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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dynamical correlations and nonequilibrium sum rules in photodoped Hubbard ladders

Using matrix product state techniques we study the nonequilibrium dynamical response of the half-filled Hubbard ladder when subject to an optical pump. Optical pumping offers a way of producing and manipulating new strongly correlated phenomena by suppressing existing magnetic correlations. The ladder allows the effects of pump directionality to be investigated, and compared to a single chain it has strong spin-charge coupling and a fully gapped excitation spectrum, promising different nonequilibrium physics. We compute time-dependent correlations, including the nonequilibrium dynamical structure factors for spin and charge. By deriving a combined spin-charge sum rule that applies both in and out-of-equilibrium, we show that spectral weight is pumped directly from the antiferromagnetic spin response into a low energy 𝜔 ∼ 0 charge response below the Mott gap. The transfer of weight is pump direction dependent: pumping directed along the legs disrupts magnetic correlations more than pumping in the rung-direction, even if the post-pump energy density is similar. The charge correlation length is dramatically enhanced by the pump, whilst the spin correlations are most strongly suppressed at nearest- and next-nearest-neighbor spacings. After the pump the system is in a nonthermal correlated metallic state, with gapless charge excitations and approximately equal spin and charge correlation lengths, emphasizing the importance of treating these degrees of freedom on an equal footing in nonequilibrium systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Weak-coupling theory of magic-angle twisted bilayer graphene

Strong correlations occur in magic-angle twisted bilayer graphene (MATBG) when the octet of flat moiré minibands centered on charge neutrality (CN) is partially occupied. The octet consists of a single valence band and a single conduction band for each of four degenerate spin-valley flavors. Motivated by the importance of Hartree electrostatic interactions in determining the filling-factor-dependent band structure, we use a time-dependent Hartree approximation to gain insight into electronic correlations. We find that the electronic compressibility is dominated by Hartree interactions, that paramagnetic states are stable over a range of density near CN, and that the dependence of energy on flavor polarization is strongly overestimated by mean-field theory. Published by the American Physical Society 2024

Materials Science↗
DOE OSTI2026

The Extent of Solar Energetic Particle Irradiation in the Sun’s Protoplanetary Disk

Solar flares emit X-rays and high-energy (MeV to GeV) ions called solar energetic particles (SEPs). Astronomical observations show solar-mass protostellar fluxes are a factor Φ ≈ 3 × 10 2 –3 × 10 3 times higher than the present-day Sun. Constraining Φ in the early solar system is important for modeling ionization in the Sun’s protoplanetary disk, the extent of magnetorotational instability or magnetocentrifugal outflows, or even production of short-lived radionuclides. Recent interpretations of meteoritic data—including cosmogenic Ne in hibonite grains, initial ( 10 Be/ 9 Be) 0 ratios in Ca-rich, Al-rich inclusions (CAIs), or even inferences of live 7 Be in CAIs—suggest values Φ > 10 5 , reaching as high as Φ ≈ 6 × 10 6 , which would make the young Sun extraordinarily active, even for a protostar. We constrain Φ by reexamining these data. We conclude that cosmogenic Ne was produced in hibonite grains as they resided in the disk; 36 Cl was created in Cl-poor grains after the disk dissipated; 10 Be was inherited from the molecular cloud, with almost no (<1%) 10 Be created in the disk; and there is no evidence whatsoever for any live 7 Be in CAIs. We show these data are consistent with a value Φ ≈ 3 × 10 3 for the first >5 Myr of the solar nebula. The early Sun evidently emitted a flux of X-rays and SEPs not atypical for a protostar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Hydropower Capacity Factor Trends & Analytics for the United States

This data repository contains all code, input data, and data generated for Turner et al. (2024)—“Hydropower capacity factors trending down in the United States”. File descriptions: – hydro-cf-trends-inputs.zip: Full set of input data used in this study, organized for direct entry into “/data” directory of hydro-cf-trends data processing pipeline. – hydro-cf-trends.zip: Full data processing pipeline, coded using the R {targets} framework. This is a snapshot release (v1.0) of the code repository stored at https://code.ornl.gov/turnersw/hydro-cf-trends/. – hydro-cf-trends-results.zip: Provides all dam level results required to reproduce results and graphics in Turner et al. (2024). Dams are identified by the “complxID” (root of the hydropower plant ID in the Existing Hydropower Assets Database, inherited from HILARRI). Results include: • dam_CF_trends.csv: Table of long-term trends in annualized capacity factors for 610 dams and modeled annualized capacity factors for 362 modeled dams (naturalized and assimilated flows). • dam_annualized_CF_gen.csv: Annualized time series of the following variables for each of 610 hydropower dams with nameplate > 5MW – Reported nameplate capacity (MW) – Implied maximum annual generation (MWh) – Reported net generation (MWh) – Computed annual capacity factor – Modeled annual capacity factor (362 modeled plants only)

