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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Rock-Forming Minerals Radiation-Induced Volumetric Expansion – Revisiting Literature Data

Neutron radiation-induced volumetric expansion (RIVE) of concrete aggregate is recognized as a major degradation mechanism causing extensive damage to concrete constituents (Hilsdorf et al. 1978; Seeberger and Hilsdorf 1982; Field et al. 2015). Nearly 400 RIVE data obtained in test-reactors on varied rock-forming minerals were collected by Denisov et al. (2012). These data were input into the Oak Ridge National Laboratory (ORNL) irradiated minerals, aggregates and concrete (IMAC) database and were reanalyzed in order to develop a general empirical model for minerals RIVE and interpret the susceptibility of silicates toward expansion. Furthermore, the empirical models best regression coefficient (r2 ≈ 0.95) is obtained by combining two different modeling techniques: (1) an interpolation-like model based on the relative distance to existing data points, and, (2) a nonlinear regression model assuming varied mathematical forms to describe RIVE as a function of the neutron fluence3 and the average irradiation temperature. The susceptibility to develop irradiation-induced expansion greatly varies with the nature of minerals. Silicates, i.e., [SiO4]4– bearing minerals show a wide range of maximum RIVEs, from a few percents to what appears as a bounding value of 17.8% for quartz. The maximum RIVE of varied silicates appears to be governed, macroscopically, by three parameters: (1) Primarily, the dimensionality of silicate polymerization (DOSP), (2) the relative number of Si–O bond per unit cell, and, (3) the relative bonding energy (RBE) of the unit cell.

58 GEOSCIENCES↗
NASA NTRS1983

Extinct radioactivities - A three-phase mixing model

A new class of models is advanced for interpreting the relationship of radioactive abundances in the early solar system to their average concentration in the interstellar medium. The model assumes that fresh radioactivities are ejected from supernovae into the hot interstellar medium, and that the time scales for changes of phase into molecular clouds determine how much survives for formation therein of the solar system. A more realistic and physically motivated understanding of the low observed concentrations of I-129, Pu-244, and Pd-107 may result.

Clayton, D. D.↗
NASA NTRS1984

Scattering Models in the Microwave Regime

Scattering from an inhomogeneous layer with irregular boundaries was calculated to model natural terrain such as a layer of vegetation or sea ice. The inhomogeneities were modeled by spherical or disc-shaped discrete scatterers which were small compared with the incident wavelength and were in the far field of one another. It was found that the cross-polarized scattering was dominated by multiple scattering effects and was sensitive to the orientations and distributions of the scatterers. This model has been applied to interpret measurements from vegetation, snow and sea ice. The scattering model was extended to handle disc-shaped scatterers which are comparable to the incident wavelength. The model was used to investigate the relative merits between active versus passive sensing of soil moisture over vegetated terrain. Results indicate that scattering mesurements are more sensitive to soil moisture changes than emission measurements. This is because while both types of measurements lose sensitivity to soil moisture because of the vegetation layer, the loss is greater for passive than active measurements.

Fung, A. K.↗
NASA NTRS1984

Theory, measurements, and models of the upper atmosphere and ionosphere of Saturn

The structure and composition of the thermosphere, exosphere, and ionosphere of saturn have been determined from observations at optical and radio wavelengths mainly by instruments aboard Voyager spacecraft. Techniques for determining the vertical profiles of temperature and density and the atmospheric vertical mixing in the upper Saturn atmosphere are discussed. Radio occultation measurements and theoretical models of Saturn's ionosphere are reviewed, and attempts to interpret the measurements using the models are discussed. Finally, mechanisms of thermospheric heating are examined.

Atreya, S. K.↗
NASA NTRS1988

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

The estimation of the spatially variable surface moisture and heat fluxes of natural, semivegetated landscapes is difficult due to the highly random nature of the vegetation (e.g., plant species, density, and stress) and the soil (e.g., moisture content, and soil hydraulic conductivity). The solution to that problem lies, in part, in the use of satellite remotely sensed data, and in the preparation of those data in terms of the physical properties of the plant and soil. The work was focused on the development and testing of a stochastic geometric canopy-soil reflectance model, which can be applied to the physically-based interpretation of LANDSAT images. The model conceptualizes the landscape as a stochastic surface with bulk plant and soil reflective properties. The model is particularly suited for regional scale investigations where the quantification of the bulk landscape properties, such as fractional vegetation cover, is important on a pixel by pixel basis. A summary of the theoretical analysis and the preliminary testing of the model with actual aerial radiometric data is provided.

