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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 307 records · Page 17

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DOE OSTI2025

Design, Fabrication and On-Sun Performance Evaluation of SiC Receiver Feature Specimens for CST Applications Using Additively Manufactured SiC Materials

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Successful development of CST systems to generate heat for industrial applications requires significant increase in temperature capability and techno-economically viability of the receiver systems. Supported by an award from the Solar Technology Office (SETO), US Department of Energy (DOE), GE Aerospace Research in collaboration with Heliogen Holdings Inc and Sandia National Lab, is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. The program objective is to demonstrate SiC receiver with air exit temperatures up to 1100 oC and high thermal efficiencies. We report design, fabrication and on-sun test results of SiC components of a prototype 50kW (thermal) HOTSSTAR module. The receiver module architecture is based on a radial airflow design and consists of a series of radial SiC receiver sectors organized around a SiC center absorber. The SiC components are fabricated using binder-jet printed SiC followed by melt-infiltration reaction bonding process. To enhance the reliability and to minimize the risk of cracking damage of SiC test articles in thermal gradient and thermal shock environment of the application, the components were laminated with GE’s MI SiC-SiC CMC. Following extensive design and lab test analyses, selected SiC component designs are tested at Heliogen Lancaster Solar field under highly concentrated solar fluxes around 2000 suns to assess the thermal performance characteristics under realistic field conditions. We report on the test results and compare the thermal performances of different HOTSSTAR SiC absorbers. Finally, we discuss fabrication and initial assembly of a 50kW prototype test module in preparation of on-sun field tests to assess the performance of our final design.

CST, CSP, high temperature, air receiver, SiC, add↗

DOE OSTI2024

Materials Characterization: A Primer for Solid Phase Processing Applications

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development (LDRD) Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). As a two-year LDRD investment requiring focused research, the MCPC project applied only a subset of the wide range of available destructive and nondestructive characterization methods to provide data to the predictive modeling and data analytics tasks. The purpose of this report is to review a wide range of destructive and nondestructive characterization methods that are relevant in solid-phase processing (SPP) applications, but not necessarily applied in the MCPC Project as a guide to the planning of characterization activities in future research. Particular attention is given to measured characteristics that can correlate to other material characteristics, with a particular interest in nondestructive evaluation (NDE) that can be applied to samples obtained in the MCPC Project. Destructive examinations include tensile tests, optical and electron microscopy, micro-hardness, and residual stress tests. NDE tests include surface visual inspection, eddy current examination for cracks, 4-point potential drop, ultrasound, x-ray, and computed tomography.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Dataset for ASME VVUQ Symposium Workshop on Regression of Validation Data to an Application Point

This dataset consists of a collection of Excel spreadsheets that contain output from analysis specified in the workshop. The analysis involves ASME V&V 20-style validation as well as the application of a supplement methodology for regression of validation comparison error and validation uncertainty to application points where experimental data does not exist for comparison. The simulation results and experimental data are provided by the workshop organizers and a NASA report, respectively.

Kirsch, Jared Roelof [Sandia National Laboratories↗

DOE OSTI2026

Radiative Cooling Paints: Application on Warehouses and Integration with Phase Change Materials

Despite extensive research on radiative cooling (RC), researchers are still working to understand its best implementation strategy and potential benefits. Understanding this could be an instrumental part of reducing building energy costs and combating climate change. In this work, we introduce two concepts to achieve this goal and broaden the application of RC paints. While building energy simulations for RC are common, most studies focus on single-layer paints in residential settings. Limited research exists on warehouses, and no literature has conducted multi-climate zone analyses or included RC technologies with >90% reflectance. As RC technology advances in efficiency, versatility, and affordability, large-scale simulations across diverse climates are crucial, particularly for warehouses as a passive indoor temperature control solution. Here, a TRNSYS-modeled single-story warehouse assesses the energy impact of BaSO4-based RC paints. Another under-explored RC application is its integration with phase change materials (PCMs), which provide thermal regulation via latent heat storage. Though RC and PCMs have been combined by placing PCMs atop, beneath, or within RC layers, studies focus on either experiments or modeling, with no validation between the two. Here, we develop a simple RC+PCM experimental and modeling setup, placing RC paint atop an aluminum cell filled with a Rubitherm PCM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Gas Hydrate Film Growth in Microfluidic Channels for Carbon Dioxide Capture and Sequestration Applications

