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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep Learning-Based Failure Prognostic Model for PV Inverter Using Field Measurements

Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.

42 ENGINEERING↗
DOE OSTI2020

EMPIRICAL VALIDATION OF MULTI-ZONE BUILDING AND HVAC SYSTEM MODELS UNDER UNCERTAINTY

This study implemented a framework of empirical validation of building energy models under uncertainty to a set of controlled experiments that aim to validate multi-zone building and HVAC system models. Energy models were created through iterative acquisitions of information and data and uses measurement data from various types of sensors as both inputs and as observations to validate predictions. Experimental and modeling uncertainties were quantified and propagated accordingly, and probabilistic accuracy metrics were used to evaluate the agreement between model predictions and observations under uncertainty. Sensitivity analysis was performed to identify the most influential uncertainties that will be prioritized to be addressed in the next steps. Current results of two cooling tests show an overall good agreement between predictions and observations on a set of HVAC system outputs despite considerable and influential uncertainty in DX cooling coil COP. Agreements on zone-level responses vary notably among individual rooms, likely because of significant uncertainties in room radiation heat gain and system supply air.

Li, Qi↗
DOE OSTI2026

Impact of light output on the timing resolution of organic glass scintillator bars in a dual-ended readout configuration

The effectiveness of detector system modeling and validation depends on the quality of characterization performed on the system. For nuclear nonproliferation applications, which require detectors to address complex and variable field conditions, access to accurate system models is essential. This study presents the timing characterization of organic glass scintillator bars used in an imaging system developed at the University of Michigan. 137 Cs and 60 Co gamma-ray sources were used to determine the coincidence time resolution as a function of measured light output for two Compton scatter events between two organic glass scintillator bars. Timing resolution for two events, each with light output ranging from 100 to 1100 keVee were characterized. The resulting data were fit to a parametric function that was validated to agree with experimental results within ±25 ps. Simulations were performed to establish appropriate calibration points for each organic scintillator, thereby ensuring accurate calibration of light output values during analysis. This work successfully characterized the light output dependence of the coincidence timing resolution of a pair of organic glass detectors for use in an imaging system. Depending on the amount of scintillation light output from the events, the full width at half maximum of the measured coincidence time resolution ranged from 653.2 ± 16.1 ps for low light yields, 100 keVee, to 121.0 ± 8.4 ps for higher yields at 1100 keVee. The insights obtained from the timing resolution behavior will allow for accurate simulation capabilities in future security and verification efforts using scatter-based imaging systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

From Fundamental Interfacial Reaction Kinetics to Macroscopic Current–Voltage Characteristics: Case Study of Solid Acid Fuel Cell Limitations and Possibilities

The unique properties of solid acid electrolytes, in particular CsH 2 PO 4 , are in many ways ideal for fuel cell operation. However, the technology is constrained by high cathode overpotentials. Here a simplified cathode geometry is employed to obtain the fundamental electrochemical parameters (exchange current density and charge transfer coefficient) describing the oxygen reduction reaction (ORR) at the CsH 2 PO 4 -Pt-gas interface. The parameters are incorporated into a 1D model of the voltage–current characteristics of realistic SAFC cathodes, which reproduced the measured polarization behavior of such cathodes without recourse to fitting adjustable parameters. Following this validation, the model is utilized to evaluate the impact of changes to cathode properties, microstructure, and operating conditions. Of these, the charge transfer coefficient, measured to have a value of ≈0.6 for ORR on Pt in the SAFC cathode environment, is found to have the greatest impact on power output. Nevertheless, even without material modifications, a combination of microstructural and operational modifications are identified with projected performance metrics meeting Department of Energy targets (0.8 V at 300 mA cm –2 , and peak power density of 1 W cm –2 ), albeit at high Pt loadings. However, the analysis indicates that truly meaningful advances will likely necessitate the discovery of alternative ORR catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Gaussian Process-Based extended Goldak heat source model for finite element simulation of laser powder bed fusion additive manufacturing process

