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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Using SMAP Level-4 Soil Moisture to Constrain MOD16 Evapotranspiration over the Contiguous USA

Evapotranspiration (ET) is a key hydrologic variable linking the Earth’s water, carbon and energy cycles. At large spatial scales, remote sensing-based (RS) models are often used to quantify ET. Despite the large number of RS ET models available, few include soil moisture as a key environmental input, which can degrade model accuracy and utility. Here, we use model assimilation enhanced soil moisture estimates from the NASA SMAP (Soil Moisture Active Passive) mission as a water supply control in the MOD16 ET algorithm framework. SMAP-derived daily surface (0-5cm depth) and root zone (0-1m depth) soil moisture are used with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation observations, and 4km gridded regional surface meteorology (Gridmet) as primary inputs for estimating daily ET and underlying model soil and stomatal conductance terms. We calibrated the model environmental response parameters using tower eddy covariance ET observations representing major North American biomes. The model ET results were validated using a holdout set of tower observations spanning a large regional climate gradient. The updated ET estimates outperform the baseline MOD16 product across all tower validation sites (RMSE = 0.758 vs 1.108 mm day-1; R2 = 0.68 vs0.45, respectively). Smaller relative improvements were obtained using a recalibrated model with 4km Gridmet meteorology, but no soil moisture control (RMSE = 0.813 mm day-1; R2 = 0.66), indicating that these changes are essential for the improved model performance. The soil moisture-constrained model improvements and relative benefits from the SMAP observations are greater in arid climates, consistent with stronger soil moisture control on ET in water-limited regions. The use of SMAP soil moisture as an additional model constraint improves MOD16 regional performance and provides a new framework for investigating both soil and atmosphere controls on ET.

SMAP↗
DOE OSTI2025

An Advanced Microscopic Energy Consumption Model for Automated Vehicle:Development, Calibration, Verification

The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.

Ma, Ke↗
NASA NTRSDate not supplied

Evaluation of a Traditional Regression Model Term Selection Process for Balance Calibration Data Sets

Key elements of a traditional regression model term selection process for the analysis of wind tunnel strain-gage balance calibration data are discussed. The traditional process is sometimes applied when regression models of the gage outputs are needed and an analyst does not have access to a term selection algorithm. In principle, the traditional process identifies supported regression model terms of a gage output by combining an analyst's subject- matter knowledge with a visual examination of selected plots of the calibration loads. Twelve empirical rules are listed that may be used to select regression model terms. Data from the manual calibration of a force balance is used to illustrate the application of the traditional term selection process. The data example shows that the traditional process can become very complex. Its successful application highly depends on an analyst's experience and skills. Therefore, it is suggested to replace the traditional term selection process with an automated term selection algorithm. Then, objective metrics are systematically applied during the term selection that make the identification of the regression model more repeatable and less dependent on an analyst's personal skills.

strain-gage balance↗
NASA NTRS2019

The Badhwar-O'Neill 2020 Model

The Badhwar-O'Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within ±15% of a large fraction of the available measurements (26,269 of 27,646 → 95%).

Slaba, Tony C.↗
NASA NTRS2020

The Badhwar-O'Neill 2020 Model

The Badhwar-O’Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high-precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within +15% of a large fraction of the available measurements (26,269 of 27,646  95%.

T C Slaba↗
DOE OSTI2025

Develop a new integrated macro→micro←nano (MMN) multiscale modeling framework to optimize high strength aluminum alloys and processes for vehicle light-weighting​

Bending tests provide a means to study plane strain fracture performance of 6000 series aluminum alloys. Metrics from bending tests have been correlated with self-pierce riveting (SPR) performance of a high strength AA6111 automotive aluminum alloy in previous works. Using the ORNL HPC resources, this project developed an innovative macro→micro←nano (MMN) multiscale microstructure-based finite element (FE) code to further understanding of the relationship between microstructure and fracture properties of high-strength 6000-series alloys. This work started with microstructural characterization in both mesoscale and nanoscale and bending performance characterization of AA6111 HS2-T6 alloy at Ford, the MMN framework was applied to this alloy to simulate 3-point VDA bending. From the results of the macro-modeling of 3-point VDA bending, the critical region of fracture was identified, and the region geometry was used to construct the micro-model. The fracture criterion of micron-scale precipitates and aluminum matrix which contains submicron and nano particles (AL-SMP-NP) in the micro-model was calibrated and validated by comparing simulated and measured bending results. With the AL-SMP-NP fracture strain obtained, the fracture strain of Al-matrix containing nano particles (ALNP) will similarly be determined by a submicron scale-model using an edge-constrained FE modeling approach developed by Hu et al. With the ALNP fracture strain obtained, the fracture strain of Al-matrix containing no particles will similarly be determined by a nano scale-model using an edge-constrained FE modeling approach. After the MMN framework is built and fracture criterion calibrated, nano-model FE simulations with virtual microstructures was performed to obtain a reduced order model (ROM) of the fracture criterion of the ALNP as a function of volume fraction, size, and distribution of the nanoparticle. This nano→submicron→micro modeling part allows exploration of the influence of different material nanostructures from different process conditions on the bending properties within the multiscale bending simulation framework and the ROM of material bendability as a function of nanoparticle size and shape was established. This obtained reduced order model (ROM) could help guide the design and selection of materials to improve existing SPR process models that could replace trial-and-error rivet/die selection and help to design new rivet/die combinations capable of robustly joining new higher strength 6000 5 series alloys in automotive body structures. This would enable lightweighting of Ford vehicles leading to greater fuel efficiency and reduce manufacturing time and energy.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification.

