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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Spatial Distribution of Volcanic Hotspots and Paterae on Io: Implications for Tidal Heating Models and Magmatic Pathways

Io, the innermost of Jupiter's Galilean satellites, is the most volcanically active body in the Solar. System. Io's global mean heat flow is approximately 2 W/square m, which is approximately 20 times larger than on Earth. High surface temperatures concentrate within "hotspots" and, to date, 172 Ionian hotspots have been identified by spacecraft and Earth-based telescopes. The Laplace resonance between Io, Europa, and Ganymede maintains these satellites in noncircular orbits and causes displacement of their tidal bulges as the overhead position of Jupiter changes for each moon. Gravitational interactions between Jupiter and Io dominate the orbital evolution of the Laplacian system and generate enormous heat within to as tidal energy is dissipated. If this energy were transferred out of Io at the same rate as it is generated, then the associated surface heat flux would be 2.24 +/- 0.45 W/square m. This estimate is in good agreement with observed global heat flow, but to better constrain tidal dissipation mechanisms and infer how thermal energy is transferred to Io's surface, it is critical to closely examine the spatial distribution of volcanic features. End-member tidal dissipation models either consider that heating occurs completely in the mantle, or completely in the asthenosphere. Mixed models typically favor one-third mantle and two-thirds asthenosphere heating. Recent models also consider the effects of mantle-asthenosphere boundary permeability and asthenospheric instabilities. Deep-mantle heating models predict maximum surface heat flux near the poles, whereas asthenosphere heating models predict maxima near the equator-particularly in the Sub-Jovian and Anti-Jovian hemispheres, with smaller maxima occurring at orbit tangent longitudes. Previous studies have examined the global distribution of Ionian hotspots and patera (i.e., irregular or complex craters with scalloped edges that are generally interpreted to be volcanic calderas), but in this study, we combine a new geospatial analysis technique with an improved hotspot and paterae database .

Hamilton, C. W.↗
NASA NTRS2000

A Strip-Yield Model for Predicting the Growth of Part-Through Cracks Under Cyclic Loading

Flaws exist in aircraft structures due to manufacturing operations and material defects. Under variable amplitude cyclic loading, these flaws grow as part-through cracks reducing the residual strength of structural components. To meet damage tolerant design requirements, accurate flaw growth predictions are needed which account for continual changes in crack shape as well as crack growth retardation and acceleration. Predicting the growth of part-through cracks under cyclic loading using an innovative and computationally efficient model is the focus of the research summarized in this report. In this research effort, a slice synthesis methodology was developed and used to construct a modified strip-yield model for the part-through semi-elliptical surface flaw, enabling prediction of plasticity-induced closure along the crack front and subsequent fatigue crack growth under constant amplitude and variable amplitude loading. While modeling the plasticity-induced closure in a part-through flaw may be performed using three dimensional elastic-plastic finite element analysis, this type of effort is impractical from an engineering perspective. A modified strip-yield model similar to that used in FASTRAN for part-through flaws is a much needed engineering design tool, particularly when computational resources are limited.

Daniewicz, S. R.↗
NASA NTRSDate not supplied

Validation of a Two-Phase CFD Model for Predicting Tank Self-Pressurization in the Ground-Based K-Site Experiment

A two-phase CFD model for self-pressurization of a cryogenic storage tank partially filled with liquid hydrogen is presented using the Volume-Of-Fluid approach for modeling two-phase flow, as well as interfacial heat, mass and momentum transfer between the liquid and the vapor regions. The CFD model is validated against self-pressurization experiment performed using the K-site flightweight hydrogen storage tank at NASA Glenn Research Center1. Laminar and turbulent simulations are performed together with conjugated heat transfer analysis. Effects of turbulence, the value of accommodation coefficient used for predicting phase change rates, as well as tank wall geometry are presented and discussed. Predicted tank pressures, fluid and wall temperatures are compared with the experimental data at 49% fill level and two different heat loads for validating this CFD model.

