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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2021

DATA DRIVEN APPROACH TO ANALYZING THE IMPACT OF POWER PLANT CYCLING ON AIR PREHEATER DEGRADATION AND REMAINING USEFUL LIFE

Due to the increased penetration of renewable energy generation sources, and fluctuations of the oil and gas prices, modern coal burning power plants deal with increased variability in the demand for power generation. These varying demands result in their intermittent under-capacity operation (cycling). Periodical ramping down and back up to follow the daily power demands causes damages to the plant components reducing its operational life. In this paper we analyze the impact of cycling on a rotary Ljungstrom air preheater (APH) unit installed at a coal fire power plant in the US. An inefficient air preheater can significantly impact boiler performance. Due to the repeated boiler’s hot-cold start, the APH experiences fluctuating operating conditions that result in accelerated degradation mechanisms, such as dew-point corrosion, fouling/deposition plugging, and air heater leakage.The analysis in this paper utilizes field data related to APH basket replacement, and the number of cycles experienced by the boiler to model the life expectancy of the baskets. The data-driven model enables preventive maintenance strategies for the APH by predicting how long the APH baskets will last in a probabilistic sense. The analysis showed that an increase in cycling for a fixed operation time can reduce the APH basket remaining useful life by about 30%.

Sharma, Himanshu↗
DOE OSTI2022

Dry in-pile fracture test (DRIFT) for separate-effects validation of ceramic fuel fracture models

Fracture is an important component of nuclear fuel behavior, and significant efforts have been invested into developing fuel performance models that are capable of accurately representing fracture. Usable data on the process of fracture propagation in nuclear fuel under realistic service conditions are very limited. To address this need, a series of separate-effects experiments were developed and performed at Idaho National Laboratory's Transient Reactor Test (TREAT) facility. These experiments employ a heat sink to radially remove heat from the fuel in a manner that approximates the effect of coolant in an operating light-water reactor (LWR). The test holder for these experiments is known as the Dry In-pile Fracture Test (DRIFT). A series of experiments employing DRIFT and TREAT were performed to provide data on the extent and nature of fracture in fresh fuel at various points during a ramp to full power. Novel aspects of these experiments include the way they employ a heat sink to replicate steady-state LWR conditions, as well as the use of fiber optic sensors for in-reactor thermal instrumentation. Details on the development of this experiment, experimental conditions, and resulting data (including in situ thermal measurements and post-irradiation imaging of fracture) are provided in this work. LWR-equivalent powers ranging from 10 to 25 kW/m were tested using this apparatus. Cracking was visible at all power levels, with increasing cracking extent as the power level increased, although there was little difference in the cracking between the two highest-power tests, which had LWR-equivalent powers of 20 and 25 kW/m.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Development of High-Temperature Bonding Techniques to Enable High-Temperature Static or Dynamic Strain Measurements

Current light-water nuclear reactors rely on a variety of different sensors and sensor applications to meet their structural health monitoring needs throughout the entirety of the reactor primary, secondary, and containment systems. Optical fiber–based sensor technologies could provide solutions to reduce the sensor system footprint while enhancing the measurement fidelity and spatial resolution by leveraging distributed monitoring techniques. Moreover, advanced reactors may require optical fiber–based sensors for structural health monitoring because their operating temperatures will exceed the limits of conventional transducers used to acquire dynamic strain or acoustic data in nuclear power plants. Therefore, this report describes experiments targeting the development of high-temperature bonding techniques that would allow for potentially long lengths of fibers to be bonded to metallic reactor components in advanced reactor systems. The high temperatures experienced within target application, next-generation nuclear reactors, necessitate a high-temperature resistant bond to limit the amount of tension on the fiber at the target application temperature. The primary bonding method investigated in this work is brazing; hot-rolling has also been investigated to a lesser extent. Both techniques are well-suited to bonding optical fibers to large reactor components such as primary coolant piping, pressure vessels, or heat exchangers. Optical frequency domain reflectometry was used to monitor the strain in metal-coated optical fibers before, during, and after the high-temperature bonding process. On select optical fibers that were successfully bonded, additional thermal cycling was performed to assess the extent to which the fiber remained bonded based on the expected thermal expansion of the test specimen material. The results of the various experiments yielded the following general conclusions: (1) brazing is a viable technique for bonding and allows significant compressive strain to be applied to the fiber at room temperature; (2) hot-rolling is a viable technique as well, which has been more optimized than the brazing technique for bonding, but less residual compressive strain has been observed with this technique; and (3) both techniques will need further development and optimization to demonstrate bonding of a long length of fiber that can provably operate at relevant temperatures for an advanced nuclear reactor application.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Operation-adversarial scenario generation

