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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PHILMS) (Technical Report)

The research work at University of California Santa Barbara (UCSB) resulted in several new developments in the areas of scientific machine learning, numerical analysis, and practical methods for data-driven modeling, prediction, reductions, and simulation. Many of the projects were carried out in collaboration with members of the national laboratories at Sandia National Laboratories (SNL), Pacific Northwestern National Laboratories (PNNL), and other institutions. Results included developing new scientific machine learning methods, related theory and mathematical frameworks for analysis and training, data-driven numerical solvers, and related tools and software for scientific computation. During the support period, over 16+ papers were submitted for publication, and 4 open-source software packages were developed and released (available at http://atzberger.org/). In addition, 7+ students and 2 post-docs were mentored in collaboration with the laboratory staff for future careers in academia, government labs, and industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Component-wise reduced order model lattice-type structure design

Lattice-type structures can provide a combination of stiffness with light weight that is desirable in a variety of applications. Design optimization of these structures must rely on approximations of the governing physics to render solution of a mathematical model feasible. In this paper, we propose a topology optimization (TO) formulation that approximates the governing physics using component-wise reduced order modeling as introduced in Huynh et al. (2013); Eftang and Patera (2013), which can reduce solution time by multiple orders of magnitude over a full-order finite element model while providing a relative error in the solution of 1%. In addition, the offline training data set from such component-wise models is reusable, allowing its application to many design problems for only the cost of a single offline training phase, and the component-wise method is nearly embarrassingly parallel. We also show how the parameterization chosen in our optimization allows a simplification of the component-wise reduced order model (CWROM) not noted in previous literature, for further speedup of the optimization process. Furthermore, the sensitivity of the compliance with respect to the particular parameterization is derived solely at the component level. In numerical examples, we demonstrate a 1000x speedup over a full-order FEM model with relative error of 1% and show minimum compliance designs for two different cantilever beam examples, one smaller and one larger. Finally, error bounds for the displacement field, compliance, and compliance sensitivity of the CWROM are derived.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

42 ENGINEERING↗
DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

SIERRA Multimechanics Module: Aria User Manual - Version 4.58

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number ( %3C 1 ) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newtons method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic -adaptivity and dynamic load balancing are some of Arias more advanced capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SIERRA Multimechanics Module: Aria Thermal Theory Manual (V.5.0)

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number ( Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newtons method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic -adaptivity and dynamic load balancing are some of Arias more advanced capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Physical Activity and Bone Density in Women

A mathematical model of bone density regulation as a function of the daily tissue "effective" stress has been derived. Using the model, the influence of daily activity in the form of a daily loading history has been related to bone density of the calcaneus. The theory incorporates a stress exponent m to account for differences in the importance of magnitude and number of load cycles experienced during daily activity. We have derived a parameter from the model, the "Bone Density Index" (BDI). We have developed a method of collecting daily habitual loading histories using an insole force sensor interfaced to a portable digital data logger carried in a fanny pack. Our goal for this study was to determine a stress exponent, m, relating GRFz history to Calcaneal Bone Mineral Density (CBMD).

Bowley, Susan M.↗
DOE OSTI2023

Projection pursuit adaptation on polynomial chaos expansions

Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Solution of geometrically nonlinear statics problems by the p-version of the finite element method

This project is concerned with the possibility of using computers for the simulation of structural systems with the same degree of reliability as full scale physical experiments. Reliable numerical simulation will make it possible to reduce the costs of engineering and improve the quality of engineering decisions based on computed information. An error of idealization is an error between the actual physical quantities on which engineering decisions are based (e.g., maximum principal stress, first natural frequency, etc.) and the same data corresponding to the exact solution of the mathematical model. An error of discretization is an error between the quantities of interest corresponding to the exact and approximate solutions of a mathematical model. A high degree of reliability can be achieved in numerical simulation only if both the errors of idealization and errors of discretization can be shown to be small.

Szabo, Barna A.↗
DOE OSTI2025

Diffusion power spectra as a window into dynamic materials architecture

Chemical recycling of commodity and specialty polymers presents a multifaceted challenge for industrial societies. On one hand, macromolecular architectures must be engineered to yield durable products that, on the other hand, rapidly deconstruct to recyclable monomers under pre-determined conditions. Polymer deconstruction is a chemical process that requires deep understanding of molecular reactivity in heterogeneous media, where porous material architectures evolve in both space and time. To build this understanding, we develop herein experimental and analytical methods describing sets of diffusive eigenmodes that exist within time-varying, non-Euclidean boundary conditions, a situation commonly encountered in the reactive deconstruction of polymers where chain fragments splay, alter their local dynamics, and evolve in their confinement of reacting media. Diffusion power spectra, discerned experimentally by NMR, yield polymer and solvent frequency-domain velocity autocorrelation functions that are analyzed in the context of physical models for chemical reactions parameterized with fractal mathematics. The results connect local motion in polymers to chemical reactivity during acidolysis of circular elastomers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO2R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. Here, we specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

