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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Point defect evolution under irradiation: Finite size effects and spatio-temporal correlations

The evolution of point defect concentrations under irradiation is controlled by their diffusion properties, and by their formation and elimination mechanisms. The latter includes the mutual recombination of vacancies and interstitials, and the elimination of point defects at sinks. Two models are traditionally used to predict the defect concentration evolution, the standard rate theory (SRT) and the atomistic kinetic Monte Carlo (AKMC). We show in this work a large discrepancy in the defect concentrations between both methods when the average number of defects in the AKMC simulation box is close or lower than one. The reason is that AKMC naturally captures strong space and time correlations between vacancies and interstitials generated by the finite size of the periodic simulation box. These correlations strongly affect the recombination rate and the point defect concentrations. SRT fails to predict such correlations, and the corresponding solution deviates from the more accurate solution given by the AKMC simulation under similar conditions. These finite size correlation effects are strong when the elimination of point defects occurs by recombination only, but can still be significant in the presence of sinks. In order to account for the spacio-temporal correlation in a continuum framework, we introduce a Correlated Pair Theory (CPT). This theory fully takes into account the correlations between vacancy and interstitial pairs and predicts point defect concentrations in good agreement with AKMC simulations. Inversely, if the goal is for the AKMC to reproduce defect concentrations in bulk using small simulation boxes, the naturally occurring correlations need to be corrected. We show here that the CPT can be used to modify the elimination rates in the AKMC simulations, so as to yield point defect concentrations in agreement with SRT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Decoding the proton’s gluonic density with lattice QCD-informed machine learning

We present a first machine learning-based decoding of the gluonic structure of the proton from lattice QCD using a variational autoencoder inverse mapper (VAIM). Harnessing the power of generative AI, we predict the parton distribution function (PDF) of the gluon given information on the reduced pseudo-Ioffe-time distributions (RpITDs) as calculated from an ensemble with lattice spacing a ≈ 0.09 fm and a pion mass of M π ≈ 310 MeV. The resulting gluon PDF is consistent with phenomenological global fits within uncertainties, particularly in the intermediate-to-high-x region where lattice data are most constraining. A subsequent correlation analysis confirms that the VAIM learns a meaningful latent representation, highlighting the potential of generative AI to bridge lattice QCD and phenomenological extractions within a unified analysis framework.

Gluon parton distribution function↗
DOE OSTI2021

Structural, Electrical, and Electromechanical Properties of Inverse Hybrid Perovskites from First-Principles: The Case of (CH 3 NH 3 ) 3 OI

Inverse-hybrid perovskites (IHPs) with large polarization have recently been predicted from first-principles computations. Here, we use one representative from the IHP class of materials, (CH 3 NH 3 ) 3 OI (MA3OI), to propose a route to the first-principles prediction of structural and electrical properties, such as polarization, polarization reversibility, and the associated coercive field for hybrid organic–inorganic perovskites. The route relies on the construction of the polarization reversal path that models experimental measurements. Such a path was found to play an important role in the ground-state search as well as in the identification of competing structural variations. The latter is believed to be the origin of the structural disorder that is characteristic of hybrid organic–inorganic perovskites. The application of such an approach to MA3OI leads to the prediction of several structural variations that are expected to result in a structurally disordered phase above 766 K and of the polar ground state with a polarization of 25.3 μC/cm 2 that is reversible with the application of an electric field. The upper estimate for the coercive field associated with homogeneous polarization reversal is 6.9 GV/m. The piezoelectric constants of MA3OI are predicted to be an order of magnitude smaller in comparison with a prototypical inorganic ferroelectric PbTiO 3 ; however, the low symmetry of the MA3OI structure yields finite values for all components of the piezoelectric tensor. The polarization in MA3OI is tunable by the epitaxial strain (11.5% change under 5% epitaxial strain), although less so as compared with PbTiO 3 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

A semiempirical model for atmospheric gamma rays from 0.3 to 10 MeV at a geomagnetic latitude of 40 deg

A model of atmospheric gamma rays based on the concept of source function is proposed for atmospheric depths from 0 to 500 g per sq cm and for an energy range of 0.3-10 MeV at a geomagnetic latitude of 40 deg. Since the source function is derived from measured fluxes, the proposed method is referred to as a semiempirical model. This model derives a source strength directly from measurements with an omnidirectional counter, thereby being an inverse of previous approaches. Source strengths are determined for 38 predicted atmospheric gamma ray lines and for 10 different energies in the continuum. Total and directional fluxes are calculated at two different atmospheric depths. The results are found to be consistent with other measurements and theoretical calculations.