In this study, laser powder bed fusion (L-PBF) additive manufacturing (AM) is a key enabling technology to manufacture highly complex and integrated metallic structures. In L-PBF AM process, the melting of the metal powders and the layers underneath can be governed by either “conduction mode” or “keyhole mode”, with the keyhole mode reportedly leading to porosity and decreased strength and ductility by many studies. In part scale simulations, finite element (FE) model is often used to study the temperature distribution during printing and to predict the residual stress, where a volumetric heat flux with a Gaussian or a double ellipsoidal (Goldak) distribution is often applied as the laser heat source. However, the above heat source models can only capture the melt pool shape in the conduction mode, and fail to capture the transition to keyhole melting mode when the process parameters change. To overcome this inaccuracy, an extended Goldak heat source model is proposed by introducing a laser penetration term as a function of laser parameters obtained from a Gaussian-Process (GP) model. The model is validated by “2D pad” AlSi10Mg L-PBF experiments under a wide range of laser power, scan speed, and laser focus offset, and the results show the model successfully captures the measured melt pool shape in all conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A mean field homogenization model for the mechanical response of ceramic matrix composites

SiC/SiC composites offer exceptional mechanical stability at high temperatures and under irradiation. These ceramic matrix composites are therefore strong candidate materials for future nuclear energy applications. Their mechanical response, which exhibits pseudo-plasticity, is mediated by matrix cracking, fiber debonding, and fiber pull-out due to slip. Here, this study introduces a mechanistic model for the behavior of unidirectionally reinforced SiC/SiC composites. Specifically a mean field homogenization approach is proposed to account for all deformation and degradation modes during mechanical deformation. The homogenization scheme relies on a Mori Tanaka method that is extended to consider the effects of the coating’s elasto-plastic response on the development of micromechanical fields. Further, the model proposed introduces a method to effectively account for the role of localized damage (i.e., cracks) on mechanical fields within both the fiber and the matrix. Upon validating the model against experimental data, the roles of interface sliding, coating dimensions and intrinsic elastic response, as well as of microstructure (e.g. porosity, fiber volume fraction) are discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Analytical simulation of meander morphology from equilibrium to long-term evolution: Impacts of channel geometry and vegetation-induced coarsening

This study introduces an innovative approach to modeling meandering river morphology, integrating and investigating the effects of geometric characteristics and vegetation-induced channel coarsening. The developed comprehensive framework combines several advanced techniques: Genetic Programming for refining the scour factor of transverse bed slope, a Leaf Area Index (LAI)-enhanced analytical model for quantifying vegetative flow resistance, and an upstream-weighted moving average method for efficient approximation of the convolution integral in meander migration calculations. The model is validated against both an idealized Kinoshita meander and a natural bend of the Tumen River (China) in equilibrium, demonstrating its robustness across diverse scales and conditions. The model's ability to simulate the long-term evolution, including cutoff events, provides valuable insight for river management strategies. The current findings demonstrate that channel geometry, particularly width-to-depth ratio, plays a dominant role in meander evolution, with wider channels prone to more complex and rapid morphological changes. Vegetation effects are most pronounced in channels with moderate width-to-depth ratios, where they can significantly influence migration rates and bed topography. A combination of channel widening and deepening, coupled with strategic vegetation management, can effectively enhance navigability while maintaining channel stability in the studied Tumen River reach. Sensitivity analyses highlight the complex interplay between hydraulic conditions, sediment characteristics, and vegetation in shaping river morphology. This research advances understanding of the multifaceted nature of meandering river systems and offers practical tools for informed decision-making in river engineering and environmental management, particularly in the context of climate change and increasing anthropogenic pressures on fluvial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Scalable simulation of coupled adsorption and transport of methane in confined complex porous media with density preconditioning

The growing significance of shales and tight formations in the transition to less carbon-intensive and clean energy drives the research endeavor to understand the physics of gas flow within these systems. However, shales are composed of massively heterogeneous physical and chemical features. Most nano-sized pores connect to millimeter-scale fractures, leading to multiscale transport. These nano-scale pore throats demonstrate non-classical flow behavior, such as non-negligible slip velocities and adsorbed gas layers at the boundary. As a result, classical computational fluid dynamics models do not capture the physics. In this work, we develop a coupling scheme for the multiple-relaxation-time (MRT) lattice Boltzmann (LB) method that integrates the Peng-Robinson equation of state into a pseudo-potential interaction model to capture the physics of methane flow in irregular networks of channels that represent nano-scale porous media. We use atomistic simulations to calibrate and validate our model in slit nano-channels. We propose a preconditioning scheme to initialize the coupled transport and adsorption simulation of methane in complex porous media. The results of this implementation of LB agree with Direct Simulation Monte Carlo (DSMC) and Molecular Dynamics (MD) simulations. We then scale up the LB implementation through vectorization and indirect addressing. We parallelize it using Message Passing Interface (MPI) and OpenMP frameworks to simulate transport and adsorption in complex media with a million lattices. Additionally, we analyze the differences between coupled and transport-only simulations in two case studies and show that considering phase behavior, i.e., adsorption, can significantly change the flow behavior. This work constitutes an important step towards bridging the gap between molecular flow and system-scale behavior of complex disordered porous media.

