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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thermonuclear performance variability near ignition at the National Ignition Facility

We describe our current understanding of the variability and degradation mechanisms observed through a series of five indirectly driven inertial fusion implosions fielded at the National Ignition Facility in the fall of 2021, four of which attempted to reproduce the first experiment to achieve Lawson's criterion for ignition with a thermonuclear yield of 1.35 MJ on August 8, 2021. A large number of absolutely calibrated (imaging, time-resolved, and spectrally resolved) x-ray and neutron diagnostics are fielded on the NIF along multiple lines of sight for each experiment. This allows for a reconstruction of the DT fuel and ablator mix injected into the hotspot around peak burn. We show that nuclear yield variations are well reproduced by numerical modeling when the measured low mode asymmetries and mix mass are included. Furthermore, these observed perturbations during burn are linked to small variations in laser delivery and capsule defects. Stringent specifications are then set to achieve robust ignition with the implosion design studied in this paper.

Divol, L. (ORCID:0000000269699898)↗
DOE OSTI2024

The impact of low-mode symmetry on inertial fusion energy output in the burning plasma state

Indirect Drive Inertial Confinement Fusion Experiments on the National Ignition Facility (NIF) have achieved a burning plasma state with neutron yields exceeding 170 kJ, roughly 3 times the prior record and a necessary stage for igniting plasmas. The results are achieved despite multiple sources of degradations that lead to high variability in performance. Results shown here, for the first time, include an empirical correction factor for mode-2 asymmetry in the burning plasma regime in addition to previously determined corrections for radiative mix and mode-1. Analysis shows that including these three corrections alone accounts for the measured fusion performance variability in the two highest performing experimental campaigns on the NIF to within error. Here we quantify the performance sensitivity to mode-2 symmetry in the burning plasma regime and apply the results, in the form of an empirical correction to a 1D performance model. Furthermore, we find the sensitivity to mode-2 determined through a series of integrated 2D radiation hydrodynamic simulations to be consistent with the experimentally determined sensitivity only when including alpha-heating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Variability in Performance of a Machine Learning Seismicity Catalog: Central Italy, 2016–2017

Machine learning (ML) catalogs contain many more earthquakes than routine catalogs, but their performance in phase picking and earthquake detection has not been fully evaluated. We develop station‐level detection probabilities using logistic regression and combine them across a seismic network to compute spatial magnitude‐of‐completeness fields. We apply this approach to two catalogs from the 2016–2017 Central Italy sequence that were constructed from the same seismic network, one routine and one ML‐based. At the station level, the ML picker increases detection sensitivity by identifying smaller magnitude events and detecting earthquakes at greater distances. Spatially, the magnitude of completeness decreases substantially, with median values shifting from 1.6 to 0.5 for P waves and from 1.7 to 0.5 for S waves. However, the ML catalog also shows greater variability in station‐level performance than the routine catalog. These results demonstrate that ML‐based improvements in detectability are widespread but spatially nonuniform, highlighting their benefits, their limitations, and the potential for further improvements.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Investigating performance and variability of NIF ICF experiments with deep learning

The parameter space involved in designing an inertial confinement fusion shot at the National Ignition Facility (NIF) is massively multi-dimensional and the cost of a single shot makes a comprehensive set of sensitivity studies in the laboratory impractical. The use of machine learning to overcome these challenges has gained popularity and has had several successful applications by the scientific community. We extend on these efforts by training a neural network (NN) on information about the experimental design, engineering elements, and drive asymmetry to predict with uncertainty the neutron yield of an experiment. We find the measured and model predicted values are in good agreement, with an R 2 value of 0.91 for a randomly selected test dataset. Almost all the predicted 95% credible intervals contain the corresponding measured value for both training and test datasets. We identify correlations picked up by the NN between the shot design, yield, and variability and use them to motivate shot sensitivity studies. The first shot to exceed the Lawson-like ignition criteria (N210808) was conducted at the NIF and subsequent shots studied the design’s robustness. In a follow-up shot to N210808, our model predicts capsule quality to be the main performance degradation mechanism that prevented the shot from repeating previous performance levels. Shot N221204 was the first shot to exceed a target energy gain of 1. Our model predicts increased yield with reduced coast time for a N221204 study and greater variability for designs with lower peak powers at constant yield. The model’s fast prediction speed and uncertainty prediction are useful for identifying interesting design paths that could warrant further investigation with conventional simulations to search for robust high yield designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Numerical Investigation of Butterfly Valve Performance in Variable Valve Sizes, Positions and Flow Regimes

Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters stands out for these requirements. Their ability to sustain the downstream flow rate, prevent reverse flow, and maintain pressure in the heat transport loop is much more efficient with the integration of sensors and intelligent algorithms. For assessing valve performance and monitoring, mechanical design and operating conditions are two important parameters. In this study, the butterfly valves of three different sizes are simulated with water and steam using STAR-CCM+ in various flow regimes and positions to analyze performance parameters to strategize an automated control system for efficiently balancing the heat–transport network. Also, flow behavior is studied using velocity and pressure fields for valve–body geometry optimization. It can be observed, through performance parameters, that the valves are suitable for operation between 30° and 90° positions with significantly low loss coefficients and high flow coefficients, and the performance parameters follow a certain pattern in both water and steam flow in each scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Improvements in optical metrology for high-performance variable-line-spacing x-ray gratings

Here, we discuss a project to develop a precision metrology system for variable-line-spacing x-ray gratings using interferometric microscopes. We are developing test standards and calibration techniques to measure and correct for geometrical distortion and blur, and data processing techniques to combine multiple measurements and measure line spacing with high accuracy.

