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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Development of Catalysts for Upgrading of Pyrolysis Vapor for Refinery Feedstocks and Intermediates (CRADA Final Report)

Catalytic fast pyrolysis (CFP) is a versatile technology platform to convert biomass into fungible hydrocarbon transportation fuels and chemical co-products. Key technical barriers to reaching this goal include increasing the product yields and achieving the desired fuel properties for gasoline, diesel, and jet range fuels or blendstocks that would be suitable for introduction into existing refinery unit operations. Overcoming these barriers will require durable catalysts that are effective at upgrading and stabilizing biomass pyrolysis vapors. Towards these goals, this CRADA leveraged NREL experience as a leader in biomass pyrolysis research and Johnson Matthey's (JM) experience as a leader in the production of advanced catalytic materials. The scope spanned CFP catalyst development, characterization, multi-scale reaction testing, and computational modeling. CRADA benefits to DOE, Participant, and U.S. Taxpayer: Assists laboratory in achieving programmatic scope, Uses the laboratory’s core competencies. The purpose of this CRADA was to develop and deploy catalysts for biomass CFP to help achieve cost-competitive biofuels and bio-based products. This was accomplished through a close collaboration between biomass conversion researchers at NREL and catalyst development researchers at JM. Summary of Research Results: Focus Area 1. Foundational research on catalytic conversion and deactivation: Key interactions between pyrolysis vapors and heterogeneous catalysts were probed through catalyst characterization, model compound reaction testing, and atomistic-scale computational modeling. Catalyst development focused on multifunctional materials, which include zeolites, oxides, carbides, and nitrides. Computational modeling identified reaction mechanisms and elucidated surface chemistry to test hypotheses regarding mechanisms of deoxygenation, coupling, cracking, dehydration, coke formation, hydrogen transfer, and aromatic ring reactions. This information was used to design multifunctional catalysts to increase product yields, control product selectivity, and reduce deactivation during CFP and downstream processing steps. The results served to increase fundamental understanding of key catalyst attributes and durability features for the upgrading of biomass pyrolysis vapors. Model compound experiments confirmed the importance of metal-acid bifunctionality for the deoxygenation of lignin-derived phenolic species under hydrodeoxygenation conditions. This insight led to the development of catalysts such as Pt/TiO2 and Mo2C, which were confirmed as high-performing materials during subsequent bench-scale experiments using biomass-derived pyrolysis vapors. This focus area also led to the identification of important catalyst deactivation mechanisms associated with the deposition of inorganic contaminants such as potassium. The molecular-level insight from model compound experiments and computational modeling, shown in Figure 1, informed the development of regeneration procedures that have been shown to be effective for restoration of > 90% of initial catalyst activity. This understanding has subsequently been translated to other catalyst systems, including zeolite materials that can be operated without requirements for co-fed hydrogen.

09 BIOMASS FUELS↗
DOE OSTI2025

Development of an FGPA-Based Cavity Simulator for Testing RF Controls

LLRF is used to precisely control the amplitude and phase of the RF field in cavities. Often times, access to test the control algorithms with RF equipment, especially in the presence of beam, is limited or beyond reach. In such cases, testing must be done through computer modeling or simulations. Computer modeling is often too slow and difficult to interface with the LLRF hardware. Analog or digital cavity simulators are preferred as they allow for interaction with the LLRF controls platform in real-time, and compared to their analog counterparts, FPGA-based digital cavity simulators allow for a more adjustable and sophisticated implementation. The newly developed FPGA-based cavity simulator includes the cavity electrical model, the cavity mechanical model including Lorentz Force Detuning and microphonics, an amplifier model which can simulate real amplifier nonlinearities, and a beam model. The simulator has been validated using measurements from BNL’s CeC 704 MHz 5-cell SRF cryomodule.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗
DOE OSTI2025

Modeling betatron radiation using particle-in-cell codes for plasma wakefield accelerator diagnostics