Gupta, Hoshin V.↗
NASA NTRS1999

Data Reduction Methods Applied to the Fastrac Engine

The Fastrac rocket engine is currently being developed for the X-34 technology demonstrator vehicle. The engine performance model must be calibrated to support accurate performance prediction. Data reduction is the process of estimating hardware characteristics from available test data, and is essential for effective performance model calibration and prediction. A new data reduction procedure was developed, implemented, and tested using data from Fastrac engine tests. The procedure selects hardware and test measurements to use in the reduction process based on examination of the model influence matrix condition number. Predicted hardware characteristics are recovered from the solution of a quadratic programming problem. Computational tests indicate that the new procedure provides a significant improvement in test data reduction capability. Enhancements include improved test data utilization and time history data reduction capability. The new method is generically applicable to other systems.

Santi, L. Michael↗
NASA NTRS2001

Thermal Analysis of a Carbon Fiber Rope Barrier for Use in the Reusable Solid Rocket Motor Nozzle Joint-2

This study provides for development and verification of analysis methods used to assess performance of a carbon fiber rope (CFR) thermal barrier system that is currently being qualified for use in Reusable Solid Rocket Motor (RSRM) nozzle joint-2. Modeled geometry for flow calculations considers the joint to be vented with the porous CFR barriers placed in the "open' assembly gap. Model development is based on a 1-D volume filling approach where flow resistances (assembly gap and CFRs) are defined by serially connected internal flow and the porous media "Darcy" relationships. Combustion gas flow rates are computed using the volume filling code by assuming a lumped distribution total joint fill volume on a per linear circumferential inch basis. Gas compressibility, friction and heat transfer are included in the modeling. Gas-to-wall heat transfer is simulated by concurrent solution of the compressible flow equations and a large thermal 2-D finite element (FE) conduction grid. The derived numerical technique loosely couples the FE conduction matrix with the compressible gas flow equations, Free constants that appear in the governing equations are calibrated by parametric model comparison to hot fire subscale test results. The calibrated model is then used to make full-scale motor predictions using RSRM aft dome environments. Model results indicate that CFR thermal barrier systems will provide a thermally benign and controlled pressurization environment for the RSRM nozzle joint-2 primary seal activation.

Clayton, J. Louie↗
NASA NTRS2002

Thermal Analysis of a Carbon Fiber Rope Barrier for Use in the Reusable Solid Rocket Motor Nozzle Joint-2

This study provides development and verification of analysis methods used to assess performance of a carbon fiber rope (CFR) thermal barrier system that is currently being qualified for use in Reusable Solid Rocket Motor (RSRM) nozzle joint-2. Modeled geometry for flow calculations considers the joint to be vented with the porous CFR barriers placed in the 'open' assembly gap. Model development is based on a 1-D volume filling approach where flow resistances (assembly gap and CFRs) are defined by serially connected internal flow and the porous media 'Darcy' relationships. Combustion gas flow rates are computed using the volume filling code by assuming a lumped distribution total joint fill volume on a per linear circumferential inch basis. Gas compressibility, friction and heat transfer are included in the modeling. Gas-to-wall heat transfer is simulated by concurrent solution of the compressible flow equations and a large thermal 2-D finite element (FE) conduction grid. The derived numerical technique loosely couples the FE conduction matrix with the compressible gas flow equations. Free constants that appear in the governing equations are calibrated by parametric model comparison to hot fire subscale test results. The calibrated model is then used to make full-scale motor predictions using RSRM aft dome environments. Model results indicate that CFR thermal barrier systems will provide a thermally benign and controlled pressurization environment for the RSRM nozzle joint-2 primary seal activation.

