sr2+ and Mg2+ on CaCO3 precipitation and subsequent crystal growth. Through chemical analysis of the aqueous phases and microscopy of the resulting calcite phases in compliment with density functional theory calculations, we elucidate the relationship between crystal growth and the resulting composition (elemental and isotopic) of calcite. The results of this experimental and modeling work suggest that Mg2+ and Sr2+ have cation-specific impacts that inhibit calcite crystal growth, including: (1) Sr2+ incorporates more readily into calcite than Mg2+ (DSr > DMg), and increasing [Sr2+]t or [Mg2+]t increases DSr; (2) the inclusion of Mg2+ into structure leads to a reduction in the calcite unit cell volume, whereas Sr2+ leads to an expansion; (3) the inclusion of both Mg2+ and Sr2+ results in a distribution of unit cell impacts based on the relative positions of the Sr2+ and Mg2+ in the lattice. These experiments were conducted at saturation indices of CaCO3 of ∼4.1, favoring rapid precipitation. This rapid precipitation resulted in observed Sr isotope fractionation confirming Sr isotopic fractionation is dependent upon the precipitation rate. We further note that the precipitation and growth of calcite favors the incorporation of the lighter 86Sr isotope over the heavier 87Sr isotope, regardless of the initial solution conditions, and the degree of fractionation increases with DSr. In sum, these results demonstrate the impact of solution environment to influence the incorporation behavior and crystal growth behavior of calcite. These factors are important to understand in order to effectively use geochemical signatures resulting from calcite precipitation or dissolution to gain specific information.
This paper presents a method for simulating evaporation in a compressible, interface-resolved framework appropriate for modeling problems of engineering interest. In order to achieve robustness and broad applicability, the method has been designed to discretely enforce consistent mass and thermal energy transport at the phase interface, to globally conserve mass, momentum, and energy, and to be capable of modeling compressible and incompressible systems. Verification is performed via the Sod-shock test, one-dimensional heat conduction, evaporation from a planar interface, and evaporation of three-dimensional droplets. Convergence with increasing mesh resolution is demonstrated in all tested configurations, and conservation is maintained near machine precision for a translating droplet. Conservation and accurate phase change rates are preserved at the low numerical resolutions commonly encountered in engineering calculations. Following verification, the method is validated by comparison to an empirical correlation for evaporating droplets in high temperature crossflow, and the presentation concludes with the simulation of an iso-octane spray at conditions representative of gasoline direct injection. Successful verification, validation, and demonstrated practical utility suggest the method to be an accurate, efficient, and robust approach for the study of phase change in engineering systems.
The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.
A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i.e., irreducible) uncertainties in model inputs (i.e., parameters). The subsequent inferential target is a distribution on parameters. Another type of inverse problem is to quantify uncertainties in estimates of “true” parameter values under the assumption that such uncertainties should be reduced as more data are incorporated into the problem, i.e., the uncertainty is considered epistemic. A major contribution of this work is the formulation and solution of such a parameter identification problem (PIP) within the measure-theoretic framework developed for the SIP. The approach is novel in that it utilizes a solution to a stochastic forward problem (SFP) to update an initial density only in the parameter directions informed by the model output data. In other words, this method performs “selective regularization” only in the parameter directions not informed by data. The solution is defined by a maximal updated density (MUD) point where the updated density defines the measure-theoretic solution to the PIP. Another significant contribution of this work is the full theory of existence and uniqueness of MUD points for linear maps with Gaussian distributions. Data-constructed Quantity of Interest (QoI) maps are also presented and analyzed for solving the PIP within this measure-theoretic framework as a means of reducing uncertainties in the MUD estimate. We conclude with a demonstration of the general applicability of the method on two problems involving either spatial or temporal data for estimating uncertain model parameters. The first problem utilizes spatial data from a stationary partial differential equation to produce a MUD estimate of an uncertain boundary condition. The second problem utilizes temporal data obtained from the state-of-the-art ADvanced CIRCulation (ADCIRC) model to obtain a MUD estimate of uncertain wind drag coefficients for a simulated extreme weather event near the Shinnecock Inlet located in the Outer Barrier of Long Island, NY, USA.
