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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Validation of energetic particle transport models for time-dependent integrated simulations of burning plasmas (Final ST Report)

The major goal of this project is to deliver a suite of validated energetic particle (EP) modeling tools for time-dependent integrated tokamak simulations (with the code TRANSP) that can be used for modeling plasma discharges featuring a substantial EP content, such as burning plasmas. During FY20, issues were resolved with an energetic particle transport model called TGLF-EP, with improvements made to the TGLF-EP workflow, generation of input files, and interpretation of runs on the NERSC supercomputing cluster. The recipient also gave input on simulations with the code TRANSP to reproduce the reference JET discharge, and assessed the accuracy and uncertainties of the simulated B field, current evolution, thermal plasma profiles, stored energy, etc. Input was also given on interpretation of fast-ion instability calculations and recommendations for modifications to JET afterglow discharge for the upcoming DT campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Predicting ductility of Mg/SiCp nanocomposite under multiaxial loading conditions based on unit cell modeling

This article presents an investigation on the ductility of Mg/SiCp under nine different multiaxial loading conditions based on a unit cell (or called representative volumetric element, RVE) finite element model. The constitutive relationship of magnesium matrix is characterized by a model combined Lode angle dependent plasticity model with the CPB06 plasticity model, which is able to describe the tension-compression asymmetry in magnesium matrix. The debonding of the matrix-particle interface and matrix fracture are considered by the modified Mohr-Coulomb (MMC) fracture model. Simulation results show that the interface debonding and subsequent matrix fracture are responsible for the ductility and fracture of nanocomposite. The computational results with parametric studies are compared and well correlated with experimental data, and it finally gives a stress-based full fracture locus of Mg/SiCp nanocomposite.

42 ENGINEERING↗
DOE OSTI2022

Numerical schemes for a multi-species BGK model with velocity-dependent collision frequency

Here, we consider a kinetic description of multi-species gas mixture modeled with Bhatnagar-Gross-Krook (BGK) collision operators, in which the collision frequency varies not only in time and space but also with the microscopic velocity. In this model, the Maxwellians typically used in standard BGK operators are replaced by a generalization of such target functions, which are defined by a variational procedure. In this paper we present a numerical method for simulating this model, which uses an Implicit-Explicit (IMEX) scheme to minimize a certain potential function, mimicking the Lagrange functional that appears in the theoretical derivation. We show that theoretical properties such as conservation of mass, total momentum and total energy as well as positivity of the distribution functions are preserved by the numerical method, and illustrate its usefulness and effectiveness with numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

Significance Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Enhancing the accuracy and generality of the Debye–Grüneisen Model: Optimizing the volume dependence for accurate predictions across varied compositions

In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.

Bond coating materials↗
DOE OSTI2025

Phase-field modeling of orientation-dependent crack growth in ductile single crystals with anisotropic elasticity

Crack growth in ductile single crystals (DuSCs) is orientation dependent due to the anisotropies of crystal plasticity and elastic tensor. This study develops a phase-field model incorporating both crystal plasticity and crack growth and proposes a general method to decompose the elastic energy into compressive and tensile parts to prevent crack growth under compression in the phase-field description. The phase-field model, in combination with three Euler angles, is employed to simulate orientation-dependent crack growth in DuSCs. The contributions from crystal plasticity and anisotropic elasticity are compared, and the former is found to dominate in the anisotropy of crack growth in copper single crystals. Furthermore, the simulation results demonstrate that crystal orientation strongly affects the heterogeneous distribution of plastic strain and the interaction between plastic strain and crack growth. High-throughput phase-field simulations are performed with exhaustive crystal orientations, and the results are explained based on the anisotropy of the Taylor factor.