Clayton, J. Louie↗
DOE OSTI2026

Robust calibration of non-perturbative models with history matching

We apply, for the first time, Bayes Linear Emulation and History Matching to the calibration of non-perturbative models in Monte Carlo event generators. In contrast to the usual approach of "Monte Carlo tuning", History Matching does not result in best-fit plus ellipsoidal parameter uncertainty estimates but instead identifies all parameter space regions that are consistent with data. This approach leads to a systematic and robust quantification of parametric uncertainties in the models, especially in those challenging cases where different, possibly disjoint, regions of parameter space deliver similar results, which are usually not properly treated with current methodology. We highlight the power of this method with the hadronisation models available through Sherpa : the built-in cluster fragmentation Ahadic and string fragmentation through an interface to Pythia .

Iskauskas, Andrew [Durham U. (main)] (ORCID:000000↗
DOE OSTI2025

Reactive Transport Modeling of Hydrogen Production from Serpentinization of Olivine

Hydrogen production from serpentinization of ultramafic rocks represents a promising natural pathway for generating carbon-free energy, yet its kinetics and controlling factors remain incompletely understood. A key challenge in advancing serpentinization research lies in the heterogeneity of porosity and permeability in rocks, which leads to nonuniform fluid velocity fields, as well as uncertainties in estimating reactive surface area and identifying appropriate mineral reaction equilibria. Additional complexities arise from the role of dissolved SiO 2 , Fe 2+ /Fe 3+ partitioning, and the limited effect of pH variations within the strongly alkaline regime on hydrogen yields. These challenges hinder straightforward extrapolation from laboratory tests to practical applications of hydrogen production from natural rocks. Here, in this work, we address these questions using a simulation-based reactive transport modeling framework calibrated against controlled laboratory experiments reported elsewhere. The model couples geochemical kinetics, multiphase flow, and mineralogical feedbacks, enabling systematic evaluation of how surface area, dissolved silica concentration, Fe redox state, temperature, and pressure govern serpentinization and H2 generation. We find that surface area exerts the strongest control on reaction rates and hydrogen yields, while Fe 2+ /Fe 3+ ratios act as secondary modulators. Elevated dissolved silica concentrations suppress hydrogen production but accelerate serpentine precipitation, whereas increasing pH beyond 12 within the strongly alkaline regime produces only marginal gains. Finally, we demonstrate that integrating targeted experiments with calibrated simulations offers a powerful and efficient approach for predicting hydrogen yields and assessing parameter trade-offs in industrial-scale applications. This integration can substantially reduce the experimental burden while improving predictive capability, thereby enhancing both the mechanistic understanding and the practical feasibility of hydrogen production from serpentinization.

08 HYDROGEN↗
DOE OSTI2025

Bayesian calibration of irradiated graphite property models under high temperatures

Graphite under high temperatures and irradiation is central to advanced reactors. We develop a Bayesian calibration framework for graphite property models that explicitly represents model-data mismatch via a Gaussian-process discrepancy. The approach propagates uncertainty from parameters, experimental noise, and model form, with a hierarchical variance structure to capture group and cross-group noise. Using two predictive models across five grades (IG-110, NBG-18, PCEA, NBG-17, 2114) and four properties-irradiation-induced dimension change, creep, Young’s modulus change ratio, and coefficient of thermal expansion change ratio-we obtain average predictive-error reductions of 54%, 65%, 17%, and 17% when discrepancy is included. We illustrate engineering impact with a multiphysics model of a very-high-temperature reactor prismatic reflector brick, analyzing stresses under high fluence and temperature. Accounting for model discrepancy markedly improves predictive accuracy and provides a robust basis for reliable graphite component design in advanced reactors.

36 - MATERIALS SCIENCE↗
NASA NTRS1980

Thermal Pollution Mathematical Model: Verification of One-Dimensional Numerical Model at Lake Keowee - Volume 2

A one dimensional model for studying the thermal dynamics of cooling lakes was developed and verified. The model is essentially a set of partial differential equations which are solved by finite difference methods. The model includes the effects of variation of area with depth, surface heating due to solar radiation absorbed at the upper layer, and internal heating due to the transmission of solar radiation to the sub-surface layers. The exchange of mechanical energy between the lake and the atmosphere is included through the coupling of thermal diffusivity and wind speed. The effects of discharge and intake by power plants are also included. The numerical model was calibrated by applying it to Cayuga Lake. The model was then verified through a long term simulation using Lake Keowee data base. The comparison between measured and predicted vertical temperature profiles for the nine years is good. The physical limnology of Lake Keowee is presented through a set of graphical representations of the measured data base.

Lee, S. S.↗