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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations

Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Secular outflows from 3D MHD hypermassive neutron star accretion disc systems

Magnetized hypermassive neutron stars (HMNSs) have been proposed as a way for neutron star mergers to produce high electron fraction, high-velocity ejecta, as required by kilonova models to explain the observed light curve of GW170817. The HMNS drives outflows through neutrino energy deposition and mechanical oscillations, and raises the electron fraction of outflows through neutrino interactions before collapsing to a black hole (BH). Here, in this study, we perform 3D numerical simulations of HMNS–torus systems in ideal magnetohydrodynamics, using a leakage/absorption scheme for neutrino transport, the nuclear APR equation of state, and Newtonian self-gravity, with a pseudo-Newtonian potential added after BH formation. Due to the uncertainty in the HMNS collapse time, we choose two different parametrized times to induce collapse. We also explore two initial magnetic field geometries in the torus, and evolve the systems until the outflows diminish significantly ($\sim\!\! 1\!\! - \!\!2\ \mathrm{s}$). We find bluer, faster outflows as compared to equivalent BH–torus systems, producing M ∼ 10 −3 M ⊙ of ejecta with Y e ≥ 0.25 and v ≥ 0.25c by the simulation end. Approximately half the outflows are launched in disc winds at times $t\lesssim 500 \ \mathrm{ms}$, with a broad distribution of electron fractions and velocities, depending on the initial condition. The remaining outflows are thermally driven, characterized by lower velocities and electron fractions. Nucleosynthesis with tracer particles shows patterns resembling solar abundances in all models. Although outflows from our simulations do not match those inferred from two-component modelling of the GW170817 kilonova, self-consistent multidimensional detailed kilonova models are required to determine whether our outflows can power the blue kilonova.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

THE CLUSTERING OF RADIO GALAXIES: BIASING AND EVOLUTION VERSUS STELLAR MASS

We study the angular clustering of ∼6 × 10{sup 5} NVSS sources on scales ≳50h{sup −1} Mpc in the context of the ΛCDM scenario. The analysis partially relies on the redshift distribution of 131 radio galaxies, inferred from the Hercules and CENSORS survey, and an empirical fit to the stellar-to-halo mass relation. For redshifts z ≲ 0.7, the fraction of radio activity versus stellar mass evolves as f{sub R{sub L}}∼M{sub ∗}{sup α{sub 0}+α{sub 1}z}, where α{sub 0} = 2.529 ± 0.184 and α{sub 1}=1.854{sub −0.761}{sup +0.708}. The estimate on α{sub 0} is largely driven by the results of Best et al., while the constraint on α{sub 1} is new. We derive a biasing factor b(z=0.5)=2.093{sub −0.109}{sup +0.164} between radio galaxies and the underlying mass. The function b(z)=0.33z{sup 2}+0.85z+1.6 fits well the redshift dependence. We also provide convenient parametric forms for the redshift-dependent radio luminosity function, which are consistent with the redshift distribution and the NVSS source count versus flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Combining Galaxy-Galaxy Lensing and Galaxy Clustering

Combining galaxy-galaxy lensing and galaxy clustering is a promising method for inferring the growth rate of large scale structure, a quantity that will shed light on the mechanism driving the acceleration of the Universe. The Dark Energy Survey (DES) is a prime candidate for such an analysis, with its measurements of both the distribution of galaxies on the sky and the tangential shears of background galaxies induced by these foreground lenses. By constructing an end-to-end analysis that combines large-scale galaxy clustering and small-scale galaxy-galaxy lensing, we also forecast the potential of a combined probes analysis on DES datasets. In particular, we develop a practical approach to a DES combined probes analysis by jointly modeling the assumptions and systematics affecting the different components of the data vector, employing a shared halo model, HOD parametrization, photometric redshift errors, and shear measurement errors. Furthermore, we study the effect of external priors on different subsets of these parameters. We conclude that DES data will provide powerful constraints on the evolution of structure growth in the universe, conservatively/ optimistically constraining the growth function to 8%/4.9% with its first-year data covering 1000 square degrees, and to 4%/2.3% with its full five-year data covering 5000 square degrees.