This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, “stressful” to the system operations and dispatch decisions. The measure of stress used in this paper is based on the operating cost increases due to net load changes. The proposed operation-adversarial cGAN (OA-cGAN) internalizes a DC optimal power flow model and seeks to maximize the operating cost and achieve a worst-case data generation. The training and testing stages employed in the proposed OA-cGAN use historical day-ahead net load forecast errors and has been implemented for the realistic NYISO 11-zone system. In conclusion, our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions.

42 ENGINEERING↗
DOE OSTI2022

A multi-chord, two-color interferometer using Hilbert transform phase detection for measuring electron density in spheromak plasmas

A new, four-chord, CO 2 /He–Ne heterodyne interferometer has been designed and built for measuring line-averaged plasma density in the HIT-SI3 and subsequent HIT-SIU sustained spheromak devices. The two-color system successfully eliminates vibration-induced errors caused by mirrors that are secured to the vacuum chamber and is able to resolve electron densities ne in the full operating range of 10 18 –10 20 m –3 in both experiments with an integrated error of 4.7 × 10 17 m –2 . Here, data are presented from high toroidal current plasma discharges, showing the time evolution of electron densities n e and j Φ /n e along multiple chords.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Experimental Analysis of the Effects of Simulator Complexity on Human Performance

Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that RL reduces waiting time by 28% to 38% for the same-day evaluation, 17% to 44% for cross-day evaluation, and 22% to 25% for cross-season evaluation compared with no rebalancing scenarios. This approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Quantitative Scanning Transmission Electron Microscopy for Materials Science: Imaging, Diffraction, Spectroscopy, and Tomography

Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Status of ICARUS NUMI Interaction Cross-Section Analysis

The ICARUS experiment, utilizing Liquid Argon Time Projection Chamber (LAr TPC) technology, has been installed at Fermilab in Chicago, Illinois, following its initial operation in Italy and subsequent refurbishment at CERN. ICARUS completed commissioning in June 2022. Currently, the experiment is in the phase of analyzing data from its two runs of physics data acquisition and gearing up for the third run. While its primary objective is to function as the far detector of the Short Baseline Neutrino program (SBN), seeking sterile neutrino signatures, ICARUS also offers diverse physics capabilities, including searches beyond the standard model and measurements of cross-sections. In addition to being exposed to the common Booster Neutrino (BNB) beamline, ICARUS also receives off-axis neutrinos from the Main Injector (NuMI) beam. Due to the off-axis angle between NuMI and ICARUS, coupled with contributions from both pion and kaon decays to neutrino fluxes, interactions of NuMI neutrinos within ICARUS can be detected over a range of several GeV in energy. These interactions present opportunities for crucial cross-section measurements and model tests within an energy range that overlaps both the SBN oscillation search and a portion of the DUNE spectrum. This poster presentation will delve into our efforts to conduct a muon-neutrino cross-section measurement, where the signal is defined by events with no pions produced in the final state of the interaction, along with some preliminary muon-neutrino inclusive measurements. Additionally, it will provide updates on the current status and future plans, including reconstruction, selection, and analysis procedures.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Smart Pixel Sensors for the HL-LHC