Ling, J. C.↗
NASA NTRS1989

Theory of microemulsions in a gravitational field

A theory of microemulsions developed previously is extended to include the effect of a gravitational field. It predicts variation with position of drop size, drop volume fraction, and area per molecule in the surfactant films within a microemulsion phase. Variation in volume fraction is greatest and occurs in such a way that oil content increases with increasing elevation, as has been found experimentally. Large composition variations are predicted within a middle phase microemulsion near optimal conditions because inversion from the water-continuous to the oil-continuous arrangement occurs with increasing elevation. Generally speaking, gravity reduces solubilization within microemulsions and promotes separation of excess phases.

Jeng, J. F.↗
NASA NTRS1992

Rigid body mode identification of the PAH-2 helicopter using the eigensystem realization algorithm

The rigid body modes of the PAH-2 'Tiger' helicopter were identified using the Eigensystem Realization Algorithm (ERA). This work complements ground vibration tests performed using DLR's traditional phase resonance technique and the ISSPA (Identification of Structural System Parameters) method. Rigid body modal parameters are important for ground resonance prediction. Time-domain data for ERA were obtained by inverse Fourier transformation of frequency response functions measured with stepped-sine excitation. Mode purity (based on the Phase Resonance Criterion) was generally equal to or greater than corresponding results obtained in the ground vibration tests. All identified natural frequencies and mode shapes correlate well with corresponding ground vibration test results. The modal identification approach discussed in this report has become increasingly attractive in recent years due to the steadily declining cost and increased performance of scientific computers. As illustrated in this application, modern time-domain methods can be successfully applied to data acquired using DLR's existing test equipment. Some suggestions are made for future applications of time domain modal identification in this manner.

Schenk, Axel↗
NASA NTRS2008

Wake Vortex Inverse Model User's Guide

NorthWest Research Associates (NWRA) has developed an inverse model for inverting landing aircraft vortex data. The data used for the inversion are the time evolution of the lateral transport position and vertical position of both the port and starboard vortices. The inverse model performs iterative forward model runs using various estimates of vortex parameters, vertical crosswind profiles, and vortex circulation as a function of wake age. Forward model predictions of lateral transport and altitude are then compared with the observed data. Differences between the data and model predictions guide the choice of vortex parameter values, crosswind profile and circulation evolution in the next iteration. Iterations are performed until a user-defined criterion is satisfied. Currently, the inverse model is set to stop when the improvement in the rms deviation between the data and model predictions is less than 1 percent for two consecutive iterations. The forward model used in this inverse model is a modified version of the Shear-APA model. A detailed description of this forward model, the inverse model, and its validation are presented in a different report (Lai, Mellman, Robins, and Delisi, 2007). This document is a User's Guide for the Wake Vortex Inverse Model. Section 2 presents an overview of the inverse model program. Execution of the inverse model is described in Section 3. When executing the inverse model, a user is requested to provide the name of an input file which contains the inverse model parameters, the various datasets, and directories needed for the inversion. A detailed description of the list of parameters in the inversion input file is presented in Section 4. A user has an option to save the inversion results of each lidar track in a mat-file (a condensed data file in Matlab format). These saved mat-files can be used for post-inversion analysis. A description of the contents of the saved files is given in Section 5. An example of an inversion input file, with preferred parameters values, is given in Appendix A. An example of the plot generated at a normal completion of the inversion is shown in Appendix B.