The analysis of plasma wakefield acceleration experimental measurements, particularly in the characterization of photons emitted through the betatron radiation mechanism, requires the development of accurate numerical models. These computational models are crucial for supporting modern instrumentation designed to measure the single-shot, double-differential angular-energy radiation spectra resulting from interactions between beams and plasmas. Motivated by the needs of such applications, this paper presents detailed numerical models of betatron radiation generated in beam-plasma acceleration experiments. These models are based on the integration of the Liénard-Wiechert (LW) potentials, applied to computed particle trajectories. The particle trajectories are generated using three distinct methods: first, by tracking particles through idealized fields in the blowout regime of PWFA; second, by obtaining trajectories using the fast quasistatic particle-in-cell (PIC) code quickpic; and third, obtaining trajectories from the fully self-consistent PIC code osiris. To ensure the accuracy and reliability of these models, the paper includes various benchmark tests using analytical expressions, as well as employing the PIC code epoch, which takes an alternative approach by using a Monte Carlo quantum electrodynamics (QED)-based radiation model. Additionally, the paper presents simulations of the expected experimental betatron radiation spectra, taking into account parameters relevant to PWFA and plasma photocathode experiments at the SLAC FACET-II facility.

Yadav, M. [University of California, Los Angeles, ↗
DOE OSTI2025

Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models

Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.

Bayesian experimental design↗
DOE OSTI2025

Performance Analysis and Simulaion of the Hydraulic Scram System in TREAT Reactor

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) serves a vital role in nuclear fuel safety research, enabling transient experiments that simulate reactivity excursions and accident scenarios. Central to these operations is the transient control rod drive system (TCRDS), which drives rapid motion of the transient control rods such that TREAT can simulate rapid power changes typical of reactor accidents. The reliability and performance of this system are critical for protecting both fuel specimens and reactor infrastructure. This study presents the initial phase of a two-year investigation into the dynamics and reliability of the TREAT hydraulic TCRDS. Conducted in collaboration with INL, the research employs a combined computational and experimental approach to analyze the system's response time, pressure transients, and potential failure modes. Emphasis is placed on understanding how fluid characteristics influence the TCRDS’s ability to achieve both rapid power changes and mechanical stability. The TRDS and the skid that powers it will be analyzed throughout this investigation. Computational modeling using computational fluid dynamics (CFD) will simulate the hydraulic response under varying conditions. In parallel, experimental testing planned at INL will validate these models and capture key performance metrics. This paper outlines the system design, analytical framework, and modeling strategies that form the foundation for later testing. Ultimately, this work aims to support improvements to the TCRDS’s design and reliability, contributing to the broader goal of enhancing nuclear fuel safety and sustaining TREAT’s mission as a premier nuclear fuel test facility.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

An Open-Source Python Package for CFD Solution Verification

Informed decision-making using computational fluid dynamics (CFD) results requires quantifying the errors and uncertainties of a simulation. Verification, validation, and uncertainty quantification (VVUQ) methods were developed to address this need and have matured. However, these VVUQ analyses are often non-trivial and require CFD analysts and practitioners to have specific skill sets. This has led to the uneven adoption of VVUQ analyses, in part, based on the availability of software tools to aid CFD analysts and practitioners. Solution verification, a procedure to evaluate the accuracy of a simulation by estimating potential errors arising from the computational model and computing the uncertainties without comparing to results from a physical system, is one of the lagging VVUQ analyses as the absence of software has forced CFD analysts and practitioners to develop their own codes or piece together incomplete software from across the internet. This work presents an opensource Python package, CFDverify, to lower the barrier of entry and fill in the technological gap in solution verification. CFDverify also provides a streamlined framework to remove some potential errors in post-processing CFD results. The hope is that CFDverify can improve the quality and quantity of CFD solution verification in scientific and research studies and attract interest in developing a communal tool. This paper describes the design, features, and an example use of CFDverify.

