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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Bayesian Gaussian process inference for neutron spin echo measurement

Neutron spin echo (NSE) spectroscopy provides unique access to microscopic dynamics, but its application is often constrained by low neutron flux, long acquisition times, and significant noise. Here, we present a Bayesian inference approach based on Gaussian process regression (GPR) to reconstruct high-quality spin echo signals from sparse and noisy data by exploiting correlations in reciprocal space. Benchmarks on synthetic datasets and validation with experimental NSE measurements of dendrimers show that GPR suppresses noise, interpolates missing intensity values, and accommodates irregular observations. The method improves accuracy, shortens acquisition times, and enables high-throughput and real-time studies. Beyond NSE, the framework is broadly applicable to other low signal-to-noise ratio scattering techniques, thereby extending the scope of neutron spectroscopy.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Measuring 3D Profilometry of SAVY-4000 Nuclear Material Storage Containers: Novacam TubeInspect Capabilities Report

The SAVY-4000 container series is a general-purpose interim storage container for nuclear materials, developed and maintained by Los Alamos National Laboratory (LANL). It is the first vented, general-use nuclear material container to be demonstrated as meeting the requirements outlined in DOE M 441.1-1, the Nuclear Material Packaging Manual. Due to the challenging radiation, thermal, and corrosive storage conditions that the SAVY containers must endure, continuous surveillance techniques are employed to ensure the containers meet all safety standards and specifications. These inspections are typically performed by human operators, who check for issues such as corrosion, O-ring deterioration, corrosion, filter integrity, and potential manufacturing defects. However, human inspections alone are not sufficient, and automated inspection technologies, such as the ATIS system, as well as other automated systems are also utilized. The MicroCam TubeInspect, developed by Novacam Technologies Inc., is designed to address the challenges of understanding how manufacturing variations in the SAVY-4000 container series may affect performance. It is a 3D profilometry measurement system that enables detailed analysis of surface features, including defects, surface roughness, and manufacturing variations. This advanced tool significantly enhances rapid surveillance techniques for both pristine and used containers. In this study, container properties such as surface roughness, thickness, and geometric attributes like circularity are measured for SAVY-4000 containers. Artificially corroded or dented containers are examined to demonstrate the MicroCam's ability to quantify defects. A sensitivity analysis is also conducted, comparing the MicroCam results to those obtained using more precise instruments such as confocal microscopy. This comparison aims to provide valuable insights into container quality, durability, and potential improvements in manufacturing processes.

42 ENGINEERING↗
DOE OSTI2026

Enabling Bipolar Membrane Manufacturing Using Microscopy

Bipolar membranes (BPMs) used for water treatment suffer from interface instabilities, causing premature failure and decreased performance. Recent research has highlighted the impacts of membrane morphology on mechanical behavior and performance. Improved material performance of thin film membranes requires enhanced transport properties coupled with better control of the mechanical characteristics to avoid premature failure of the film. Thus, morphology is a key parameter when developing and evaluating novel membrane materials. Microscopy offers unique advantages for nano-scale characterization of thin film membrane interfaces and morphologies. In this study, the interface junction of BPMs was evaluated using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX). BPMs were fabricated using (1) hot pressing lamination, (2) bench-scale solution casting, and (3) roll-to-roll sequential casting (R2R). Both commercial and in-house fabricated ion exchange membranes were used in the BPMs. The BPMs were comprised of a cation exchange membrane and an anion exchange membrane with a catalyst layer sandwiched in between. Cross sectional SEM and EDX images of the membrane interface junction were compared among the different fabrication techniques and materials. The bench cast catalyst layers were nonuniform compared to the R2R and hot pressed membranes, demonstrating the importance of precise catalyst application conditions. Different thicknesses of the BPMs and individual membrane layers revealed that polymer solution concentration needs to be optimized during manufacturing. This work shows that microscopy can identify key processing parameters that affect BPM interface junction quality at the microscale to enable manufacturing of high performance BPMs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enabling Roll-to-Roll Manufacturing of Bipolar Membranes Using Microscopy