Calibration methods↗
DOE OSTI2026

Data-Driven Optimization of Pixelated CdZnTe Spectrometers for Uranium Enrichment Assay

Here, in recent work [Vavrek et al. (2025)], we developed the performance optimization framework spectre-ml for gamma spectrometers with variable performance across many readout channels. The framework uses non-negative matrix factorization (NMF) and clustering to learn groups of similarly-performing channels and sweep through various learned channel combinations to optimize the performance tradeoff of including worse-performing channels for better total efficiency. In this work, we integrate the pyGEM uranium enrichment assay code with our spectre-ml framework, and show that the U-235 enrichment relative uncertainty can be directly used as an optimization target. We find that this optimization reduces relative uncertainties after a 30 -minute measurement by an average of 20%, as tested on six different H3D M400 CdZnTe spectrometers, which can significantly improve uranium non-destructive assay measurement times in nuclear safeguards contexts. Additionally, this work demonstrates that the spect re-ml optimization framework can accommodate arbitrary end-user spectroscopic analysis code and performance metrics, enabling future optimizations for complex Pu spectra.

Gamma-ray detection↗
DOE OSTI2024

Early experiences on the OLCF Frontier system with AthenaPK and Parthenon–Hydro

The Oak Ridge Leadership Computing Facility (OLCF) has been preparing the nation's first exascale system, Frontier, for production and end users. Frontier is based on HPE Cray's new EX architecture and Slingshot interconnect and features 74 cabinets of optimized 3rd Gen AMD EPYC CPUs for HPC and AI and AMD Instinct 250X accelerators. As a part of this preparation, “real-world” user codes have been selected to help assess the functionality, performance, and usability of the system. This article describes early experiences using the system in collaboration with the Hamburg Observatory for two selected codes, which have since been adopted in the OLCF test harness. Experiences discussed include efforts to resolve performance variability and per-cycle slowdowns. Results are shown for a performance portable astrophysical magnetohydronamics code, AthenaPK, and a mini-application stressing the core functionality of a performance portable block-structured adaptive mesh refinement framework, Parthenon-Hydro. Here, these results show good scaling characteristics to the full system. At the largest scale, the Parthenon-Hydro miniapp reaches a total of $1.7$ $\times$ $10^{13}$ zone-cycles/s on 9216 nodes (73,728 logical GPUs) at ≈92% weak scaling parallel efficiency (starting from a single node using a second-order, finite-volume method).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rapid assessment of the creep rupture life of metals: A model enabling experimental design

Prediction of the creep rupture life of engineering metals is critical for qualification and design of new materials. The use of long-term creep tests and the need to quantify the performance variability in a priori similar systems hinder the rapid creep assessment of a given material. Therefore, it is essential to develop methods that can extrapolate the long-term performance of alloys and the associated variability from short-term experiments. To this end, this study introduces a new model which enables the estimation of the rupture life of a material for a given stress and temperature. This model relies on two components. First, a new relation for the minimum creep rate (MCR) of materials is introduced. It includes a stress dependent stress exponent allowing the model to capture the variation of MCR across a wide range of temperatures and stresses. Second, employing the Monkman-Grant (MG) law, we establish a relation between stress, temperature and creep rupture life. Together, these two elements yield a new closed-form mathematical expression for the Larson Miller parameter as a function of stress and temperature. This expression captures the creep rupture time for many metals (Gr91, Copper, Gr122 and 347H) and compares favorably with alternate empirical approaches. The model is then used to assess the minimum duration of creep rates necessary to qualify the material up to 100000h. Furthermore, it is found that depending on the material system, creep tests as few as five limited to 5000 h for steels (Gr91, Gr122, 347H) and 100 h for copper are sufficient to model creep lifetimes. Finally, using a Bayesian inference-based approach to calibrate the model, we demonstrate that variability in rupture life can be captured via the quantification of the uncertainty in the model parameters and extrapolated from a limited number of short to moderately short creep tests; thereby paving the way for accelerated creep testing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Carbon Capture through Membranes - Leveraging Multiphysics Modeling, Dimensional Analysis and Machine Learning to Scale up and Optimize Devices and Processes for Decarbonization

We study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow (DFfeed). Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗
DOE OSTI2024

