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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Many-body electronic structure of LaScO 3 by real-space quantum Monte Carlo

We present real space quantum Monte Carlo (QMC) calculations of the scandate LaSc0 3 that proved to be challenging for traditional electronic structure approaches due to strong correlation effects resulting in inaccurate band gaps from DFT and GW methods when compared with existing experimental data. Besides calculating an accurate QMC band gap corrected for supercell size biases and in agreement with numerous experiments, we also predict the cohesive energy of the crystal using the standard fixed-node QMC without any empirical or non-variational parameters. We show that promotion (optical) gap and fundamental gap agree with each other illustrating a clear absence of significant excitonic effects in the ideal crystal. We obtained these results in perfect consistency in two independent tracks that employ different basis sets (plane wave vs. localized gaussians), different codes for generating orbitals (QUANTUM ESPRESSO vs. CRYSTAL), different QMC codes (QMCPACK vs. QWALK) and different high-accuracy pseudopotentials (ccECPs vs. Troullier-Martins) presenting the maturity and consistency of QMC methodology and tools for studies of strongly correlated problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Online evolutionary neural architecture search for multivariate non-stationary time series forecasting

Time series forecasting (TSF) is one of the most important tasks in data science. TSF models are usually pre-trained with historical data and then applied on future unseen datapoints. However, real-world time series data is usually non-stationary and models trained offline usually face problems from data drift. Models trained and designed in an offline fashion can not quickly adapt to changes quickly or be deployed in real-time. To address these issues, this work presents the Online NeuroEvolution-based Neural Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Without any pre-training, ONE-NAS utilizes populations of RNNs that are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world, large-scale multivariate wind turbine data as well as the univariate Dow Jones Industrial Average (DJIA) dataset. These results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies. Additionally, results show that utilizing multiple populations of RNNs which are periodically repopulated provide significant performance improvements, allowing this online neural network architecture design and training to be successful.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Higher-order LaSDI: Reduced order modeling with multiple time derivatives

Solving complex partial differential equations (PDEs) is essential across scientific disciplines but often requires numerical models that can be prohibitively expensive in time-sensitive applications. Reduced-order models (ROMs) address this challenge by exploiting low-dimensional structure to create fast approximations. The Latent Space Dynamics Identification (LaSDI) framework has demonstrated success in learning ROMs for parameterized PDE families, but remains limited to first-order systems. Here, in this paper, we propose Higher-Order LaSDI (HLaSDI), which extends the LaSDI framework to PDEs with arbitrary order of time derivatives. This generalization significantly expands the applicability of LaSDI-based methods to systems previously outside their scope, including hyperbolic PDEs. We demonstrate HLaSDI’s accuracy and efficiency on several linear and nonlinear benchmark problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Dynamic Graph Sequence Data from Simulated Neutron Reflectometry Measurements

This dataset comprises dynamic graph sequences derived from simulated in-situ neutron reflectometry measurements, capturing the gradual evolution of a layer structure over time. Each graph sequence represents a synthetic sample, with node features detailing the scattering vector and corresponding reflectivity measurements, while adjacency matrices have corresponding reference material parameters attached as metadata. The dataset spans multiple sets, each with a different number of sequences, offering a comprehensive basis for training models that handle dynamic input sequences with embedded physics. This dataset is particularly suited for tackling inverse problems with hidden physical states that evolve over time, challenges that are typically difficult to address using conventional iterative fitting methods.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Are You Resilient? Site Energy and Water Resilience Indicators

There is growing interest among operators of individual or co-located facilities to plan for utility disruptions caused by natural hazards and threats. Energy and water resilience at the site level can be characterized as the ability to withstand, adapt, and recover from an energy and/or water supply disruption. But how can a site’s energy and water resilience posture be measured? An evaluation framework with clear performance measures or indicators can help organizations establish what it means to be energy and water resilient and assess progress towards meeting resilience goals across sites and over time. Tracking a consistent set of indicators can help organizations identify problem areas that should be addressed and ascertain if energy and water resilience has improved as a result of investments or operational or policy changes. Resilience indicators also provide a means for organizations with geographically distributed sites to identify common problem areas that may require higher-level attention. This paper outlines an approach that organizations can use to measure site energy and water resilience. This approach was developed by Pacific Northwest National Laboratory for the U.S. Department of Energy’s Federal Energy Management Program as part of an effort to help federal agencies plan for energy and water resilience in a more systematic and rigorous way. The paper defines a general structure for defining energy and water resilience indicators, and provides example indicators and potential rating methods.

resilience, metrics for resilience, site resilienc↗
DOE OSTI2020

Equilibrium carbon isotope fractionation factors of hydrocarbons: Semi-empirical force-field method