Lai, David↗
DOE OSTI2023

Pragmatic Uncertainty Quantification and Propagation in Inverse Estimation of Structural Dynamics Parameters given Material Property Uncertainties and Limited Sensor Data

In this report we demonstrate some relatively simple and inexpensive methods to effectively account for various sources of epistemic lack-of-knowledge type uncertainty in inverse problems. The demonstration problem involves inverse estimation of six parameters of a bolted joint that attaches a kettlebell shaped object to a thick plate. The parameters are efficiently inverted in a modal-based model calibration using gradient-based optimization. Two material properties of the kettlebell are treated as uncertain to within given epistemic uncertainty bounds. We apply and test interval and sparse-sample probabilistic approaches to account for uncertainty in the estimated parameters (and various scalar functionals of the parameters as generic quantities of interest, QOIs) due to uncertainties in the material properties. We also investigate the error effects of limited numbers of vibration sensors (accelerometers) on the kettlebell and plate, and therefore abbreviated excitation/response information in the parameter inversions. We propose and demonstrate a Leave-K-Sensors-Out “cross-prediction” UQ approach to estimate related uncertainties on the parameters and QOI functionals. We indicate how uncertainties from material properties and limited sensors are treated in a combined manner. The economical combined UQ approach involves just three to five samples (i.e. three to five inverse simulations), with no added complication or error/uncertainty from use of surrogate models for affordability. Finally, we describe a related economical UQ approach for handling potential parameter solution non-uniqueness and numerical optimization related precision uncertainties in the estimated parameter values. Indicated further research is identified.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bayesian Neural Network Variational Autoencoder Inverse Mapper (BNN-VAIM) and its application in Compton Form Factors extraction

Abstract We extend the Variational Autoencoder Inverse Mapper (VAIM) framework for the inverse problem of extracting Compton Form Factors (CFFs) from deeply virtual exclusive reactions, such as the unpolarized Deeply virtual exclusive scattering (DVCS) cross section. VAIM is an end-to-end deep learning framework to address the solution ambiguity issue in ill-posed inverse problems, which comprises of a forward mapper and a backward mapper to simulate the forward and inverse processes, respectively. In particular, we incorporate Bayesian Neural Network (BNN) into the VAIM architecture (BNN-VAIM) for uncertainty quantification. By sampling the weights and biases distributions of the BNN in the backward mapper of the VAIM, BNN-VAIM is able to estimate prediction uncertainty associated with each individual solution obtained for an ill-posed inverse problem. We first demonstrate the uncertainty quantification capability of BNN-VAIM in a toy inverse problem. Then, we apply BNN-VAIM to the inverse problem of extracting 8 CFFs from the unpolarized DVCS cross section.

Instruments & Instrumentation↗
DOE OSTI2025

The Principle of Maximum Conformality Correctly Resolves the Renormalization-Scheme-Dependence Problem

In this paper, we clarify a serious misinterpretation and consequent misuse of the Principle of Maximum Conformality (PMC), which also can serve as a mini-review of PMC. In a recently published article, P. M. Stevenson has claimed that “the PMC is ineffective and does nothing to resolve the renormalization-scheme-dependence problem”, concluding incorrectly that the success of PMC predictions is due to the PMC being a “laborious, ad hoc, and back-door” version of the Principle of Minimal Sensitivity (PMS). We show that such conclusions are incorrect, deriving from a misinterpretation of the PMC and an overestimation of the applicability of the PMS. The purpose of the PMC is to achieve precise fixed-order pQCD predictions, free from conventional renormalization schemes and scale ambiguities. We demonstrate that the PMC predictions satisfy all the self-consistency conditions of the renormalization group and standard renormalization-group invariance; the PMC predictions are thus independent of any initial choice of renormalization scheme and scale. The scheme independence of the PMC is also ensured by commensurate scale relations, which relate different observables to each other. Moreover, in the Abelian limit, the PMC dovetails into the well-known Gell-Mann–Low framework, a method universally revered for its precision in QED calculations. Due to the elimination of factorially divergent renormalon terms, the PMC series not only attains a convergence behavior far superior to that of its conventional counterparts but also deftly curtails any residual scale dependence caused by the unknown higher-order terms. This refined convergence, coupled with its robust suppression of residual uncertainties, furnishes a sound and reliable foundation for estimating the contributions from unknown higher-order terms. Anchored in the bedrock of standard renormalization-group invariance, the PMC simultaneously eradicates the factorial divergences and eliminates superfluous systematic errors, which inversely provides a good foundation for achieving high-precision pQCD predictions. Consequently, owing to its rigorous theoretical underpinnings, the PMC is eminently applicable to virtually all high-energy hadronic processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A numerical study of the spectral radiative properties of packed bed with mixed bauxite and silica spheres