13 HYDRO ENERGY↗
DOE OSTI2025

Accelerated Fuel Qualification of Fast Modular Reactor Fuel in a Thermal Reactor: Modeling and Simulation Paired with Irradiation Testing

The accelerated fuel qualification (AFQ) methodology is applied by simulating accelerated fuel tests of the General Atomics Electromagnetic Systems’ fuel system for its 44-MW(electric) gas-cooled, fast-spectrum fast modular reactor (FMR). This fuel is comprised of UO 2 pellets in SiGA® cladding, a silicon carbide ceramic matrix composite. Fast reactors, like the FMR, offer many benefits, including high fuel utilization and flexibility, but may require a lengthy material design process if tests are performed using fast neutron irradiation alone. A thermal neutron irradiation can instead be used to rapidly test how well key components of the current material models extend to high burnup. Thermal neutrons produce a different radial power distribution within the pin than fast neutrons. However, the temperature and burnup values for the two neutron types are comparable, and the differences between the simulated fuel responses are relatively small, demonstrating the weak sensitivity of the physics-based fuel model calculations on the neutron type and the irradiation rate. Furthermore, the deformation of the SiGA cladding saturates after about 1 displacement per atom for both neutron spectra. In an accelerated fuel test, the irradiation time required to reach the target fuel burnup can be reduced by a factor of 3 by using a small rodlet with a 45% smaller pellet diameter while maintaining the same linear power. Therefore, the time for data collection up to high burnup can be significantly reduced while maintaining the same temperature profile, which largely determines the material response. Tests of fuel rodlets of standard and compact size will be carried out in the Idaho National Laboratory’s Advanced Test Reactor (ATR), including full size and compact rodlets with varying gap sizes. By applying physics-based mechanistic modeling and simulation in accordance with the AFQ methodology, this type of compact rodlet testing in a thermal test reactor captures the necessary phenomena to test fuel material models up to high burnup and to simulate the expected impact of fast neutron radiation on the fuel in FMR operations. Finally, this approach to testing fast reactor fuels in existing thermal test reactors, paired with advanced physics-based mechanistic modeling and simulation, is expected to be applicable to a range of advanced fuels and will decrease the overall fuel qualification timeframe from decades to years.

Advanced test reactor (ATR)↗
DOE OSTI2024

Size resolved particle volatility for 30, 60, and 90 nm particles collected at the EPCAPE Mount Soledad site from 04/23/2023 to 06/15/2023

VTDMA GF-PDF: Growth factor probability density functions from the Gysel inversion are located here, where probability is out of 200. The instrument measured three different diameters 30 nm, 60nm and 90nm each diameter was selected for an hour. During that hour three different temperatures where scanned (40°C, 80°C, 160°C) in between each temperature a bypass scan was taken at room temperature. Each scan took 10-minutes and scanned growth factors from 0.2-1.2. Calibrations of the instrument with NaCl were performed on 4/26/2023 from 11:20 to 14:20, on 5/16/2023 from 11:40 to 16:00, and on 5/30/2023 from 10:20 to 14:20. Calibrations with (NH4)2SO4 were performed on 5/8/2023 from 11:45 to 14:40, on 5/23/2023 from 11:20 to 3:00, and on 6/14/2023 from 13:00 to 16:00. Time is recorded in seconds since 1/1/1904. GF_Vol_avg: Growth factor volume average calculated by the Gysel inversion. The instrument measured three different diameters 30 nm, 60nm and 90nm each diameter was selected for an hour. During that hour three different temperatures where scanned (40°C, 80°C, 160°C) in between each temperature a bypass scan was taken at room temperature. Each scan took 10-minutes and scanned growth factors from 0.2-1.2. Calibrations of the instrument with NaCl were performed on 4/26/2023 from 11:20 to 14:20, on 5/16/2023 from 11:40 to 16:00, and on 5/30/2023 from 10:20 to 14:20. Calibrations with (NH4)2SO4 were performed on 5/8/2023 from 11:45 to 14:40, on 5/23/2023 from 11:20 to 3:00, and on 6/14/2023 from 13:00 to 16:00. Time is recorded in seconds since 1/1/1904.