Bipolar membranes (BPMs) used for water treatment suffer from interface instabilities, causing premature failure and decreased performance. Recent research has highlighted the impacts of membrane morphology on mechanical behavior and performance. Improved material performance of thin film membranes requires enhanced transport properties coupled with better control of the mechanical characteristics to avoid premature failure of the film. Thus, morphology is a key parameter when developing and evaluating novel membrane materials. Microscopy offers unique advantages for nano-scale characterization of thin film membrane interfaces and morphologies. In this study, the interface junction of BPMs was evaluated using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX). BPMs were fabricated using (1) hot pressing lamination, (2) bench-scale solution casting, and (3) roll-to-roll sequential casting (R2R). Both commercial and in-house fabricated ion exchange membranes were used in the BPMs. The BPMs were comprised of a cation exchange membrane and an anion exchange membrane with a catalyst layer sandwiched in between. Cross sectional SEM and EDX images of the membrane interface junction were compared among the different fabrication techniques and materials. The bench cast catalyst layers were nonuniform compared to the R2R and hot pressed membranes, demonstrating the importance of precise catalyst application conditions. Different thicknesses of the BPMs and individual membrane layers revealed that polymer solution concentration needs to be optimized during manufacturing. This work shows that microscopy can identify key processing parameters that affect BPM interface junction quality at the microscale to enable manufacturing of high performance BPMs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ranking and Classifying AI Benchmarks

We created a set of standards to efficiently evaluate AI benchmarks through objective means. Although prevalent, especially in recent times, AI benchmarks have no single way to measure their effectiveness. The MLCommons team provided a set of criteria for evaluating benchmarks, although the criteria lacks a clearly defined set of evaluation rules. We created a rubric with preset factors to efficiently and objectively evaluate a benchmark s quality. We created a software framework for processing lists of benchmarks for visualization. The framework and rating system allows researchers to quickly check if their benchmarks are effective.

Shiraishi, Reece C. [Cornell U.]↗
DOE OSTI2026

High waste loading glass formulation development for High-Mn HLW

One of the primary objectives of the work described herein was to develop and identify HLW glass compositions and glass forming additive blends that achieve high waste loadings and processing rates for high manganese HLW streams while maintaining acceptable glass properties. Another objective was to determine the effect of the form of manganese (MnO, MnO2, MnCO3) on feed processing properties, glass production rates, and product quality while vitrifying a high manganese HLW stream. This was accomplished through a combination of crucible-scale tests, vertical gradient furnace tests, and confirmation tests on a DM100 melter system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Glass Formulation Development for Al, Fe, and Na Phosphate HLW

The primary objective of the work described herein was to develop and identify HLW glass compositions and glass forming additive blends that achieve high waste loadings and processing rates for high phosphorus HLW streams while maintaining acceptable glass properties. Another objective was to determine the effect of phosphate form (aluminum, iron, sodium) on feed processing properties, glass production rates, and product quality while vitrifying a high phosphorus HLW stream. This was accomplished through a combination of crucible-scale tests, vertical gradient furnace tests, and confirmation tests on a DM100 melter system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Maximizing Efficiency and Quality: Leveraging Automated Testing for Laboratory Commissioning

The traditional commissioning process uses sampling to select equipment for functional acceptance testing when large quantities of equipment are present. Although this approach is generally effective in identifying wide-spread issues, it has several shortcomings: it fails to evaluate equipment not included in the sample, provides only a one-time validation of equipment operation, and the standard documentation is a simple checklist of pass/fail questions. During the construction and commissioning process of the new Research and Innovation Laboratory (RAIL) in Golden, CO, the National Renewable Energy Laboratory team engaged Group14 Engineering to implement a Connected Commissioning process using fault detection and diagnostic software for automated functional acceptance testing. This presentation highlights the advantages offered by automated functional testing in this critical laboratory setting: (1) sampling 100% of BAS-connected equipment during functional testing, (2) testing results backed by data beyond the traditional pass/fail checklist, and (3) an automated test process that can be regularly executed by the building management team for ongoing commissioning throughout the life of the building. The presentation will also cover technical challenges associated with Connected Commissioning and the important conversations with key stakeholders that need to occur well before functional acceptance testing in order to successfully implement the automated testing processes.

automated testing↗
DOE OSTI2024

Power Electronics Materials and Bonded Interfaces - Reliability and Lifetime

The need for reliable bonded interface materials is critical to realize the performance benefits of wide-bandgap devices in power electronic modules, especially in operating temperatures greater than 150 degrees Celsius. In this paper, we investigate the thermomechanical performance of sintered copper (Cu) as a large-area attachment, bonded between Cu baseplates and active-metal-bonded substrates, under accelerated thermal shock (-40 degrees Celsius to 200 degrees Celsius) conditions. In the fabrication phase of the samples, we used different stencil patterns and found out that the grid and stripe patterns resulted in a better outgassing of the residual organics during the sintering process, thereby ensuring a substantially improved bond quality than a full-area print. The paste consisted of Cu microflakes and we performed sintering using a Budatec SP300 sintering press at 275 degrees Celsius with 15 MPa of bonding pressure for 5 minutes in a nitrogen atmosphere. Under accelerated experiments, we monitored the degradation of the sintered Cu bond in the samples through C-mode scanning acoustic microscope (C-SAM) images. To quantify the defect percentage in C-SAM images, we investigated image denoising techniques to exclude the pattern prints. Finally, we cross-sectioned a sample and obtained digital microscope images, which revealed adhesive fracture as the dominant failure mechanism.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Classifying and rating AI benchmarks

