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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Experimental validation of a co-simulation architecture for modeling whole-building and detailed electrical distribution performance

This article presents an experimental validation of a co-simulation architecture for simultaneously modeling whole-building energy performance and detailed building electrical distribution system performance. The co-simulation architecture consists of a whole-building energy model (EnergyPlus®) embedded within a Modelica-based building electrical distribution system library called the Building Electrical Efficiency Analysis Model (BEEAM) using the Functional Mock-up Interface standard. We validate the model using experimental data collected at a full-scale test cell within Lawrence Berkeley National Laboratory’s FLEXLAB® facility. In conclusion, we show that the co-simulation model accurately predicts the electrical, mechanical, and thermal performance of the test cell for typical loads with both an AC and a DC electrical distribution topology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Low Activity Waste Glass Optimization with Property Models from Machine Learning, Part 2: Experimental Validation and Active Learning

The United States Department of Energy is responsible for managing legacy nuclear waste stored in underground tanks at the Hanford Site. To treat the waste, it is planned as the current baseline to separately vitrify low-activity waste (LAW) and high-level waste fractions. Previously, machine learning (ML) based glass property models (e.g., chemical durability, viscosity, electrical conductivity and SO3 solubility) were developed with prediction uncertainties. A waste glass optimization approach was then established to enable the capability of using these ML models in LAW glass formulation. In this study, the previous ML models were first experimentally validated, and the results were incorporated back into the database to update the ML models. The updated models and formulations showed increased waste loading while reducing the failure rate, demonstrating improved predictive accuracy, reduced uncertainties, and the effectiveness of active learning in guiding high-dimensional, nonlinear LAW glass design. This represents the first experimental validation of ML based LAW glass formulation, with practical benefits such as higher waste loading, shorter mission duration, and lower operational risk.

Lu, Xiaonan (ORCID:0000000179708148)↗
DOE OSTI2021

Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications

Animal experimentation is key in the evaluation of cardiac efficacy and safety of novel therapeutic compounds. However, interspecies differences in the mechanisms regulating excitation-contraction coupling can limit the translation of experimental findings from animal models to human physiology and undermine the assessment of drugs’ efficacy and safety. Here, we built a suite of translators for quantitatively mapping electrophysiological responses in ventricular myocytes across species. We trained these statistical operators using a broad dataset obtained by simulating populations of our biophysically detailed computational models of action potential and Ca 2+ transient in mouse, rabbit, and human. We then tested our translators against experimental data describing the response to stimuli, such as ion channel block, change in beating rate, and β-adrenergic challenge. We demonstrate that this approach is well suited to predicting the effects of perturbations across different species or experimental conditions and suggest its integration into mechanistic studies and drug development pipelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

An experimentally validated mesoscale model for the effective thermal conductivity of U-Zr fuels

An experimentally validated mesoscale model was developed to simulate the effective thermal conductivity (ETC) of metallic U and U–Zr nuclear fuels. The effects of microstructure, temperature, composition, and interfacial thermal resistance (ITR) were investigated. Companion experiments were conducted to validate the model. The numerical simulations clearly demonstrate that accounting for the interface (Kapitza) thermal resistance and Zr precipitation is necessary to improve the model predictions. The dependence of the effective Kapitza resistance of depleted-U and U-10Zr on temperature was determined. Finally, for both materials, the largest difference between the model calculations and the experimental data was about 4.5%, which is within the precision of the experimental measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

High-throughput native mass spectrometry as experimental validation for in silico drug design

In this project, we developed automated workflows for both experimental validation and computational prediction of protein-ligand interactions. The ultimate goal is to establish an integrated pipeline for high throughput design of inhibitors to enzymes relevant to all areas of biological research. Our experimental approach is based on native mass spectrometry (native MS), which measures accurate masses and quantify the relative abundance of protein-ligand complexes to define binding affinity. We set up an in-house built autosampler with highly flexible configurations to minimize the manual steps for high throughput native MS. In parallel, we also performed manual native MS to characterize the binding of substrates and inhibitors of SARS-Cov-2 nonstructural protein nsp10/16 in order to optimize the experimental parameters for future automation. On the computational side, we streamlined the pipeline to achieve minimal manual intervention for predicting enzyme inhibitors via simulation, using the same nsp10/16 system as an example. Using the native MS method we examined 8 top-ranked designed compounds, 2 of which showed weak binding of ~50 µM. The information from native MS experiment provided critical insights and the foundation for a fully integrated workflow for enzyme inhibitor design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High-fidelity wind farm simulation methodology with experimental validation

The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University’s National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.