Kepler Observations of Rapid Optical Variability in the BL Lac Object W2r192+42

We present the first Kepler monitoring of a strongly variable BL Lac, W2R1926+42. The light curve covers 181 days with approx. 0.2% errors, 30 minute sampling and >90% duty cycle, showing numerous delta-I/I > 25% flares over timescales as short as a day. The flux distribution is highly skewed and non-Gaussian. The variability shows a strong rms-flux correlation with the clearest evidence to date for non-linearity in this relation. We introduce a method to measure periodograms from the discrete autocorrelation function, an approach that may be well-suited to a wide range of Kepler data. The periodogram is not consistent with a simple power-law, but shows a flattening at frequencies below 7x10(exp -5) Hz. Simple models of the power spectrum, such as a broken power law, do not produce acceptable fits, indicating that the Kepler blazar light curve requires more sophisticated mathematical and physical descriptions than currently in use.

galaxies↗
NASA NTRS1973

Analysis of embedded shock waves calculated by relaxation methods.

The requirements for uniqueness of the calculated jump conditions across embedded shock waves are investigated for type-dependent difference systems used in transonic flow studies. A mathematical analysis shows that sufficient conditions are (1) the equations should be differenced in conservative form and (2) a special difference operator should be used when switching from a hyperbolic to an elliptic operator. The latter results in a consistency condition on the integral equations, rather than the differential, at these points. Calculated jump conditions for several embedded and detached shock waves are analyzed in the physical and hodograph planes. Comparisons are made with previous results, a time-dependent calculation, and data.

Murman, E. M.↗
NASA NTRS1987

A global time-dependent model of thunderstorm electricity. I - Mathematical properties of the physical and numerical models

A time-dependent model is introduced that can be used to simulate the interaction of a thunderstorm with its global electrical environment. The model solves the continuity equation of the Maxwell current, which is assumed to be composed of the conduction, displacement, and source currents. Boundary conditions which can be used in conjunction with the continuity equation to form a well-posed initial-boundary value problem are determined. Properties of various components of solutions of the initial-boundary value problem are analytically determined. The results indicate that the problem has two time scales, one determined by the background electrical conductivity and the other by the time variation of the source function. A numerical method for obtaining quantitative results is introduced, and its properties are studied. Some simulation results on the evolution of the displacement and conduction currents during the electrification of a storm are presented.

Browning, G. L.↗
DOE OSTI2020

Impact of Proton Activity on Fuel-Cell Performance (CRADA Final Report)

The key attribute that is being investigated is the dependence of the oxygen-reduction and hydrogen-oxidation reaction kinetics on proton activity. The influence of proton activity on the kinetics is also important in the determination of limiting factors that impact overall fuel-cell performance. At lower humidities, which are required to simplify fuel-cell water and thermal management (especially for higher-temperature operation) and enable next generation fuel cells, the ionomer water content will be different than that in today’s fuel cells. Such changes in water content directly impact the activity and concentration of protons. Thus, it is necessary to quantify this impact of proton activity on the reaction kinetics, and to determine means to mitigate any detrimental impacts to enable high performance. To realize the above goals of understanding the intrinsic interactions of proton activity on hydrogen-oxidation and oxygen-reduction kinetics, systematic investigations were carried out and a novel technique, a rapid microelectrode with environmental control, was realized. The experimental campaign at LBNL worked alongside that of TokyoTech and both were complemented by mathematical modeling of the key phenomena. The results demonstrated a robust method to analyze the kinetic behavior and an impact of proton activity, although greater delineation in terms of ion concentration versus activity was not obtained fully due in part to limited lab access during the COVID pandemic.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

AI-Based Integrated Modeling and Observational Framework for Improving Seasonal to Decadal Prediction of Terrestrial Ecohydrological Extremes

Focal Areas: (1) Insight gleaned from complex data (both observed and simulated) using artificial intelligence(AI), big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fermilab Booster Beam Emittances from Quadrupole Modes Measured BPMs

The measurement of beam emittances by extracting the quadrupole mode signal from a 4 plate beam position monitor (BPM) was published at least 40 years ago. Unfortunately, in practice, this method suffers from poor signal to noise ratio and requires a lot of tuning to extract out the emittances. In this paper, an improved method where multiple BPMs are used together with better mathematical analysis is described. The BPM derived emittances are then compared with those measured by the Ion Profile Monitor (IPM). Surprisingly, the BPM measured emittances behave very well and are more realistic than those measured by the IPM.

43 PARTICLE ACCELERATORS↗