Here, we have calculated the reduced partition function ratios for carbon isotopes (β-factor) of 67 hydrocarbons (alkanes, alkenes, alkynes, cycloalkanes, and aromatics), including their 267 single-substituted isotopomers. The calculations were performed using the harmonic oscillator – rigid rotator model and the Urey / Bigeleisen-Mayer approach. Normal frequencies of molecular vibrations of the isotopologues were calculated from the molecular structures data and valence field force constants stored in the Light-handled Elucidation of Vibrations (LEV) database elaborated by Dr. Gribov and his colleagues in Russia. The LEV database is constructed from experimental spectroscopic and structural data by solving inverse problems of molecular vibrations. The LEV is internally consistent and the most comprehensive database available to date. The β-factor were calculated in the temperature range of 200–800 K with a 10 K step. Our calculations predict that the β-factors increase with an increasing number of C atoms within the same groups of hydrocarbons (e.g., alkanes). Our calculations also show a general descending order of 13 C enrichments among the different groups of hydrocarbons: cycloalkanes, aromatics, alkenes (double bonds) and isoalkanes, alkanes, alkynes (triple bonds). Position-specific, intramolecular isotope effects within hydrocarbons are determined by the β-factors of C in the different functional groups in the order: quaternary (C), tertiary (methine, CH), secondary (methylene, CH 2 ), primary (methyl, CH 3 ) and double bond (C=C), saturated bond (C - C), triple bond (HC≡). Our calculations on bulk and position-specific carbon isotope β-factors of the hydrocarbons, which are generally consistent with very limited ab initio calculations in the literature, are internally consistent and the most comprehensive to date for future applications to position-specific isotope geochemistry of hydrocarbons.

58 GEOSCIENCES↗
DOE OSTI2024

Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems

Rapid, wide-scale deployment of green process systems, such as carbon capture or water desalination systems, is essential for combatting climate change. Methods relying on traditional design or modularity fail to capture the benefits of both economies of numbers and economies of scale. We have proposed process family design, which designs a family of processes simultaneously exploiting opportunities for common elements. In previous work, we explored different optimization formulations to solve this problem. In this work, we develop a decomposition approach to tackle larger problems efficiently. We solve a water desalination case study, which is too large to solve within a reasonable timeframe with the discretization formulation. We exploit the block angular structure of the discretization problem to decompose and solve using Progressive Hedging (PH). We use the open-source Python package mpi-sppy to execute PH which allows us to leverage parallelization and a HPC cluster to further improve solution time.

Stinchfield, Georgia↗
DOE OSTI2023

On a fully-implicit VMS-stabilized FE formulation for low Mach number compressible resistive MHD with application to MCF

This study presents the development and evaluation of a fully-implicit variational multiscale (VMS) stabilized unstructured finite element (FE) formulation for compressible magnetohydrodynamics (MHD) model, at low Mach number regime. The model describes the dynamics of a compressible conducting fluid in the low Mach number limit in the presence of electromagnetic fields and can be used to study aspects of astrophysical phenomena, important science and technology applications, and basic plasma physics phenomena. The specific applications that motivate this study are macroscopic simulations of the longer time-scale stability and disruptions of magnetic confinement fusion (MCF) devices, specifically the ITER tokamak. The discussion considers the development of the VMS FE representation, the structure of the stabilizing terms that deal with significant convective flows, the stabilization of the nearly incompressible response of the fluid flow, and the stabilization of the constraint that enforces the solenoidal involution on the magnetic field. The nonlinear discretized system is solved with scalable preconditioned Newton–Krylov iterative methods, which employs a multiphysics block preconditioning method based on approximate block factorizations and Schur complements. The study presents an evaluation of the VMS method on a 2D cartesian tearing mode instability, and illustrates the scalability of the solvers on MCF relevant problems. A set of results are also presented for longer time-scale stability and disruptions for the ITER tokamak. These include a vertical displacement event (VDE), and a (1,1) internal kink mode. Here, the formulation is demonstrated to be scalable and also reasonably robust with respect to the Lundquist number scaling.

42 ENGINEERING↗
DOE OSTI2022

Automatic parameter selection for electron ptychography via Bayesian optimization

Abstract Electron ptychography provides new opportunities to resolve atomic structures with deep sub-angstrom spatial resolution and to study electron-beam sensitive materials with high dose efficiency. In practice, obtaining accurate ptychography images requires simultaneously optimizing multiple parameters that are often selected based on trial-and-error, resulting in low-throughput experiments and preventing wider adoption. Here, we develop an automatic parameter selection framework to circumvent this problem using Bayesian optimization with Gaussian processes. With minimal prior knowledge, the workflow efficiently produces ptychographic reconstructions that are superior to those processed by experienced experts. The method also facilitates better experimental designs by exploring optimized experimental parameters from simulated data.

