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At least 1,387 records · Page 77

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DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Study on ammonia and dimethyl ether oxidation and kinetic interaction up to 100 atm

NH 3 and DME dual fuel oxidation and kinetic coupling are experimentally studied by using a supercritical pressure jet-stirred reactor (SP-JSR) at 20 and 100 atm, over a temperature range of 500–900 K, and at fuel-lean and stoichiometric conditions with NH 3 to DME molar ratios of 4 and 0.62, respectively. An HP-Mech model for high-pressure NH3/DME oxidation is developed based on our previous studies and it shows generally better performance than other models in the literature on high-pressure oxidation experiments. Due to DME’s strong low-temperature reactivity, it dramatically enhances the oxidation of NH3 at low temperature. However, the effect of NH 3 on DME oxidation varies with temperature. At low temperatures, NH 3 inhibits low-temperature reactivity by consuming OH radicals. In addition, the NH 2 and NO x formation from NH 3 further suppresses the low-temperature DME reactivity by reducing alkylperoxyl and O 2 QOOH radicals via RO 2 + NH 2 = RO + H 2 NO, RO 2 + NO = RO + NO 2 and O 2 QOOH + NO = 2CH 2 O + HO 2 + NO 2 . At intermediate temperatures, due to enhanced kinetic coupling of NH 2 /NO x /HO 2 chemistry, DME oxidation is significantly promoted. It is found that there are two major NH 2 /NO x /HO 2 coupling pathways for OH radical production brought by NH 3 : (1) NH 2 + HO 2 = H 2 NO + OH and (2) NH 2 + NO 2 = H 2 NO + NO with NO + HO 2 = OH + NO 2 . Moreover, the resulting H 2 NO will further contribute to OH production via the H 2 NO/NO x /HONO coupling pathways: H 2 NO + NO 2 = HNO + HONO, HNO + NO 2 = HONO + NO, and HONO (+M) = OH + NO (+M). These new NH 2 /NO x /HO 2 and H 2 NO/NO x /HONO pathways play a critical role in promoting DME oxidation at intermediate temperatures. Novelty and Significance Statement: Blending NH 3 with DME can significantly enhance the reactivity of NH 3 and facilitate its practical application in advanced internal combustion engines. NH 3 /DME oxidation is studied in a novel supercritical pressure jet-stirred reactor up to 100 atm, a much higher pressure than previous studies. An interesting non-monotonic effect of NH3 addition on fuel oxidation has been found. Unique kinetic couplings of NH2/HO2/NOx and H2NO/NOx/HONO brough by NH3 and DME blending are identified to play important roles in radical generation and reactivity promotion. The valuable experimental data and key kinetics revealed in the current work can tremendously improve our understanding of NH 3 /DME oxidation via NO x /RO 2 and NH 2 / H 2 NO/HO 2 /NO x kinetic couplings at low to intermediate temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Finite element model for brittle fracture and fragmentation

A new computational model for brittle fracture and fragmentation has been developed based on finite element analysis of non-linear elasticity equations. The proposed model propagates the cracks by splitting the mesh nodes alongside the most over-strained edges based on the principal direction of strain tensor. To prevent elements from overlapping and folding under large deformations, robust geometrical constraints using the method of Lagrange multipliers have been incorporated. In conclusion, the model has been applied to 2D simulations of the formation and propagation of cracks in brittle materials, and the fracture and fragmentation of stretched and compressed materials.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Analysis of Photovoltaic Energy: CRADA Number CRD-21-17862 (Cooperative Research and Development Final Report)

For this project, NREL will work with the Participant to model phase change material performance, model photovoltaic thermodynamics performance, and predict photovoltaic module durability based on environmental conditions to help develop optimized photovoltaic roofing designs. This project will develop thermodynamic models of integrated photovoltaic roofing with phase change materials. These models will be used to optimize the materials and design selections based on reducing photovoltaic operating temperatures while balancing cost effective integration of phase change materials.