Computational Fluid Dynamics↗
DOE OSTI2021

Reliable modeling and prediction of precipitation & radiation for mountainous hydrology

This White Paper focuses on data-driven atmospheric process model emulation and atmospheric process surrogate model development. It proposes leveraging recent AI advances in these approaches to fill in unavoidable observational gaps and enable high-fidelity modeling/predictability of the atmosphere and land-surface interactions in mountainous watersheds. This approach will support studies and predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Validation of a Two-Phase CFD Model for Predicting Tank Self-Pressurization in the Ground-Based K-Site Experiment

A two-phase CFD model for self-pressurization of a cryogenic storage tank partially filled with liquid hydrogen is presented using the Volume-Of-Fluid approach for modeling two-phase flow, as well as interfacial heat, mass and momentum transfer between the liquid and vapor regions. The CFD model is validated against self-pressurization experiment performed using the K-site flightweight hydrogen storage tank at NASA Glenn Research Center 1 . Laminar and turbulent simulations are performed together with conjugate heat transfer analysis. Effects of turbulence, the value of accommodation coefficient used for predicting phase change rates, as well as tank wall geometry are presented and discussed. Predicted tank pressures, fluid and wall temperatures are compared with the experimental data at 49% fill level and two different heat loads for validating this CFD model.

Computational Fluid Dynamics↗
DOE OSTI2024

Exclusive η Electro-Production Beam Spin Asymmetry Measurements using CLAS12 at Jefferson Lab

The exploration of nucleon structure and electromagnetic transitions from ground-state to excited-state is a cornerstone of nuclear physics research. Meson electro-production experiments have opened new avenues for investigating these phenomena, particularly in the 12 GeV era at Jefferson Lab with the CLAS12 spectrometer. The ?N final states, accessible only through isospin resonances I = 1/2, provide a unique tool for studying nucleon excitations. By simplifying the analysis and enabling a cleaner extraction of resonance properties compared to the extensively studied ?N final states, ? electroproduction offers a complementary approach to unraveling the structure of excited nucleons. This work presents the first-ever measurement of the beam spin asymmetry (BSA) in exclusive ? electroproduction, covering a previously unexplored kinematic region with 1.6 ? W ? 2.2 GeV. The BSA is extracted from the CLAS12 data using a comprehensive analysis framework that carefully considers the statistical limitations of the data set. The results are compared to predictions from theoretical models, such as the Jülich-Bonn-Washington (JBW) and MAID, as well as compared to previously published cross-section and spin observable results from CLAS and SLAC. Notably, the extracted BSA exhibits discrepancies with the model predictions, highlighting the potential for refining theoretical descriptions of nucleon resonances and their electromagnetic couplings through the incorporation of these new data. The high precision data obtained in this previously unmeasured kinematic region now serve as valuable input for theorists to refine their models.

Illari, Isabella↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
NASA NTRS1999

Dynamics of the Auroral Luminosity Boundary of the Polar Cap During Substorms

The area of the polar cap during substorms has been measured using images from the Polar Ultraviolet Imager (UVI) for different interplanetary magnetic field (IMF) conditions. Changes in the poleward boundary of auroral luminosity have been analyzed in relation to substorm phase and IMF orientation. Reconnection models of flux transport into the polar cap during the substorm growth phase, and loss from the polar cap during the expansion phase, provide a framework by which these UVI observations can be analyzed. By comparison of the observations with the model predictions we can determine to what extent these models accurately predict the polar cap dynamics, and also where anomalous behavior calls for a new understanding of the dynamics beyond what these models provide. It was found that the polar cap boundary near noon and midnight usually shifted down in latitude by 1-2 degrees and 3-4 degrees respectively, increasing the area of the polar cap during the substorm growth phase as predicted. However, this growth phase phenomenon also unexpectedly occurs independently of the IMF Bz component, as shown for a substorm on January 9, 1997. The polar cap area also increased due to motion of the dawn and dusk aurora to lower latitudes, although the latitudinal shifts were asymmetric, not always concurrent, and continued well into the substorm expansion phase. The polar cap area decreased immediately following the expansion phase due to the poleward motion of the aurora on the nightside, consistent with the model prediction. What is not explained by the models is that the poleward auroral boundary in the nightside region sometimes reached very high latitudes (greater than 80 degrees MLat) greatly decreasing the polar cap area, independent of the magnitude of the substorm.