42 ENGINEERING↗
DOE OSTI2025

Selective electrified polyethylene upcycling by pore-modulated pyrolysis

Plastic waste is a increasing problem, accumulating in landfills and the environment. Pyrolysis is a promising and industrially relevant approach for transforming plastic waste into value-added chemicals. However, the selectivity and yield of traditional plastic pyrolysis are poor, with products featuring broad molar mass distributions. Here we report a highly selective, energy-efficient and catalyst-free pyrolysis method that can upcycle plastic into value-added chemicals via pore-modulated pyrolysis. Using a Joule-heated carbon column, we demonstrate the pivotal role of the reactor’s graded porous structure in decreasing the polydispersity of the reaction intermediates, enabling high product selectivity and yield. The decreasing pore size of the reactor modulates the mass transport in an apparent gating effect—preventing high-molar-mass species from exiting the reactor before sufficient pyrolysis has occurred. Using polyethylene as a model reactant, we demonstrate a high yield of 65.9 ± 5.2% and up to 80.8% selectivity toward value-added aviation fuel precursor (C8–C18 hydrocarbons) without the use of any catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multiwavelength anomalous X-ray diffraction for combined imaging of atomic displacement and strain

The X-ray Bragg coherent diffractive imaging (CDI) technique assumes that the structure factor holds constant over the measured crystal. This approximation breaks down for materials exhibiting variations in the unit-cell configuration, such as piezo- and ferroelectrics. In that case, the strain field cannot be reliably determined from the reconstruction because the lattice deformation and the structure factor contribute concomitantly. Proposed here is a solution to this problem achieved by combining Bragg CDI and the multiwavelength anomalous diffraction approach that measures a Friedel pair of reflections at two different photon energies near an absorption edge. Comparing the obtained reconstructions with a parametric model that includes calculating the scattering amplitude as a function of wavelength and the unit-cell configuration, the contributions of the lattice deformation and the structure factor are separated. Simulations of the ferroelectric material BaTiO 3 demonstrate the possibility of simultaneous probing of the strain and displacement of the Ti atoms. The proposed method opens up an opportunity to apply coherent X-ray diffraction for nanoscale-resolved 3D mapping of polarization domains in micro- and nanocrystals.

lattice deformation↗
DOE OSTI2023

Modeling support for the development of material surveillance specimens and procedures

This report describes modeling and simulation activities performed at Argonne National Laboratory supporting the development of passively actuated mechanical test articles for material surveillance in Molten Salt Reactors (MSRs). These test articles are a critical technology in formulating material surveillance programs for future MSRs to monitor the degradation in the structural properties of the materials in critical plant components. The main activity described in this report is the development of a method for inferring the amount of mechanical damage a test article has experienced during some duration of exposure to plant thermal and environmental conditions, using only mechanical test data collected from the test articles before and after exposure. The basic approach is to develop a model of the test article, including a description of mechanical degradation through continuum damage mechanics, and then use this model to cast the problem of inferring mechanical degradation in the test article materials into a shooting problem for a set of ordinary differential equations. We can then solve the shooting problem to determine the amount of damage accumulated in the sample. The report also details a few miscellaneous simulation studies completed at Argonne to support the development of the test articles themselves at Idaho National Laboratory.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Wind Turbine Blade Repurposing and Recycling: Coupling Repurposing Methods with 3D Printing Technology

Fiber-reinforced thermoplastic composites continue to be implemented in several marketsbecause of their strength capacity and light weight. Especially in the wind industry, wind bladesare made of this material which makes them ideal for wind energy generation, but bladesbecome a problem at end-of-life. Because recycling thermoplastic composites technologiesare still not cost and environmentally effective, research in this space is needed. This paperpresents a case study for coupling repurposed wind turbine blades and 3D printing technology.The purpose is to remanufacture decommissioned wind blades into bus stop roof and seatingsections to then use recycled glass fiber from a decommissioned wind turbine blade to 3Dprint structural and/or fixtures needed in the assembly. We provide an overview of the designprocess, and the development process steps in repurposing and 3D printing the material,delving into key parameters required for a successful implementation.

Henao, Yulizza↗
DOE OSTI2020

Ionic/electronic conductivity regulation of n-type polyoxadiazole lithium sulfonate conductive polymer binders for high-performance silicon microparticle anodes

Low-cost silicon microparticles (SiMP), as a substitute for nanostructured silicon, easily suffer from cracks and fractured during the electrochemical cycle. A novel n-type conductive polymer binder with excellent electronic and ionic conductivities as well as good adhesion, has been successfully designed and applied for high-performance SiMP anodes in lithium-ion batteries to address this problem. Its unique features are attributed to the strong electron-withdrawing oxadiazole ring structure with sulfonate polar groups. The combination of rigid and flexible components in the polymer ensures its good mechanical strength and ductility, which is beneficial to suppress the expansion and contraction of SiMP s during the charge/discharge process. By fine-tuning the monomer ratio, the conjugation and sulfonation degrees of the polymer can be precisely controlled to regulate its ionic and electronic conductivities, which has been systematically analyzed with the help of an electrochemical test method, filling in the gap on the conductivity measurement of the polymer in the doping state. The experimental results indicate that the cell with the developed n-type polymer binder and SiMP (~0.5 μm) anodes achieves much better cycling performance than traditional non-conductive binders. It has been considered that the initial capacity of the SiMP anode is controlled by the synergetic effect of ionic and electronic conductivity of the binder, and the capacity retention mainly depends on its electronic conductivity when the ionic conductivity is sufficient. Here, it is worth noting that the fundamental research of this work is also applicable to other battery systems using conductive polymers in order to achieve high energy density, broadening their practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Validation of the single-event method for low-energy electron transport via stopping power calculations with $\mathrm{MCNP}$

Monte Carlo simulations of low-energy ( <50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. Here in this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