Eagleson, Peter S.↗
NASA NTRSDate not supplied

Models, In situ, and Remote sensing of Aerosols (MIRA)

The Models, In situ, and Remote sensing of Aerosols (MIRA) working group connects members of the different aerosol communities through collaborative projects. This abstract, additional details, and a list of current projects can be found at https://science.larc.nasa.gov/mira-wg/. Join our email distribution list of 200+ members at https://espo.nasa.gov/lists/listinfo/mira.In this talk, we will describe some current MIRA projects that are relevant to AeroCom (e.g., the Table of Aerosol Optics, or TAO) and the benefits of presenting your multi-disciplinary work at MIRA sessions and meetings. What is MIRA? MIRA is forum that fosters international collaborations amongst the aerosol Modeling, In situ, and Remote sensing specialties. MIRA advances scientific knowledge about aerosol properties and improves our understanding of air quality, weather, and climate through the pursuit of specific goals. MIRA emphasizes areas of study where the aerosol specialties overlap, and MIRA projects seek additional data and insight from others (observational and modeled). Why? The purpose of MIRA is to contextualize both observations and model results through the encouragement of holistic projects and collaborations. Linking aerosol disciplines removes gaps in understanding that hinder our interpretation of observations and model results. Such linkages improve our understanding of air quality, weather, and climate. How does MIRA differ from other working groups? MIRA focuses on interdisciplinarity to improve measurements and their utility, so MIRA complements the activities of other groups. Other interdisciplinary aerosol groups have different primary foci. For instance, AeroCom mainly focuses on improving global aerosol models, AeroSat focuses on strengthening collaboration amongst satellite aerosol retrieval groups, and ICAP focuses on aerosol forecasting. We hope that many MIRA projects engage these groups.

Gregory Schuster↗
DOE OSTI2025

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

42 ENGINEERING↗
DOE OSTI2017

The low salinity effect at high temperatures

The mechanism(s) of low salinity water flooding (LSWF) must be better understood at high temperatures and pressures if the method is to be applied in high T/P kaolinite-bearing sandstone reservoirs. We measured contact angles between a sandstone and an oil (acid number, AN = 3.98 mg KOH/g, base number, BN = 1.3 mg KOH/g) from a reservoir in the Tarim Field in western China in the presence of various water chemistries. We examined the effect of aqueous ionic solutions (formation brine, 100X diluted formation brine, and softened water), temperature (60, 100 and 140 °C) and pressure (20, 30, 40, and 50 MPa) on the contact angle. We also measured the zeta potential of the oil/water and water/rock interfaces to calculate oil/brine/rock disjoining pressures. A surface complexation model was developed to interpret contact angle measurements and compared with DLVO theory predictions. Contact angles were greatest in formation water, followed by the softened water, and low salinity water at the same pressure and temperature. Contact angles increased slightly with temperature, whereas pressure had little effect. DLVO and surface complexation modelling predicted similar wettability trends and allow reasonably accurate interpretation of core-flood results. Water chemistry has a much larger impact on LSWF than reservoir temperature and pressure. As a result, low salinity water flooding should work in high temperature and high pressure kaolinite-bearing sandstone reservoirs.

02 PETROLEUM↗
DOE OSTI2019

Data for secondary-electron production from ion precipitation at Jupiter III: Target and projectile processes in H{sup +}, H, and H{sup −} + H{sub 2} collisions