The Z machine is a current driver producing up to 30 MA in 100 ns that utilizes a wide range of diagnostics to assess accelerator performance and target behavior conduct experiments that use the Z target as a source of radiation or high pressures. Here, we review the existing suite of diagnostic systems, including their locations and primary configurations. The diagnostics are grouped in the following categories: pulsed power diagnostics, x-ray power and energy, x-ray spectroscopy, x-ray imaging (including backlighting, power flow, and velocimetry), and nuclear detectors (including neutron activation). We will also briefly summarize the primary imaging detectors we use at Z: image plates, x-ray and visible film, microchannel plates, and the ultrafast x-ray imager. The Z shot produces a harsh environment that interferes with diagnostic operation and data retrieval. We term these detrimental processes “threats” of which only partial quantifications and precise sources are known. Finally, we summarize the threats and describe techniques utilized in many of the systems to reduce noise and backgrounds.
Roychoudhury, Subhayan; Shulda, Sarah; Goyal, Anuj; Bell, Robert T.; Sainio, Sami; Strange, Nicholas A.; Park, James E.; Coker, Eric N.; Lany, Stephan; Ginley, David S.; Prendergast, David
BaCe0.25Mn0.75O3−δ (BCM), a non-stoichiometric oxide with a layered perovskite-like crystal structure, has recently emerged as a prospective contender for application in renewable energy harvesting by solar thermochemical hydrogen generation. Using solar-thermal energy and a reducing environment, oxygen vacancies can be created in high-temperature BCM, and the reduced crystal so obtained can, in turn, produce H2 by stripping oxygen from H2O. Therefore, a first step toward understanding the working mechanism and optimizing the performance of BCM is a thorough and comparative analysis of the electronic structure of the pristine and the reduced material. In this paper, we probe the electronic structure of BCM using the combined effort of first-principles calculations and experimental O K-edge X-ray absorption spectroscopy (XAS). The computed projected density of states (PDOS) and orbital plots are used to propose a simplified model for orbital mixing between the oxygen and metal atoms. With the help of state-of-the-art simulations, we are able to find the origins of the XAS peaks and categorize them on the basis of contribution from Ce and Mn. For the reduced crystal, the calculations show that the change in electron density resulting from the reduction is strongly localized around the oxygen vacancy. Experimental measurements reveal a marked lowering of the first O K-edge peak in the reduced crystal. Using theoretical analysis, this is shown to result from lifting of spin degeneracy in the absorption peaks as well as from a diminished O 2p contribution to the frontier unoccupied orbitals, in accordance with the tight binding scheme. The simulated results serve as a reference for the extent of spectral change as a function of the percentage of oxygen vacancies in the reduced crystal. Our study paves the way for the investigation of the working mechanism of BCM and for computational and experimental efforts aimed at design and discovery of efficient water-splitting oxides.
Here we used aerosol mass spectrometry coupled with tunable synchrotron photoionization to measure radical and closed-shell species associated with particle formation in premixed flames and during pyrolysis of butane, ethylene, and methane. We analyzed photoionization (PI) spectra for the C7H7 radical to identify the isomers present during particle formation. For the combustion and pyrolysis of all three fuels, the PI spectra can be fit reasonably well with contributions from four radical isomers: benzyl, tropyl, vinylcyclopentadienyl, and o-tolyl. Although there are significant experimental uncertainties in the isomeric speciation of C7H7, the results clearly demonstrate that the isomeric composition of C7H7 strongly depends on the combustion or pyrolysis conditions and the fuel or precursors. Fits to the PI spectra using reference curves for these isomers suggest that all of these isomers may contribute to m/z 91 in butane and methane flames, but only benzyl and vinylcyclopentadienyl contribute to the C7H7 isomer signal in the ethylene flame. Only tropyl and benzyl appear to play a role during pyrolytic particle formation from ethylene, and only tropyl, vinylcyclopentadienyl, and o-tolyl appear to participate during particle formation from butane pyrolysis. There also seems to be a contribution from an isomer with an ionization energy below 7.5 eV for the flames but not for the pyrolysis conditions. Kinetic models with updated and new reactions and rate coefficients for the C7H7 reaction network predict benzyl, tropyl, vinylcyclopentadienyl, and o-tolyl to be the primary C7H7 isomers and predict negligible contributions from other C7H7 isomers. These updated models provide better agreement with the measurements than the original versions of the models but, nonetheless, underpredict the relative concentrations of tropyl, vinylcyclopentadienyl, and o-tolyl in both flames and pyrolysis and overpredict benzyl in pyrolysis. Our results suggest that there are additional important formation pathways for the vinylcyclopentadienyl, tropyl, and o-tolyl radicals and/or loss pathways for the benzyl radical that are currently unaccounted for in the present models.