Computational Solid Mechanics↗
DOE OSTI2022

Simulation of dilatancy-controlled gas migration processes in saturated bentonite using a coupled multiphase flow and elastoplastic H 2 M model

Dilatancy-controlled gas flow in preferential pathways plays a key role in the safety analysis of radioactive waste repositories. This is particularly the case for bentonite, an often-preferred barrier material. Gas flow in preferential pathways is characterized by localization and spontaneous behavior, which is challenging to simulate in numerical models due to strong hydro-mechanical coupling. To analyze a laboratory experiment in the framework of the DECOVALEX-2023 project, this study introduced a new approach of combining continuous modelling methods with spatial material properties derived from material heterogeneities and experimental observations. The proposed model utilized hydro-mechanical spatial distributions, namely Young's modulus and gas entry pressure, and elastoplasticity combined with a linear swelling model. A conceptual strain-dependent permeability approach simulated dilatancy-controlled gas flow based on hydro-mechanical coupling. To test the effectiveness of the presented approach, a gas injection test in a compacted, saturated bentonite sample was simulated using the open-source code OpenGeoSys 5.8 and compared with experimental observations. The presented methodology is capable of simulating localized gas flow in preferential pathways. The spatial distributions of Young's modulus and gas entry pressure affect the swelling pressure, relative permeability and, in combination with the strain-dependent permeability model, also the intrinsic permeability.

42 ENGINEERING↗
DOE OSTI2026

Variation in surface properties as error sources in dynamic temperature measurements

Most temperature measurement methods rely upon the assumption of a single uniform source as part of the analysis in inferring an observed temperature T from the surface of a material. This is true for the radiometric and spectroscopic methods we rely upon in shock physics. However, there are obvious cases where this is a poor assumption that can cause large errors in analysis, such as the measurement of emission from a surface composed of more than one material, such as a rusted surface of Fe and Fe 2 O 3 , or a multi-material surface that could be produced through alloying methods (e.g., mokume gane) or 3D printing. Here we perform an initial examination of the effects of varied surface properties upon the analysis using idealized test data to constrain when we may have better confidence in the resulting calculations. We also examine the structures of the fits to test whether, in principle, comparing the fit with ideal gray bodies would reveal such a structure and enable the researcher to identify when a two-temperature fitting model should be used instead of a single- T model or a wavelength-dependent emissivity model.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Temperature Dependence of the Langmuir Adsorption Model for a Single-Site Metal–Organic Framework

The single-site Langmuir adsorption model, also known as the Langmuir isotherm equation, is one of the simplest possible descriptions of adsorption phenomena, and yet finds widespread applicability across a range of disciplines. In its simplest form, it is deployed to treat adsorption equilibria at constant temperature (i.e., along isotherms); however, at the heart of its derivation is a more general class of models that each incorporate an explicit temperature dependence, subject to assumptions about the spatial/translational degrees of freedom of the adsorbed species. In this work, measurements of the temperature dependence of supercritical adsorption of H 2 on a single-site metal-organic framework (MOF) are presented, and fitted using a range of Langmuir models with distinct treatments of degrees of freedom in the adsorbed phase. Surprisingly, all of the models can be used to adequately represent the measured data, despite yielding significantly different values for binding energy and the temperature dependence of the isosteric enthalpy of adsorption (i.e., the isosteric heat, qst). Furthermore, a critical finding is that the mean-temperature isosteric enthalpy of adsorption remains consistent across all models within experimental error, highlighting its reliability for evaluating adsorption thermodynamics.

08 HYDROGEN↗
DOE OSTI2022

Assessing models of force-dependent unbinding rates via infrequent metadynamics

Protein–ligand interactions are crucial for a wide range of physiological processes. Many cellular functions result in these non-covalent “bonds” being mechanically strained, and this can be integral to proper cellular function. Broadly, two classes of force dependence have been observed—slip bonds, where the unbinding rate increases, and catch bonds, where the unbinding rate decreases. Despite much theoretical work, we cannot predict for which protein–ligand pairs, pulling coordinates, and forces a particular rate dependence will appear. Here, we assess the ability of MD simulations combined with enhanced sampling techniques to probe the force dependence of unbinding rates. We show that the infrequent metadynamics technique correctly produces both catch and slip bonding kinetics for model potentials. We then apply it to the well-studied case of a buckyball in a hydrophobic cavity, which appears to exhibit an ideal slip bond. Finally, we compute the force-dependent unbinding rate of biotin–streptavidin. Here, the complex nature of the unbinding process causes the infrequent metadynamics method to begin to break down due to the presence of unbinding intermediates, despite the use of a previously optimized sampling coordinate. Allowing for this limitation, a combination of kinetic and free energy computations predicts an overall slip bond for larger forces consistent with prior experimental results although there are substantial deviations at small forces that require further investigation. Furthermore, this work demonstrates the promise of predicting force-dependent unbinding rates using enhanced sampling MD techniques while also revealing the methodological barriers that must be overcome to tackle more complex targets in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Mesoscale modeling of microstructure-dependent thermal conductivity in U-Zr fuels