42 ENGINEERING↗
DOE OSTI2025

A Full-Cell Model for Direct Toluene Electro-Hydrogenation Electrolysis

Liquid organic hydrogen carriers (LOHCs) are organic molecules that undergo a hydrogenation/dehydrogenation cycle to enable storage and transportation of hydrogen fuel under ambient conditions. One promising LOHC candidate is toluene, which can be converted to methylcyclohexane (MCH) electrochemically, enabling a decarbonized process when green electricity is used. In this study, we developed a full-cell model for the direct electro-hydrogenation of toluene to MCH, utilizing a zero-gap membrane electrode assembly architecture. The model incorporates electrochemical kinetics, ionic transport, water transport across the membrane, and mass transport effects. Electrochemical kinetics are characterized using Tafel analysis on Pt/Ru catalyst. The model is validated against experimental data, including polarization curves, Faradaic efficiencies, and water crossover. A voltage breakdown analysis shows that the performance is dominated by kinetic losses, and the model is used to carry out a comparison of different toluene electro-hydrogenation reaction catalysts. Finally, a sensitivity analysis is conducted on key design parameters illustrating which can be modified to maximize electrolyzer performance. The cathode specific surface area and cathode porous transport layer thickness (PTL) have the largest impact on the current density, while the PTL thickness and Pt loading in the PTL have the largest impact on Faradaic efficiency.

Ehlinger, Victoria M. [Lawrence Livermore National↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Enhancement of effective thermal conductivity of rGO/Mg nanocomposite packed beds

Engineering thermophysical properties of metal hydrides nanocomposites is crucial for effective thermal management during hydrogen storage reactions; however, the effect of microstructure on thermal transport mechanisms is still unclear. Here, we employed an integrated experiment-modeling approach to investigate microstructural factors that determine the effective thermal conductivity of individual reduced graphene oxide-magnesium (rGO/Mg) nanocomposites and their packed bed. Here the effective thermal conductivity of the rGO/Mg nanocomposite packed bed was measured by using guarded hot-plate method under various atmospheric conditions (i.e., vacuum, Ar and He). A microstructure-aware mesoscopic modeling revealed that anisotropy of the effective thermal conductivity of individual rGO/Mg nanocomposites plays an important role in determining the effective thermal conductivity of their packed bed. The validated mesoscopic model also disclosed a nontrivial interplay between the intrinsic rGO properties and the extrinsic composite structural features. Finally, quantitative sensitivity analysis based on the modeling framework is used to provide practical engineering guidance for controlling the thermal transport within nanocomposite packed beds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling the Impacts of Hydrogen Extraction on Anaerobic Fermentation Systems Using a Modified ADM1 Model

Here, this paper presents a modified ADM1 (mADM1) model for use in assessing the influence of active dissolved H 2 extraction on biogas production from anaerobic fermentation reactors, specifically in the context of high-strength brewery wastewater as the feed. The modified model adds regulation factors to account for the varying product stoichiometry of glucose degradation reactions as a function of H 2 concentration in anaerobic digestion, and incorporates the use of both lactate and ethanol as intermediates. The changes to the model are expected to better predict the effects of H 2 removal on the production of H 2 in fermentation processes. Both ADM1 and mADM1 were calibrated against experimental data from a fluidized-bed reactor containing encapsulated acidogenic-acetogenic microbial communities treating real brewery wastewater. The calibrated models were validated against another fluidized-bed reactor identical to the calibration reactor, with the addition of a hollow-fiber membrane module that was used to reduce dissolved H 2 concentrations in the validation reactor. It was found that ADM1 over-estimated the H 2 production rates of the validation reactor (117 mL/day simulated vs. 16.48 mL/day experimental), whereas mADM1 was able to successfully predict fermentation, and specifically the H 2 production rate (17 mL/day). The presented model is a first step towards a model that can predict the effects of dissolved H 2 removal on fermentation processes.

