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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

JAMES BUTTLE REVIEW: Interflow, subsurface stormflow and throughflow: A synthesis of field work and modelling

Interflow, throughflow and subsurface stormflow are interchangeable terms that refer to the lateral subsurface flow above a restricting layer of lower hydraulic conductivity that occurs during and following storm events. Interflow (used here) is a more dominant process in steeper catchments with high infiltration capacity soils overlying a more impermeable soil or geologic layer. Interflow as a runoff process was first recognised in the early 1900s, yet hydrologists still struggle to predict its occurrence, persistence, importance, interaction with other streamflow generation processes, and potential to connect to valleys and streams during and following storms. We review the history of interflow research and address some of the challenges in understanding its role in runoff production. We argue that characterising the controls on interflow initiation and occurrence relies on detailed field observations of subsurface properties, which exist only in limited experimental settings. This data shortcoming contributes to our inability to predict interflow or determine its contribution to streamflow more broadly. There remain many opportunities to advance our understanding of interflow that include both modelling and experimental or observational approaches in hydrology.

hillslope hydrology↗
DOE OSTI2025

Step-loaded creep testing of Zircaloy-4 cladding at higher temperatures in the α-phase

A refined understanding of zirconium-based cladding thermomechanical performance during rapid transients is essential for enhancing the safety and operation of light-water reactors. Traditional models for zirconium alloys under accident conditions generally assume that creep dominates fuel cladding performance. Here, these historic models have largely remained unchanged and serve as the basis for safety criteria development. As the U.S. nuclear industry pursues higher burnup levels, the increased release of fission gases during transients raises the risk of cladding failure in the low-temperature hcp α-phase, making the fidelity of these models of greater importance. Creep testing was conducted from 550–700°C with 25–120 MPa applied hoop stresses to investigate Zircaloy-4 deformation at accident-relevant temperatures in the α-phase. Step-loading was employed to capture creep behavior across a wide stress range from a single sample. The stress-strain rate data at higher temperatures (650 and 700°C) were well-described by isotropic versions of the Erbacher and Kaddour models, while the lower temperature data (550 and 600°C) were underpredicted by both anisotropic and isotropic model variants. Greater strain rates during the initial loading step at 650 and 700°C were attributed to recrystallization and grain growth of sub-micron crystallites. Yet, texture analysis revealed the basal split texture remained after testing. These observations produced results suggesting Zircaloy-4 claddings experience higher creep rates across the α-phase than previously thought, possibly related to dynamic anisotropy due to temperature dependent activation of deformation mechanisms, effects of biaxial loading, and variation in material condition between the current testing used in previous model development.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance and Durability of Heavy-Duty Fuel Cell Systems with an Advanced Ordered Intermetallic ORR Alloy Catalyst and Novel Support

Ordered PtCo intermetallic (OIM) catalyst (L1 0 -PtCo/C) is a promising candidate as the oxygen reduction reaction (ORR) catalyst in hybrid fuel cell systems (FCS) for class-8 heavy duty (HD) trucks. Compared to a baseline annealed Pt on high surface area carbon (a-Pt/HSC) catalyst, its mass activity (MA) is 71% higher initially and 144% higher after 90,000 potential cycles in an accelerated stress test (AST). Analysis of the AST data indicates that the ORR kinetic constants do not change with aging and the degradation in the OIM catalyst activity is linearly proportional to the loss in the electrochemically active surface area (ECSA). Several operational strategies are investigated to mitigate catalyst degradation and achieve 25,000-h electrode lifetime and 2.5 kW g −1 Pt utilization on a HD truck duty cycle including load sharing with the hybrid battery, regulating the radiator fan power to maintain the coolant temperature close to 60 °C, clipping the maximum cell voltage below 850 mV, limiting the ECSA loss to 55%, and oversizing the active area of the membrane electrode assemblies by 20%. Drive cycle simulations indicate that the lifetime average voltage degradation rate is about 1.8 μV h −1 and the integrated stack and FCS drive cycle efficiencies decrease by 3.5 to 3.9%.

