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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression

Here, an interpretable machine learning method, physics-informed genetic programming-based symbolic regression (P-GPSR), is integrated into a continuum thermodynamic approach to developing constitutive models. The proposed strategy for combining a thermodynamic analysis with P-GPSR is demonstrated by generating a yield function for an idealized material with voids, i.e., the Gurson yield function. First, a thermodynamic-based analysis is used to derive model requirements that are exploited in a custom P-GPSR implementation as fitness criteria or are strongly enforced in the solution. The P-GPSR implementation improved accuracy, generalizability, and training time compared to the same GPSR code without physics-informed fitness criteria. The yield function generated through the P-GPSR framework is in the form of a composite function that describes a class of materials and is characteristically more interpretable than GPSR-derived equations. The physical significance of the input functions learned by P-GPSR within the composite function is acquired from the thermodynamic analysis. Fundamental explanations of why the implemented P-GPSR capabilities improve results over a conventional GPSR algorithm are provided.

42 ENGINEERING↗
DOE OSTI2018

Spatial distribution of pressure resonance in compressible cavity flow

The development of the unsteady pressure field on the floor of a rectangular cavity was studied at Mach 0.9 using high-frequency pressure-sensitive paint. Power spectral amplitudes at each cavity resonance exhibit a spatial distribution with a streamwise-oscillatory pattern; additional maxima and minima appear as the mode number is increased. This spatial distribution also appears in the propagation velocity of modal pressure disturbances. This behavior was tied to the superposition of a downstream-propagating shear-layer disturbance and an upstream-propagating acoustic wave of different amplitudes and convection velocities, consistent with the classical Rossiter model. The summation of these waves generates a net downstream-traveling wave whose amplitude and phase velocity are modulated by a fixed envelope within the cavity. This traveling-wave interpretation of the Rossiter model correctly predicts the instantaneous modal pressure behavior in the cavity. Here, subtle spanwise variations in the modal pressure behavior were also observed, which could be attributed to a shift in the resonance pattern as a result of spillage effects at the edges of the finite-width cavity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1986

Dynamic pressure model derived from an observation by Sakigake for Comet Halley on 31 December, 1985

An outstanding disconnection event (DE)-like knot was observed on 31 Dec. 1985 in P/Halley's tail. Analysis of the Sakigake/IMF data reveals that comet Halley did not encounter the heliospheric neutral sheet on the day, demanding a new explanation of the DE-like event. During this event, the comet encountered a high-speed solar wind from a coronal hole tongue of the Sun. The event can be explained by a dynamic pressure model, according to which the DE-like plasmoid was caused by a sudden increase in the dynamic pressure of the solar wind. A simulation result is found to support this interpretation. The dynamic pressure model for a comet can be compared with the mechanism of a possible geotail disturbance during a spacecraft triggered auroral substorm.

Saito, Takao↗
NASA NTRS1992

Application of theoretical models to active and passive remote sensing of saline ice

The random medium model is used to interpret the polarimetric active and passive measurements of saline ice. The ice layer is described as a host ice medium embedded with randomly distributed inhomogeneities, and the underlying sea water is considered as a homogeneous half-space. The scatterers in the ice layer are modeled with an ellipsoidal correlation function. The orientation of the scatterers is vertically aligned and azimuthally random. The strong permittivity fluctuation theory is employed to calculate the effective permittivity and the distorted Born approximation is used to obtain the polarimetric scattering coefficients. We also calculate the thermal emissions based on the reciprocity and energy conservation principles. The effects of the random roughness at the air-ice, and ice-water interfaces are accounted for by adding the surface scattering to the volume scattering return incoherently. The above theoretical model, which has been successfully applied to analyze the radar backscatter data of the first-year sea ice near Point Barrow, AK, is used to interpret the measurements performed in the CRRELEX program.

Han, H. C.↗
DOE OSTI2022

A Universal Machine Learning Model for Elemental Grain Boundary Energies

The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small Σ (Σ<10) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m –2 . More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Measurable characteristics of lysozyme crystal growth

The behavior of protein crystal growth is estimated from measurements performed at both the microscopic and molecular levels. In the absence of solutal flow, it was determined that a model that balances the macromolecular flux toward the crystal surface with the flux of the crystal surface well characterizes crystal growth observed using microscopic methods. Namely, it was determined that the model provides accurate estimates for the crystal-growth velocities upon evaluation of crystal-growth measurements obtained in time. Growth velocities thus determined as a function of solution supersaturation were further interpreted using established deterministic models. From analyses of crystal-growth velocities, it was found that the mode of crystal growth varies with respect to increasing solution supersaturation, possibly owing to kinetic roughening. To verify further the hypothesis of kinetic roughening, crystal growth at the molecular level was examined using atomic force microscopy (AFM). From the AFM measurements, it was found that the magnitude of surface-height fluctuations, h(x), increases with increasing solution supersaturation. In contrast, the estimated characteristic length, xi, decreases rapidly upon increasing solution supersaturation. It was conjectured that the magnitude of both h(x) and xi could possibly determine the mode of crystal growth. Although the data precede any exact theory, the non-critical divergence of h(x) and xi with respect to increasing solution supersaturation was nevertheless preliminarily established. Moreover, approximate models to account for behavior of both h(x) and xi are also presented.