The present contribution emphasizes the formation of oligomeric products in various depolymerization approaches of lignin, namely reductive catalytic fractionation, oxidative catalytic fractionation, and pyrolysis. Three possible routes to form such oligomers in these depolymerization processes are summarized and compared from various studies conducted on model compounds. Next, the main identification techniques for characterizing oligomeric products are highlighted. Particular focus is given to 2D-HSQC-NMR, GPC, Maldi-TOF-MS and FT-ICR-MS, which represent the state-of-art characterization of lignin. Special attention was paid to the transferability of these techniques for depolymerized oligomeric lignin. Finally, both the existing and expected potential lignin valorization routes are discussed for these oligomers, and technical hurdles and recommendations are provided in an attempt to catalyze the development of new discoveries and enabling technologies.
Tritium population thermodynamics and transport kinetics critically define the tritium storage performance of zirconium tritides that can be used for a variety of nuclear applications including tritium-producing burnable absorber rods. Both thermodynamic and kinetic properties can be sensitive to grain sizes of materials and can be significantly altered by irradiated defects during operation under the reactor environments. A thorough experimental characterization of how these properties evolve under different reactor conditions and different initial grain structures is extremely challenging. Here molecular dynamics simulations are used to investigate tritium population and diffusion in zirconium with and without different planar symmetric and asymmetric tilt grain boundaries and irradiated defects. Here, we found that in addition to trapping tritium, the most significant effect of planar grain boundaries is to increase tritium diffusivity on the boundary plane. Furthermore, fine grain structures are found to mitigate the change of tritium diffusivity due to irradiated point defects as these point defects are likely to migrate to and sink at grain boundaries.
Herein, the International Commission on Illumination (CIE) designed its color space to be perceptually uniform so that a given numerical change in the color code corresponds to perceived change in color. This color encoding is demonstrated to be advantageous in scientific visualization and analysis of vector fields. The specific application is analysis of ice motion in the Arctic where patterns in smooth monthly-averaged ice motion are seen. Furthermore, fractures occurring in the ice cover result in discontinuities in the ice motion. This vector jump in displacement can also be visualized. We then analyze modeled and observed fractures through the use of a metric on the color space, and image amplitude and phase metrics. Amplitude and phase metrics arise from image registration that is accomplished by sampling images using space filling curves, thus reducing the image registration problem to the more reliable functional alignment problem. We demonstrate this through an exploration of the metrics to compare model runs to an observed ice crack.
Here, the effectiveness of continuous vibro-impact forcing representations for the cantilevered pipe that conveys fluid is explored and analyzed. The previously accepted forcing model utilizing a smoothened trilinear spring is estimated using three continuous forcing representations, namely, polynomial, rational polynomial, and hyperbolic tangent. The accuracy of the estimated forcing functions is investigated and analyzed by calculating the root mean square error, and bifurcation diagrams are generated and compared to the nominal system. Additionally, the dynamic response of the system is further characterized using Poincare maps, power spectra, and basins of attraction. Once all continuous forcing representations are analyzed and compared to the nominal system, the computational cost of each method is examined, and further limitations of the hyperbolic tangent method are discovered. It is proved that the hyperbolic tangent forcing representation most accurately captures the dynamic response of the pipeline, and the least accurate representation is the rational polynomial representation. Additionally, considerable computational cost is saved when employing the hyperbolic tangent representation compared to the discontinuous representation.
In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.
