SEARCH · Engineering Papers

Results for “simulation decomposition”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42

All sources NASA NTRS DOE OSTI

DOE OSTI2024

StOKeDMD: Streaming Occupation kernel dynamic mode decomposition

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems in partial differential equations (PDEs). However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes, particularly involving turbulence, is yet to be fully established. Here, this study introduces NeuroSEM, a hybrid framework integrating PINNs with the highfidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages the strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into the Nektar++ solver. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where the data is available. We applied NeuroSEM to the Rayleigh-B´enard convection system, including cases with missing thermal boundary conditions and noisy datasets. Finally, we applied the proposed NeuroSEM framework to real particle image velocimetry (PIV) data to capture flow patterns characterized by horseshoe vortical structures. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework’s plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

42 ENGINEERING↗

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↗

DOE OSTI2023

Pressure-induced evolution of the lattice dynamics for selected UO 3 polymorphs

Uranium trioxide (UO 3 ) is a stable chemical form of uranium oxide with multiple polymorphs found throughout the nuclear fuel cycle. The pressure-induced changes in the structure and lattice dynamics of four of these polymorphs are simulated with density functional perturbation theory and analyzed. Two phases, α- and δ-UO 3 are found to exhibit an isotropic response to pressure and do not undergo any changes in coordination geometry up to ~40 GPa. In contrast, the other two phases investigated, β- and γ-UO 3 , exhibit an anisotropic response to pressure. Decomposition of the phonon eigenvectors allows us to assign specific pressure-induced structural changes to individual phonon modes. Here, this analysis has been performed on a per atom basis for the relatively simple α- and δ-UO 3 structures, which have one symmetrically unique uranium site, and on a per coordination environment basis for β- and γ-UO 3 , which have multiple U sites.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Statistical analysis of Al III and C III ] emission lines as virial black hole mass estimators in quasars

We test the usefulness of the intermediate ionisation lines Al IIIλ1860 and C III]λ1909 as reliable virial mass estimators for quasars. We identified a sample of 309 quasars from the Sloan Digital Sky Survey Data Release 16 (SDSS DR16) in the redshift range 1.2 ≤ z ≤ 1.4 that have [O II]λ3728 recorded on the same spectrum as Al IIIλ1860, Si III]λ1892, and C III]λ1909. We set the systemic quasar redshift using careful measurements of [O II]λ3728. We then classified the sources as Population A (Pop. A), extreme Population A (Pop. xA), and Population B (Pop. B), and analysed the 1900 Å blend using multi-component models to look for systematic line shifts of Al IIIλ1860 and C III]λ1909 along the quasar main sequence. We do not find significant shifts of the Al IIIλ1860 line peak in Pop. B or in the vast majority of Pop. A. For Pop. xA, a small median blueshift of -250 km s -1 was observed, motivating a decomposition of the Al III line profile into a virialised component centred at rest frame and a blueshifted component for an outflow emission. For Pop. B objects, we proved the empirical necessity to fit a redshifted very broad component, clearly seen in C III], and analysed the physical implications on a Pop. B composite spectrum using CLOUDY simulations. We find consistent black hole mass estimations using Al III and C III] as virial estimators for the bulk of Pop. A. Al III (and even C III]) is a reliable virial black hole mass estimator for Pop. A and Pop. B objects. The Pop. xA sources deserve special attention due to the significant blueshifted excess observed in the line profile of Al III, although it is not as large as the excess observed in C IVλ1549.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1974

Decomposition of NO studied by infrared emission and CO laser absorption

A diagnostic technique for monitoring the concentration of NO using absorption of CO laser radiation was developed and applied in a study of the decomposition kinetics of NO. Simultaneous measurements of infrared emission by NO at 5.3 microns were also made to validate the laser absorption technique. The data were obtained behind incident shocks in NO-N2O-Ar (or Kr) mixtures, with temperatures in the range 2400-4100 K. Rate constants for dominant reactions were inferred from comparisons with computer simulations of the reactive flow.

Hanson, R. K.↗

NASA NTRS2005

An Approximate Ablative Thermal Protection System Sizing Tool for Entry System Design

A computer tool to perform entry vehicle ablative thermal protection systems sizing has been developed. Two options for calculating the thermal response are incorporated into the tool. One, an industry-standard, high-fidelity ablation and thermal response program was integrated into the tool, making use of simulated trajectory data to calculate its boundary conditions at the ablating surface. Second, an approximate method that uses heat of ablation data to estimate heat shield recession during entry has been coupled to a one-dimensional finite-difference calculation that calculates the in-depth thermal response. The in-depth solution accounts for material decomposition, but does not account for pyrolysis gas energy absorption through the material. Engineering correlations are used to estimate stagnation point convective and radiative heating as a function of time. The sizing tool calculates recovery enthalpy, wall enthalpy, surface pressure, and heat transfer coefficient. Verification of this tool is performed by comparison to past thermal protection system sizings for the Mars Pathfinder and Stardust entry systems and calculations are performed for an Apollo capsule entering the atmosphere at lunar and Mars return speeds.