14 SOLAR ENERGY↗

DOE OSTI2023

Mechanistic modeling of copper corrosions in data center environments

Air-side economizers are increasingly used to take advantage of “free-cooling” in data centers with the intent of reducing the carbon footprint of buildings. However, they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology equipment. Here, to evaluate the reliability of information technology equipment under various thermal and air-pollution conditions, a mechanistic model based on multi-ion transport and chemical reactions was developed. The model was used to predict Cu corrosion caused by Cl2-containing pollutant mixtures. It also accounted for the effects of temperature (25 °C and 28 °C), relative humidity (50%, 75%, and 95%), and synergism. It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator, which agreed well with the experimental results. The average root mean square error of the prediction was 13.7 Å. The model can be used to evaluate the thermal guideline for data centers design and operation when Cl2 is present based on pre-established acceptable risk of corrosion in data centers’ environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Thermodynamic analysis of a MEMS based differential scanning calorimeter model

Thin film based differential scanning calorimeter (DSC) was developed and utilized for the study of phase-transition kinetics in protein samples. In this paper, we proposed a MEMS based DSC model. The distance between the sample and reference cell was optimized based on the thermal analysis result. The influence of the response time to the shape of the DSC thermograms and the transition temperature was also studied systematically. Meanwhile, Lumry-Eyring model was used to investigate the relationship between the scanning rate and the transition temperature. The modeling result was verified by the lysozyme denaturation tests. This study provided a pathway for the optimization of MEMS based DSC array and the correction of the DSC thermograms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Chapter 21 - Future Developments Without Targeted Policies

Together with other tools, model-based scenario analysis provides a useful method to explore whether environment related targets of the Sustainable Development Goals (SDGs) and related Multilateral Environmental Agreements (MEAs) are going to be achieved (well established). Assessing whether current trends are leading to fulfilment of the selected targets outlined in Chapter 20 is complex: it requires insights into the interactions of different trends and systems, inertia and cross-scale relationships. A combination of qualitative storylines and quantitative scenario tools can help to explore possible futures trends, while taking the many complexities into account. While scenarios can never be forecasts, because surprises may occur, they can inform decision makers of the likely implications of current trends. {21.2}

Lucas, Paul↗

DOE OSTI2023

Verification and Benchmarking of High Fidelity Physics Peat Smoldering Model

Peat fires are a major contributor to greenhouse gas emissions. The estimates of these emissions currently contain major uncertainties, due to the difficulty of determining the mass of peat burned in a fire. To address these uncertainties, we develop a computational physics-based peat smoldering model, which will be leveraged for high-fidelity quantitative estimates of peat fire emissions relevant to climate change. We present the verification of the 2-D axisymmetric model, a first step towards developing a full 3-D model. Verification includes the solution verification against a literature model for the 0-D smoldering case and verification of the heat transfer problem in 1-D and 2-D. Also presented is the effect of reaction mechanism on the smoldering model, for which we found a relatively simple three-step reaction mechanism is able to capture key behavior. These verification results provide the foundation for moving forward with validation against experimental data of the 2-D model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗

DOE OSTI2010

Molecular Simulation of the Diffusion of Uranyl Carbonate Species in Aqueous Solution

Molecular dynamics simulations of aqueous uranyl carbonate species were carried out with two different potential models to gain molecular-level insight into the hydration properties of these species and evaluate the ability of the two models to reproduce published ab initio and experimental data. The simulation results were used to estimate the self-diffusion coefficients of uranyl carbonate species that often dominate uranyl speciation in groundwater systems. The first potential model was based on a series of shell models developed by Parker and co-workers (including (DE LEEUW and PARKER, 1998; KERISIT and PARKER, 2004; PAVESE et al., 1996). The second potential model was a rigid-ion model based on the flexible SPC water model (TELEMAN et al., 1987), the uranyl model of Guilbaud and Wipff (GUILBAUD and WIPFF, 1996), and the parameters for the carbonate ion given by Greathouse and co-workers (GREATHOUSE and CYGAN, 2005; GREATHOUSE et al., 2002). Analysis of structural (mean interatomic distances and coordination numbers) and dynamical (water residence times in hydration shell and self-diffusion coefficients) properties showed that, overall, the first potential model performed best when compared to published data, although the only major discrepancy with the second model was a misrepresentation of the configuration adopted by the alkaline-earth uranyl carbonate ions. The diffusion coefficients obtained for the alkaline-earth cations and the uranyl ion were compared with three variants of the Stokes-Einstein (SE) equation and it was found that none of the three SE models were able to reproduce both the absolute values and the overall trend determined from the molecular dynamics simulations. However, as would be expected based on the SE equation, a plot of the diffusion coefficients of the uranyl carbonate complexes as a function of the inverse of the equivalent spherical radius showed a general linear dependence with the two models yielding almost identical gradients. The nature of the alkaline-earth cation in the uranyl carbonate complexes was found to have only a small effect on the ion’s diffusion coefficient.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Generative Electrolyte Solvent and Formulation Discovery

