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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Operator inference for non-intrusive model reduction with quadratic manifolds

This paper proposes a novel approach for learning a data-driven quadratic manifold from high-dimensional data, then employing this quadratic manifold to derive efficient physics-based reduced-order models. The key ingredient of the approach is a polynomial mapping between high-dimensional states and a low-dimensional embedding. This mapping consists of two parts: a representation in a linear subspace (computed in this work using the proper orthogonal decomposition) and a quadratic component. The approach can be viewed as a form of data-driven closure modeling, since the quadratic component introduces directions into the approximation that lie in the orthogonal complement of the linear subspace, but without introducing any additional degrees of freedom to the low-dimensional representation. Combining the quadratic manifold approximation with the operator inference method for projection-based model reduction leads to a scalable non-intrusive approach for learning reduced-order models of dynamical systems. Further, applying the new approach to transport-dominated systems of partial differential equations illustrates the gains in efficiency that can be achieved over approximation in a linear subspace.

42 ENGINEERING↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗
DOE OSTI2022

Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green’s function

Computing the numerical solution of the Kadanoff–Baym equations, a set of nonlinear integral differential equations satisfied by the two-time Green's functions derived from many-body perturbation theory for a quantum many-body system away from equilibrium, is a challenging task. Recently, we have successfully applied dynamic mode decomposition (DMD) to construct a data driven reduced order model that can be used to extrapolate the time-diagonal of a two-time Green's function from numerical solutions of the KBE within a small time window. In this paper, we extend the previous work and use DMD to predict off-diagonal elements of the two-time Green's function. We partition the two-time Green's function into a number of one-time functions along the diagonal and subdiagonals of the two-time window as well as in horizontal and vertical directions. We use DMD to construct separate reduced order models to predict the dynamics of these one-time functions in a two-step procedure. We extrapolate along diagonal and several subdiagonals within a subdiagonal band of a two-time window in the first step. In the second step, we use DMD to extrapolate the Green's function outside of the sub-diagonal band. In conclusion, we demonstrate the efficiency and accuracy of this approach by applying it to a two-band Hubbard model problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

HTGR PBR Reactor Optimization Methodology

This presentation showcases the work done under the ART work package in FY23 regarding optimization and sensitivity analysis of pebble-bed reactors. The slides include a description of the problem, the proposed methodology, the developed model used for analysis, and the methodologies for sensitivity and reduced-order modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A reduced order sulfuric acid decomposition model for a nuclear-powered hybrid sulfur cycle

A reduced order model of sulfuric acid decomposition within a bayonet chemical reactor was developed to support the U. S. Department of Energy Integrated Energy System program, and address the lack of knowledge in scaling and integration for joint chemical and nuclear processes. Sulfuric acid decomposition within a bayonet reactor was modeled to provide chemical and thermodynamic data relevant to advanced nuclear reactor-driven integrated energy systems based on desired operational scale and operational conditions. The temperature range required for high-temperature advanced nuclear reactor integrated energy systems, 750-850 °C, was shown to produce reasonable agreement (within a few percent relative error) with past models and experimental data, and yielded good efficiency results for bayonet reactor operations. The results of the reduced order model agreed with previous work from Savannah River National Labs within a maximum of 3.4% error on the decomposition of sulfur trioxide, and on previous Hybrid Sulfur flowsheets from Gorensek and Summers that showed operational temperature, pressure, and composition ranges for efficiency which made the Hybrid Sulfur cycle competitive with water electrolysis. Here, the agreement with previous high-fidelity models provided a framework for future Integrated Energy System grid evaluations with an advanced nuclear reactor and large-scale hydrogen production using a mathematical model to represent chemical operations.

08 HYDROGEN↗
DOE OSTI2022

Physics-constrained deep learning of nonlinear normal modes of spatiotemporal fluid flow dynamics

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method.