Large-scale particle physics experiments produce tens of terabytes of data every second. Innovative methods to manage the data rate at the HL-LHC, which expects to operate at 10x the luminosity of what the LHC was initially designed for, are needed. AI-on the chip provides a way to intelligently filter out low momentum clusters in the pixel detector. This will open up an opportunity to use the pixel detector for the first time in the CMS Level-1 trigger, and lead to increased sensitivity to new physics measurements and searches. We have taped out our first chip, which incorporates a $p_T$ filtering algorithm on an ASIC chip. Our initial $p_T$ filtering algorithm considers clusters that are tracked by CMS. We will report on ongoing studies seeking to enhance the performance of our filter by utilizing unsupervised learning on untracked clusters, thus increasing background rejection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Data Driven Approach to Analyzing The Impact of Power Plant Cycling on Air Preheater Degradation and Remaining Useful Life

Due to the increased penetration of renewable energy generation sources, and fluctuations of the oil and gas prices, modern coal burning power plants deal with increased variability in the demand for power generation. These varying demands result in their intermittent under-capacity operation (cycling). Periodical ramping down and back up to follow the daily power demands causes damages to the plant components reducing its operational life. In this paper we analyze the impact of cycling on a rotary Ljungstrom air preheater (APH) unit installed at a coal fire power plant in the US. An inefficient air preheater can significantly impact boiler performance. Due to the repeated boiler’s hot-cold start, the APH experiences fluctuating operating conditions that result in accelerated degradation mechanisms, such as dew-point corrosion, fouling/deposition plugging, and air heater leakage. The analysis in this paper utilizes field data related to APH basket replacement, and the number of cycles experienced by the boiler to model the life expectancy of the baskets. The data-driven model enables preventive maintenance strategies for the APH by predicting how long the APH baskets will last in a probabilistic sense. Furthermore, the analysis showed that an increase in cycling for a fixed operation time can reduce the APH basket remaining useful life by about 30%.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Investigations into the Ternary NaF-KF-UF4 Salt System – Phase A

A knowledge gap exists in the data and understanding of fresh fuel salt and irradiated multicomponent fuel salt systems thermophysical properties. Quantifying these properties is necessary for the design and construction of test reactors, as well as the licensing of future commercial molten-salt reactors. To facilitate thermal property determination on a proposed fuel salt composition for Seaborg Technologies, several samples containing depleted uranium tetrafluoride (UF4), sodium fluoride (NaF), and potassium fluoride (KF) were blended, and a melt temperature analysis was performed. From the melting temperature analysis, it was determined that sample Seaborg-7, a ternary salt composition of 26.4UF4-24.7KF-48.9NaF (mol%), was very near a ternary eutectic point. Therefore, thermal properties such as melting temperature, salt stability, density, heat capacity, thermal diffusivity, and viscosity were experimentally determined on the Seaborg-7 salt. These measurements document the baseline properties of fresh fuel salt as a function of temperature, where future experiments on irradiated fuel salt will provide a holistic perspective on the change of thermophysical properties during reactor operations. Several precision instruments were used to collect property data, and instrument calibrations and data collection were performed and documented in a standardized and reproducible manner with meticulous detail. This process ensured that the measurement procedures and resulting data can readily be duplicated elsewhere. The Seaborg-7 salt was shown to be stable at temperatures up to 900°C, as no mass change was observed upon repeated heating and cooling. The peak melting temperature was determined to be 547°C (557°C endset). The enthalpy of fusion (??H?_fus^o) was determined to be 167.5 ± 2.7 J/g while the enthalpy of crystallization (??H?_c^o) was determined to be -147.8 ± 13.3 J/g. In addition to the eutectic melting peak, upon heating, several pre eutectic peaks were observed, occurring at 470°C (onset) and 499°C (peak). Specific heat capacity measurements showed a slightly increasing trend with respect to temperature in the solid phase, while the liquid-specific heat capacity showed a somewhat flat trend with an average value of 106.1 ± 1.24 J/mol·K between 600 to 800°C. Three independent trials using the Seaborg-7 salt determined the density to be ?(T) = 4.908 – 0.000363·T(°C), validated between 32 to 200°C, and ?(T) = 4.808 – 0.00113·T(°C), validated between ~575 to 850°C. Thermal diffusivity was determined for the liquid state and is represented by the linear equation y = 0.1581 + 0.000207·T(°C) between 550 to 850°C. The viscosity was determined from 600 to 800°C and is represented by the exponential fit equation, ? (mPa·s) = 736.58e^(-0.006·T(°C)). This report documents the conclusion of fuel salt thermophysical property measurements for the Seaborg SPP, Phase A project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Design of a High-Assay Low-Enriched Uranium Tri-Structural Isotropic Critical Experiment for Advanced Reactor Validation