Molecular mixtures and/or formulations are of great importance in fields ranging from materials science to pharmaceuticals to chemistry. In batteries, electrolytes are complex molecular mixtures consisting of multiple salts and solvents and additives at different concentrations that dictate battery capacity, safety, and cycle life, among others. Unfortunately, due to the complex composition and infinite design space as well as the conflicting property requirements, electrolyte design is the rate-determining step in the design of next generation battery chemistries. In this work, we develop a transformer-based generative AI model − ElectrolyteGPT − capable of generating solvents and electrolyte formulations to satisfy a wide range of desired property requirements. First, we curate an electrolyte-relevant database and develop a new line notation for formulations. Then, we show that ElectrolyteGPT can generate solvents and formulations conditioned on a wide range of important electrolyte properties such as ionic conductivity, oxidative stability, Coulombic efficiency, viscosity, and more. Finally, we experimentally synthesize the generated solvents and fabricate the electrolyte formulations and show that they can meet the desired property requirements and enable longterm cycling in energy-dense anode-free lithium metal batteries. Our work showcases the ability of generative models to address challenges in molecular mixture design for next generation batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Optimization to Generate Equations of State for Hydrogen Production

On a high level, the larger project in question, HydroGEN, aims to develop software used for finding equations of state (EOS) to optimize catalyst configuration for H 2 production through water splitting. In particular, this summer project focused on solving the nonlinear equations used in fitting the equations. This problem involved using Python to solve a linear system with nonlinear constraints. In order for this to be achieved, Pyomo was used to build a model and the solver Ipopt, interior point optimizer, was used. Pyomo is a Python-based language developed at Sandia; it is an optimization modeling language. Rather than solving the entire problem at once, a toy problem was created, simplifying the problem down to the most important focus. This problem had a known solution, comparable to the calculated solution to assess accuracy and as progress was made towards finding solutions, complexity was gradually added to the problem. After building and solving the toy problem, it was found that it gave reasonably accurate solutions, better compared to the two existing solvers previously used with this project in terms of functionality. The solver is now ready for implementation into the project’s main software.

08 HYDROGEN↗

DOE OSTI2017

Parameter estimation in a thermoelastic composite problem via adjoint formulation and model reduction

Advances in nondestructive material characterization are providing a wealth of information that could be exploited to gain insight into general aspects of material performance and, in particular, discover relationships between microstructure and thermo-mechanical properties in polycrystalline and other complex composite materials. Here, in order to facilitate the integration of such measurements into existing models, as well as inform new physics-based predictions, we developed a C++/MPI computational framework for sensitivity analysis and parameter estimation. The framework utilizes a micro-mechanical modeling based on fast Fourier transforms, direct and adjoint formulations, and Markov chain Monte Carlo sampling techniques. We illustrate the characteristics of this framework and demonstrate its utility by computing the residual stresses arising from thermal expansion of an elastic composite and using data from simulated experiments. Lastly, we show that the availability of nondestructive 3-D measurements is crucial to reduce the uncertainty in predictions, emphasizing the importance of an integrated experimental/modeling/data analysis approach for improved material characterization and design.

36 MATERIALS SCIENCE↗

DOE OSTI2017

(U) Physics Validation of the RMI-Based Ejecta Source Model Implementation in FLAG: L2 Milestone #6035 Report