In uranium-zirconium (U-Zr) based metallic fuels, different phases can form at different compositions and temperatures. Typically, lamellar δ-UZr 2 and α-U phases are the dominant microstructures in U-rich U-Zr alloys at temperatures below 880 K. In this work, a finite element method based mesoscale modeling technique is used to calculate the effective thermal conductivities of such heterogeneous microstructures, using the thermal conductivities of two individual phases and their interphase thermal resistance (Kapitza resistance) as input parameters. The Kapitza resistance between δ-UZr 2 and α-U is determined at different temperatures, which shows an approximately T 3 dependence in the temperature range between 300 and 800 K. In addition, the Kapitza resistance exhibits a strong dependence on the aspect ratio of the δ-UZr 2 phase. Further, an analytical model is therefore developed to quantify the effects of both temperature and δ-UZr 2 aspect ratio on the Kapitza resistance. Using this newly developed Kapitza resistance model, the effective thermal conductivities of a number of δ-UZr 2 + α-U heterogeneous microstructures in U-Zr alloys, including non-lamellar microstructures, can be estimated accurately.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Predicting encapsulant delamination in photovoltaic modules bridging photochemical reaction kinetics and fracture mechanics

Abstract Photovoltaic (PV) modules are subjected to environmental stressors (UV exposure, temperature, and humidity) that cause degradation within the encapsulant and its interfaces with adjacent glass and cell substrates. To save experimental time and to enable long‐term assessment with intensive degradation only taking place after many years, the development of predictive models is indispensable. Previous works have modeled the delamination of the ethylene vinyl acetate (EVA) encapsulant/glass and encapsulant/cell interfaces under field aging conditions with fundamental photochemical degradation reactions that lead to molecular scission and loss of interfacial adhesion, characterized by the fracture resistance, G c . However, these models were fundamentally limited in that the following aspects were not incorporated: (i) molecular crosslinking in the field, (ii) synergistic autocatalytic interactions of degradation mechanisms, (iii) connection between degraded encapsulant structure and its mechanical properties, and (iv) rigorous treatment of the plasticity contribution to G c with finite element models. Here, we present a time‐dependent multiscale model that addresses these limitations and is applicable to a wide range of encapsulants and interfaces. For the reference EVA encapsulant and its interfaces with the glass and cell, the presented model predicts an initial rise in G c in the first 3 years of field aging from crosslinking, then a subsequent sharp decline from degradation mechanisms. We used nanoindentation to measure the changes in EVA mechanical properties over exposure time to tune the model parameters. The model predictions of G c and mechanical properties match with experimental data and show an improvement compared to previous models. The model can even predict switches in failure interfaces, such as the observed EVA/cell to EVA/glass transition. We also conducted a sensitivity analysis study by varying the degradation and crosslinking kinetic parameters to demonstrate their effects on G c . Model extensions to polyolefin elastomer‐ and silicone‐encapsulants and their interfaces are also demonstrated.

Liu, Kuan↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗
DOE OSTI2022

Continuous-time probabilistic models for longitudinal electronic health records

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. Here, we present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
DOE OSTI2021

densegranFoam

densegranFoam is a dense granular material flow solver developed using the OpenFoam Computational Fluid Dynamics (CFD) tool. It is developed using Euler-Euler framework. The transport model is implemented using the shear rate dependant constitutive model. The primary focus of the solver is to simulate shear rate dependency on the dense granular flow bulk behavior.

Ahsan, Syed↗
DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