Tropical Ocean Global Atmosphere CD-ROM Set
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Tropical Ocean Global Atmosphere Project Physical ↗
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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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Tropical Ocean Global Atmosphere CD-ROM Set
Explore the source record for details and available documents.
Active Archive Center↗
The NASDA Global Rain Forest Mapping (GRFM) Project Releases First CD-ROM
The goal of the Global Rain Forest Mapping project (GRFM) is to acquire continuous, high resolution L-band Synthetic Aperture Radar (SAR) imagery of the major tropical rain forests of the world using the Japanese Earth Resources Satellite (JERS-1) SAR.
rain forest mapping SAR Global Rain Forest Mapping↗
Physics-Informed ROM Development for ISS EMU LLB-2 Condition-Based Monitoring
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Parameter inference↗
ROM-enabled Multifidelity Uncertainty Quantification for High-Speed Flows
Abstract for conference proceedings (presentation materials to follow later)
Eldred, Michael S. [Sandia National Laboratories (↗
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↗
A Machine Learning Framework for Error Compensation in Radiative Transfer Calculations
Radiative heat transfer influences the amount of heat flux transferred to the surface of the hypersonic vehicle, which is essential to evaluate the performance of thermal protection systems. The radiative heat flux is found to be computationally prohibitive while accounting for the variation in spatial, angular, and spectral domains. A new methodology has been recently developed to alleviate the cost of computation in the spectral domain by constructing flow-agnostic reduced-order models (ROMs). The developed spectral ROM databases provide grouping strategies that account for non-equilibrium absorption and emission as well as interaction between disparate species due to spectral overlap in associated radiative processes. However, the developed ROMs need to be optimized for a specific combination of interacting gas species and would need to re-calibrated in case individual species are added/omitted. In this work, we use various machine learning (ML) techniques to approximate the radiative intensities determined by a ROM optimized for a specific gas mixture. The ML model relies on the ROM databases developed for a single species which ignores any spectral overlap. Thus, radiation evaluation starts with a simple summation of radiative intensities predicted using these non-calibrated ROMs for the contributing species. The ML framework then provides a correction to account for the interplay in the frequency, i.e., emission of photons by one species and absorption by another, and yields mixture-specific radiation fields. Once trained on the individual ROM databases, the ML framework offers instantaneous corrections that serves as a time/cost effective alternative to the optimization of ROMs for a specific gas mixture. The ML framework is trained on both the high fidelity and ROM evaluated line of sight (LOS) data from Orion, Stardust, and FIRE II cases to obtain a general purpose correction model for earth re-entry scenarios when radiation contributions from both atomic nitrogen and atomic oxygen are considered. A geometric length scale parameter is used in the training process to account for errors introduced in the ROM databases as a consequence of high optical thickness. The efficacy of the ML framework is underscored through extensive analysis of train and test errors with respect to all the re-entry scenarios. The applicability of such an ML framework was further corroborated by embedding it in a state-of-the-art US3D - NERO system for determining the radiative heat flux transferred to the hypersonic vehicle surface.
Radiation↗
Local reduced-order modeling for electrostatic plasmas by physics-informed solution manifold decomposition
Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.
Electrostatic plasmas↗
Toward real-time optimization through model reduction and model discrepancy sensitivities
Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essential to solve these optimization problems within wall-clock-time limitations. However, this is often unattainable for complex systems, such as those modeled by nonlinear partial differential equations. One strategy for mitigating this issue is to construct a reduced-order model (ROM) that enables more rapid optimization. In particular, the use of nonintrusive ROMs—those that do not require access to the full-order model at evaluation time—is popular because they facilitate the computation of optimization solutions within the wall-clock time requirements. However, the optimization solution will be unreliable if the iterates move outside the ROM training data. This article proposes the use of hyper-differential sensitivity analysis with respect to model discrepancy (HDSA-MD) as a computationally efficient tool to augment ROM-constrained optimization and improve its reliability. The proposed approach consists of two phases: (i) an offline phase where several full-order model evaluations are computed to train the ROM, and (ii) an online phase where a ROM-constrained optimization problem is solved, a limited number of full-order model evaluations are computed, and HDSA-MD is used to enhance the optimization solution. Numerical results are demonstrated for two examples, atmospheric contaminant control and wildfire ignition location estimation, in which a ROM is trained offline using inaccurate atmospheric data. In conclusion, the HDSA-MD update yields a significant improvement in the ROM-constrained optimization solution using only one full-order model evaluation online with corrected atmospheric data.
