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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Comprehensive Review and Qualitative Analysis of Micro-Combined Heat and Power Modeling Approaches

Concurrent production of electrical and thermal energy from a Combined Heat and Power (CHP) device is an attractive tool to address the growing energy needs of the planet. Micro CHP (µCHP) systems can reduce a building’s primary energy consumption, reduce carbon footprint, and enhance resiliency. Modeling of the µCHP helps understand the system from multiple perspectives and helps discover errors earlier, improves impact analysis and simulation of system solutions for ease of integration with the building. Consequently, there is a need for analysis of the impact of µCHP modeling approach on its reliability and flexibility. The primary objective of this paper is to review the state-of-the art models in the µCHP space with a focus towards internal combustion engine as the primary mover (PM) and limit the study to system modeling, calibration, and validation methodologies. Based on the analysis, recommendations for further model considerations and refinements are presented.

42 ENGINEERING↗
DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Calibration of Predictor Models Using Multiple Validation Experiments

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

Crespo, Luis G.↗
DOE OSTI2011

Bio-energy feedstock yields and their water quality benefits in Mississippi

Cellulosic and agricultural bio-energy crops can, under careful management, be harvested as feedstock for bio-fuels production and provide environmental benefits. However, it is required to quantify their relative advantages in feedstock production and water quality. The primary objective of this research was to evaluate potential feedstock yield and water quality benefit scenarios of bioenergy crops: Miscanthus (Miscanthus-giganteus), Switchgrass (Panicum virgatum), Johnsongrass (Sorghum halepense), Alfalfa (Medicago sativa L.), Soybean {Glycine max (L.) Merr.}, and Corn (Lea mays) in the Upper Pearl River watershed (UPRW), Mississippi using a Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated (January 1981 to December 1994) and validated (January 1995 to September 2008) using monthly measured stream flow data. The calibrated and validated model determined good to very good performance for stream flow prediction (R2 and E from 0.60 to 0.86). The RMSE values (from 14 m3 s-1 to 37 m3 s-1) were estimated at similar levels of errors during model calibration and validation. The long-term average annual potential feedstock yield as an alternative energy source was determined the greatest when growing Miscanthus grass (373,849 Mg) as followed by Alfalfa (206,077 Mg), Switchgrass (132,077 Mg), Johnsongrass (47,576 Mg), Soybean (37,814 Mg), and Corn (22,069 Mg) in the pastureland and cropland of the watershed. Model results determined that average annual sediment yield from the Miscanthus grass scenario determined the least (1.16 Mg/ha) and corn scenario the greatest (12.04 Mg/ha). The SWAT model simulated results suggested that growing Miscanthus grass in the UPRW would have the greatest potential feedstock yield and water quality benefits.

Biofuels↗
DOE OSTI2019

Improving Representation of Deforestation Effects on Evapotranspiration in the E3SM Land Model

Abstract Evapotranspiration (ET) plays an important role in land‐atmosphere coupling of energy, water, and carbon cycles. Following deforestation, ET is typically observed to decrease substantially as a consequence of decreases in leaf area and roots and increases in runoff. Changes in ET (latent heat flux) revise the surface energy and water budgets, which further affects large‐scale atmospheric dynamics and feeds back positively or negatively to long‐term forest sustainability. In this study, we used observations from a recent synthesis of 29 pairs of adjacent intact and deforested FLUXNET sites to improve model parameterization of stomatal characteristics, photosynthesis, and soil water dynamics in version 1 of the Energy Exascale Earth System Model (E3SM) Land Model (ELMv1). We found that default ELMv1 predicts an increase in ET after deforestation, likely leading to incorrect estimates of the effects of deforestation on land‐atmosphere coupling. The calibrated model accurately represented the FLUXNET observed deforestation effects on ET. Importantly, the search for global optimal parameters converged at values consistent with recent observational syntheses, confirming the reliability of the calibrated physical parameters. Applying this improved model parameterization to the globe scale reduced the bias of annual ET simulation by up to ~600 mm/year. Analysis on the roles of parameters suggested that future model development to improve ET simulation should focus on stomatal resistance and soil water‐related parameterizations. Finally, our predicted differences in seasonal ET changes from deforestation are large enough to substantially affect land‐atmosphere coupling and should be considered in such studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