Park, Youngsoo↗
DOE OSTI2025

DESI DR2 results. II. Measurements of baryon acoustic oscillations and cosmological constraints

We present baryon acoustic oscillation (BAO) measurements from more than 14 million galaxies and quasars drawn from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2), based on three years of operation. For cosmology inference, these galaxy measurements are combined with DESI Lyman-𝛼 forest BAO results presented in a companion paper (M. Abdul-Karim et al., companion paper, Phys. Rev. D 112, 083514 2025.). The DR2 BAO results are consistent with DESI DR1 and the Sloan Digital Sky Survey, and their distance-redshift relationship matches those from recent compilations of supernovae (SNe) over the same redshift range. The results are well described by a flat Λ cold dark matter (Λ⁢CDM) model, but the parameters preferred by BAO are in mild, 2.3⁢𝜎 tension with those determined from the cosmic microwave background (CMB), although the DESI results are consistent with the acoustic angular scale 𝜃 * that is well measured by Planck. This tension is alleviated by dark energy with a time-evolving equation of state parametrized by 𝑤0 and 𝑤𝑎, which provides a better fit to the data, with a favored solution in the quadrant with 𝑤 0 >−1 and 𝑤 𝑎 <0. This solution is preferred over Λ ⁢CDM at 3.1⁢𝜎 for the combination of DESI BAO and CMB data. When also including SNe, the preference for a dynamical dark energy model over Λ⁢ CDM ranges from 2.8 − 4.2⁢𝜎 depending on which SNe sample is used. We present evidence from other data combinations which also favor the same behavior at high significance. From the combination of DESI and CMB we derive 95% upper limits on the sum of neutrino masses, finding ∑𝑚 𝜈 < 0.064 eV assuming Λ ⁢CDM and ∑𝑚 𝜈 < 0.16 eV in the 𝑤0⁢𝑤𝑎 model. Unless there is an unknown systematic error associated with one or more datasets, it is clear that Λ⁢ CDM is being challenged by the combination of DESI BAO with other measurements and that dynamical dark energy offers a possible solution.

Baryon acoustic oscillations↗
DOE OSTI2023

Quantifying uncertainties due to optical potentials in one-neutron knockout reactions

One-neutron knockout reactions have been widely used to extract information about the single-particle structure of nuclei from the valley of stability to the drip lines. The interpretation of knockout data relies on reaction models, where the uncertainties are typically not accounted for. In this work, we quantify uncertainties of optical potentials used in these reaction models and propagate them, for the first time, to knockout observables using a Bayesian analysis. We study two reactions in the present paper, the first of which involves a loosely bound halo projectile, 11 Be, and the second a tightly bound projectile, 12 C. We first quantify the parametric uncertainties associated with phenomenological optical potentials. Complementing this approach, we also quantify the model uncertainties associated with the chiral forces that can be used to construct microscopic optical potentials. For the phenomenological study, we investigate the impact of the imaginary terms of the optical potential on the breakup and stripping components of the knockout cross sections as well as the impact of the angular range. For the 11 Be case, the theoretical uncertainty from the phenomenological method is on the order of the experimental uncertainty of the knockout observables; however, for the 12 C case, the theoretical uncertainty is significantly larger. Additionally, the widths of the uncertainty bands for the knockout observables obtained for the microscopic study and the phenomenological approach are of similar orders of magnitude. Based on this work we conclude that structure information inferred from the ratio of the knockout cross sections will carry a theoretical uncertainty of at least 20% for halo nuclei and at least 40% for tightly bound nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

COMPARING THE LIGHT CURVES OF SIMULATED TYPE Ia SUPERNOVAE WITH OBSERVATIONS USING DATA-DRIVEN MODELS

We propose a robust, quantitative method to compare the synthetic light curves of a Type Ia supernova (SN Ia) explosion model with a large set of observed SNe Ia, and derive a figure of merit for the explosion model's agreement with observations. The synthetic light curves are fit with the data-driven model SALT2 which returns values for stretch, color, and magnitude at peak brightness, as well as a goodness-of-fit parameter. Each fit is performed multiple times with different choices of filter bands and epoch range in order to quantify the systematic uncertainty on the fitted parameters. We use a parametric population model for the distribution of observed SN Ia parameters from large surveys, and extend it to represent red, dim, and bright outliers found in a low-redshift SN Ia data set. We discuss the potential uncertainties of this population model and find it to be reliable given the current uncertainties on cosmological parameters. Using our population model, we assign each set of fitted parameters a likelihood of being observed in nature, and a figure of merit based on this likelihood. We define a second figure of merit based on the quality of the light curve fit, and combine the two measures into an overall figure of merit for each explosion model. We compute figures of merit for a variety of one-, two-, and three-dimensional explosion models and show that our evaluation method allows meaningful inferences across a wide range of light curve quality and fitted parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

