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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Yao, Weigang↗
DOE OSTI2020

Online adaptive basis refinement and compression for reduced-order models via vector-space sieving

In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate solutions are accurate; their accuracy depends on the ability of the low-dimensional trial basis to represent the FOM solution. As a result, ROMs can generate inaccurate approximate solutions, e.g., when the FOM solution at the online prediction point is not well represented by training data used to construct the trial basis. To address this fundamental deficiency of standard model-reduction approaches, this work proposes a novel online-adaptive mechanism for efficiently enriching the trial basis in a manner that ensures convergence of the ROM to the FOM, yet does not incur any FOM solves. The mechanism is based on the previously proposed adaptive $h$-refinement method for ROMs, but improves upon this work in two crucial ways. First, the proposed method enables basis refinement with respect to any orthogonal basis (not just the Kronecker basis), thereby generalizing the refinement mechanism and enabling it to be tailored to the physics characterizing the problem at hand. Second, the proposed method provides a fast online algorithm for periodically compressing the enriched basis via an efficient proper orthogonal decomposition (POD) method, which does not incur any operations that scale with the FOM dimension. We report these two features allow the proposed method to serve as (1) a failsafe mechanism for ROMs, as the method enables the ROM to satisfy any prescribed error tolerance online (even in the case of inadequate training), and (2) an efficient online basis-adaptation mechanism, as the combination of basis enrichment and compression enables the basis to adapt online while controlling its dimension.

42 ENGINEERING↗
DOE OSTI2023

Scalable Reduced Order Model with Discontinuous Galerkin Domain Decomposition

scaleupROM is a scalable, physics-constrained reduced order model (ROM). It aims to provide robust, accelerated physics predictions at extrapolated scales, based on the small, component-level data. This is implemented by combining projection-based ROM with discontinuous Galerkin domain decomposition, in the framework of MFEM and libROM. It currently supports the Poisson equation and Stokes flow equation, and more work is in progress toward general, nonlinear physics systems.

Chung, Seung Whan↗
DOE OSTI2021

Space-Time Reduced-Order Modeling for Uncertainty Quantification

This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Evaluation of Linear, Inviscid, Viscous, and Reduced-Order Modeling Aeroelastic Solutions of the AGARD 445.6 Wing Using Root Locus Analysis

Reduced-order modeling (ROM) methods are applied to the CFD-based aeroelastic analysis of the AGARD 445.6 wing in order to gain insight regarding well-known discrepancies between the aeroelastic analyses and the experimental results. The results presented include aeroelastic solutions using the inviscid CAP-TSD code and the FUN3D code (Euler and Navier-Stokes). Full CFD aeroelastic solutions and ROM aeroelastic solutions, computed at several Mach numbers, are presented in the form of root locus plots in order to better reveal the aeroelastic root migrations with increasing dynamic pressure. Important conclusions are drawn from these results including the ability of the linear CAP-TSD code to accurately predict the entire experimental flutter boundary (repeat of analyses performed in the 1980's), that the Euler solutions at supersonic conditions indicate that the third mode is always unstable, and that the FUN3D Navier-Stokes solutions stabilize the unstable third mode seen in the Euler solutions.

Silva, Walter A.↗
NASA NTRS2019

Active Flutter Suppression Using Reduced-Order Modeling for Transonic Aeroservoelastic Control Law Development

In this paper, several aerodynamic reduced-order models (ROMs) are generated and coupled with structural models to form aeroelastic ROMs. The aerodynamic ROMs generated here include the effects of control surface motion and are appropriate for use in aeroservoelastic applications. Simple observer-based full-state feedback controllers were designed from these aeroelastic ROMs. Additionally, observer gain matrices were designed from and coupled to the aeroelastic ROMs. Each (linear) observer was then used to estimate the dynamics of a (nonlinear) stand-alone computational fluid-structure dynamics simulation. Then, using the estimated states and the full-state feedback controller, control surface commands were fed back into the computational fluid-structure dynamics simulation to successfully achieve active flutter suppression. The process, as well as some results, are presented in this paper.

Waite, Josiah M.↗
DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Reduced-Order Modeling Method for Phase-Change Thermal Energy Storage Heat Exchangers

Thermal energy storage can facilitate the effective utilization of renewable energy. To speed up the design process of thermal energy storage devices, it is critical to develop fast and accurate modeling methods for phase change material embedded heat exchangers (PCM HXs). This study developed and compared two approximation-assisted reduced-order PCM HX models for the simulation of thermal storage components and systems, which were verified against a validated finite-volume model. They are a pure black-box model, and a grey-box model based on the Number of Transfer Units (or effectiveness-NTU) approach. We used the reduced-order models to predict the performance of standalone PCM HX for 1000 cases with different fluid inlet conditions and HX designs. We also integrated the models with a vapor compression system to predict the compressor energy consumption and total charging time under various conditions. The results show that overall, the black-box model gave more accurate results than the grey-box model. On average, the mean absolute deviation in the PCM HX fluid outlet temperature was 0.05 K and 0.1 K for the black-box model and grey-box model, respectively. The grey-box model gave larger temperature deviation toward the end of the phase change process, due to the simplified two-node PCM representation. On the system level, the mean absolute deviation in compressor energy consumption was 0.2% and 0.3% for the black-box model and grey-box model, respectively. Their mean absolute deviation in total charging time was 1.1% and 2.6%, respectively. In terms of computation efficiency, the system simulation speedup ratio gained by using the reduced-order models was 11 to 57. System simulation time was decreased from an average of 1465 s to an average of 59 s. This shows that the proposed reduced-order modeling methods can be used to predict system performance of PCM HXs with less than 3% accuracy penalty, and 25 times less computational time than finite-volume models, enabling faster design and evaluation of PCM thermal storage devices.