WHA Sphere Impacting RHA Plate: Modeling Brittle Behavior in Metal Alloys

Brittle behavior of metal alloys is often critical to modeling ballistic impact and penetration. The ALEGRA multiphysics finite element software incorporates calibrated models for the equation of state, elasticity, yield stress, plasticity and fracture, but simulations do not always capture expected metal fracture. Here we report concerted efforts to do so for one important case where experiments clearly show shear fractures: a tungsten sphere impacting a steel plate at various angles. Our best simulations show fractures that are qualitatively similar to experiments, but there are significant differences in quantitative metrics. Specifically, velocities of tracers used to quantify simulated plug parameters consistently fall short of measured plug velocities. Also, simulated plugs break apart more than expected from experimental evidence. We attribute these shortfalls to the lack of an explicit shear fracture mechanism in the material models, leading to over-estimated resistance to plug formation and movement.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Geothermal Heat Pump System Showcase: Short-Term Validation of Borehole Heat Exchanger Performance from Field Data to Numerical Modeling: Preprint

Since 2011, a geothermal heat pump (GHP) system has been operating to provide space heating and cooling for the Solar Radiation and Research Laboratory building at the National Laboratory of the Rockies (NLR) in Golden, Colorado. The system consists of 23 vertical boreholes, each extending to a depth of 300 ft (91 m), connected to 11 water-to-air heat pump units and four circulation pumps. Between fiscal years 2023 and 2025, additional power meters and temperature sensors were retrofitted to support detailed system performance assessment and model development. This study presents preliminary monitoring results and the development of an initial numerical model of the borehole heat exchanger field. The model incorporated site-specific geometry, ground thermal properties derived from thermal response tests, and ambient temperatures, and simulated system behavior over a representative operating day in September. Model predictions of outlet temperatures were compared against corresponding field measurements. Results showed that modeling initialized with a simplified linear subsurface temperature gradient presents systematic discrepancies in outlet temperature, whereas incorporating depth-resolved borehole temperature measurements for initialization yields substantially improved agreement with observations. The findings highlight the sensitivity of short-term predictive modeling to the representation of initial subsurface thermal conditions and underscore the value of high-resolution field measurements for model calibration and validation. These preliminary results inform ongoing efforts to extend the modeling framework to longer time horizons and to refine monitoring and modeling strategies that support the design guidance and operational optimization of GHP systems in research and commercial buildings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Model Independent Approach to the Single Photoelectron Calibration of Photomultiplier Tubes

The accurate calibration of photomultiplier tubes is critical in a wide variety of applications for which it is necessary to know the absolute number of detected photons or precisely determine the resolution of the signal. Conventional calibration methods rely on fitting the photomultiplier response to a low intensity light source with analytical approximations to the single photoelectron distribution. We show that this approach often leads to biased estimates due to an inability to model the full distribution accurately, especially at low charge values. We present a simple statistical method to extract the relevant single photoelectron calibration parameters (first two central moments) without making any assumptions about the underlying single photoelectron distribution. We illustrate the use of this method through the calibration of a Hamamatsu R11410 photomultiplier tube and study the accuracy and precision of the method using Monte Carlo simulations. The method is found to have significantly reduced bias compared to conventional methods and works under a wide range of light intensities, making it suitable for the simultaneous calibration of large arrays of photomultiplier tubes where uniform illumination may not be possible.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Modeling gas migration through clay-based buffer material using coupled multiphase fluid flow and geomechanics with stress-dependent gas permeability

A model for gas migration through clay-based buffer material is developed for modeling gas generation and migration associated with deep geologic nuclear waste disposal. The model is based on a multiphase fluid flow and geomechanics simulator that is adapted to consider enhanced gas flow when gas pressure is high enough to approach the confining stress magnitude. A key feature in the model is a direct coupling between gas permeability and stress, through a non-linear stress-dependent permeability function. The model was first tested and calibrated by modelling two different laboratory gas migration tests on Wyoming (MX-80) bentonite samples. The calibrated model was then applied to model gas migration through a bentonite buffer of a large-scale gas injection test (Lasgit) conducted at the Äspö Hard Rock Laboratory in Sweden. Observed preferential gas migration along interfaces (between compacted blocks and along the canister surface) required explicit representation of such interfaces in the model. The model with the stress-dependent gas permeability accurately captured observed experimental responses in terms of gas breakthrough time, peak gas pressure, and cumulative gas flow rates. The calibrated model was finally applied to simulate migration of hydrogen gas generated within a breached nuclear waste canister over 10,000 years, involving migration of much larger gas volumes. For the considered gas generation rate and host rock properties, the generated gas could migrate through the bentonite buffer and released into the surrounding host rock at a maximum gas pressure somewhat higher than the initial total stress, though a significant amount of hydrogen remained within the buffer. This modelling sets the stage for further detailed analysis of the impact of hydrogen gas generation on the long-term performance of nuclear waste repositories.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1999

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Tripp, John S.↗
DOE OSTI2019

Predictions of Yield Strength Evolution Due to Physical Aging of 828 DGEBA/DEA using the Simplified Potential Energy Clock Model