Brittnacher, M. J.↗
NASA NTRS2000

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated with Oxidation and Interdiffusion of Overlay-Coated Substrates

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating fife based on a concentration dependent failure criterion (e.g., surface solute content drops to two percent). The computer code, written in an extension of FORTRAN 77, employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Nesbitt, James A.↗
NASA NTRS2001

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated With Oxidation and Interdiffusion of Overlay-Coated Substrates

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating life based on a concentration dependent failure criterion (e.g., surface solute content drops to 2%). The computer code is written in FORTRAN and employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Nesbitt, James A.↗
DOE OSTI2024

A Generation-Storage Coordination Dispatch Strategy for Power System Based on Causal Reinforcement Learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model - the crux of model-based reinforcement learning - thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

causal reinforcement learning↗
DOE OSTI2025

Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data. The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made. The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library. Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.

Coal ash composition↗
NASA NTRS1990

Development of a computer model to predict platform station keeping requirements in the Gulf of Mexico using remote sensing data

Offshore operations such as oil drilling and radar monitoring require semisubmersible platforms to remain stationary at specific locations in the Gulf of Mexico. Ocean currents, wind, and waves in the Gulf of Mexico tend to move platforms away from their desired locations. A computer model was created to predict the station keeping requirements of a platform. The computer simulation uses remote sensing data from satellites and buoys as input. A background of the project, alternate approaches to the project, and the details of the simulation are presented.

Barber, Bryan↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗
NASA NTRS1992

Modeling Characteristics Of Surfaces For Radar Polarimetry

Paper reviews mathematical models of polarimetric radar backscattering characteristics of various types of terrain; forests, grasslands, and lava fields. Represents approach to imaging radar polarimetry in which one accumulates models predicting realistic polarization signatures and represent distinct scattering processes, without attempting full vector solutions of Maxwell's equations in all cases. Idea to develop ability to invert models to identify unknown terrain depicted in polarimetric radar images. Describes models, major scattering characteristics predicted by models, and interpretation of characteristics in terms of dominant scattering mechanisms. Models predict realistic polarization signatures.

Van Zyl, Jakob J.↗
NASA NTRSDate not supplied

Thermal Model of Ice Growth in Vacuum for Lunar Water Production

To support NASA’s goal of sustained lunar presence, the use of in-situ resource utilization will reduce launch weight and frequency of resupply missions. With the discovery of water in the permanently shadowed regions of the moon, the prospect of harvesting water has generated particular interest. A more fundamental understanding of water storage and capture in lunar conditions is necessary for effective and efficient water capture and transport. The present study derives a thermal model for ice height, heat flux, chamber pressure, and mass flow rate in sub-atmospheric pressures below the triple point. The thermal model compares ice layer height against the results of the experiment at two pressures (300 Pa and 500 Pa) and temperatures from -18°C to -3°C. The thermal model predicts the trends seen experimentally with an overall root mean square error of 0.1135 cm and coefficient of efficiency of 0.994, indicating the model predicts the experimental results. The thermal model can be utilized to examine the impacts of varying pressure, temperature, and cold plate area to determine the most effective ice growth conditions for efficient capture of water on the lunar surface. The thermal model will provide a useful tool for NASA in the design of the lunar ice tanker and shows the promise of effective capture water on the lunar surface.

Ice growth↗