Bauxite and silica particles have gained increasing attention for applications in the field of concentrated solar power. In this work, a Monte Carlo ray-tracing simulation is performed to predict the radiative properties (absorptance, reflectance, and transmittance) of packed beds with mixed bauxite and silica spherical particles at wavelengths of 0.5, 2.6, 9.0, and 9.35 µm. These wavelengths are representative for the visible, near-infrared, and the mid-infrared regions that are important for solar and thermal radiation, respectively. A repeating unit-column approach is used to mathematically represent the particle bed. The effects of particle mixing ratio, volume fraction, and wavelength on the predicted radiative properties are examined. The obtained radiative properties are inputted to an inverse method to retrieve the effective absorption and scattering coefficients as well as the scattering albedo, which may be later used in a continuous-scale radiative heat transfer analysis. Furthermore, the independent scattering model is used to obtain the absorption coefficient and scattering albedo based on the absorption and scattering cross sections predicted by a Monte Carlo algorithm for a single particle. It is shown that the independent scattering model underpredicts the scattering coefficient for opaque particles but overpredicts the scattering coefficient for semitransparent particles for sufficiently high particle volume fractions. Here, the radiative properties calculated from the independent scattering model are compared to the full Monte Carlo simulation of the particle bed to examine the influence of particle mixing on dependent scattering.

42 ENGINEERING↗
DOE OSTI2020

Three-dimensional fracture continuum characterization aided by surface time-domain electromagnetics and hydrogeophysical joint inversion—proof-of-concept

Efficient and safe production of hydraulically fractured reservoirs benefits from the prediction of their geometrical attributes. Geophysical methods have the potential to provide data that are sensitive to fracture geometries, alleviating the typically sparse nature of in situ reservoir observations. Moreover, surface-based methods can be logistically and economically attractive since they avoid operational interference with the injection well infrastructure. Here, we investigate the potential of the surface-based time-domain electromagnetic (EM) method. EM methods can play an important role owing to their sensitivity to injection-induced fluid property changes. Two other advantageous factors are the EM signal-enhancing effect of vertical steel-cased wells and the fact that injected proppants can be enhanced to produce a stronger electrical conductivity contrast with the reservoir’s connate fluid. Nevertheless, an optimal fracture characterization will no doubt require the integration of EM and reservoir injection and production data. We hence carry out our investigations within a hydrogeophysical parameter estimation framework where EM data and injection flow rates are combined in a fully coupled way. Given the interdisciplinary nature of coupled hydrogeophysical inverse modeling, we dedicate one section to laying out key aspects in a didactic manner.

58 GEOSCIENCES↗
DOE OSTI2026

A new interatomic potential for mixed Mg-Al-Ga-In spinels

While density functional theory (DFT) has become the de facto approach for accurate simulation of materials at the atomic scale, there are many aspects of materials that are simply out of reach of DFT methods. In particular, finite temperature properties such as diffusivities, the structure and properties of grain boundaries and interfaces, and the study of defect properties in complex alloys are computationally challenging for DFT methods. Recently, a new class of spinels in which three cations order over two sublattices was discovered. In order to predict the properties of these types of structures, classical potentials are a must. Here, in this work, we derive a new classical potential for Mg-bearing spinels in which the B cations are Al, Ga, and/or In. The potential does well in describing the DFT energetics of various spinel structures as a function of chemistry and inversion. In particular, it reproduces the thermodynamically favorable MgAlGaO 4 structure while correctly predicting that neither MgAlInO 4 nor MgGaInO 4 are stable. Further, it reproduces physical trends in elastic properties as compared against experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

Here, in this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks to approximate the model parameters as functions of the operating conditions. This method allows the integration of VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training process.

25 ENERGY STORAGE↗
DOE OSTI2021

Nonlinear Hall effect in Weyl semimetals induced by chiral anomaly

In this work, we predict a nonlinear Hall effect in certain Weyl semimetals with broken inversion symmetry. When the energy dispersions about pairs of Weyl nodes are skewed-the Weyl cones are "tilted"-the concerted actions of the anomalous velocity and the chiral anomaly give rise to the nonlinear Hall effect. This Hall conductivity is linear in both electric and magnetic fields and depends critically on the tilting of the Weyl cones. We also show that this effect does not rely on a finite Berry curvature dipole, in contrast with the intrinsic quantum nonlinear Hall effect that was recently observed in type-II Weyl semimetals.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Evidence for a Dayside Thermal Inversion and High Metallicity for the Hot Jupiter WASP-18b