Modeling and Scaling up of Membrane Modules Leveraging Dimensional Analysis

In the current analysis, we study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow. Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗
DOE OSTI2024

Light Water Reactor Sustainability Program: Use of Time Distributions to Predict Operator Procedure Performance in Dynamic Human Reliability Analysis

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework affords software capable of conducting human reliability analysis (HRA) using a dynamic approach built around operating procedures (OPs) from nuclear power plants (NPPs). Previous HUNTER reports document the development of this software tool, the coupling of HUNTER to the simulator code, the collection of operator performance data by using simulators to calibrate HUNTER models, and linking HUNTER to probabilistic risk assessment (PRA) software. The present report largely addresses two topics. The first is a new function in HUNTER called the HUNTER Procedure Performance Predictor (P3). HUNTER P3 uses HUNTER’s built in Monte Carlo tools featuring human performance variability to identify potential error traps in procedures. The second topic is time distribution analysis to generate time inputs for dynamic HRA. The current analysis was performed to investigate time distributions for task primitives, which are the minimum task unit of analysis used in dynamic HRA modeling. Using the time distribution data, the elapsed time for human actions in an extended loss of AC power (ELAP) scenario is then investigated. Time data and prediction are essential for modeling procedure performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
DOE OSTI2026

EVSE Characterization

NextGen Profiles' EVSE characterization efforts explored performance variability in production EVSE through the use of EV emulation equipment and assessed how different operational conditions influence charging behavior. Data were collected at a frequency of 10 Hz from both the EV emulator and EVSE during each charge session and stored in a time-series database for further analysis. As part of the NextGen Profiles project, characterization of high-power EVSE was performed on both conductive and wireless charging infrastructure; however, only conductive charging data are currently included in this repository. This EVSE characterization was performed over a range of DC output currents and voltages, covering both nominal and off-nominal test conditions. This EVSE characterization dataset includes high-power charging data from two types of 350-kW-capable EVSE using liquid-cooled Combined Charging System-1 (CCS1, North American version) cables and connectors. To protect confidentiality, all EVSE metadata are anonymized, and the publicly released datasets are metered at 10-Hz frequency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2026

Effect of fiber sizing and glass fiber laminate hybridization on vibration damping and mechanical properties of banana fiber reinforced polypropylene composites

Modern automotive applications demand lightweight, multifunctional materials to reach mileage goals and natural fiber reinforced composites (NFRCs) are one of the classes of materials proposed as a solution. NFRCs exhibit good vibration damping properties and have low density, but are often limited by processing challenges, poor-fiber matrix compatibility and variable performance. Herein, we investigate non-woven wet-lay of comingled banana fiber (BF), recycled glass fiber (rGF), and polypropylene (PP) fibers to in situ sizing and preparation of composite feedstocks for compression molding. BF and rGF hybrids were prepared by stacking rGF layers during compression molding to produce composites with various fiber ratios. The effect of fiber content, in-situ sizing and ratio of BF to rGF on tensile, flexural and vibration damping performance are investigated. Key results are the significant increase in tensile strength by in situ sizing (40 % sized at 60 wt% BF) and in flexural modulus (+58 % sized at 60 wt% BF) and flexural strength (+41 % sized 60 wt% BF) compared to the unsized equivalent. For BF-rGF hybrid composites with40 wt% total fiber content, flexural strength and modulus were improved by 51 % and 231 % respectively for a 1:1 ratio BF:rGF compared to BF reinforced system. Lastly, identifying the cross-over point where damping and stiffness are optimized for a hybrid composite. These findings demonstrate that these composites can be used as alternative to synthetic fiber or mineral filled composites in automotive applications, particularly where weight reduction, vibration damping and stiffness are desired.

Banana fiber↗
DOE OSTI2023

Correlating chemistry and mass transport in sustainable iron production

Steelmaking contributes 8% to the total CO 2 emissions globally, primarily due to coal-based iron ore reduction. Clean hydrogen-based ironmaking has variable performance because the dominant gas–solid reduction mechanism is set by the defects and pores inside the mm- to nm-sized oxide particles that change significantly as the reaction progresses. While these governing dynamics are essential to establish continuous flow of iron and its ores through reactors, the direct link between agglomeration and chemistry is still contested due to missing measurements. In this work, we directly measure the connection between chemistry and agglomeration in the smallest iron oxides relevant to magnetite ores. Using synthesized spherical 10-nm magnetite particles reacting in H 2 , we resolve the formation and consumption of wüstite (Fe 1-x O)—the step most commonly attributed to whiskering. Using X-ray diffraction, we resolve crystallographic anisotropy in the rate of the initial reaction. Complementary imaging demonstrated how the particles self-assemble, subsequently react, and grow into elongated “whisker” structures. Our insights into how morphologically uniform iron oxide particles react and agglomerate in H 2 reduction enable future size-dependent models to effectively describe the multiscale aspects of iron ore reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