17 WIND ENERGY↗
DOE OSTI2024

Destabilizing high-capacity high entropy hydrides via earth abundant substitutions: From predictions to experimental validation

The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, in this study, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb) 75 Cr 25-x Fe x system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design, Analysis, Comparison, and Experimental Validation of Insulated Metal Substrates for High-Power Wide-Bandgap Power Modules

Direct bonded copper (DBC) substrates used in power modules have limited heat spreading and manufacturing capability due to ceramic properties and manufacturing technology. The ceramic and copper bonding is also subject to high mechanical stress due to coefficient of thermal expansion mismatch between the copper and the ceramic. For wide-bandgap (WBG) devices, it is of interest exploring new substrate technologies that can overcome some of the challenges of direct bonded copper substrates. In this technical paper, the design, analysis, and comparison of insulated metal substrates (IMSs) for high-power wide-bandgap semiconductor-based power modules are discussed. This paper starts with technical description and discussion of state-of-the-art DBC substrates with different ceramic insulators such as aluminum nitride (AlN), Al 2 O 3 , and Si 3 N 4 . Next, an introduction of IMSs and their material properties, and a design approach for SiC (silicon carbide) metal-oxide-semiconductor field-effect transistor (MOSFET)-based power modules for high-power applications is provided. The influence of dielectric thickness on the power handling capability of the substrate are also discussed. The designed IMS and DBC substrates were characterized in terms of steady-state and transient thermal performance using finite element simulation. Finally, the performance of the IMS and DBC are validated in an experimental setup under different loading and cooling temperature conditions. We report the simulation and experimental results showed that the IMS can provide high steady-state thermal performance for high-power modules based on SiC MOSFETs. Furthermore, the IMS provided enhanced transient thermal performance, which provided a reduced junction temperature when the module is operated at low fundamental output frequencies in traction drive systems.

42 ENGINEERING↗
DOE OSTI2024

Optimization and Experimental Validation of Annular Finned PCM-HX for a Domestic Hot Water Heater Application

The load profile for domestic water heating is time-dependent and can result in high energy demand during peak operating times. Shifting this peak load can have significant environmental and economic impacts. Phase change material (PCM)-based thermal energy storage (TES) is a potentially useful technology for peak load shifting in domestic hot water (DHW) applications thanks to its high latent heat and energy density. In this study, an annular finned-tube PCM-HX design concept was optimized for a load-shifting TES unit to meet the Department of Energy standard for a medium-usage DHW heater using a resistance-capacitance model (RCM) integrated with a Multi-Objective Genetic Algorithm. The optimized design comprised 70 identical annular finned-tube PCM-HX units connected in parallel and utilizing RT62HC as the PCM. A single PCM-HX unit was prototyped and tested in a vertically oriented setup with upward heat transfer fluid (HTF) flow. The hot water supply time was defined based on a cutoff temperature of 51.7°C. The as-designed mass flow rate (1.5 g/s) was tested to assess the performance of the prototyped PCM-HX unit for RCM validation. For the experimental investigation, RTD sensor bundles measured HTF temperature at the PCM-HX inlet and outlet, and a Coriolis flow meter accurately measured the HTF mass flow rate. The simulated discharging power underpredicted the experimental result by about 12%, and the simulated hot water supply time underpredicted the experimental result by approximately 13% for the as-designed mass flow rate (1.5 g/s). The average deviation of the hot water supply temperature between the experimental and RCM results during the complete PCM solidification process was 1.3 K for the as-designed mass flow rate. The overall good agreement between the experimental and RCM results provides confidence that computationally efficient models such as RCM can be utilized for design optimization of PCM-HXs.