PDE-constrained optimization↗
Recent Enhancements to the Development of CFD-Based Aeroelastic Reduced-Order Models
Recent enhancements to the development of CFD-based unsteady aerodynamic and aeroelastic reduced-order models (ROMs) are presented. These enhancements include the simultaneous application of structural modes as CFD input, static aeroelastic analysis using a ROM, and matched-point solutions using a ROM. The simultaneous application of structural modes as CFD input enables the computation of the unsteady aerodynamic state-space matrices with a single CFD execution, independent of the number of structural modes. The responses obtained from a simultaneous excitation of the CFD-based unsteady aerodynamic system are processed using system identification techniques in order to generate an unsteady aerodynamic state-space ROM. Once the unsteady aerodynamic state-space ROM is generated, a method for computing the static aeroelastic response using this unsteady aerodynamic ROM and a state-space model of the structure, is presented. Finally, a method is presented that enables the computation of matchedpoint solutions using a single ROM that is applicable over a range of dynamic pressures and velocities for a given Mach number. These enhancements represent a significant advancement of unsteady aerodynamic and aeroelastic ROM technology.
Silva, Walter A.↗
Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems
Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.
Conditional VAEs↗
Kinematics and Kinetics of Squat and Deadlift Exercises with Varying Stance Widths
The primary motion of squat and deadlift exercise involves flexion and extension of the hips, knees, and ankles, but each exercise can be performed with variations in stance width. These variations may result in differing kinematics and ground reaction forces (GRF), which may in turn affect joint loading. PURPOSE: The purpose of this investigation was to compare ankle, knee, and hip kinematics and kinetics of normal squat (NS), wide-stance squat (WS), normal deadlift (ND), and sumo deadlift (SD). We hypothesized that hip joint kinematics and work at each joint would differ between exercise variations. METHODS: Six subjects (3 m/3 f; 70.0 plus or minus 13.7 kg; 168 plus or minus 9.9 cm) performed each lift in normal gravity on the ground-based version of the Advanced Resistive Exercise Device (ARED) used on the International Space Station. The ARED provided resistance with a combination vacuum tube/flywheel mechanism designed to replicate the gravitational and inertial forces of free weights. Subjects completed each lift with their 10-repetition maximum load. Kinematic data were collected at 250 Hz by a 12-camera motion-capture system (Smart-D, BTS Bioengineering, Milan, Italy), and GRF data were collected at 1000 Hz with independent force platforms for each leg (Model 9261, Kistler Instruments AG, Winterhur, Switzerland). All data were captured simultaneously on a single workstation. The right leg of a single lift for each motion was analyzed. Modeling software (OpenSim 2.2.0, Simbios, Palo Alto, CA) determined joint kinematics and net positive and negative work at each lower extremity joint. Total work was found as the sum of work across all joints and was normalized by system mass. Effect sizes and their 95% confidence intervals were computed between conditions. RESULTS: Peak GRF were similar for each lift. There were no differences between conditions in hip flexion range of motion (ROM). For hip adduction ROM, there were no differences between the NS, WS, and SD. However, hip adduction ROM was greater during the NS and SD than during the ND. Hip rotation ROM was greater during the WS than during the NS and SD, and was greater during the SD than during the ND. For knee and ankle flexion ROM, the ND, WS, and SD were not different, but ROM was greater during the NS than the ND and greater during the WS than the SD. Total eccentric work was greater during the WS than the SD. Otherwise, there were no differences in eccentric or concentric work between conditions. CONCLUSIONS: Although squat and deadlift exercises consist of similar motions, there are kinematic differences between them that depend on stance width. Total eccentric and concentric work are similar for different lifts, but differing kinematics may require activation of different musculature for each variation. With respect to each condition, in the ND the ROM of each joint tended to be less, and the WS tended to trade knee motion for hip motion. PRACTICAL APPLICATIONS: Knowledge of differences in kinematics and kinetics between different squat and deadlift variations is important for coaches and rehabilitation personnel to understand when prescribing exercise. Our results suggest that each variation of the squat and deadlift should be considered a separate exercise that may induce different long-term training effects.