25 ENERGY STORAGE↗
DOE OSTI2025

Surrogate Modelling of 3rd Integer Resonant Extraction at Fermilab Delivery Ring

We present an ongoing work in which a surrogate model is being developed to reproduce the response dynamics of the third-integer resonant extraction process in the Delivery Ring (DR) at Fermilab. This effort is in pursuit of smoothly extracting circulating beam to the Mu2e Experiment s production target, wherein the goal is to extract a uniform slice of the circulating $1e12$ protons in the DR over 25,000 turns (43~ms). The DR contains 3 harmonic sextupoles which excite a third-integer resonance as well as three fast, tune-ramping quadrupole magnets which drive the horizontal tune towards the $29/3$ resonance. In our initial work the surrogate model trains on a semi-analytical simulation provided in the same format as live data. Using Reinforcement Learning (and other potential ML methods), the trained surrogate acts as the environment in which a simple ML control agent could learn to dynamically adjust the quadrupole ramp at 430 break points within the 43 microsecond spill window. The control agent will be hosted on a dedicated Arria 10 FPGA, introducing its own requirements on control agent architecture. In this work we report the accuracy and fidelity of surrogate models in comparison to the response dynamics of the physics simulator.

Narayanan, Aakaash [Fermilab] (ORCID:0000000157944↗
DOE OSTI2025

Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet

Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the long-time instability of the surrogates and the sparsity of the observations, filtering estimates can remain accurate in the long-time horizon. As a case study, we integrate FourCastNet, a weather surrogate model, within a variational data assimilation framework using partial, noisy ERA5 data. Our results show that filtering estimates remain accurate over a year-long assimilation window and provide effective initial conditions for forecasting tasks, including extreme event prediction.

Adrian, Melissa [Univ. of Chicago, IL (United Stat↗
DOE OSTI2024

Initial Calibration of Large Timing Arrays for the LHC

In preparation for HL-LHC operation a number of new detector systems are being constructed with timing precision on physics objects of <50 picoseconds. These time stamps will reduce the level of pileup induced backgrounds in this LHC phase where the number of interactions per crossing will reach of order 100-200. In the case of CMS, three new systems have initially to be corrected for the usual amplitude walk resulting from the effect of variations in signal size on leading edge timing. In these systems the resulting timing spread (ie walk) ranges from one to four nanoseconds. In the following note we advocate approaching this initial calibration for walk as a calculable correction given early calibration during commissioning -- rather than depending on special collider data to perform the calibration. We derive a simple analytic expression for the walk correction and confirm its effectiveness with lab data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis – Simulated Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wind turbine. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen . While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the simulated wind energy profiles, NLR used OpenFAST to simulate a 3.4-MW International Energy Agency (IEA) reference wind turbine. The hour-long wind energy profiles varied over wind turbulence intensity (Class A or Class C) and average wind speed (5, 7, or 9 m/s). To match the power limits of the 1.25-MW electrolyzer and 3.4-MW IEA wind turbine most effectively and to maximize the efficiency of hydrogen production at a given average wind speed, the profiles were sometimes scaled by two times. This means that, in some cases, the experimental setup assumed two 1.25-MW electrolyzers were coupled with the wind turbine, representing a total maximum electrolysis load of 2.5 MW. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}-{average wind speed}-{turbulence class}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “windIEA3.4-5ms-C_2-400.zip” represents the hour-long experiment using the IEA 3.4-MW turbine, subjected to an average wind speed of 5 m/s and Class C wind turbulence, and connected to two 1.25-MW electrolyzers with the power supply set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wind turbine power. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis .

08 HYDROGEN↗
DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Wave

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wave energy conversion device. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen. While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the wave energy, NLR used a wave energy converter model from PacWave. These devices can be equipped with accumulators and pressure relief values to smooth the power output by storing and releasing hydraulic energy. Using a peak power output of 10 MW, the model created two 25-minute profiles: one with and one without the accumulators and pressure relief valves. To down select the profile data from the native resolution of 20 Hz to 1 Hz, NLR took the mean of every 20 data points. NLR experimented with two simulated wave energy power plants: one that peaks at 10 MW, and one that peaks at 5 MW. These profiles were scaled for the physical 1.25 MW electrolyzer by multiplying the original profiles by one eighth and one quarter, respectively. The first profile matches the capacity rating of eight of the 1.25 MW electrolyzers, while the second matches four electrolyzers. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wave electrolysis experiment and is formatted as follows: {technology}-{accumulator?}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “wavePacWave-Noacc_4-400.zip” represents the 25 minute-long experiment using the PacWave’s wave energy converter model, equipped with no accumulator, connected to four 1.25-MW electrolyzers with their power supplies set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. An experiment, labeled “characterization_200.zip”, demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all wave profiles combined into one dataset labeled "combined_wave_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis.