ON INFERRING EXTINCTION LAWS IN z {approx} 6 QUASARS AS SIGNATURES OF SUPERNOVA DUST

Unusual extinction curves of high-redshift QSOs have been taken as evidence that dust is primarily produced by supernovae at high redshift. In particular, the 3000 A Todini-Ferrara-Maiolino kink in the extinction curve of the z = 6.20 SDSS J1048+4637 has been attributed to supernova dust. Here we discuss the challenges in inferring robust extinction curves of high-redshift QSOs and critically assess previous claims of detection of supernova dust. In particular, we address the sensitivity to the choice of intrinsic QSO spectrum, the need for a long wavelength baseline, and the drawbacks in fitting theoretical extinction curves. In a sample of 21 QSOs at z {approx} 6 we detect significant ultraviolet extinction using existing broadband optical, near-infrared, and Spitzer photometry. The median extinction curve is consistent with a Small Magellanic Cloud curve with A{sub 1450} {approx} 0.7 mag and does not exhibit any conspicuous (rest frame) 2175 A or 3000 A features. For two QSOs, SDSS J1044-0125 at z = 5.78 and SDSS J1030+0524 at z = 6.31, we further present X-shooter spectra covering the wavelength range 0.9-2.5 {mu}m. The resulting non-parametric extinction curves do not exhibit the 3000 A kink. Finally, in a re-analysis of literature spectra of SDSS J1048+4637, we do not find evidence for a conspicuous kink. We conclude that the existing evidence for a 3000 A feature is weak and that the overall dust properties at high and low redshifts show no significant differences. This, however, does not preclude supernovae from dominating the dust budget at high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The radius distribution of planets around cool stars

We calculate an empirical, non-parametric estimate of the shape of the period-marginalized radius distribution of planets with periods less than 150 days using the small yet well-characterized sample of cool (T{sub eff} < 4000 K) dwarf stars in the Kepler catalog. In particular, we present and validate a new procedure, based on weighted kernel density estimation, to reconstruct the shape of the planet radius function down to radii smaller than the completeness limit of the survey at the longest periods. Under the assumption that the period distribution of planets does not change dramatically with planet radius, we show that the occurrence of planets around these stars continues to increase to below 1 R{sub ⊕}, and that there is no strong evidence for a turnover in the planet radius function. In fact, we demonstrate using many iterations of simulated data that a spurious turnover may be inferred from data even when the true distribution continues to rise toward smaller radii. Finally, the sharp rise in the radius distribution below ∼3 R{sub ⊕} implies that a large number of planets await discovery around cool dwarfs as the sensitivities of ground-based transit surveys increase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

A CONSTANT LIMITING MASS SCALE FOR FLAT EARLY-TYPE GALAXIES FROM z {approx} 1 TO z = 0: DENSITY EVOLVES BUT SHAPES DO NOT

We measure the evolution in the intrinsic shape distribution of early-type galaxies from z {approx} 1 to z {approx} 0 by analyzing their projected axis-ratio distributions. We extract a low-redshift sample (0.04 < z < 0.08) of early-type galaxies with very low star formation rates from the Sloan Digital Sky Survey, based on a color-color selection scheme and verified through the absence of emission lines in the spectra. The inferred intrinsic shape distribution of these early-type galaxies is strongly mass dependent: the typical short-to-long intrinsic axis ratio of high-mass early-type galaxies (>10{sup 11} M{sub Sun }) is 2:3, whereas at masses below 10{sup 11} M{sub Sun} this ratio narrows to 1:3, or more flattened galaxies. In an entirely analogous manner, we select a high-redshift sample (0.6 < z < 0.8) from two deep-field surveys with multi-wavelength and Hubble Space Telescope/Advanced Camera for Surveys imaging: GEMS and COSMOS. We find a seemingly universal mass of {approx}10{sup 11} M{sub Sun} for highly flattened early-type systems at all redshifts. This implies that the process that grows an early-type galaxy above this ceiling mass, irrespective of cosmic epoch, involves forming round systems. Using both parametric and non-parametric tests, we find no evolution in the projected axis-ratio distribution for galaxies with masses >3 Multiplication-Sign 10{sup 10} M{sub Sun} with redshift. At the same time, our samples imply an increase of 2-3 Multiplication-Sign in comoving number density for early-type galaxies at masses >3 Multiplication-Sign 10{sup 10} M{sub Sun }, in agreement with previous studies. Given the direct connection between the axis-ratio distribution and the underlying bulge-to-disk ratio distribution, our findings imply that the number density evolution of early-type galaxies is not exclusively driven by the emergence of either bulge- or disk-dominated galaxies, but rather by a balanced mix that depends only on the stellar mass of the galaxy. The challenge for galaxy formation models is to reproduce this overall non-evolving ratio of flattened to round early-type galaxies in the context of a continually growing population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Constraining the scatter in the galaxy–halo connection at Milky Way masses