Titanium alloys are used in a large array of applications. In this work we focus our attention on the most used alloy, Ti-6Al-4V (Ti64), which has excellent mechanical and biocompatibility properties with applications in aerospace, defense, biomedical, and other fields. Here we present high-fidelity experimental shock compression data measured on Sandia's Z machine. We extend the principal shock Hugoniot for Ti64 to more than threefold compression, up to over 1.2 TPa. We use the data to validate our ab initio molecular dynamics simulations and to develop a highly reliable, multiphase equation of state (EOS) for Ti64, spanning a broad range of temperature and pressures. The first-principles simulations show very good agreement with Z data and with previous three-stage gas gun data from Sandia's STAR facility. The resulting principal Hugoniot and the broad-range EOS and phase diagram up to 10 TPa and 105 K are suitable for use in shock experiments and in hydrodynamic simulations. The high-precision experimental results and high-fidelity simulations demonstrate that the Hugoniot of the Ti64 alloy is stiffer than that of pure Ti and reveal that Ti64 melts on the Hugoniot at a significantly lower pressure and temperature than previously modeled.
Experiments have shown that pitting corrosion can develop in aluminum surfaces at potentials > − 0.5 V relative to the standard hydrogen electrode (SHE). Until recently, the onset of pitting corrosion in aluminum has not been rigorously explored at an atomistic scale because of the difficulty of incorporating a voltage into density functional theory (DFT) calculations. We introduce the Quantum Continuum Approximation (QCA) which self-consistently couples explicit DFT calculations of the metal-insulator and insulator-solution interfaces to continuum Poisson-Boltzmann electrostatic distributions describing the bulk of the insulating region. By decreasing the number of atoms necessary to explicitly simulate with DFT by an order of magnitude, QCA makes the first-principles prediction of the voltage of realistic electrochemical interfaces feasible. After developing this technique, we apply QCA to predict the formation energy of chloride atoms inserting into oxygen vacancies in Al(111)/α-Al2O3 (0001) interfaces as a function of applied voltage. We predict that chloride insertion is only favorable in systems with a grain boundary in the Al2O3 for voltages > − 0.2 V (SHE). Our results roughly agree with the experimentally demonstrated onset of corrosion, demonstrating QCA’s utility in modeling realistic electrochemical systems at reasonable computational cost.
Mixtures of gas-phase hydrogen isotopologues (diatomic combinations of protium, deuterium, and tritium) can be separated using columns containing a solid such as palladium that reversibly absorbs hydrogen. A temperature-swing process can transport hydrogen into or out of a column by inducing temperature-dependent absorption or desorption reactions. We consider two designs: a thermal cycling absorption process, which moves hydrogen back and forth between two columns, and a simulated moving bed (SMB), where columns are in a circular arrangement. We present a numerical mass and heat transport model of absorption columns for hydrogen isotope separation. It includes a detailed treatment of the absorption-desorption reaction for palladium. By comparing the isotope concentrations within the columns as a function of position and time, we observe that SMB can lead to sharper separations for a given number of thermal cycles by avoiding the remixing of isotopes.
Fireballs produced from the detonation of high explosives often contain particulates primarily composed of various phases of carbon soot. The transport and concentration of these particulates is of interest for model validation and emission characterization. This work proposes ultra-high-speed imaging techniques to observe a fireball's structure and optical depth. An extinction-based diagnostic applied at two wavelengths indicates that extinction scales inversely with wavelength, consistent with particles in the Rayleigh limit and dimensionless extinction coefficients which are independent of wavelength. Within current confidence bounds, the extinction-derived soot mass concentrations agree with expectations based upon literature reported soot yields. Results also identify areas of high uncertainty where additional work is recommended.
Vertical-axis wind turbines (VAWTs) have a long history, with a wide variety of turbine archetypes that have been designed and tested since the 1970s. While few utility-scale VAWTs currently exist, the placement of the generator near the turbine base could make VAWTs advantageous over tradition horizontal-axis wind turbines for floating offshore wind applications via reduced platform costs and improved scaling potential. However, there are currently few numerical design and analysis tools available for VAWTs. One existing engineering toolset for aero-hydro-servo-elastic simulation of VAWTs is the Offshore Wind ENergy Simulator (OWENS), but its current modeling capability for floating systems is non-standard and not ideal. This article describes how OWENS has been coupled to several OpenFAST modules to update and improve modeling of floating offshore VAWTs and discusses the verification of these new capabilities and features. The results of the coupled OWENS verification test agree well with a parallel OpenFAST simulation, validating the new modeling and simulation capabilities in OWENS for floating VAWT applications. These developments will enable the design and optimization of floating offshore VAWTs in the future.