25 ENERGY STORAGE↗
DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗
DOE OSTI2021

Reduced Order Model of Transactive Bidding Loads

Transactive energy (TE) has been identified to provide better grid efficiency and reliability by market-based transactive exchanges between energy producers and energy consumers. Simulations of TE systems are crucial to evaluate the benefits and impacts of different transactive mechanisms. However, such simulations can be time consuming due to the information exchange between various participants and complex co-simulation environments. In this paper, we develop a reduced order model to speed up the simulation of transactive systems in TE simulation platform (TESP) while achieving very low error between the reduced order and full model results. Specifically, the developed reduced order model consists of an aggregate responsive load agent which utilizes two Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTMs) to enable transactive elements to collectively participate in the TE system. The proposed aggregate responsive load (ARL) agent is able to produce similar transactive behaviors to the full simulation model while achieving significant simulation time reduction. Finally, we also show that the developed model enables generalization of simulation results across different dates and across different number of loads included in the simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition

The Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophysics, atmospheric sciences and climate, geophysics, and fusion energy. Analytical methods cannot be applied to explain the long-time behavior of the Rayleigh-Taylor instability, and therefore, numerical simulation of the full problem is required. However, in order to capture the growth of amplitude of perturbations accurately, both the spatial and temporal discretizations need to be extremely fine for traditional numerical methods, and long-time simulation may become prohibitively expensive. In this paper, we propose efficient reduced order model techniques to accelerate the simulation of the Rayleigh-Taylor instability in compressible gas dynamics. Here, we introduce a general framework for decomposing the solution manifold to construct the temporal domain partition and temporally-local reduced order model construction with varying Atwood number. We propose two practical approaches in this framework, namely decomposition by physical time and by penetration distance. Numerical results are presented to examine the performance of the proposed approaches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling

Uncertainty quantification of computational fluid dynamics (CFD) simulations is a complicated procedure which still relies in many cases on engineering judgment and factors of safety. This is in part because the computational cost of measuring the simulation's sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data is already available. In this work, fluid reduced-order models are utilized to quantify a flow's sensitivity to certain physical parameters for the purposes of improved uncertainty quantification. Characteristic observability and sensitivity are both explored. The resulting sensitivity quantifications enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data.

uncertainty quantification↗
NASA NTRSDate not supplied

Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling

Uncertainty quantification of computational fluid dynamics (CFD) simulations is a complicated procedure which still relies in many cases on engineering judgment and factors of safety. This is in part because the computational cost of measuring the simulation's sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data is already available. In this work, fluid reduced-order models are utilized to quantify a flow's sensitivity to certain physical parameters for the purposes of improved uncertainty quantification. Characteristic observability and sensitivity are both explored. The resulting sensitivity quantifications enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data.

uncertainty quantification↗
DOE OSTI2024

A generalized wind turbine cross section as a reduced-order model to gain insights in blade aeroelastic challenges

In this work, we present an approach to study the aeroelastic stability of a wind turbine by focusing on the dynamics of a blade cross section. We present a methodology to obtain a reduced-order model of the blade dynamics in the form of generalized cross-sectional quantities that approximates the aerodynamic and structural properties of the full blade. The motivation for the work is to gain a physical understanding of the influence of aerodynamic models such as dynamic wake and dynamic stall on the frequency and damping of the structure using a reduced-order model with low computational cost. The model may be coupled to two-dimensional computational fluid dynamics softwares or engineering unsteady airfoil aerodynamics models accounting for dynamic wake and dynamic stall. In the latter case, we can obtain monolithic state-space forms of the aeroelastic system of equations, which simplifies the determination of the modal parameters and therefore the study of stability. The work investigates wind turbines in operation or at standstill, where vortex-induced vibrations and stall-induced vibrations, respectively, might be an issue. The implementation is made available as part of the open-source Python package WELIB and as part of the open-source unsteady aerodynamic driver of OpenFAST.

17 WIND ENERGY↗
DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

The reduced order model problem in distributed parameter systems adaptive identification and control

The research concerning the reduced order model problem in distributed parameter systems is reported. The adaptive control strategy was chosen for investigation in the annular momentum control device. It is noted, that if there is no observation spill over, and no model errors, an indirect adaptive control strategy can be globally stable. Recent publications concerning adaptive control are included.

Johnson, C. R., Jr.↗
DOE OSTI2025

A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Anisotropy↗
DOE OSTI2026

Efficient data-driven regression for reduced-order modeling of spatial pattern formation

We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learned non-intrusively from available training data of physically accurate numerical simulations. The method can be applied to general nonlinear systems through the use of polynomial model form, while not requiring knowledge of the underlying physical model, governing equations, or numerical solvers. The process of learning ROMs is posed as a low-cost least-squares problem in a reduced-order subspace identified via Proper Orthogonal Decomposition (POD). Numerical experiments on classical pattern-forming systems–including the Schnakenberg and Mimura–Tsujikawa models–demonstrate that higher-order surrogate models significantly improve prediction accuracy while maintaining low computational cost. The proposed method provides a flexible, non-intrusive model reduction framework, well suited for the analysis of complex spatio-temporal pattern formation phenomena.

Data-driven modeling↗