In this work, we develop and implement two new methods for constraining the scatter in the relationship between galaxies and dark matter haloes. These new techniques are sensitive to the scatter at low halo masses, making them complementary to previous constraints that are dependent on clustering amplitudes or rich galaxy groups, both of which are only sensitive to more massive haloes. In both of our methods, we use a galaxy group finder to locate central galaxies in the Sloan Digital Sky Survey main galaxy sample. Our first technique uses the small-scale cross-correlation of central galaxies with all lower mass galaxies. This quantity is sensitive to the satellite fraction of low-mass galaxies, which is in turn driven by the scatter between haloes and galaxies. The second technique uses the kurtosis of the distribution of line-of-sight velocities between central galaxies and neighbouring galaxies. This quantity is sensitive to the distribution of halo masses that contain the central galaxies at fixed stellar mass. Theoretical models are constructed using peak halo circular velocity, Vpeak, as our property to connect galaxies to haloes, and all comparisons between theory and observation are made after first passing the model through the group-finding algorithm. We parametrize scatter as a lognormal distribution in M* at fixed Vpeak, σ[M*|Vpeak]. The cross-correlation technique yields a constraint of σ[M*|Vpeak] = 0.27 ± 0.05 dex at a mean Vpeak of 168 km s-1, corresponding to a scatter in log M* at fixed Mh of σ[M*|Mh] = 0.38 ± 0.06 dex at $M_\text{h}=10^{11.8}\, \text{M}_\odot$. The kurtosis technique yields σ[M*|Vpeak] = 0.30 ± 0.03 at Vpeak = 209 km s-1, corresponding to σ[M*|Mh] = 0.34 ± 0.04 at $M_\text{h}=10^{12.2}\, \text{M}_\odot$. The values of σ[M*|Mh] are significantly larger than the constraints at higher masses, in agreement with the results of hydrodynamic simulations. This increase is only partly due to the scatter between Vpeak and Mh, and it represents an increase of nearly a factor of 2 relative to the values inferred from clustering and group studies at high masses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A copula-based Bayesian method for probabilistic solar power forecasting

With increased penetration of solar energy sources, solar power forecasting has become more crucial and challenging. This paper proposes a copula-based Bayesian approach to improve probabilistic solar power forecasting by capturing the joint distribution between solar power and ambient temperature. A prior forecast distribution is first obtained using different underlying point forecasting models. Parametric and empirical copulas of solar power and temperature are then developed to update the prior distribution to the posterior forecast distribution. A public solar power database is used to demonstrate effectiveness of the proposed method. Numerical results show that the copula-based Bayesian method outperforms the forecasting method that directly uses temperature as a feature. The Bayesian method is also compared with persistent models and show improved performance. Furthermore, this article includes supplementary material (data and code) for reproducibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Two-dimensional parametric design of short pulse laser driven iron opacity experiments

High intensity short pulse lasers are commonly used to create high temperature and high density plasmas. These plasmas are used to study fundamental properties such as the equation of state and opacity. The targets often use small-diameter, thin buried layers of the materials of interest, surrounded by a tamper of low atomic number material such as plastic. Computational modeling is used to design and interpret experiments with short pulse lasers. Most of the modeling to date has assumed one-dimensional plane-parallel geometry. In this paper, the effects of radial gradients in the irradiation of thin planar targets are studied with one- and two-dimensional radiation/hydrodynamic simulations. It is found that the main effect of radial gradients is the averaging of plasma conditions and x-ray emission over the pattern of irradiation. Additionally, differences between one- and two-dimensional simulations arise because the plasma conditions and x-ray emission are, in general, nonlinear functions of the temperature of the plasma and thereby also nonlinear functions of the irradiation intensity. The differences increase with the ratio of the buried layer radius to the laser spot radius. The root mean square difference in the inferred iron L-shell opacity is less than 30% when the ratio is less than 1.0 and about 70% when the ratio is 1.5.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Sensitivity of Simulated Radiative Emission from Nitrogen Flows to Chemical Parameters