08 HYDROGEN↗
DOE OSTI2026

A data-driven approach to real-time vertical position estimation for NSTX-U vertical stability control

In this paper, a database of 77 996 plasma equilibrium reconstructions from 727 discharges during the initial operation of the NSTX-U spherical tokamak is analyzed to develop a statistically robust model of the plasma vertical position for real-time control. A variety of regression models are developed and tested, ranging in complexity from linear models to deep neural networks, and including input signals ranging from the four pairs of flux loops used historically on NSTX-U up to the full set of 389 real-time signals available to the plasma control system. A linear model based on 140 real-time magnetics signals is found to offer excellent accuracy, with a coefficient of determination R 2 = 0.906. The robustness of this model to limited training data, new operating scenarios, and signal errors is tested, and a procedure is demonstrated to tune the model parameters to optimize its robustness. A time-dependent plasma equilibrium solver, TokaMaker, is used to simulate vertical stability control in NSTX-U, demonstrating that it should be possible to iteratively tune the parameters of a linear vertical position model to stabilize both positive and negative triangularity plasmas in future experiments.

magnetic diagnostics↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN↗
DOE OSTI2024

Search for low-mass resonances decaying into two jets and produced in association with a photon or a jet at √s = 13 TeV with the ATLAS detector

A search is performed for localized excesses in the low-mass dijet invariant mass distribution, targeting a hypothetical new particle decaying into two jets and produced in association with either a high transverse momentum photon or a jet. The search uses the full Run 2 data sample from LHC proton-proton collisions collected by the ATLAS experiment at a center-of-mass energy of 13 TeV during 2015–2018. Two variants of the search are presented for each type of initial-state radiation: one that makes no jet flavor requirements and one that requires both of the jets to have been identified as containing b-hadrons. No excess is observed relative to the Standard Model prediction, and the data are used to set upper limits on the production cross section for a benchmark Z' model and, separately, for generic, beyond the Standard Model scenarios which might produce a Gaussian-shaped contribution to dijet invariant mass distributions. The results extend the current constraints on dijet resonances to the mass range between 200 and 650 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Initial Mobility Analysis for ORNL VA-EDH Synthetic Populations

Travel burdens are a major barrier to healthcare access among US Veteran patient populations, particularly those residing in rural areas. Spatial accessibility to points of care for US Veteran populations is commonly assessed in two ways. The first approach uses open data from the US Census to represent collective travel burdens, for example the distance between population-weighted census tract centroids and VHA points of care. The second approach uses restricted-access VHA patient data to measure travel costs (e.g., distance, time) for accessing points of care with respect to geolocated patient addresses and real or approximated transportation networks. While the advantage of the open data approach lies in its reproducibility, it has notable limitations in its tendency to infer individual travel behavior from aggregate population characteristics, a problem known as ecological fallacy. Conversely, while the patient data approach is able to account for individual travel behavior, its ability to account for localized access disparities (e.g., a neighborhood with exceptionally high transportation costs) and patient demographics is limited as protecting individual patient data requires their storage in closed systems with limited capacity for adequately modeling real-world travel patterns or for supplementing patient attributes. Additionally, the patient data approach cannot account for veterans who are not enrolled in the VHA system but who may be eligible for care. These challenges limit the ability to perform “what if” analyses on the effects of place-specific interventions on veteran populations with high access barriers to healthcare. To address these challenges, we explore the application of realistic synthetic populations to examine travel burdens and spatial accessibility issues among veteran patient populations. Synthetic populations provide a virtual, individually-resolved and cross-sectional representation of the veteran patient population that enables investigation of spatial access to points of care in ways in which aggregate data and patient data do not. First, synthetic populations allow one to directly assess how individuals access points of care, from synthesized residential locations to outpatient facilities on real-world transportation networks. Modeling access to points of care at the individual scale addresses the ecological fallacy problem associated with using aggregated census data to represent veteran populations and patterns of movement. Second, synthetic populations provide a means of completely representing an area’s veteran population using only publicly available, anonymized census microdata from the American Community Survey (ACS) to ensure the privacy of real-world individuals. Generating synthetic populations from the ACS also expands descriptive characteristics beyond what patient data typically offers to include socio-demographic, economic, housing, and mobility attributes. More detailed profiles of both VHA patient populations and veterans not enrolled in the VA system will provide a comprehensive picture of groups that may benefit from interventions or outreach. As an initial exercise for using synthetic populations to measure veteran travel burdens to VA care, we apply Oak Ridge National Laboratory’s (ORNL) UrbanPop capability to generate a series of synthetic VHA patient populations for 9 Veterans Integrated Services Networks (VISN) market areas in 9 Census Divisions across the continental United States, which are listed in Table 1. We use UrbanPop to produce synthetic populations for the VISN markets selected for each US Census Division, then assign VA outpatient clinic destinations to synthetic VHA patients based on travel about each VISN market’s road network. To demonstrate using the synthetic populations to evaluate healthcare travel burdens, we compare the time-based impedance between simulated home locations and VA outpatient clinics in each VISN market. We then perform validation exercises on the synthetic populations with respect to neighborhood (block group) demographic composition as well as patient mobility, comparing aggregate origin-destination statistics for the synthetic population to outpatient visits available in restricted patient data from the VA’s Corporate Data Warehouse (CDW) database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2024