Weinmeister, Justin [ORNL] (ORCID:0000000160090237↗
DOE OSTI2024

FENIX: Towards a Fully Integrated Multiphysics Framework for Plasma Facing Component Modeling

Computational tools have a crucial role to play in accelerating the deployment of fusion as a clean, reliable, abundant, and sustainable energy source. Multiphysics, high-fidelity simulation capabilities can help model, study, and predict intricate interactions between materials performance, plasma exposure, neutron irradiation, and engineering processes. As such, they can assist in the resolution of scientific and engineering challenges underpinning design, construction, and commission of fusion power plants. To address these needs, ongoing efforts are leveraging the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework and delivering new computational tools for the fusion community. These tools inherit crucial attributes from MOOSE. They are open-source, modular, integrated with nuclear industry-standard software quality assurance processes, and enable multiphysics, multi-fidelity, fully integrated, zero- to three-dimensional, and massively parallel simulations. After a short overview of these capabilities, we will present the development of Fusion ENergy Integrated multiphys-X (FENIX), a MOOSE-based application designed to enable plasma facing component design and performance evaluation. Throughout their lifetime, plasma facing components are exposed to extreme thermal loads, repeated thermal shocks, and irradiation by plasma ions, neutral particles, and high-energy neutrons. Consequently, designing a plasma facing component with acceptable lifetime degradation is extremely challenging. FENIX aims to model the multiphysics environment in which plasma facing components evolve to accelerate their design studies. To that end, FENIX couples existing MOOSE capabilities such as heat transfer, thermomechanics, and thermal hydraulics, with tritium transport via the MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8), with neutronics via the MOOSE-based high-fidelity neutron-photon transport and fluid dynamics code Cardinal, and finally with Particle-in-Cell plasma simulation capabilities being developed in this project. In this study, we present the current FENIX capabilities and preliminary results of its application to model the Tritium Plasma Experiment set up at Idaho National Laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Robust Solution Verification Experiments on Nonuniform Meshes

The activities of verification, validation, and uncertainty quantification (VVUQ) provide a comprehensive means to assess the credibility of computational models. Within VVUQ, solution verification assesses numerical errors and evaluates whether the simulation is sufficiently accurate for its intended applications. As computational modeling gains traction in the development of complex, high-consequence systems, the need for robust solution verification intensifies, particularly because experimental data for these systems are often limited. This work examines improvements in the robustness of Richardson extrapolation (RE), a method commonly used in solution verification to study the discretization error of computational models using a power law. Nonuniform mesh refinement is discussed alongside other pollutants that affect the robustness of the power law model. Maximum likelihood estimation (MLE) is proposed as a robust strategy to address the uncertainty generated by nonuniform mesh refinement. An exploratory computational fluid dynamics (CFD) study of a 2D planar Poiseuille flow is conducted to determine if nonuniform mesh noise can be modeled with this MLE approach for more robust RE.

Weinmeister, Justin [ORNL] (ORCID:0000000160090237↗
DOE OSTI2025

Flow instabilities in helical-coil steam generators for small modular reactors: A review

Here, this study covers the research and discoveries in two-phase flow-boiling instabilities available in the literature—specifically for a helical-coil steam generator (HCSG), including experimental findings, theoretical research, computational models, and system code analyses—supporting research and development of representative small modular reactors (SMRs). Like other new and advanced reactor systems, water-cooled SMRs require experimental data from both integral and separate thermal-hydraulics test facilities for the verification and validation (V&V) of the computational models and computer codes in order to design and obtain regulatory approval. The complex dynamics of two-phase flow-boiling instabilities includes flow regimes physics phenomena, flow-channel geometries, heat-transfer behavior, and interactions among the solid–liquid-gas within the system boundary, all of which are pivotal for understanding the design and operational challenges of SMRs. This study focuses on identifying the relevant knowledge gaps on boiling instabilities—specifically for a HCSG—and provides insights about future research direction optimizing the transport of thermal energy, mass-flow rates, and boundary conditions that ensure the adequate heat-transfer performance, operational stability, and safety associated with SMR systems.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities.