Gas or clathrate hydrates are a solid, crystalline compound composed of water and guest molecules that typically form at high pressure and low temperature conditions. Carbon dioxide (CO2) hydrates may be involved in several carbon dioxide capture and sequestration (CCS) applications, including CO2 pipeline transportation and CO2 offshore sequestration. Within these applications, the formation mechanism and kinetics must be well understood to manage the CCS processes, either by preventing or promoting hydrate formation. In this work, a high-pressure glass microfluidic reactor is used in tandem with visual microscopy and in-situ Raman spectroscopy to study both the morphological and kinetic behavior of gas hydrate crystals. Subcooling, pressure, and CO2 flow rate are investigated for their impact on the thickening behavior of pure CO2 hydrates, with flow rate being the only parameter to have a significant effect. Visual and Raman spectroscopy evidence show that both a dense hydrate layer and a porous hydrate layer form, and the latter may provide a path for mass transfer to continue hydrate crystallization. A first principles mass transfer model is developed to describe CO2 hydrate crystal thickening at the interface between gas and water. The impacts of gas impurities and channel wettability are also studied. This method is further applied to investigate the conversion of methane hydrate to CO2 hydrate for combined energy recovery and methane hydrate formation. The authors acknowledge the US Department of Energy Basic Energy Science award # DE-SC0022162.

Wadsworth, Lindsey [Colorado School of Mines, Gold↗

DOE OSTI2025

Application of Electrochemical Methods to Molten Salt Reactors: Draft TLR Documenting Assessment of Electrochemical Monitoring

Nuclear Regulatory Commission (NRC) is developing the regulatory framework and technical expertise to support regulatory review of advanced non-water reactors, including molten salt reactors (MSRs). In an MSR, it is essential that the salt chemistry be maintained in a desired range in terms of redox potential for reliable operation and mitigation of corrosion to structural materials in the reactor, particularly the reactor vessel and heater exchanger. Measuring the redox potential of the salt in the reactor would also allow for the material lifetimes to be predicted more accurately, and chemical issues to be diagnosed more quickly. In addition to the chemical composition analysis by ICP-MS, electrochemical methods including potentiometry and linear wave scanning (LSC) were also used in molten salt reactor experiment (MSRE) for redox potential monitoring purposes. Electrochemcial methods offers unique advantages such as quick turnaround in results and unique capability of in-line monitoring of redox potential, and are considered popular electroanalytical techniques that can be used to monitor redox potentials and salt chemistry including impurities. The last two decades have seen significant advances in science and engineering of electrochemical methods for potential application to molten salts. The primary goal of this report is to assist NRC in understanding the monitoring of the salt chemistry by electrochemical methods and provide NRC reviewers with necessary information and tools to support regulatory review of MSR designs. This reports consists of two majors parts—Part 1, chemical potential of molten salts and effects by fission process in MSR; Part 2, assessment of electrochemical methods for application to MSRs. The TRLs of the typical relevant electrochemical methods for molten salts were evaluated based on the DOE TRL guidelines and upon a review of the current status of the electrochemical methods for two typical salt systems, fluoride and chloride, for MSRs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Trident Smyth: Near Real Time Sensor Fusion and Track Stitching Application

Sandia’s Sensor Data Fusion application is a NGA sponsored project that fuses tracks from different sensors to provide more accuracy than any single track. The application serves as the unclassified architecture for data ingestion, processing, and near real-time sensor fusion.