Utah FORGE Project 3-2417: Meteorological Data During 2023 Completion/Circulation

This preliminary data archive includes meteorological data recorded at the Utah FORGE facility over the period of time including the completion/cementing of well 78-16B and the initial 2023 circulation test, largely occurring in June/July/August of 2023. This information may prove useful for understanding seismic and other instrumentation responses during these activities. The meteorological station was installed June 23rd, 2023 during drilling; the circulation test occurred in mid July of 2023 (July 4-19). The included files span this period. All sensors were installed at the 16 pad and hence reflect weather state at the site. This dataset was acquired by the FOGMORE R&D project (Fiber Optic MOnitoring for Reservoir Evolution), Utah FORGE R&D Project 3-2417.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗
DOE OSTI2024

Validation of prediction capability of operating space for plasma initiation in MAST-U

DYON is a plasma initiation modelling code that solves the differential equation system of the full circuit equations (plasma current, active coil currents and eddy currents in full passive structures) and 0D global energy and particle balance equations (Kim 2022 Nucl. Fusion 62 126012). In order to test the capability of the full electromagnetic plasma initiation model to predict individual discharges in experiments and thus the operating space in the device, a dedicated experimental database was built in MAST-U by scanning the prefilled gas pressure p 0 and the induced loop voltage V loop . In the experimental operating space of p 0 and V loop the lower and the upper limits of p 0 are determined by the plasma breakdown failure and the plasma burn-through failure, respectively. The lower limit of V loop is determined by the plasma burn-through failure. By directly reading the control room data used in each discharge (i.e. currents in the solenoid, poloidal field coils, and toroidal field coils, p 0 , and gas puffing rate), the full electromagnetic DYON consistently predicted the failed breakdown, failed burn-through, and successful plasma initiation discharges in the experimental database, demonstrating its capability to predict the operating space for inductive plasma initiation. The Paschen curve calculated with the effective connection length in MAST-U indicates a much higher p 0 required for plasma breakdown than the experimental data, indicating that individual field line evaluation is necessary to calculate the quantitative requirements for Townsend breakdown. The demonstration in this paper shows that the full electromagnetic DYON could be a useful simulation tool to assess the feasibility of inductive plasma initiation and to optimise operating scenarios in future devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Using Neural Networks to Identify Mixture Components in Hyperspectral Reflectance Data

Neural networks have been employed to identify materials of interest from hyperspectral data (generally imagery) based on their unique spectral signatures. This approach assumes that there is a single material that is standing out from the rest of the spectrum to be identified. However, pixels often contain more than one material, or a material of interest may itself be a mixture of multiple materials. Neural networks are only as good as the data used to train them, and it takes a great deal of work in the laboratory to identify, make, and measure all potential mixtures of interest. Thus, researchers often calculate synthetic spectra using algorithms with varying degrees of fidelity to the physics that govern the interactions between light and multiple materials. In this work, we have (1) adapted a neural network designed to identify mixture components from Raman spectroscopy to work with visible to near‐infrared reflectance data and (2) tested three common mixture algorithms to determine the most accurate and least computationally expensive method to build synthetic training datasets. With our initial test dataset, we have achieved accuracies of > 90% and found that the synthetic training dataset produced using the Hapke mixture model provides the best results.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Measurement of the A dependence of the ν μ charged-current quasielasticlike cross section as a function of muon and proton kinematics at ⟨E ν ⟩ ∼ 6 GeV

The first simultaneous measurements of the 𝜈 𝜇 quasielasticlike cross section on C, CH, H 2 ⁡O, Fe, and Pb targets as a function of kinematic imbalance variables in the plane transverse to the incoming neutrino direction are presented. These variables combine the muon and proton information to provide a new way to disentangle the effects of the nucleus in quasielasticlike processes. The data were obtained using a wideband 𝜈 𝜇 beam with ⟨E 𝜈 ⟩ ∼ 6 GeV. Cross-section ratios of the different target materials to CH are also shown. These measurements are used to explore the nature of the cross-section 𝐴 scaling, as well as initial and final state interaction effects. Comparisons are made to predictions from a number of commonly used neutrino Monte Carlo event generators. The range of predictions of the different models tends to cover the data but the degree and consistency of the agreement suffers in regions, and on higher 𝐴 targets, where the final state interactions are expected to be more pronounced.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