To extend the range of data required for modeling the secondary-electron production from ion precipitation into the upper atmosphere of Jupiter, inelastic processes for collisions of 1 keV to 25 MeV H{sup +}, H, and H{sup −} with H{sub 2} are considered. As in other work treating the dominant heavy-ion species of magnetospheric origin, O and S ions (Schultz et al., 2017, 2019; Gharibnejad et al., 2019) the classical trajectory Monte Carlo method is employed to describe the secondary-electron-producing channels (single and double ionization, transfer ionization, and single and double stripping) as well as the other inelastic channels (single and double charge transfer and projectile and target excitation) required to model the energy loss and charge state evolution of the precipitating ions in their passage through the atmospheric gas. Data is described and tabulated both as directly obtained from these calculations and normalized to widely accepted recommended values (Hunter et al., 1990) for channels for which recommendations exist. As in the previous work, the overall accuracy and completeness of the data presented is verified by use of a Monte Carlo ion-transport simulation to obtain the stopping power and ion-fraction populations as a function of impact energy in comparison with accepted values. The addition of the present data to models of secondary-electron production in Jupiter’s atmosphere improves such model’s ability to interpret in situ observations of the precipitating ions’ effect by the spacecraft Juno as well as enhancing the physical reality of models of the coupling of the Jovian magnetosphere, ionosphere, and atmosphere.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Lorentz group equivariant autoencoders

Abstract There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $$\textrm{SO}^+(3,1)$$ SO + ( 3 , 1 ) , with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring CO 2 reduction and crossover in membrane electrode assemblies

Electrochemical CO 2 reduction (CO 2 R) using renewable electricity is a key pathway toward synthesizing fuels and chemicals. In this study, multi-physics modeling is used to interpret experimental data obtained for CO 2 R to CO using Ag catalysts in a membrane electrode assembly. The one-dimensional model is validated using measured CO 2 crossover and product formation rates. The kinetics of CO formation are described by Marcus–Hush–Chidsey kinetics, which enables accurate prediction of the experimental data by accounting for the reorganization of the solvent during CO 2 R. Further, the results show how the performance is dictated by competing phenomena including ion formation and transport, CO 2 solubility, and water management. The model shows that increasing the ion-exchange capacity of the membrane and surface area of the catalyst increases CO formation rates by >100 mA cm –2 without negatively impacting CO 2 utilization. Here we provide insights into how to manage the trade-off between productivity and CO 2 utilization in CO 2 electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

Galactic X-ray sources

Data on galactic X-ray sources from rocket and balloon experiments and the UHURU satellite are reported. The outline emphasizes general discrete source models and begins with some a priori considerations about X-ray emission mechanisms and their applicability, after which contemporary data are interpreted according to these models. Topics covered include the production of X-rays by various mechanisms, source regimes, observational parameters, stellar distributions, individual stars, flare stars, supernovae and pulsars, the Crab nebula, old and young remnants, binary systems, and others.

Holt, S. S.↗
NASA NTRS1987

Cloud chemistry on Jupiter

Chemical equilibrium models used currently to interpret observations of Jupiter are reexamined using new data defining thermal profiles, which are substantially different from those used in the previous models. A model is developed for the chemical reactions controlling the composition of the upper troposphere on Jupiter, specifically the cloud-forming region from 10 bar to 0.1 bar, which includes, for the first time, the effects of aqueous chemistry on the composition and the vertical distribution of many measurable species in the atmosphere, identifying the factors influencing their abundances above the H2O cloud. The thermodynamic data for potential condensates on Jupiter, i.e., NH3(s), NH4SH(s), (NH4)2S(s), and H2S(s), are reexamined, recognizing the lack of data on sulfides for the temperature range of interest on Jupiter. Vertical profiles of mixing ratios for CO2, H2S, NH3, and H2, obtained for several assumed bulk abundances with respect to solar, are presented.

Carlson, Barbara E.↗
DOE OSTI2026

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

Cao, Shuxiang↗
NASA NTRS1999

Testing the Pair Reflection Model for AGN

This project involved the interpretation of X-ray observations of AGN, and more specifically, evaluation and interpretation of the spectra in terms of the pair-reflection model. The grant was originally awarded on 3/14/94. We have now completed this work, which led to a number of publications. We found that reflection features are common in Seyfert galaxies, and may also be present in their radio-loud analogs, the broad-emission-line radio galaxies. In the latter case, however, a strong, nonthermal component is also present, and can swamp the thermal reflection spectrum.

Urry, C. Megan↗
DOE OSTI2024

Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

25 ENERGY STORAGE↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