Burley, Stephen K.↗
DOE OSTI2025

CFD Simulation of Aerobic Gas Fermentation to Enable Commercial Conversion of CO 2 into Aquaculture and Animal Feed: Cooperative Research and Development (Final Report)

NovoNutrients’ fermentation technology uses energy from hydrogen to transform industrial CO2 emissions into premium animal feed ingredients and other valuable products. A single NovoNutrients’ commercial manufacturing plant will capture and convert over 200,000 tons/yr of CO2 into over 100,000 tons/yr of high-protein feed. Key to the rapid and widespread deployment of the technology is maximization of its productivity and energy efficiency. Robust, physically based computational models of the technology will significantly increase productivity and efficiency, accelerating NovoNutrients’ technology to manufacturing scale. NREL has unique capabilities for creating and running such computational models. NREL's existing aerobic bioreaction computational fluid dynamics (CFD) models will be adapted to NovoNutrients’ gas fermentation (CO2, H2, O2) technology. The multiphysics CFD simulations require thousands of high-performance computing (HPC) node hours to simulate the complex geometries and contents of NovoNutrients’ industrial bioreactors. The experimentally validated CFD models were used to identify optimally efficient and productive bioreactor designs and operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

FY25 Report on Water NSTF Testing: Parametric and Accident Testing with Lower Tank Inlet

The Natural Convection Shutdown Heat Removal Test Facility (NSTF) at Argonne National Laboratory has continued to generate empirical validation data on the performance of water-based reactor cavity cooling system (RCCS) for seven years. This data is actively being used to support the development of passive decay heat removal systems for advanced reactors. Distinguishing this facility are 1) the large, ½ scale of the facility and 2) its governance under an NQA-1 qualified program for producing data of the highest pedigree for advanced reactor designers and regulators. In addition to the experimental activities discussed in this report, a computational modeling program continues to support the experimental program and further accuracy and understanding of the computational models. Together, the experimental and computational work create a mutually beneficial relationship integral to the overall program objective of advancing the understanding of the RCCS technology.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Elastic Bayesian Model Calibration

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Computational flow modeling of triply periodic minimal surfaces as feed channel spacers in ultra-high pressure reverse osmosis applications

Triply periodic minimal surfaces (TPMS) are a special class of mathematical surfaces characterized by a high surface area-to-volume ratio. They have generated considerable interest in fields such as acoustics, heat transfer, and membrane-based filtration processes. This study evaluates the performance of four different TPMS designs—Schoen Gyroid, Schoen Crossed Layers of Parallels (CLP), Schoen Transverse Crossed Layers of Parallels (tCLP), and Schwarz-Primitive—when used as feed channel spacers under ultra-high pressure reverse osmosis (UHPRO) conditions, at approximately 200 bar. Our experimentally validated computational fluid dynamics model reveal different flow patterns within the feed channels for each of the four TPMS designs, leading to varying hydrodynamic and permeation properties. Under the simulated UHPRO conditions, the Gyroid and tCLP designs yield up to a 23% increase in average permeate velocity and a 14% reduction in average membrane-surface concentration relative to a non-woven spacer of the same porosity. Furthermore, the enhanced performance comes with an increased feed channel pressure drop, although it only constitutes less than 4% of the operating pressure when extrapolated for a meter-long membrane module. Additionally, the study analyzes the effects of varying inlet velocity and spacer porosity on membrane performance. Overall, this research provides valuable insights into the potential use of TPMS spacers in UHPRO applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermal Management for Planar Package Power Electronics (CRADA Final Report)

The National Renewable Energy Laboratory (NREL) and John Deere Electronic Solutions (JDES) collaborated to develop and evaluate computer models and simulations related to the thermal performance of semiconductor device packaging for an inverter of an off-road vehicle ("Semiconductor Packaging"). The objective of the research was for NREL and JDES to develop a two or three-dimensional computer-aided design model of the Semiconductor Packaging ("Computer Model") for thermal performance evaluation in a simulation. The project developed computer models of a Semiconductor Package of silicon-carbide semiconductor devices with appropriate thermal dissipation via one or more of the following thermal features: a double-sided planar cooling configuration, an air-cooled configuration, a liquid-cooled configuration for off-road inverter applications in a relevant simulated operating environment. It is noted that surface area and prototype product embodiment consisting of air-cooled and liquid-cooled configurations may and may not be in direct contact with power semiconductor chips. The idea was to explore and develop packaging and thermal management technology for power semiconductors that was most effective in the performance yet had least burden in overall product cost for a given application.

33 ADVANCED PROPULSION SYSTEMS↗