We created a set of standards to efficiently evaluate AI benchmarks through objective means. Although prevalent, especially in recent times, AI benchmarks have no single way to measure their effectiveness. The MLCommons team provided a set of criteria for evaluating benchmarks, although the criteria lacks a clearly defined set of evaluation rules. We created a rubric with preset factors to efficiently and objectively evaluate a benchmark’s quality. We created a software framework for processing lists of benchmarks for visualization. The framework and rating system allows researchers to quickly check if their benchmarks are effective.

Shiraishi, Reece [Fermilab]↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
DOE OSTI2024

NuMI/LBNF Horn and Stripline Welding

Focusing horns for secondary particles are critical components for creating a stable beam of neutrinos. These components need to survive in a harsh environment and withstand high stresses. Extending the lifetime of the horns is critical as spare fabrication takes approximately two years and has many subcomponents with strict quality control. Two key aspects of the fabrication process include the inner conductor CNC TIG welding and the friction stir welding of the stripline pieces. The process for welding requires steps such as sample welding, x-ray imaging, and tensile pull tests. Having a perfect weld retains as much of the original strength of the base metal and reduces the risk of failure. As FNAL ramps up in power to 2.4MW, the lifetime of the horn and stripline will more heavily rely on continuing to have high quality welding procedures and thorough quality assurance.

Orea, Adrian↗
DOE OSTI2025

Process intensification in hydrothermal liquefaction of biomass: A review

Hydrothermal liquefaction (HTL) presents a promising pathway for converting wet biomass resources into biofuels, offering significant advantages over conventional methods. However, numerous challenges must be addressed for HTL scale-up, including energy provision for the endothermic process, heat and mass transfer limitations, slurry concentration and pumpability challenges, char and coke formation, and continuous phase separation. This review explores key strategies such as autothermal HTL, which improves process efficiency and reduces external energy requirements by coupling exothermic and endothermic reactions within the same reactor, thereby simplifying reactor design and reducing operational costs. Additionally, multistage HTL processes are highlighted for their ability to optimize biocrude quality and yield by fractionating biomass conversion stages, resulting in higher energy returns on investment and better-quality biocrude. Solvothermal HTL and integration techniques for aqueous phase are also discussed. Furthermore, the HTL patent landscape is discussed to provide insights into current technological advancements. This review aims to offer a comprehensive understanding of process intensification in HTL, highlighting innovative solutions to enhance the efficiency and scalability of the process for sustainable biofuel production.

Biocrude oil↗
DOE OSTI2026

Screening and qualification methodology for SiC end plug processing methods

Deployment of SiC-ceramic-based fuel cladding for light water reactors requires a hermetic end plug–to–cladding joint that can withstand neutron irradiation during normal operation and maintain integrity during design-basis accidents. Reactor experiments have shown that some SiC composite tubes with SiC end plugs can retain hermeticity after irradiation. However, achieving consistent joint performance under irradiation remains a key challenge. Resolving this issue is essential to enable integral irradiation testing and to demonstrate fuel integrity under commercial-reactor irradiation conditions. This report aims to: (1) provide guidance for designing radiation-tolerant end plug joints for SiC cladding; (2) demonstrate experimental methods to detect processing defects that are unstable under neutron irradiation at light-water-reactor-relevant temperatures and doses; and (3) outline a step-by-step approach for designing and conducting reactor experiments to screen joining methods. The resulting data will be used to improve joint processing and to define critical defect types and sizes that must be detected and eliminated through non-destructive evaluation for quality assurance. Based on prior irradiation experiments at the High Flux Isotope Reactor, differential swelling among the cladding, bonding layer, and end plug was identified as an underlying mechanism for irradiation-induced joint degradation. Accordingly, this effect must be considered in the design of radiation-tolerant joining techniques. In this work, miniature SiC end plug joint specimens irradiated during the previous project were analyzed using X-ray computed tomography to characterize the joint microstructure. Digital volume correlation of the tomography data quantified radiation-induced microstructural changes and enabled evaluation of defect-related risks. Finally, ongoing neutron irradiation efforts using larger specimen volumes are presented. These efforts aim to statistically assess joint performance and to build a microstructure–performance (e.g., leak-tightness) dataset to inform processing improvements and quality control.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2024

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