42 ENGINEERING↗
DOE OSTI2026

Second-order wave excitation forces in WEC-Sim/MOST: Implementation, experimental validation, and code-to-code comparison

Accurate prediction of second-order hydrodynamic loads is essential for floating bodies, including floating offshore wind turbines, wave energy converters, and hybrid wind–wave platforms. These nonlinear effects, arising from both sum- and difference-frequency forcing, are critical for capturing key response characteristics but remain challenging to model efficiently. In this work, we extend the open-source Wave Energy Converter Simulator / MATLAB for Offshore Simulation Tool by implementing second-order wave excitation forces, supporting both the full Quadratic Transfer Function formulation and the Newman approximation. The full Quadratic Transfer Function method is used for all code-to-code comparisons and experimental validation, while the Newman approximation is provided as a computationally lighter alternative. To benchmark the new capability, we perform a code-to-code comparison with OpenFAST and OrcaFlex. We then validate the enhanced model using wave-tank measurements of a 1:96 scale DeepCwind semi-submersible, showing that second-order effects are required to reproduce platform motions. The implementation employs a computationally efficient pre-computation strategy for second-order wave excitation forces, reducing simulation cost while maintaining engineering accuracy. Overall, this work advances the tool as an open-source and versatile tool for modelling floating offshore renewable-energy systems requiring second-order hydrodynamic fidelity.

17 WIND ENERGY↗
DOE OSTI2022

Multi-scale and multi-physics analysis, design optimization, and experimental validation of heat exchangers utilizing high performance, non-round tubes

Air-to-refrigerant heat exchangers (HXs) are fundamental components in HVAC&R systems, and considerable research has been dedicated designing continually smaller, lighter, and more efficient HX designs. In recent years, researchers have leveraged advancements in Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and optimization algorithms to consider primary tube shape and topology optimization to design highly compact, high performance HXs for a multitude of applications. In this research, we present a computationally efficient, comprehensive, multi-scale, and multi-physics analysis and optimization method for air-to-refrigerant HXs featuring automated CFD and FEA simulations and approximation-assisted optimization. This methodology was utilized to design HXs with shape-optimized, non-round tubes which outperform current state-of-the-art tube-fin HXs without compromising structural integrity. The optimal HXs were shown to deliver similar thermal performance to the baseline HXs while also achieving more than 20% reductions in airside pressure drop and core envelope volume and more than 30% reduction in internal volume. Comprehensive experimental validation of the optimization methodology was conducted through the testing of two prototypes in a standardized wind tunnel facility under multiple operating conditions. For prototype #1 under dry evaporator conditions, the predicted heat load agreed within ± 10% of measured values and the predicted airside pressure drop agreed within ± 30%, while for dehumidifying conditions, the predicted sensible and latent heat loads agreed within ± 10% and ± 20% of the measured values, respectively. For prototype #2, the predicted condenser heat load agreed within ± 3.0% of measured values, and the predicted airside pressure drop agreed within ± 27%. The acceptable agreement between simulation and experimental results for the present application highlights the flexibility of the novel optimization methodology to design next generation HXs with improved performance and reduced volume, weight, and environmental impact.

42 ENGINEERING↗
DOE OSTI2021

Experimental Validation of Subsystem Models for a Novel Variable Displacement Hydraulic Motor

A novel, variable displacement, low-speed high-torque hydraulic motor is being developed that is expected to be highly efficient across a broad operating range. To ensure the final hardware achieves the expected performance, the models used in the development of the motor must be experimentally validated and revised, as necessary. The specific focus in this work is on mechanical energy loss models that were used to guide the design of a single-cylinder motor prototype and on experimental tests used for model validation. Ideally each model, whether friction loss in a piston/cylinder interface or energy loss due to leakage in a valve , would be individually validated by an independent test. This granular approach would remove any question of where the error lies and result in highly accurate models. However, many motor components have multiple forms of energy loss, creating difficulty in validating individual losses. Additionally, it is not physically realizable to divide many components into an individual model equivalent test. Testing the motor as a full assembly is possible, but pinpointing the source of discrepancies between the model and the hardware becomes difficult with dozens of models potentially being partially responsible. A compromise was found by separating the motor into functional component groups that are characterized by the type of loss and ability to test each subcomponent independently. By checking for correlation between test observations and model predictions, revisions could be implemented into the models. This allows future solutions to be more accurately predicted in the design phase to drive the design of better machines.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Experimental validation of a collision-radiation dataset for molecular hydrogen in plasmas