High-assay low-enriched uranium (HALEU) fuel is a key component of many small modular reactor designs. Critical experiments are an important way to understand the neutronic performance of systems by obtaining nuclear data validations through measurements. Data reduce uncertainty and risk by showing that systems respond as predicted to changes such as temperature, subsequently advancing the overall technology readiness level of the materials within. Numerous critical experiments have been performed at the National Criticality Experiments Research Center (NCERC) operated by Los Alamos National Laboratory at the Nevada National Security Site since it became operational in 2011. However, the first experiment with HALEU fuel did not occur until 2024. Through extensive engineering, the experiment described in this paper was successfully designed and executed for the Comet vertical lift assembly at NCERC to perform measurements with HALEU tri-structural isotropic fuel that will assist in validation of nuclear data and computational modeling of small modular reactors for years to come.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Computing rank‐revealing factorizations of matrices stored out‐of‐core

This paper describes efficient algorithms for computing rank-revealing factorizations of matrices that are too large to fit in main memory (RAM), and must instead be stored on slow external memory devices such as disks (out-of-core or out-of-memory). Traditional algorithms for computing rank-revealing factorizations (such as the column pivoted QR factorization and the singular value decomposition) are very communication intensive as they require many vector-vector and matrix-vector operations, which become prohibitively expensive when data is not in RAM. Randomization allows to reformulate new methods so that large contiguous blocks of the matrix are processed in bulk. The paper describes two distinct methods. The first is a blocked version of column pivoted Householder QR, organized as a “left-looking” method to minimize the number of the expensive write operations. The second method results employs a UTV factorization. It is organized as an algorithm-by-blocks to overlap computations and I/O operations. As it incorporates power iterations, it is much better at revealing the numerical rank. Numerical experiments on several computers demonstrate that the new algorithms are almost as fast when processing data stored on slow memory devices as traditional algorithms are for data stored in RAM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Engagement opportunities in OECD NEA benchmark development

A myriad of opportunities is available to collaborate via international benchmark exercises and experimental data preservation activities. Many such opportunities abound under the auspices of the Nuclear Science Committee of the Organisation for Economic Co-operation and Development Nuclear Energy Agency (NEA). Key projects and activities of relevance to the development of advanced reactors design include the International Criticality Safety Benchmark Evaluation Project (ICSBEP), the International Reactor Physics Experiment Evaluation Project (IRPhEP), the International Assay Data of Spent Nuclear Fuel Database (SFCOMPO), the Shielding Integral Benchmark and Archive Database (SINBAD), and The International Experimental Thermal HYdraulicS Database (TIETHYS), and various cooperative benchmark exercises. Interested participants are encouraged to contact the leadership and secretariat of the various Technical Working Groups and Working Parties to become more engaged. This paper provides a summary of the current benchmark exercises and experimental databases available for international participation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Design optimization of an ethanol heavy-duty engine using design of experiments and bayesian optimization

Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools. In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165~candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9~percentage point increase in thermal efficiency from the baseline design, yet only required 65\% of the design samples investigated using the DoE approach.

Tekgul, Bulut↗