Anaerobic Digestion↗
DOE OSTI2023

Microkinetic insights into the role of catalyst and water activity on the nucleation, growth, and dissolution during COF-5 synthesis

The chemical pathway for synthesizing covalent organic frameworks (COFs) involves a complex medley of reaction sequences over a rippling energy landscape that cannot be adequately described using existing theories. Even with the development of state-of-the-art experimental and computational tools, identifying primary mechanisms of nucleation and growth of COFs remains elusive. Other than empirically, little is known about how the catalyst composition and water activity affect the kinetics of the reaction pathway. In this work, for the first time, we employ time-resolved in situ Fourier transform infrared spectroscopy (FT-IR) coupled with a six-parameter microkinetic model consisting of ~10 million reactions and over 20 000 species. The integrated approach elucidates previously unrecognized roles of catalyst pK a on COF yield and water on growth rate and size distribution. COF crystalline yield increases with decreasing pK a of the catalysts, whereas the effect of water is to reduce the growth rate of COF and broaden the size distribution. The microkinetic model reproduces the experimental data and quantitatively predicts the role of synthesis conditions such as temperature, catalyst, and precursor concentration on the nucleation and growth rates. Furthermore, the model also validates the second-order reaction mechanism of COF-5 and predicts the activation barriers for classical and non-classical growth of COF-5 crystals. The microkinetic model developed here is generalizable to different COFs and other multicomponent systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Understanding carbon sourcing and transport originating from the helicon antenna surfaces during high-power helicon discharge in DIII-D Tokamak

The high-power helicon wave system in the DIII-D tokamak could potentially introduce new plasma–material interaction (PMI) challenges owing to rectified RF sheath potentials that develop near the antenna and surrounding plasma-facing components. We present the first application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to helicon-induced PMIs, extending previous STRIPE studies of ICRH antennas by incorporating net erosion, local re-deposition, and three-dimensional global impurity transport. The integrated workflow couples SOLPS, COMSOL, RustBCA, GITR, and GITRm to simulate carbon erosion, re-deposition, and impurity transport for two experimentally constrained DIII-D H-mode helicon operating scenarios with different antenna–plasma gaps, coupled RF powers, and edge plasma conditions. COMSOL predicts rectified RF sheath potentials of 1–5 kV localized near the lower portion of the antenna, where the magnetic field intersects the surface at grazing incidence. Carbon self-sputtering dominates the erosion source, whereas RF-accelerated D + ions contribute approximately 1% of the total gross erosion. The smaller-gap operating scenario exhibits substantially stronger gross erosion, enhanced local re-deposition (∼12%), and a larger confined carbon inventory owing to increased plasma accessibility and broader RF sheath coverage. Comparison with available DIII-D measurements shows no distinct change in the global carbon signal that correlates with the helicon RF pulse, consistent with the simulations indicating that the helicon-generated carbon source remains small compared with the existing background carbon inventory under the present graphite-wall operating conditions. These results demonstrate the capability of STRIPE to integrate RF sheath modeling, plasma transport, surface interaction physics, and three-dimensional impurity transport for the interpretation of helicon-induced PMIs. The study further identifies the principal sources of modeling uncertainty, including grazing-angle RF sheath physics, slow-wave resolution, plasma-background extrapolation, and trace-impurity transport assumptions, providing a framework for future validation and model development.

Kumar, Atul [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2021

Impact of confined geometries on hopping and trapping of motile bacteria in porous media

We use a random walk particle-tracking (RWPT) approach to elucidate the impact of porous media confinement and cell-cell interactions on bacterial transport. The model employs stochastic alternating motility states consisting of hopping movement and trapping reorientation. The stochastic motility patterns are defined based on direct visualization of individual trajectory data. We validate our model against experimental data, at single-cell resolution, of bacterial E. coli motion in three-dimensional confined porous media. Results show that the model is able to efficiently simulate the spreading dynamics of motile bacteria as it captures the impact of cell-cell interaction and pore confinement, which marks the transition to a late-time subdiffusive regime. Furthermore, the model is able to qualitatively reproduce the observed directional persistence. Lastly, our RWPT model constitutes a meshless simple method which is easy to implement and does not invoke ad hoc assumptions but represents the basis for a multiscale approach to the study of bacterial dispersal in porous systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

elm-diagnostics

elm-diagnostics is a Python package for computing diagnostic analyses and visualizations for the E3SM Land Model (ELM) component and is meant to support new feature development in ELM. The tool reads model history files and performs quantitative analyses including budget-closure checking, variable transformations, temporal aggregations, and statistical summaries to support model evaluation, validation, and scientific interpretation. The framework is designed for extensibility, with modular architecture enabling straightforward addition of new diagnostic methods, derived variables, analysis types, visualization approaches, and model-specific adaptations

Hoffman, Matt [Los Alamos National Laboratory]↗