30nm growth factor volume average at 160C (GF_vol_↗
DOE OSTI2026

Adaptive Control for Load-Following of Boiling Water Reactors Part I: Linear Systems and Fully-Observable Dynamics

Automation control is a key strategy to improve the economic competitiveness of nuclear power plants. Not only does it help reduce operational costs, but it also extends the value proposition of these plants to nontraditional markets, including unattended operations in remote villages and space. However, the dynamics of the operating environments of nuclear reactors are subject to changes over time, and there are no widely adopted methods to ensure that the automation strategy will remain effective over the extended durations required for these applications. Adaptive control is a discipline that offers the possibility to accommodate such changes online. However, it relies on mathematical assumptions that must be respected to ensure robustness and reliability. In this work, we derive an adaptive control formulation for linear systems in which all states are observable and apply it to an instance of load-follow operation for Boiling Water Reactors. We assumed uncertainty in two factors: the temperature coefficient and the control rod worth, both of which are affected over time by the evolution of the nuclear reactor core environment. With an arbitrary penalty factor of 5, we found that the mean absolute and integral time absolute errors can be reduced by more than 90%, underscoring the strength of adaptive control. To extend the application to more challenges, different uncertainties and load-follow trajectories, as well as new formulations that include non-linearity and partial observability, are currently being developed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fuzzy spheres in stringy matrix models: quantifying chaos in a mixed phase space

We consider a truncation of the BMN matrix model to a configuration of two fuzzy spheres, described by two coupled non-linear oscillators dependent on the mass parameter μ. The classical phase diagram of the system generically (μ ≠ 0) contains three equilibrium points: two centers and a center-saddle; as μ → 0 the system exhibits a pitchfork bifurcation. We demonstrate that the system is exactly integrable in quadratures for μ = 0, while for very large values of μ, it approaches another integrable point characterized by two harmonic oscillators. The classical phase space is mixed, containing both integrable islands and chaotic regions, as evidenced by the classical Lyapunov spectrum. At the quantum level, we explore indicators of early and late time chaos. The eigenvalue spacing is best described by a Brody distribution, which interpolates between Poisson and Wigner distributions; it dovetails, at the quantum level, the classical results and reemphasizes the notion that the quantum system is mixed. We also study the spectral form factor and the quantum Lyapunov exponent, as defined by out-of-time-ordered correlators. These two indicators of quantum chaos exhibit weak correlations with the Brody distribution. We speculate that the behavior of the system as μ → 0 dominates the spectral form factor and the quantum Lyapunov exponent, making these indicators of quantum chaos less effective in the context of a mixed phase space.

AdS-CFT correspondence↗
DOE OSTI2025

Simulation-driven design optimization of reaction injection molding (RIM) process for polydicyclopentadiene (pDCPD): Minimizing cycle time, defects, and warpage

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this study, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. In conclusion, this optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption.

42 ENGINEERING↗
DOE OSTI2025

Splitting amplitudes at N 3 LO in QCD

In the limit where partons become collinear to each other, scattering amplitudes factorize into a product of universal, process-independent building blocks and scattering amplitudes involving fewer partons. We compute these universal building blocks — known as splitting amplitudes — for two collinear QCD partons up to third loop order in QCD. Our results describe arbitrary time-like splitting processes. Due to the violation of strict collinear factorization in space-like splitting processes, we specifically present space-like splitting amplitudes for three-parton QCD scattering amplitudes at third loop order. To achieve our results, we perform a collinear expansion of three-loop scattering amplitudes using a new expansion-by-subgraph technology, which is based on the method of regions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Phenomena Identification and Ranking Tables (PIRT) analysis of wind turbine blade leading edge erosion

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 Phase 2 to undertake cooperative research in the key topic of blade erosion. The purpose of Task 46 Erosion Phase 2 is to further improve understanding of erosion driving factors, develop datasets and model tools to enhance prediction of leading-edge erosion likelihood, identify damage at the earliest possible stage and advance potential solutions. The scope of work covers several technical areas, reflecting the multidisciplinary nature of the challenge. Participants in the task are given in Table 1.