Muramidase/chemistry↗
DOE OSTI2024

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗
DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Non-local Thermodynamic Equilibrium Stellar Spectroscopy with 1D and 〈3D〉 Models. II. Chemical Properties of the Galactic Metal-poor Disk and the Halo

From exploratory studies and theoretical expectations it is known that simplifying approximations in spectroscopic analysis (local thermodynamic equilibrium (LTE), 1D) lead to systematic biases of stellar parameters and abundances. These biases depend strongly on surface gravity, temperature and, in particular, for LTE versus non-LTE (NLTE), on metallicity of the stars. Here we analyze the [Mg/Fe] and [Fe/H] plane of a sample of 326 stars, comparing LTE and NLTE results obtained using 1D hydrostatic models and averaged 〈3D〉 models. We show that compared to the 〈3D〉 NLTE benchmark, the other three methods display increasing biases toward lower metallicities, resulting in false trends of [Mg/Fe] against [Fe/H], which have profound implications for interpretations by chemical evolution models. In our best 〈3D〉 NLTE model, the halo and disk stars show a clearer behavior in the [Mg/Fe]–[Fe/H] plane, from the knee in abundance space down to the lowest metallicities. Our sample has a large fraction of thick disk stars and this population extends down to at least [Fe/H] ∼ −1.6 dex, further than previously proven. The thick disk stars display a constant [Mg/Fe] ≈ 0.3 dex, with a small intrinsic dispersion in [Mg/Fe] that suggests that a fast SN Ia channel is not relevant for the disk formation. The halo stars reach higher [Mg/Fe] ratios and display a net trend of [Mg/Fe] at low metallicities, paired with a large dispersion in [Mg/Fe]. These indicate the diverse origin of halo stars from accreted low-mass systems to stochastic/inhomogeneous chemical evolution in the Galactic halo.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Self-consistent models for triaxial galaxies with flat rotation curves - The disk case

We examine the possibility of constructing scale-free triaxial logarithmic potentials self-consistently, using Schwarzschild's linear programing method. In particular, we explore the limit of nonaxisymmetric disks. In this case it is possible to reduce the problem to the self-consistent reconstruction of the disk surface density on the unit circle, a considerably simpler problem than the usual 2D or 3D one. Models with surface densities of the form Sigma = (x exp n + (y/q) exp n) exp - 1/n with n = 2 or 4 are investigated. We show that the complicated shapes of the 'boxlet' orbit families (which replace the box orbit family found in potentials with smooth cores) limit the possibility of building self-consistent models, though elliptical disks of axis ratio above 0.7 and a restricted range of boxier models can be constructed. This result relies on using sufficiently fine bins, smaller than the 10 deg bins commonly used in 2D or 3D investigations. It also indicates the need for caution in interpreting N-body models of triaxial halos in which the core of the potential is numerically smoothed.

Kuijken, Konrad↗
NASA NTRS2010

TPSAS-NF1676L-10206-DND

NASA is at fore-front in developing active and passive remote sensing technologies and unique capabilities towards space-based observations for understanding the complexities and interactions among Earth system components. The world is facing significant environmental challenges and a robust, integrated, and flexible system of observations and models are needed for understanding the short-and long term impact on the Earth system. A fundamental challenge for the coming decade is to ensure that space-based observations, analyses, better interpretive understanding, enhanced predictive models, broadened international community participation, and improved means for information assimilation and disseminations are well coordinated to realize the full economic, societal, and security benefit of Earth science. This presentation will provide an overview of NASA?s future vision for Earth science missions for global observations and the challenges associated in applying them for societal benefit.

Upendra N Singh↗
NASA NTRSDate not supplied

NASA's Future Earth Science Missions for Global Observations

NASA is at fore-front in developing active and passive remote sensing technologies and unique capabilities towards space-based observations for understanding the complexities and interactions among Earth system components. The world is facing significant environmental challenges and a robust, integrated, and flexible system of observations and models are needed for understanding the short-and long term impact on the Earth system. A fundamental challenge for the coming decade is to ensure that space-based observations, analyses, better interpretive understanding, enhanced predictive models, broadened international community participation, and improved means for information assimilation and disseminations are well coordinated to realize the full economic, societal, and security benefit of Earth science. This presentation will provide an overview of enabling active and passive remote sensing technologies and techniques, NASA's future vision for Earth science missions for global observations, and the challenges associated in applying them for societal benefit.

Upendra N Singh↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27491-DND

NASA is at fore-front in developing lidar technologies and unique active/passive remote sensing capabilities towards space-based observations for understanding the complexities and interactions among Earth system components. The world is facing significant environmental challenges and a robust, integrated, and flexible system of observations and models are needed for understanding the short-and long term impact on the Earth system. A fundamental challenge for the coming decade is to ensure that space-based observations, analyses, better interpretive understanding, enhanced predictive models, broadened international community participation, and improved means for information assimilation and disseminations are well coordinated to realize the full economic, societal, and security benefit of Earth science. This presentation will provide an overview of enabling lidar technologies and techniques from ground and space towards NASA's future vision for Earth science missions for global observations, and the challenges associated in applying them for societal benefit.