Mechanics↗
DOE OSTI2025

On the Efficacy of Repeat Voltage Holds for Conditioning and Calendar Life Testing of Graphite and Silicon Cells

Voltage-hold (V-hold) protocols have shown promise toward calendar lifetime analysis of cells with graphite (Gr) and silicon (Si) anodes. In this work, repeat V-holds are performed on Gr and Si cells paired with lithium iron phosphate cathodes to delineate their beneficial role in formation and conditioning. We find that V-hold at the top of charge supplements constant current cycling in conditioning the cell to higher capacities for both Gr and Si cells after the first V-hold. A reduced order model provides the irreversible capacity proportions of each V-hold. With each repeat V-hold, parasitic loss of lithium to the solid electrolyte interphase (SEI) decreases on both Gr and Si cells. Gr cells show the square-root-of-time capacity loss behavior within 200 h of V-hold, indicative of its fast relaxation and low impact of reference performance test cycles on the SEI growth. Lifetime estimates from repeat V-holds on Gr can reach years. Si exhibits longer transition times from kinetic to diffusion-limited SEI growth, evidenced by the 400 h and 200 h holds showing square-root-of-time and linear behavior, respectively. Lifetime predictions from repeat V-holds on Si only reach 1–2 months, highlighting its limitations. Recommended duration of V-holds for Si cells should be ≥400 h.

25 ENERGY STORAGE↗
DOE OSTI2026

Reduced-Order CFD Modeling to Support Waste Loading Optimization in Hanford WTP Vitrification

The U.S. DOE Hanford Site stores over 56 million gallons of radioactive liquid tank waste that must be treated and immobilized for long-term disposal The Waste Treatment and Immobilization Plant (WTP) will vitrify this waste by feeding it into Joule-heated melters, where it is incorporated into a stable borosilicate glass Computational fluid dynamics (CFD) simulations of glass melters can provide insight into the maximum achievable waste loading under varying melter operating conditions Fully resolved VOF multiphase simulations were used as the reference model to capture bubble-driven convection in the melter, including bubble formation, rise behavior, and induced glass melt circulation Effective bubble column diameter and rise velocity were extracted from the resolved simulations, compared with empirical correlations, and refit across relevant viscosity and gas flow rate conditions Explicit gas–liquid interface tracking was replaced with a single-phase momentum source term model, enabling faster steady-state CFD simulations while preserving the dominant hydrodynamic effects of bubbling New empirical correlations were developed for effective bubble column diameter and bubble rise velocity by fitting resolved simulation data across expected melter viscosity and gas flow rate ranges, providing improved inputs for the momentum source term model compared with existing literature correlations The momentum source term model reduced fluid-domain mesh size by 89% and achieved an 8.4× computational speedup relative to resolved bubbling simulations The validated momentum source term approach enables prediction of process-relevant heat transfer behavior in the integrated melter model, including heat transfer from the molten glass to the cold cap, plenum, refractory walls, and surrounding structural regions under varying melter operating conditions

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2021

Performance of a Natural Gas Solid Oxide Fuel Cell System With and Without Carbon Capture

The fuel cell program at the United States Department of Energy (DOE) National Energy Technology Laboratory (NETL) is focused on the development of low-cost, highly efficient, and reliable fossil-fuel-based solid oxide fuel cell (SOFC) power systems that can generate environmentally-friendly electric power with at least 90 percent carbon capture. NETL’s SOFC technology development roadmap is aligned with near-term market opportunities in the distributed generation sector to validate and advance the technology while paving the way for utility-scale natural gas (NG)- and coal-derived synthesis gas-fueled applications via progressively larger system demonstrations. The present study represents a part of a series of system evaluations being carried out at NETL to aid in prioritizing technological advances along research pathways to the realization of utility-scale SOFC systems, a transformational goal of the fuel cell program. In particular, the system performance of utility-scale NG fuel cell (NGFC) systems with and without carbon dioxide (CO2) capture is presented. The NGFC system analyzed features an external auto-thermal reformer (ATR) feeding the fuel to the SOFC system consisting of planar anode-supported SOFC with separated anode and cathode off-gas streams. In systems with CO2 capture, an air separation unit (ASU) is used to provide the oxygen for the ATR and for the combustion of unutilized fuel in the SOFC anode exhaust along with a CO2 purification unit to provide a nearly pure CO2 stream suitable for transport for usage in enhanced oil recovery operations or for storage in underground saline formations. Remaining thermal energy in the exhaust gases is recovered in a bottoming steam Rankine cycle while supplying any process heat requirements. A reduced order model (ROM) developed at the Pacific Northwest National Laboratory (PNNL) is used to predict the SOFC performance. The ROM, while being computationally effective for system studies, provides other detailed information about the state of the stack, such as the internal temperature gradient, generally not available from simple performance models often used to represent the SOFC. Such additional information can be important in system optimization studies to preclude operation under off-design conditions that can adversely impact overall system reliability. The NGFC system performance was analyzed by varying salient system parameters, including the percent of internal (to the SOFC module) NG reformation—ranging from 0 to 100 percent—fuel utilization, and current density. The impact of advances in underlying SOFC technology on electrical performance was also explored.