17 WIND ENERGY↗
DOE OSTI2025

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

14 SOLAR ENERGY↗
DOE OSTI2025

Particle Markov Chain Monte Carlo Approach to Inference in Transient Surface Kinetics

Here, in this work, we develop a novel Bayesian approach to study the adsorption and desorption of CO onto a Pd(111) surface, a process of great importance in natural sciences. The motivation for this work comes from the recent availability of time-resolved infrared spectroscopy data and the need for model interpretability and uncertainty quantification in chemical processes. The objective is to learn the relevant parameters that characterize the process: coverage with time, rate constants, activation energies, and pre-exponential factors. Our approach consists of three main schemes: (i) a problem design and probabilistic model for the whole system, (ii) a particle Markov chain Monte Carlo sampler to learn the hidden coverages and rate constant parameters, and (iii) two Bayesian formulations to infer the activation energies and pre-exponential factors. The flexibility of the Bayesian framework allows for uncertainty quantification where possible and integration of mathematical constraints in the model to reflect the system physically. We found that our results for the activation energies and pre-exponential factor are in agreement with those reported in the experimental literature, independently, and we provide discussions on the advantages and disadvantages as well as applicability to other systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A polysulfide/ferricyanide redox flow battery with extended cycling

The inexpensive sulfur raw material is promising to enable cost-effective redox flow batteries for long duration energy storage. But the catastrophic through-membrane crossover of polysulfides remains a severe challenge resulting in irreversible performance degradation and short cycle life. In this work, we demonstrate that use of a permselective cation exchange membrane yields a two orders of magnitude enhancement in polysulfide retention compared to the benchmark Nafion membrane. Combined physico-chemical, spectroscopic, and microscopic analyses suggest more disordered sidechain structures, which lead to the more hydrophobic nature and smaller hydrophilic domains in the membrane. The microstructural features contribute to the effective mitigation of polysulfide crossover. As a result, the cycle life of polysulfide/ferricyanide flow cells is boosted over a substantially extended test time. This finding sheds light on the fundamental membrane factors that cause polysulfide permeation and can provide feasible directions in the development of permselective membranes for polysulfide flow batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Laser beam pointing stabilization using analog position-sensitive diodes

Laser wire scanners have been used for ion beam profile and emittance measurements at the Spallation Neutron Source linear accelerator. Due to propagation distances exceeding 100 m, reliable measurements require laser beam stabilization, previously accomplished with a feedback control loop using a digital camera as a position sensor [Hardin et al., Opt. Express 19, 2874 (2011)]. Here, this paper presents an upgraded pointing stabilization system utilizing analog position-sensitive diodes (PSDs) as the position detector, optimized with an optical diffuser. The new system significantly surpasses the previous system by offering an order-of-magnitude improvement in radiation tolerance and a bandwidth limited only by the burst repetition rate of the laser beam. Operating with a 60-Hz burst mode laser, the new system effectively suppresses laser beam drifts up to 30 Hz, compared to the previous system’s 4 Hz limit. In addition, the amplitude of spectral components below 0.1 Hz was reduced by a factor of more than 100, which is over seven times greater than was achieved by the previous system.

Data acquisition↗
DOE OSTI2024

Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants

The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Furthermore, our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Low-temperature etching of silicon oxide and silicon nitride with hydrogen fluoride

Etching of high aspect ratio features into alternating SiO 2 and SiN layers is an enabling technology for the manufacturing of 3D NAND flash memories. In this paper, we study a low-temperature or cryo plasma etch process, which utilizes HF gas together with other gas additives. Compared with a low-temperature process that uses separate fluorine and hydrogen gases, the etching rate of the SiO 2 /SiN stack doubles. Both materials etch faster with this so-called second generation cryo etch process. Pure HF plasma enhances the SiN etching rate, while SiO 2 requires an additional fluorine source such as PF 3 to etch meaningfully. Further, the insertion of H 2 O plasma steps into the second generation cryo etch process boosts the SiN etching rate by a factor of 2.4, while SiO 2 etches only 1.3 times faster. We observe a rate enhancing effect of H 2 O coadsorption in thermal etching experiments of SiN with HF. Ammonium fluorosilicate (AFS) plays a salient role in etching of SiN with HF with and without plasma. AFS appears weakened in the presence of H 2 O. Density functional theory calculations confirm the reduction of the bonding energy when NH 4 F in AFS is replaced by H 2 O.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