Upendra N Singh↗
DOE OSTI2017

Physiologically based pharmacokinetic toolkit to evaluate environmental exposures: Applications of the dioxin model to study real life exposures

Chlorinated dibenzo-p-dioxins (CDDs) are a series of mono- to octa-chlorinated homologous chemicals commonly referred to as polychlorinated dioxins. One of the most potent, well-known, and persistent member of this family is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). As part of translational research to make computerized models accessible to health risk assessors, we present a Berkeley Madonna recoded version of the human physiologically based pharmacokinetic (PBPK) model used by the U.S. Environmental Protection Agency (EPA) in the recent dioxin assessment. This model incorporates CYP1A2 induction, which is an important metabolic vector that drives dioxin distribution in the human body, and it uses a variable elimination half-life that is body burden dependent. To evaluate the model accuracy, the recoded model predictions were compared with those of the original published model. The simulations performed with the recoded model matched well with those of the original model. The recoded model was then applied to available data sets of real life exposure studies. The recoded model can describe acute and chronic exposures and can be useful for interpreting human biomonitoring data as part of an overall dioxin and/or dioxin-like compounds risk assessment. - Highlights: • The best available dioxin PBPK model for interpreting human biomonitoring data is presented. • The original PBPK model was recoded from acslX to the Berkeley Madonna (BM) platform. • Comparisons were made of the accuracy of the recoded model with the original model. • The model is a useful addition to the ATSDR's BM based PBPK toolkit that supports risk assessors. • The application of the model to real-life exposure data sets is illustrated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Search for resonant pair production of Higgs bosons in the $\textrm{b}\overline{\textrm{b}}\textrm{b}\overline{\textrm{b}}$ final state using large-area jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search is presented for the resonant production of a pair of standard model-like Higgs bosons using data from proton-proton collisions at a centre-of-mass energy of 13 TeV, collected by the CMS experiment at the CERN LHC in 2016–2018, corresponding to an integrated luminosity of 138 fb −1 . The final state consists of two b quark-antiquark pairs. The search is conducted in the region of phase space where at least one of the pairs is highly Lorentz-boosted and is reconstructed as a single large-area jet. The other pair may be either similarly merged or resolved, the latter reconstructed using two b-tagged jets. The data are found to be consistent with standard model processes and are interpreted as 95% confidence level upper limits on the product of the cross sections and the branching fractions of the spin-0 radion and the spin-2 bulk graviton that arise in warped extradimensional models. The limits set are in the range 9.74–0.29 fb and 4.94–0.19 fb for a narrow radion and a graviton, respectively, with masses between 1 and 3 TeV. For a radion and for a bulk graviton with widths 10% of their masses, the limits are in the range 12.5–0.35 fb and 8.23–0.23 fb, respectively, for the same masses. These limits result in the exclusion of a narrow-width graviton with a mass below 1.2 TeV, and of narrow and 10%-width radions with masses below 2.6, and 2.9 TeV, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗
NASA NTRS2018

TPSAS-NF1676L-30074-DND

NASA is at forefront in developing unique active/passive remote sensing capabilities towards space-based observations for understanding the complexities and interactions among Earth system components. The world is facing significant environmental challenges and a robust, integrated, and flexible system of observations and models are needed for understanding the short-and long-term impact on the Earth system. A fundamental challenge for the coming decade is to ensure that space-based observations, analyses, better interpretive understanding, enhanced predictive models, broadened international community participation, and improved means for information assimilation and disseminations are well coordinated to realize the full economic, societal, and security benefit of Earth science. This presentation will provide an overview of active/passive remote sensing technologies and techniques from ground and space towards NASA’s future vision for Earth science missions for global observations, and the challenges associated in applying them for societal benefit.

Upendra N Singh↗
NASA NTRS1987

Theoretical interpretation of the HEAO-3 observations of Cygnus X-3 under the HEAO-3 Guest Investigator Program

A model of the galactic X-ray source Cygnus X-3 (Cyg X-3) is presented which deviates from previous models by positing that the X-rays originate in a jet rather than a binary system consiting of an ordinary star and a collapsed object. In the new model, the 4.8 hour period of Cyg X-3 is caused by variable absorption which occurs as the jet precesses. The primary role of the accretion disk corona (ADC) in modulating Cyg X-3 radiation is to make the observed intensity of a blob of material in a jet appear dimmer by absorption. The needed derivation of the positional dependence of the density of the ADC is freed of some complications by assuming that only the inner regions of the disk are precessing, with a period shorter than 4.8 hours. This model permits the secondary star to be a supergiant, as indicated by the luminosity of the system. The model is especially helpful in interpreting production of radio outbursts and very high energy gamma rays.

Marscher, Alan P.↗