The Los Alamos Physics and Engineering Models (PEM) program has developed a model for Richtmyer-Meshkov instability (RMI) based ejecta production from shock-melted surfaces, along with a prescription for a self-similar velocity distribution (SSVD) of the resulting ejecta particles. We have undertaken an effort to validate this source model using data from explosively driven tin coupon experiments. The model’s current formulation lacks a crucial piece of physics: a method for determining the duration of the ejecta production interval. Without a mechanism for terminating ejecta production, the model is not predictive. Furthermore, when the production interval is hand-tuned to match time-integrated mass data, the predicted time-dependent mass accumulation on a downstream sensor rises too sharply at early times and too slowly at late times because the SSVD overestimates the amount of mass stored in the fastest particles and underestimates the mass stored in the slowest particles. The functional form of the resulting m(t) is inconsistent with the available time-dependent data; numerical simulations and analytic studies agree on this point. Simulated mass tallies are highly sensitive to radial expansion of the ejecta cloud. It is not clear if the same effect is present in the experimental data but if so, depending on the degree, this may challenge the model’s compatibility with tin coupon data. The current implementation of the model in FLAG is sensitive to the detailed interaction between kinematics (hydrodynamic methods) and thermodynamics (material models); this sensitivity prohibits certain physics modeling choices. The appendices contain an extensive analytic study of piezoelectric ejecta mass measurements, along with test problems, excerpted from a longer work (LA-UR-17-21218).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

The effect of cell-size dispersity on the mechanical properties of closed-cell aluminum foam

Highlights: • The effect of cell-size dispersity on the Young’s modulus of foam was investigated through experiments and simulations • Crushing experiments were conducted on 27 foam specimens in order measure the relative Young’s modulus • Three types of numerical models, including monodispersed, bidispersed and polydispersed models were studied • The numerical convergence on the model size for different models was also studied • The difference among numerical results obtained from the three models was insignificant - Abstract: The mechanical properties of open-celled and closed-celled metallic foam are strongly influenced by the cellular microstructure and material properties of the base material from which the foam is made. The present study investigated the microstructure and mechanical properties of ALPORAS closed-cell Al foam. In particular, the effect of cell-size dispersity on the Young's modulus of foam was investigated through crushing experiments and numerical simulations. Crushing experiments were first conducted on two mutually perpendicular directions. Numerical models of varying cell-size dispersity and complexity were subsequently developed, including the monodispersed model that is based on the Kelvin structure, the bidispersed model, and the polydispersed model that is based on the random Laguerre tessellation algorithm. The numerical models were discretized using shell elements, and the simulations were conducted using the ABAQUS finite element method software. Relative Young's moduli with various relative densities were predicted, and the numerical convergence on the model size was studied accordingly. Numerical results obtained from all three numerical models developed were found to overestimate the experimental results by a factor of approximately 3. The difference between the numerical and experimental results can be attributed to the difference in detailed microstructural characteristics. Furthermore, the difference among numerical results obtained from the three models was insignificant.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX-GHG

Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process-based model TRIPLEX-GHG was developed by coupling it with the new MEND (Microbial-ENzyme-mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX-MICROBE) shows considerable improvement over the previous version (TRIPLEX-GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well-regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated. We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC) and mineral-associated organic carbon (MOC). Furthermore, our work represents the first step towards a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

PETN Coarsening - Predictions from Accelerated Aging Data

Ensuring good ignition properties over long periods of time necessitates maintaining a good level of porosity in powders of initiator materials and preventing particle coarsening. To simulate porosity changes of such powder materials over long periods of time a common strategy is to perform accelerated aging experiments over shorter time spans at elevated temperatures. In this paper we examine historical accelerated-aging data on powders of Pentaerythritol Tetranitrate (PETN), an important energetic material, and make predictions for long-term aging under ambient conditions. Lastly, we develop an evaporation-condensation- based model to provide some mechanistic understanding of the coarsening process.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Mechanical and Microstructural Effects of Thermal Aging on Cast Duplex Stainless Steels by Experiment and Finite Element Method

Cast duplex stainless steel piping in light water nuclear reactors expe- rience thermal aging embrittlement during operational service. Interest in extending the operational life to 80 years requires an increased understanding of the microstructural evolution and corresponding changes in mechanical behavior. We analyze the evolution of the microstructure during thermal aging of cast CF-3 and CF-8 stainless steels using electron microscopy and atom probe tomography. The evolution of the mechanical properties is measured concurrently by mechanical methods such as tensile tests, Charpy V-notch tests, and instrumented nanoinden- tation. A microstructure-based finite element method model is developed and uti- lized in conjunction with the characterization results in order to correlate the local stress-strain effects in the microstructure with the bulk measurements. This work is supported by the DOE Nuclear Energy University Programs (NEUP), contract number DE-NE0000724.

36 MATERIALS SCIENCE↗