The capability of the Simplified Potential Energy Clock Model (SPEC) to represent the uniaxial compression yield strength evolution under isothermal aging conditions is evaluated for a widely used epoxy thermoset encapsulation material, 828 DGEBA/DEA. A baseline model calibration is used. We note that this calibration did not consider yield strength behavior close to the glass transition temperature ( T g ), but in this work, the model is exercised in this temperature range to evaluate the ability to predict changes in material response with aging as equilibrium is approached. Some model alterations were needed to remove negative Prony weights in the thermal and bulk relaxation function ( f 1 ), which is chiefly responsible for aging in this analysis, but otherwise, the model was not altered. Model predictions of yield stress evolution are quantitatively different compared with experiments, but the rate of change of yield stress with respect to aging time is in reasonable agreement with respect to experiments for the first 1000 hours of aging. After this aging time, the measured yield stress stops evolving, but the model continues to evolve for several more decades in time. Parametric studies and model alterations are considered to investigate how yield strength evolution predictions are affected by modeling choices. It is clear that the baseline calibration must be re-examined in order to represent the aging data quantitatively.

42 ENGINEERING↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris‐Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris-Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817

The source of the gravitational-wave (GW) signal GW170817, very likely a binary neutron star merger, was also observed electromagnetically, providing the first multi-messenger observations of this type. The two-week-long electromagnetic (EM) counterpart had a signature indicative of an r-process-induced optical transient known as a kilonova. This Letter examines how the mass of the dynamical ejecta can be estimated without a direct electromagnetic observation of the kilonova, using GW measurements and a phenomenological model calibrated to numerical simulations of mergers with dynamical ejecta. Specifically, we apply the model to the binary masses inferred from the GW measurements, and use the resulting mass of the dynamical ejecta to estimate its contribution (without the effects of wind ejecta) to the corresponding kilonova light curves from various models. The distributions of dynamical ejecta mass range between ${M}_{\mathrm{ej}}={10}^{-3}-{10}^{-2}\,{M}_{\odot }$ for various equations of state, assuming that the neutron stars are rotating slowly. In addition, we use our estimates of the dynamical ejecta mass and the neutron star merger rates inferred from GW170817 to constrain the contribution of events like this to the r-process element abundance in the Galaxy when ejecta mass from post-merger winds is neglected. We find that if ≳10% of the matter dynamically ejected from binary neutron star (BNS) mergers is converted to r-process elements, GW170817-like BNS mergers could fully account for the amount of r-process material observed in the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

A Modeling Study of Hydrodynamic Circulation in a Fjord of the Pacific Northwest

Increased eutrophication and degraded water quality in estuarine and coastal waters have been a worldwide environmental concern. While it is commonly accepted that anthropogenic impact plays a major role in many emerging water quality issues, natural conditions such as restricted water circulations controlled by geometry may also substantially contribute to unfavorable water quality in certain ecosystems. To elucidate the contributions from different factors, a hydrodynamic-water quality model that integrates both physical transport and pollutant loadings is particularly warranted. A preliminary modeling study using the Environmental Fluid Dynamic Code (EFDC) is conducted to investigate hydrodynamic circulation and low dissolved oxygen (DO) in Hood Canal, a representative fjord in the U.S. Pacific Northwest. Because the water quality modeling work is still ongoing, this paper focuses on the progress in hydrodynamic modeling component. The hydrodynamic model has been set up using the publicly available forcing data and was calibrated against field observations or NOAA predictions for tidal elevation, current, salinity and temperature. The calibrated model was also used to estimate physical transport timescales such as residence time in the estuary. The preliminary model results demonstrate that the EFDC Hood Canal model is capable of capturing the general circulation patterns in Hood Canal, including weak tidal current and strong vertical stratification. The long residence time (i.e., on the order of 100 days for the entire estuary) also indicates that restricted water circulation could contribute to low DO in the estuary and also makes the system especially susceptible to anthropogenic disturbance, such as excess nutrient input.

circulation↗
DOE OSTI2024

"Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date"

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

Ameli, Mostafa↗
NASA NTRS2023

Efficient Calibration of Expensive Computational Models

Accounting for uncertainty when calibrating expensive computational models is a common challenge faced by scientists and engineers. Often Bayesian techniques are adopted to estimate a probability density function over the model parameters given noisy empirical data. The methods used to perform this type of probabilistic calibration are computationally prohibitive in that they require a large number of evaluations of the expensive model. In these cases, surrogate modeling -- that is, using a fast-to-evaluate, lower fidelity stand-in for the original computational model -- may be the only option to alleviate this computational burden. However, the upfront cost of generating training data to build a surrogate model can itself be expensive. As such, it is important to be judicious when selecting training points at which the full-fidelity model is evaluated. Here, an active learning approach is proposed that enables efficient selection of training points using approximate samples of the calibrated parameter probability density function. In this way, the training points can be concentrated in regions where the calibration algorithm requires high model accuracy.

active learning↗