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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 OSTI2025

An analysis of physics limited dispatch of nuclear renewable integrated energy systems using deep reinforcement learning and dynamic modeling

Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. Furthermore, this work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

14 - SOLAR ENERGY↗
DOE OSTI2026

Continuum contribution to charged-current absorption of low-energy $ν_e$ on $^{40}$Ar

Accurate modeling of the absorption of tens-of-MeV $ν_e$ on $^{40}$Ar is needed to enable measurements of astrophysical neutrinos using large liquid argon time projection chamber (LArTPC) detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE). We revisit the MARLEY neutrino interaction model used in present estimates of DUNE sensitivity to supernova and solar neutrino signals. Multiple theoretical refinements are pursued, especially in the unbound continuum region of nuclear excitation energy. Inclusive charged-current neutrino-argon cross sections are calculated using a hybrid strategy. Nuclear transitions to unbound states are treated using a Hartree-Fock Continuum Random Phase Approximation (HF-CRPA) model, including forbidden contributions. Allowed transitions to low-lying discrete levels are also included using indirect measurements and approximate corrections for the momentum transfer dependence. Exclusive predictions are obtained by coupling these calculations with a statistical nuclear de-excitation model. The impact on observables of interest for DUNE and similar experiments is examined in terms of both total and differential cross sections. Our refined calculations predict a lower allowed portion of the cross section relative to the prior MARLEY model. At neutrino energies appreciably below 100 MeV, the inclusion of forbidden transitions does not fully compensate for the loss of allowed strength. For a representative neutrino burst from a galactic core-collapse supernova, our results suggest that MARLEY 1.2.0 overestimates the event yield in a DUNE-like detector by approximately 20%. However, because this overestimation is more severe at backwards angles, use of the charged-current $ν_e$-$^{40}$Ar reaction for supernova pointing may be more feasible than previously expected.

Gardiner, Steven [Fermilab]↗
DOE OSTI2026

FY27 Annual LDRD Call for Proposals - external release

Proposals are now being accepted for the fiscal year 2027 (FY-27) Laboratory Directed Research and Development (LDRD) call, the FY-27 Annual LDRD Call for Proposals. Idaho National Laboratory (INL) has renewed its research and development (R&D) portfolio to align with its strategy, and this call seeks high-quality proposals that address these renewed research priorities. The LDRD Office encourages proposals that offer the potential to achieve breakthroughs in the three science and technology (S&T) strategic initiatives: • Unleashing America’s Energy Abundance: Enabling the Quadrupling of Nuclear Power by 2050; • Securing America’s Future by Developing Domestic Supply Chains for Critical and Strategic Minerals and Materials (CSMM); and • Delivering Critical National Security Technology. Proposals that support INL’s four strategic thrust areas are also encouraged: • Enabling Future Fusion Energy Systems; • Delivering Small Nuclear Reactor Technology for Space Exploration and Defense Missions; • Accelerating Technologies for Hybrid Energy and Nuclear Applications; and • Securing America’s Energy Future Through Artificial Intelligence (AI).

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Flexible Nuclear-Based Integrated Energy System

Presentation on a conceptual study of combining a small modular nuclear reactor (SMR) with a solid oxide fuel cell, solid oxide electrolysis cell, and a gas turbine as an integrated energy system with high operational flexibility and rapid load following. Virtually presented at the MILLENNIUM CLEAN and SUSTAINABLE POWER workshop 2025, University of Genoa, Italy.

flexibility↗
DOE OSTI2025

Metallurgical Analysis and Forward Modeling of Weld Distortion in SMR Containment Vessels

This work aimed to apply Sandia’s expertise in metallurgy and modeling to enable the use of hybrid laser arc welding for building nuclear reactor containment structures, via a collaboration with Holtec International. Experimental observations were coupled with finite element analysis to resolve microstructure development, mechanical properties, distortion, and residual stress in welds relevant to the production of the Holtec SMR-160. High residual stresses were observed in welds that were not subjected to preheat. Meanwhile, the microstructure of the welds generally exhibited a narrow heat affected zone relative to conventional arc welds. FEA appeared to be effective in simulating the thermal/mechanical conditions that occur during hybrid laser arc welding of simplified and instrumented test welds. Subsequently, FEA was used to perform sensitivity analyses for various weld geometries that would be prohibitively costly to assess with physical experiments. Insights from the study were used to inform Holtec’s welding process, and successful production welds were performed in 2025.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Covalent integration of polymers and porous organic frameworks

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research.