Dec, John A.↗

NASA NTRS2006

An Approximate Ablative Thermal Protection System Sizing Tool for Entry System Design

Dec, John A.↗

NASA NTRS2010

Algorithm for Stabilizing a POD-Based Dynamical System

This algorithm provides a new way to improve the accuracy and asymptotic behavior of a low-dimensional system based on the proper orthogonal decomposition (POD). Given a data set representing the evolution of a system of partial differential equations (PDEs), such as the Navier-Stokes equations for incompressible flow, one may obtain a low-dimensional model in the form of ordinary differential equations (ODEs) that should model the dynamics of the flow. Temporal sampling of the direct numerical simulation of the PDEs produces a spatial time series. The POD extracts the temporal and spatial eigenfunctions of this data set. Truncated to retain only the most energetic modes followed by Galerkin projection of these modes onto the PDEs obtains a dynamical system of ordinary differential equations for the time-dependent behavior of the flow. In practice, the steps leading to this system of ODEs entail numerically computing first-order derivatives of the mean data field and the eigenfunctions, and the computation of many inner products. This is far from a perfect process, and often results in the lack of long-term stability of the system and incorrect asymptotic behavior of the model. This algorithm describes a new stabilization method that utilizes the temporal eigenfunctions to derive correction terms for the coefficients of the dynamical system to significantly reduce these errors.

Kalb, Virginia L.↗

NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗

DOE OSTI1961

Laboratory Development of a Pot Calcination Process for Converting Liquid Wastes to Solids

Simulated Darex, Purex, Thorex, and TBP-25 wastes were batch-evaporated and calcined to solids. The weight of residue was decreased to ~ 8% of the original waste for Darex, to ~ 4% for Purex, to ~ 2% for Thorex, and ~ 7% for TBP-25 by calcination to 1200°C. Semicontinuous evaporation and calcination to solids at 900°C of Darex and Purex wastes gave volume reduction factors of ~ 8 and 7 to 10, respectively. The nitrate contents of the residues from batch calcination to 800°C were from ~ 0.07 to 0.5 wt % while the nitrate contents of the residues from semicontinuous calcination to 900°C were from ~ 0.1 to 0.7 wt %. Sodium, calcium, and magnesium additives to Purex waste decreased the percent of sulfate volatilized into the condensate from ~ 30% to <0.5%. Replacement of the atmosphere above the waste with nitric oxide decreased ruthenium volatility from 50 to 60% of that originally present in Purex waste to 0.5 to 3.5%. The thermal conductivities for the calcined wastes, measured in situ, were all >0.1 Btu/hr ft °F at ~ 400°F and increased almost linearly with increasing temperature to >0.3 Btu/hr ft °F at ~ 1600°F in all cases studied.

052001* -- Nuclear Fuels-- Waste Processing↗

DOE OSTI2024

A reduced-order model for nonlinear radiative transfer problems based on moment equations and POD-Petrov-Galerkin projection of the normalized Boltzmann transport equation

A data-driven projection-based reduced-order model (ROM) for nonlinear thermal radiative transfer (TRT) problems is presented. The TRT ROM is formulated by (i) a hierarchy of low-order quasidiffusion (aka variable Eddington factor) equations for moments of the radiation intensity and (ii) the normalized Boltzmann transport equation (BTE). The multilevel system of moment equations is derived by projection of the BTE onto a sequence of subspaces which represent elements of the phase space of the problem. Exact closure for the moment equations is provided by the Eddington tensor. A Petrov-Galerkin (PG) projection of the normalized BTE is formulated using a proper orthogonal decomposition (POD) basis representing the normalized radiation intensity over the whole phase space and time. The Eddington tensor linearly depends on the solution of the normalized BTE. By linear superposition of the POD basis functions, a low-rank expansion of the Eddington tensor is constructed with coefficients defined by the PG projected normalized BTE. The material energy balance (MEB) equation is coupled with the effective gray low-order equations which exist on the same dimensional scale as the MEB equation. The resulting TRT ROM is structure and asymptotic preserving. A detailed analysis of the ROM is performed on the classical Fleck-Cummings (F-C) TRT multigroup test problem in 2D geometry. Numerical results are presented to demonstrate the ROM's effectiveness in the simulation of radiation wave phenomena. Importantly, the ROM is shown to produce solutions with sufficiently high accuracy while using low-rank approximation of the normalized BTE solution. Essential physical characteristics of supersonic radiation wave are preserved in the ROM solutions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