solid oxide fuel cell (SOFC), natural gas fuel cel↗
DOE OSTI2022

Physically rigorous reduced-order flow models of fractured subsurface environments without explosive computational cost

Fractured media models comprise discontinuities of multiple lengths (e.g. fracture lengths and apertures, wellbore area) that fall into the relatively insignificant length scales spanning millimeter-scale fractures to centimeter-scale wellbores in comparison to the extensions of the field of interest, and challenge the conventional discretization methods imposing highly-fine meshing and formidably large numerical cost. By utilizing the recent developments in the finite element analysis of electromagnetics that allow to represent material properties on a hierarchical geometry, this project develops computational capabilities to model fluid flow, heat conduction, transport and induced polarization in large-scale geologic environments that possess geometrically-complex fractures and man-made infrastructures without explosive computational cost. The computational efficiency and robustness of this multi-physics modeling tool are demonstrated by considering various highly-realistic complex geologic environments that are common in many energy and national security related engineering problems.

42 ENGINEERING↗
DOE OSTI2023

DPE Techno-Economic Analyses

This National Energy Technology Laboratory (NETL) report presents the results of an extensive set of techno-economic analyses for several open-cycle coal fuel oxy-fired direct power extraction (DPE) concepts. These power cycles make use of a conducting plasma accelerated through a magnetic field to generate direct current directly without the need for moving parts. Similar to gas turbines, the efficiency of the power cycle increases with increasing working fluid temperature and typically, plasma temperatures are much higher than the inlet temperature of even the most advanced gas turbine available today, affording the opportunity for very high cycle efficiencies. Most of the study uses high-fidelity models to assess a coal-fueled oxy-combustion DPE system with an AUSC steam bottoming cycle. Two baseline configurations were explored, one with relatively conservative operating point parameters and another with relatively aggressive operating point parameters. Additional analysis on the high-fidelity models as well as reduced order models were performed to analyze the cost and performance potential more thoroughly for the baseline systems and to compare their performance to two alternate open-cycle DPE systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Digital Twin Development of FASTR

This work package is focused on creating a digital twin (DT) of the Facility to Alleviate Salt Technology Risks (FASTR) experiment. FASTR is a high-profile facility tasked with maturing molten salt applications such as liquid salt energy storage, concentrated solar power facilities, and molten salt cooling systems supporting other advanced reactor technologies, all of which may be incorporated as components within the Integrated Energy Systems (IES) program. As part of creating a digital twin (DT), verification and validation (V&V) has been performed for the physics-based model, and gaps have been identified. Additionally, a preliminary integration of the FASTR model has been completed with the existing physics-based model of the Thermal Energy Distribution System (TEDS)/Microreactor AGile Non-nuclear Experimental Testbed (MAGNET) facility at Idaho National Laboratory (INL). A Functional Mock-up Unit (FMU) of the physics-based system model and a reduced-order model (ROM) were both used to test for a preliminary hardware in the loop operation when the physical loop was offline. Future tasks could include improving the physics-based model to better match the available experimental data, improving the hardware in the loop integration, and improving the integration with the TEDS model.

14 SOLAR ENERGY↗