Hossain, Md Amjad↗
DOE OSTI2025

Virtual Critical Coupling in High-Power Resonant Systems

Power reflections are a fundamental challenge in high-power resonant systems, causing energy loss, limiting per- formance, and potentially damaging critical components. This problem is particularly acute in applications such as lower hybrid current drive (LHCD) for tokamak-based nuclear fusion, where efficient, stable power transfer to the plasma is paramount. In this work, we introduce and experimentally demonstrate the virtual critical coupling (VCC) mechanism, a nonmechanical method to achieve reflection-free excitation in high-power S-band resonators. VCC utilizes a temporally shaped excitation signal with a precise complex frequency, tailored to the resonator’s characteristics to enable complete energy absorption. Using a custom low-level RF system and a 5-MW S-band klystron, we conducted experiments from low power (32 mW) to high power (600 kW). Furthermore, the results demonstrate a greater than ninefold reduction in the reflection coefficient compared to conventional monochromatic excitation. This signal processing-based approach offers a robust path toward enhancing the efficiency and stability of high-power resonant systems, with significant implications for particle accelerators and the advancement of nuclear fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

New Methods for Predicting Non-Born-Oppenheimer Chemistry

Current methods for modeling non-adiabatic molecular dynamics face fundamental limitations when treating geometric phase effects: quantum mechanical phenomena where nuclear wavepackets acquire phase shifts when encircling conical intersections. Existing approaches either neglect these effects entirely or rely on potential energy surfaces arising from the Born-Oppenheimer approximation, which introduce artificial singularities and can overestimate geometric phase contributions. This project developed a new theoretical framework based on exact factorization (XF) methods to overcome these limitations. We derived mathematical formulations for hybrid quantum-classical XF dynamics that selectively treat critical nuclear degrees of freedom quantum mechanically while propagating others classically. This approach addresses the computational intractability that has previously limited exact methods to toy systems. Key innovations include a new approach to systematically identifying nuclear coordinates requiring quantum treatment, as well as novel implementation strategies that interface with existing quantum chemistry codes. The project also developed a proof-of-concept code for treating Jahn-Teller systems and creation of educational materials on non-adiabatic dynamics geared at the graduate level. The theoretical framework developed will enable future systematically improvable calculations of nuclear quantum effects in realistic molecular systems, filling a critical gap in non-adiabatic dynamics methods. This foundation supports future development of predictive tools for designing energy-relevant photochemical processes where quantum coherence effects may be exploited to control reaction outcomes.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗
DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2025

Development of thin-gap GEM- μ RWELL hybrid detectors

Micro Pattern Gaseous Detectors (MPGDs) are used for tracking in High Energy Physics and Nuclear Physics because of their large area, excellent spatial resolution capabilities and low cost. However, for high energy charged particles impacting at a large angle with respect to the axis perpendicular to detector plane, the spatial resolution degrades significantly because of the long trail of ionization charges produced in clusters all along the track in the drift region of the detector. The long ionization charge trail results in registering hits from large number of strips in the readout plane which makes it challenging to precisely reconstruct the particle’s position using simple center of gravity algorithm. As a result, the larger the drift gap, the more severe the deterioration of spatial resolution for inclined tracks. For the same reason, the position resolution is also severely degraded in a large magnetic field, where the Lorentz E x B effect causes the ionization charges to follow a curved and longer path in the detector gas volume. In this paper, we report on the development of thin-gap MPGDs as a way to maintain excellent spatial resolution capabilities of MPGD detectors over a wide angular range of incoming particles. In a thin-gap MPGD, the thickness of the gas volume in the drift region is reduced from typically 3 mm to 1 mm or less. We present preliminary test beam results demonstrating the improvement in spatial resolution from 400 m with a standard 3 mm gap μ RWELL prototype to ~140 μ m with a double amplification GEM- μ RWELL thin-gap hybrid detector. Here, we also discuss the impact of a thin-gap drift volume on other aspects of the performance of MPGD technologies such as the efficiency and detector stability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Ameletus Mayflies (Ephemeroptera: Ameletidae) of the Eastern Nearctic