Sorroche, Joseph John [Sandia National Laboratorie↗

DOE OSTI2025

Applications of Federated Learning in Semiconductor Manufacturing [Poster]

As semiconductor manufacturers explore advanced data analytics and modeling techniques and data hungry machine learning models increase in popularity due to their accuracy in solving generalized problems and ability to learn complex relationships, federated learning emerges as a privacy preserving machine learning technique for preserving data privacy and ensuring intellectual property protection. Federated Learning is a machine learning technique focused on training models using distributed data that never needs to be centrally stored, allowing the use of advanced machine learning techniques without compromising data privacy, and in the semiconductor manufacturing industry advanced machine learning techniques can reduce cost and time, but maintaining data privacy is essential to maintaining a competitive advantage. This paper systematically reviews existing literature on applications of federated learning in the semiconductor manufacturing industry with a focus on identifying common themes, algorithms, and gaps within the literature to drive future research directions. The findings reveal five key themes, including improvements in quality assurance, virtual models, privacy preservation, reliable data practices, and emerging trends and developments. By identifying key themes in literature on federated learning and semiconductor manufacturing and analyzing gaps and discussed methodologies, this study highlights several potential future research directions to expand the application of federated learning techniques in the semiconductor manufacturing domain.

42 ENGINEERING↗

DOE OSTI2026

Low-Cost Highly Recyclable Structural Composites Utilizing Vitrimers and Natural Fibers (Basalt) Manufactured via a Novel Pultrusion Method for High-Volume Applications

The project’s overall goal is to explore and demonstrate as proof-of-concept that a highly recyclable and repairable composite material system, that is reinforced with natural fibers, can be used to produce lightweight structural components using a low-cost manufacturing process for high-volume automotive applications. More specifically, the project’s objectives will focus on vitrimer resins (which are a hybrid polymeric system of thermoplastics and thermosets) reinforced with basalt fibers and manufactured using pultrusion technologies. Pultrusion, as a method, is well-known to be one of the lowest-cost manufacturing processes for high-volume applications. However, to date, the validity and viability of such attractive objectives have not been demonstrated in support of the automotive industry.

36 MATERIALS SCIENCE↗

DOE OSTI2026

CyTRICS™ Assessment Report: Whole Home Battery Applications

This report examines the software supply chain security posture of mobile applications developed for consumer whole-house battery and energy-management products. While these applications are not currently integrated with critical infrastructure, their growing role in connected energy domain spaces underscores the importance of understanding the external dependencies, permission structures, and runtime behaviors that could introduce systemic risk; particularly, if adoption expands into more critical environments.

25 ENERGY STORAGE↗

DOE OSTI2026

SAM Finite Volume Method Development Status Update: GCR Application, Restart, and MultiApp

The System Analysis Module (SAM) is being developed as a modern system analysis code for advanced non-light-water-reactor safety analysis under the U.S. DOE NEAMS program. Previous feasibility studies have demonstrated that a staggered-grid finite volume method (SG-FVM), implemented under the MOOSE framework, can deliver more than an order of magnitude speedup over the existing continuous Galerkin finite element method (CG-FEM) solver for liquid-cooled, incompressible but thermally expandable flow systems. This work extends the previous effort to compressible, gas-cooled reactor applications, where pressure couples directly into the mass equation adding additional nonlinearity into the equation system. New code capabilities are implemented for pebble bed high-temperature gas-cooled reactor (PB-HTGR) analysis, including a pebble bed CoreChannel component, built-in pebble bed effective thermal conductivity model and channel-to-channel crossflow model. The capabilities are tested, benchmarked, and demonstrated for problems with increased level of model and physical complexities, including the HTTU effective thermal conductivity test, the SANA passive cooling test, and a demonstration case using the GPBR200 reactor design covering steady-state operation, DLOFC and PLOFC transients. Across all cases, the SG-FVM solver demonstrated strong robustness and efficiency, and the solutions agree well with reference results and data. The finding of this work proves that SG-FVM is a viable and efficient solver pathway for compressible, gas-cooled reactor system analysis in SAM. In addition, work has been done to successfully support SAM-FVM recover/restart code feature that is essential to reactor safety analysis applications, and MultiApp code feature that is essential to multi-scale and multi-physics simulations. In summary, this work continued from previous feasibility studies, and further demonstrated that the SG-FVM will serve as a strong foundation for SAM’s advanced solver algorithm for future deployment.