Numerical models of hypersonic flows rely on chemistry data inferred from experiments and quantum calculations. Experiments from NASA's Electric Arc Shock Tube (EAST) measure the spectral emission of strong shocks in pure nitrogen flows. These spectral profiles are only approximately captured by numerical simulations. To address these discrepancies, one wishes to calibrate some of the chemical parameters to better match experiment. Because there are hundreds of parameters in the numerical model, a sensitivity analysis was carried out on the entire chemistry database to determine which parameters are most important to analyze in the future. In order to perform this work it was necessary to generate a consistent chemical database that interfaced with both the CFD and line-by-line radiation solver. Using Monte Carlo methods, the total Sobol index was calculated for important parameters. Twenty parameters were identified as important across the nitrogen system. However, it was noted that in some spectral regions, the parametric uncertainty was unable to bound EAST measurements. Further improvements could be made by increasing the parameter uncertainties or addressing other sources of modeling error.

Kaelan B Hansson↗
NASA NTRSDate not supplied

Sensitivity of Simulated Radiative Emission from Nitrogen Flows to Chemical Parameters

Numerical models of hypersonic flows rely on chemistry data inferred from experiments and quantum calculations. Experiments from NASA's Electric Arc Shock Tube (EAST) measure the spectral emission of strong shocks in pure nitrogen flows. These spectral profiles are only approximately captured by numerical simulations. To address these discrepancies, one wishes to calibrate some of the chemical parameters to better match experiment. Because there are hundreds of parameters in the numerical model, a sensitivity analysis was carried out on the entire chemistry database to determine which parameters are most important to analyze in the future. In order to perform this work it was necessary to generate a consistent chemical database that interfaced with both the CFD and line-by-line radiation solver. Using Monte Carlo methods, the total Sobol index was calculated for important parameters. Twenty parameters were identified as important across the nitrogen system. However, it was noted that in some spectral regions, the parametric uncertainty was unable to bound EAST measurements. Further improvements could be made by increasing the parameter uncertainties or addressing other sources of modeling error.

Kaelan Hansson↗
DOE OSTI2018

Inferring Geothermal Reservoir Processes at the Raft River Geothermal Field, Idaho, USA, Through Modeling InSAR-Measured Surface Deformation

Ground surface deformations detected with Interferometric Synthetic Aperture Radar (InSAR) provide valuable information for inferring subsurface reservoir processes that are difficult to observe directly. This study aims at building a reservoir model that honors the available geological, hydrological and geo-mechanical data and also produces ground surface deformation consistent with InSAR measurements at the Raft River Geothermal Field. In the thermo-hydro-mechanical (THM) coupled model developed, the reservoir deforms as a result of the rock’s poroelastic response to changes in hydrologic pressure and thermal expansion/contraction. The results indicate that the observed deformation is the result of pressure decrease in the deep production reservoir and pressure increase in a shallower reservoir that accommodates the injected fluid (likely in the Salt Lake Formation). The combination of the uplift around injection wells with subsidence around the production wells, affected by the Bridge Fault as a flow barrier, creates a complex pattern of surface deformation in which the center of subtle subsidence significantly deviates from the location of the production wells. A parametric study suggests that (1) the Bridge Fault Zone is likely a flow barrier, (2) the surface deformation appears to be insensitive to the presence of the Narrows Structure, and (3) additional flow barriers likely exist to shape the flow system. This case study demonstrates the utility of a high-fidelity forward model that honors available known information and THM coupled processes in understanding geothermal reservoir characteristics.

58 GEOSCIENCES↗
DOE OSTI2020

Non-autoregressive time-series methods for stable parametric reduced-order models

Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accuracy and robustness are crucial. There has been significant interest in the use of techniques borrowed from machine learning to reduce the computational expense and/or improve the accuracy of predictions for these systems. These rely on the identification of a basis that reduces the dimensionality of the problem and the subsequent use of time series and sequential learning methods to forecast the evolution of the reduced state. Often, however, machine-learned predictions after reduced-basis projection are plagued by issues of stability stemming from incomplete capture of multiscale processes as well as due to error growth for long forecast durations. To address these issues, we have developed a non-autoregressive time series approach for predicting linear reduced-basis time histories of forward models. In particular, we demonstrate that non-autoregressive counterparts of sequential learning methods such as long short-term memory (LSTM) considerably improve the stability of machine-learned reduced-order models. Further, we evaluate our approach on the inviscid shallow water equations and show that a non-autoregressive variant of the standard LSTM approach that is bidirectional in the principal component directions obtains the best accuracy for recreating the nonlinear dynamics of partial observations. Moreover-and critical for many applications of these surrogates-inference times are reduced by three orders of magnitude using our approach, compared with both the equation-based Galerkin projection method and the standard LSTM approach.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