Fourteen Ameletus species are recognized in the eastern Nearctic (south of the Artic zone), including six described as new. Keys to adult males and full-grown larvae are provided. Taxonomic decisions were based on morphologic and genetic evidence. Their justification is discussed in depth and four species groups are proposed. The vast majority of Ameletus encountered in the eastern Nearctic are members of one of three triploid, clonal parthenogenetic species, at least two of which are of hybrid origin. Bisexual progenitors of the parthenogens were inferred using a combination of mitochondrial and nuclear genetic markers. The parthenogens likely arose during the Late Pleistocene when glacial advances brought previously allopatric species/populations into contact, and as glaciers retreated the parthenogens rapidly expanded their range while the sexual lineages remained in presumed glacial refugia. Although parthenogenesis is relatively common in Ephemeroptera, these Ameletus represent the first known cases of polyploidy and hybrid origin.

Funk, David H.↗
DOE OSTI2025

Towards a new generation of solid total-energy detectors for neutron-capture time-of-flight experiments with intense neutron beams

Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have low sensitivity to neutrons, can quickly recover from the so-called γ-flash, and offer the highest possible detection sensitivity. In this paper, we present several steps towards such advanced systems. Specifically, we describe the performance of a high-sensitivity experimental setup at CERN n_TOF EAR2. It consists of nine sTED detector modules in a compact cylindrical configuration, two conventional used large-volume C 6 D 6 detectors, and one LaCl 3 (Ce) detector. The performance of these detection systems is compared using 93 Nb(n, γ) data. We also developed a detailed G EANT small4 Monte Carlo model of the experimental EAR2 setup, which allows for a better understanding of the detector features, including their efficiency determination. This Monte Carlo model has been used for further optimization, thus leading to a new conceptual design of a γ detector array, STAR, based on a deuterated-stilbene crystal array. Finally, the suitability of deuterated-stilbene crystals for the future STAR array is investigated experimentally utilizing a small stilbene-d12 prototype. The results suggest a similar or superior performance of STAR with respect to other setups based on liquid-scintillators, and allow for additional features such as neutron-gamma discrimination and a higher level of customization capability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Engineering diamond interfaces free of dark spins

Nitrogen-vacancy (NV) centers in diamond are utilized extensively as quantum sensors for imaging fields at the nanoscale. The ultrahigh sensitivity of NV magnetometers has enabled the detection and spectroscopy of individual electron spins, with potentially far-reaching applications in condensed matter physics, spintronics, and molecular biology. However, the surfaces of these diamond sensors naturally contain electron spins, which create a background signal that can be hard to differentiate from the signal of the target spins. In this study, we develop a surface modification approach that eliminates the unwanted signal of these so-called dark electron spins. Our surface passivation technique, based on coating diamond surfaces with a thin titanium oxide (Ti⁢O 2 ) layer, reduces the dark spin density. The observed reduction in dark spin density aligns with our findings on the electronic structure of the diamond-Ti⁢O 2 interface. The reduction, from a typical value of 2000 μm −2 to a value below that set by the detection limit of our NV sensors (200 μm −2 ), results in a twofold increase in Hahn-echo coherence time of near surface NV centers. Furthermore, we derive a comprehensive spin model that connects dark spin relaxation with NV coherence, providing additional insights into the mechanisms behind the observed spin dynamics. Our findings are directly transferable to other quantum platforms, including nanoscale solid-state qubits and superconducting qubits.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Applications of quantum materials in nuclear physics experiments (Final Report)

The goals of this project are to search for hypothesized spin-hall effects of neutrons and variants of neutron-electron hybrid spin hall effects in quantum materials with strong spin-orbit-coupling (SOC) – such as: • Deflection of neutrons dependent on spin (polarization) states (a); • Electrical current induced electron spin polarization or (dynamically polarized) nuclear spin polarization (as suggested in PI’s prior work) may affect polarization states of transmitted or reflected neutron beam • Spin polarized neutrons transfer some spin angular momenta to electrons, which get converted to electronic charge voltage via electronic inverse spin Hall effect (b), which if realized, offers an electrical method to detect neutron spins.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