Zou, Ling↗

DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗

DOE OSTI2025

An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications

Nuclear sensors are essential for detecting and measuring nuclear radiation in various applications, including nuclear power plants, medical imaging, and environmental monitoring. Traditional nuclear sensors have served these fields for decades, but recent advancements in emerging sensor technologies offer novel improvements in accuracy, sensitivity, and reliability. This review presents an up-to-date overview of recent progress in the advancements of nuclear sensor technologies, their diverse applications, challenges in implementation, and opportunities for future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Respiration Signal Pattern Analysis for Doppler Radar Sensor with Passive Node and Its Application in Occupancy Sensing of a Stationary Subject

Doppler radar node occupancy sensors are promising for applications in smart buildings due to their simple circuits and price advantage compared to quadrature radar sensors. However, single-channel sensitivity limitations may result in low sensitivity and misinterpreted motion rates if the detected subject is at or close to “null” points. We designed and tested a novel method to eliminate such limits, demonstrating that passive nodes can be used to detect a sedentary person regardless of position. This method is based on characteristics of chest motion due to respiration, found via both simulations and experiments based on a sinusoidal model and a more realistic model of cardiorespiratory motion. In addition, respiratory rate variability is considered to distinguish a true human presence from a mechanical target. Sensor node data were collected simultaneously with an infrared camera system, which provided a respiration signal reference, to test the algorithm with 19 human subjects and a mechanical target. The results indicate that a human presence was detected with 100% accuracy and successfully differentiated from a mechanical target in a controlled environment. The developed method can greatly improve the occupancy detection accuracy of single-channel radar-based occupancy sensors and facilitate their adoption in smart building applications.

Song, Chenyan↗

DOE OSTI2024

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Foliar Application of Nettle and Japanese Knotweed Extracts on Vitis vinifera: Consequences for Plant Physiology, Biochemical Parameters, and Yield

Climate change is expected to affect grapevine physiology, berry quality, and yield in the Douro Demarcated Region (DDR). In this study, nettle (NE) and Japanese knotweed (JKE) extracts were tested to verify their biostimulant effect on the physiological and biochemical parameters of grapevine leaves and in vine yields. In fact, some parameters were improved after foliar application of the plant extracts, namely the photosynthetic activity and consequently, the levels of photosynthetic pigments (Clb), starch, and total soluble sugars. We also observed a reduction in lipid peroxidation, which could play a crucial role in protecting cell membranes from oxidative damage induced by the climatic conditions prevalent in this region. Therefore, we confirmed that the foliar application of plant extracts, along with the enhancement of secondary metabolites and the upregulation of plant defense genes, as previously reported, resulted in the enhancement of grapevine physiology, while also increasing the yield at harvest. In the future, these plant extracts could serve as a vital tool for winegrowers in mitigating the effects of expected changes in climatic conditions.

Monteiro, Eliana (ORCID:0000000231675540)↗

DOE OSTI2025

Laboratory Testing to Assess the Feasibility of Polyurethane Flat Belts for Marine Energy Applications

Polyurethane flat belts have received limited scientific attention as load-bearing elements in marine energy systems, particularly in applications involving dynamic tensile and bending loads. This study evaluates their potential as a replacement for traditional wire ropes in marine energy applications, with a focus on their ability to be integrated into winch-driven wave energy converters where bending and tensile stresses can make long-term operation difficult. Polyurethane belts are hypothesized to offer enhanced fatigue resistance due to their reduced thickness in the bending plane and therefore lower bending stresses. This research involves a series of tests utilizing the National Renewable Energy Laboratory’s (NREL) Large-Amplitude Motion Platform to replicate the dynamic conditions experienced by mooring lines of winch-based point-absorber-type marine energy converters. The conditions tested include unequal coiling and uncoiling tensions and load cases resulting from the device’s unconstrained movement relative to its anchor, such as twisting and off-axis loading. Results from this study show that polyurethane flat belts can achieve more than 198 percent of the fatigue life of a conventional wire rope under similar load profiles. The stress concentrations resulting from off-axis loading and cumulative twist beyond the system’s allowable limits have been identified as potential failure modes for flat belt mooring lines used in winch-driven wave energy converters deployed in ocean environments. To mitigate these risks, the use of anti-spin systems and fairleads designed to accommodate off-axis loading while limiting twist accumulation is recommended.

13 HYDRO ENERGY↗