A Hotspot’s Better Half: Non-Equilibrium Intra-Molecular Strain in Shock Physics

Shockwave interactions with a material’s microstructure localizes energy into hotspots, which act as nucleation sites for complex processes such as phase transformations and chemical reactions. To date, hotspots have been described via their temperature fields. Nonreactive, all-atom molecular dynamics simulations of shock-induced pore collapse in a molecular crystal show that more energy is localized as potential energy (PE) than can be inferred from the temperature field and that PE localization persists beyond thermal diffusion. Furthermore, the origin of the PE hotspot is traced to large intramolecular strains, storing energy in modes readily available for chemical decomposition.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Gaseous cyclodextrin- closo -dodecaborate complexes χCD·B 12 X 12 2– (χ = α, β, and γ; X = F, Cl, Br, and I): electronic structures and intramolecular interactions

A fundamental understanding of cyclodextrin- closo -dodecaborate inclusion complexes is of great interest in supramolecular chemistry. Herein, we report a systematic investigation on the electronic structures and intramolecular interactions of perhalogenated closo -dodecaborate dianions B 12 X 12 2– (X = F, Cl, Br and I) binding to α-, β-, and γ-cyclodextrins (CDs) in the gas phase using combined negative ion photoelectron spectroscopy (NIPES) and density functional theory (DFT) calculations. The vertical detachment energy (VDE) of each complex and electronic stabilization of each dianion due to the CD binding (ΔVDE, relative to the corresponding isolated B 12 X 12 2– ) are determined from the experiments along α-, β- and γ-CD in the form of VDE (ΔVDE): 4.00 (2.10), 4.33 (2.43), and 4.30 (2.40) eV in X = F; 4.09 (1.14), 4.64 (1.69), and 4.69 (1.74) eV in X = Cl; 4.11 (0.91), 4.58 (1.38), and 4.70 (1.50) eV in X = Br; and 3.54 (0.74), 3.88 (1.08), and 4.05 (1.25) eV in X = I, respectively. All complexes have significantly higher VDEs than the corresponding isolated dodecaborate dianions with ΔVDE spanning from 0.74 eV at (α, I) to 2.43 eV at (β, F), sensitive to both host CD size and guest substituent X. DFT-optimized complex structures indicate that all B 12 X 12 2– prefer binding to the wide openings of CDs with the insertion depth and binding motif strongly dependent on the CD size and halogen X. Dodecaborate anions with heavy halogens, i.e. , X = Cl, Br, and I, are found outside of α-CD, while B 12 F 12 2– is completely wrapped by γ-CD. Partial embedment of B 12 X 12 2– into CDs is observed for the other complexes via multipronged B–X … H–O/C interlocking patterns. The simulated spectra based on the density of states agree well with those of the experiments and the calculated VDEs well reproduce the experimental trends. Molecular orbital analyses suggest that the spectral features at low binding energies originated from electrons detached from the dodecaborate dianion, while those at higher binding energies are derived from electron detachment from CDs. Energy decomposition analyses reveal that the electrostatic interaction plays a dominating role in contributing to the host–guest interactions for the X = F series partially due to the formation of a O/C–H … X–B hydrogen bonding network, and the dispersion forces gradually become important with the increase of halogen size.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Eureka: Enabling Fine-Grained Access and Range Queries on Compressed Scientific Data via Data-Index Co-Compression

Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.

Yan, Ning↗

NASA NTRS1981

Mathematical specifications of the Onboard Navigation Package (ONPAC) simulator (revision 1)

The mathematical theory of the computational algorithms employed in the onboard navigation package system is described. This system, which simulates an onboard navigation processor, was developed to aid in the design and evaluation of onboard navigation software. The mathematical formulations presented include the factorized UDU(T) form of the extended Kalman filter, the equations of motion of the user satellite, the user clock equations, the observation equations and their partial derivatives, the coodinate transformations, and the matrix decomposition algorithms.

Dunham, J. B.↗

NASA NTRS1985

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Juang, J. N.↗

NASA NTRS2015

Advanced Background Subtraction Applied to Aeroacoustic Wind Tunnel Testing

An advanced form of background subtraction is presented and applied to aeroacoustic wind tunnel data. A variant of this method has seen use in other fields such as climatology and medical imaging. The technique, based on an eigenvalue decomposition of the background noise cross-spectral matrix, is robust against situations where isolated background auto-spectral levels are measured to be higher than levels of combined source and background signals. It also provides an alternate estimate of the cross-spectrum, which previously might have poor definition for low signal-to-noise ratio measurements. Simulated results indicate similar performance to conventional background subtraction when the subtracted spectra are weaker than the true contaminating background levels. Superior performance is observed when the subtracted spectra are stronger than the true contaminating background levels. Experimental results show limited success in recovering signal behavior for data where conventional background subtraction fails. They also demonstrate the new subtraction technique's ability to maintain a proper coherence relationship in the modified cross-spectral matrix. Beam-forming and de-convolution results indicate the method can successfully separate sources. Results also show a reduced need for the use of diagonal removal in phased array processing, at least for the limited data sets considered.

Bahr, Christopher J.↗