DeWitt, John K.↗
Model Order Reduction of Aeroservoelastic Model of Flexible Aircraft
This paper presents a holistic model order reduction (MOR) methodology and framework that integrates key technological elements of sequential model reduction, consistent model representation, and model interpolation for constructing high-quality linear parameter-varying (LPV) aeroservoelastic (ASE) reduced order models (ROMs) of flexible aircraft. The sequential MOR encapsulates a suite of reduction techniques, such as truncation and residualization, modal reduction, and balanced realization and truncation to achieve optimal ROMs at grid points across the flight envelope. The consistence in state representation among local ROMs is obtained by the novel method of common subspace reprojection. Model interpolation is then exploited to stitch ROMs at grid points to build a global LPV ASE ROM feasible to arbitrary flight condition. The MOR method is applied to the X-56A MUTT vehicle with flexible wing being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies demonstrated that relative to the fullorder model, our X-56A ROM can accurately and reliably capture vehicles dynamics at various flight conditions in the target frequency regime while the number of states in ROM can be reduced by 10X (from 180 to 19), and hence, holds great promise for robust ASE controller synthesis and novel vehicle design.
aeroelasticity↗
Reduced Order Modeling for Transonic Aeroservoelastic Control Law Development
As aircraft become more flexible, aeroelastic considerations become increasingly important and complex, particularly for transonic flight where nonlinearities in the flow render linear analysis tools less effective. In order to analyze these aeroelastic interactions between the fluid and the structure efficiently, reduced order models (ROMs) are sometimes generated from and used in place of computational fluid dynamics solutions. In this paper, several aerodynamic ROMs are generated and coupled with structural models to form aeroelastic ROMs. The aerodynamic ROMs generated here include the effects of control surface motion. Hence, the aeroelastic ROMs presented here are appropriate for use in aeroservoelastic applications and are intended to be used for aeroservoelastic control law development. These ROMs are used to simulate a number of test cases with and without control surface involvement. Results show that several of the ROMs generated in the paper are able to predict results similar to solutions of higher-order computational methods.
Waite, Josiah M.↗
Reduced-order CFD modeling of cryogenic hydrogen isotope extrusion for pellet fueling
This study presents a reduced-order model (ROM) for computational fluid dynamics (CFD) simulations of cryogenic hydrogen isotope extrusions, focusing on protium (H₂) and deuterium (D₂) piston extruders. Using a 2D axisymmetric ROM in ANSYS-Polyflow, significant computational savings were achieved (runtime reduced from 9∼24 h to 3∼5 min), with extrusion force discrepancies between the 2D ROM and 3D models being on the order of 1%. Parametric studies identified optimal cutoff shear rates in the viscosity model (0.01/s for H₂ and 0.001/s for D₂), providing recommendations for future simulations. Finally, a comprehensive comparison of ROM results with experimental data was performed across varying geometries, cryogenic materials, temperatures, extrusion lengths, and piston velocities. Predictions at low extrusion temperatures met the objective of providing quick and efficient solutions with an acceptable extrusion force error of approximately 10% or less, validating the effectiveness of the 2D ROM approach. However, at high temperatures closer to the triple point, extrusion force error grows, which necessitates developing an improved model that accounts for temperature effects, e.g. melting. Nevertheless, the findings still represent a significant improvement in efficiency of CFD modeling of cryogenic hydrogenic extrusion. The ROM framework can also be extended to tritium (T2) and screw extruders, which will ultimately provide a fast and effective tool for optimizing pellet injector design for ITER and future reactor systems.
Fan, Joy [ORNL] (ORCID:0000000229751735)↗
Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems
Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.
58 GEOSCIENCES↗