DOE maintains an up-to-date documentation of the number of available full drawdowns of each of the caverns at the U.S. Strategic Petroleum Reserve (SPR). This information is important for assessing the SPR’s ability to deliver oil to domestic oil companies expeditiously if national or world events dictate a rapid sale and deployment of the oil reserves. Sandia was directed to develop and implement a process to continuously assess and report the evolution of drawdown capacity, the subject of this report. This report covers impacts on drawdown availability due to SPR operations during Calendar Year 2022. A cavern has an available drawdown if, after that drawdown, the long-term stability of the cavern, the cavern field, or the oil quality are not compromised. Thus, determining the number of available drawdowns requires the consideration of several factors regarding cavern and wellbore integrity and stability, including stress states caused by cavern geometry and operations, salt damage caused by dilatant and tensile stresses, the effect of enhanced creep on wellbore integrity, and the sympathetic stress effect of operations on neighboring caverns. Finite-element geomechanical models have been used to determine the stress states in the pillars following successive drawdowns. By computing the tensile and dilatant stresses in the salt, areas of potential structural instability can be identified that may represent red flags for additional drawdowns. These analyses have found that many caverns will maintain structural integrity even when grown via drawdowns to dimensions resulting in a pillar-to-diameter ratio of less than 1.0. The analyses have also confirmed that certain caverns should only be completely drawn down one time. As the SPR caverns are utilized and partial drawdowns are performed to remove oil from the caverns (e.g., for oil sales, purchases, or exchanges authorized by the Congress or the President), the changes to the cavern caused by these procedures must be tracked and accounted for so that an ongoing assessment of the cavern’s drawdown capacity may be continued. A methodology for assessing and tracking the available drawdowns for each cavern is reiterated. This report is the latest in a series of annual reports, and it includes the baseline available drawdowns for each cavern, and the most recent assessment of the evolution of drawdown expenditures. A total of 222 million barrels of oil were released in calendar-year 2022. A nearly-equal amount of raw water was injected, resulting in an estimated 34 million barrels of cavern leaching. Twenty caverns have now expended a full drawdown. Cavern BC 18 has expended all its baseline available drawdowns, and has no drawdowns remaining. Cavern BM 103 has expended one of its two baseline drawdowns, and is now a single-drawdown cavern. All other caverns with an expenditure went from at-least-5 to at-least-4 remaining drawdowns.
The future mission success of the Nuclear Security Enterprise (NSE) relies on our workforce and our workplace. The 2022 Nuclear Posture Review notes that “the health of the enterprise depends critically on recruiting and retaining a skilled and diverse workforce” and the 2022 National Nuclear Security Administration (NNSA) Strategic Vision articulates a commitment to “recruit, invest in, and nourish a high-performing, diverse, and flexible workforce that can meet the unique policy, technical, and leadership needs of our mission today and well into the future.”
Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps and constitutive models simultaneously is currently lacking. This paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.
We present EASEE (Edge Advertisements into Snapshots using Evolving Expectations) for partitioning streaming communication data into static graph snapshots. Given streaming communication events (A talks to B), EASEE identifies when events suffice for a static graph (a snapshot). EASEE uses combinatorial statistical models to adaptively find when a snapshot is stable, while watching for significant data shifts - indicating a new snapshot should begin. If snapshots are not found carefully, they poorly represent the underlying data - and downstream graph analytics fail: We show a community detection example. We demonstrate EASEE's strengths against several real-world datasets, and its accuracy against known-answer synthetic datasets. Synthetic datasets' results show that (1) EASEE finds known-answer data shifts very quickly; and (2) ignoring these shifts drastically affects analytics on resulting snapshots. We show that previous work misses these shifts. Further, we evaluate EASEE against seven real-world datasets (330 K to 2.5B events), and find snapshot-over-time behaviors missed by previous works. Finally, we show that the resulting snapshots' measured properties (e.g., graph density) are altered by how snapshots are identified from the communication event stream. In particular, EASEE's snapshots do not generally 'densify' over time, contradicting previous influential results that used simpler partitioning methods.