We find evidence for a strong thermal inversion in the dayside atmosphere of the highly irradiated hot Jupiter WASP-18b (equatorial temperature equals 2411 degrees Kelvin, mass equals 10.3 times the mass of Jupiter) based on emission spectroscopy from Hubble Space Telescope secondary eclipse observations and Spitzer eclipse photometry. We demonstrate a lack of water vapor in either absorption or emission at 1.4 microns. However, we infer emission at 4.5 microns and absorption at 1.6 microns that we attribute to CO, as well as a non-detection of all other relevant species (e.g., TiO, VO). The most probable atmospheric retrieval solution indicates a C/O ratio of 1 and a high metallicity (C/H equals 283 from plus 395 to minus 138 times solar). The derived composition and temperature/pressure profile suggest that WASP-18b is the first example of both a planet with a non-oxide driven thermal inversion and a planet with an atmospheric metallicity inconsistent with that predicted for Jupiter-mass planets at greater than 2 sigma. Future observations are necessary to confirm the unusual planetary properties implied by these results.

Sheppard, Kyle B.↗
NASA NTRS2003

Non-Thermal Hard X-Ray Emission in Galaxy Clusters Observed with the BeppoSAX PDS

We study the X-ray emission in a Sample of galaxy clusters using the BeppoSAX PDS instrument in the 20 - 80 keV energy band. We estimate the non-thermal hard X-ray cluster emission (HXR) by modeling the thermal contribution from the cluster gas and the non-thermal contamination from the unobscured AGN in the clusters. We also evaluate the systematic uncertainties due to the background fluctuations. Assuming negligible contamination from the obscured AGN, the resulting non-thermal component is detected at a 2 sigma level in approx. 50% of the non-significantly AGN-contaminated clusters: A2142, A2199, A2256, A3376, Coma, Ophiuchus and Virgo. The data are consistent with a scenario whereby relaxed clusters have no hard X-ray component of non-thermal origin, whereas merger clusters do. The co-added spectrum of the above clusters indicates a power-law spectrum for the HXR with a photon index of 2.8+0.3-0.4 in the 12 - 115 keV band, and we find indication that it has extended distribution. These indications argue against significant contamination from obscured AGN, which have harder spectra and centrally concentrated distribution. These results are supportive of the assumption of the merger shock acceleration of electrons in clusters, which has been proposed as a possible origin of the non-thermal hard X-ray emission models. Assuming that the Cosmic Microwave Background photons experience Inverse Compton scattering from the merger-accelerated relativistic electrons, and thus produce the observed HXR, the measured hard X-ray slope corresponds to a differential momentum spectra of the relativistic electrons with a slope of mu = 3.8 - 5.0. In presence of cluster magnetic fields this relativistic electron population produces synchrotron emission with a spectral index of 1.4 - 2.1, consistent with radio halo observations of merger clusters. Thus both hard X-ray and radio observations of merger clusters are consistent with the Inverse Compton model. The observed slope of HXR is also consistent with that predicted by the non-thermal bremsstrahlung, which chus cannot be ruled by the fit to the current data, even though this model requires an extreme, untenable cluster energetics. Assuming centrally concentrated distribution of HXR, the data requires a harder slope for the HXR spectrum, which is consistent with secondary electron models, but this model yields a worse fit to the PDS data and thus seems to be disfavored over the primary electron Inverse Compton model.

Nevalainen, J.↗
DOE OSTI2020

Statistical Treatment of Inverse Problems Constrained by Differential Equations-based Models with Stochastic Terms

This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution represented numerically by an ensemble of simulations. The goal is to formulate the inverse problem, in particular the objective function, to find the closest forward distribution (i.e., the output of the stochastic forward problem) that best explains the distribution of the observations in a certain metric. We use proper scoring rules, a concept employed in statistical forecast verification, namely energy, variogram, and hybrid (i.e., combination of the two) scores. We study the performance of the proposed formulation in the context of two applications: a coefficient field inversion for subsurface flow governed by an elliptic partial differential equation with a stochastic source and a parameter inversion for power grid governed by differential-algebraic equations. In both cases we show that the variogram and the hybrid scores produce better parameter inversion results than does the energy score, whereas the energy score leads to better probabilistic predictions.

97 MATHEMATICS AND COMPUTING↗