Quantitative spectroscopy of molecular hydrogen has generated substantial demand, leading to the accumulation of diverse elementary process data encompassing radiative transitions, electron-impact transitions, predissociations, and quenching. However, their rates currently available are still sparse, and there are inconsistencies among those proposed by different authors. In this study, we demonstrate an experimental validation of such a molecular dataset by composing a collisional-radiative model (CRM) for molecular hydrogen and comparing experimentally obtained vibronic populations across multiple levels. From the population kinetics of molecular hydrogen, the importance of each elementary process in various parameter space is studied. In low-density plasmas (electron density ne≲1017 m−3) the excitation rates from the ground states and radiative decay rates, both of which have been reported previously, determine the excited state population. The inconsistency in the excitation rates affects the population distribution the most significantly in this parameter space. However, in higher density plasmas (ne≳1018 m−3), the excitation rates from excited states become important, which have never been reported in the literature, and may need to be approximated in some way. In order to validate these molecular datasets and approximated rates, we carried out experimental observations for two different hydrogen plasmas; a low-density radio frequency heated plasma (ne≈1016 m−3) and the Large Helical Device (LHD) divertor plasma (ne≳1018 m−3). The visible emission lines from EF1Σg+, HH¯1Σg+, D1Πu±, GK1Σg+, I1Πg±, J1Δg±, h3Σg+, e3Σu+, d3Πu±,g3Σg+, i3Πg±, and j3Δg± states were observed simultaneously and their population distributions were obtained from their intensities. We compared the observed population distributions with the CRM prediction, in particular the CRM with the rates compiled by Janev et al., Miles et al., and those calculated with the molecular convergent close-coupling (MCCC) method. The MCCC prediction gives the best agreement with the experiment, particularly for the emission from the low-density plasma. However, the population distribution in the LHD divertor shows a worse agreement with the CRM than those from low-density plasma, indicating the necessity of the precise excitation rates from excited states. We also found that the rates for the electron attachment is inconsistent with experimental results. This requires further investigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Experimental Validation of a Module Cell Cracking Model

The What's Cracking app can predict how changes in crystalline silicon photovoltaic (PV) module materials, design, and mounting affect its susceptibility for cell fracture under uniform loading. This work has experimentally validated the app. A set of commercial crystalline silicon PV modules was obtained for this study. The modules were uniformly loaded at three different mounting points, and their subsequent cell fractures were recorded. A large sample size allowed for the development of an experimental statistical model for cell fracture. Here, the comparison of the experiment to predictions from the app is in excellent agreement. Both experimental and modeling results also elucidate how moving the module mounting points toward the center of the module increases the probability of cell fracture.

14 SOLAR ENERGY↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental Validation of a Kinetic Ballooning Mode in High-Performance High-Bootstrap Current Fraction Fusion Plasmas

We report the observation of a set of coherent high frequency electromagnetic fluctuations that leads to a turbulence induced self-regulating phenomenon in the DIII-D high bootstrap current fraction plasma. The fluctuations have frequency of 130~220kHz, the poloidal wave length and phase velocity are 16~30 m -1 and ~30 km/s, respectively in the outboard midplane with the estimated toroidal mode number n~5- 9. The fluctuations are located in the internal transport barrier (ITB) region at large radius and are experimentally validated to be kinetic ballooning modes (KBM). Furthermore, quasilinear estimation predicts the KBM to be able to drive experimental particle flux and non-negligible thermal flux, suggesting its significant role in regulating the ITB saturation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