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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transient Modeling and Simulation of a Generic Stable Salt Reactor

A SAM system-level model of a generic stable salt reactor has been developed to investigate thermal-hydraulic behavior and safety performance under steady and transient conditions. The model integrates information generated from a reactor physics analysis using PROTEUS and PERSENT, and a computation fluid dynamics (CFD) analysis using STAR-CCM+. A loose, iterative coupling scheme between PROTEUS and SAM is implemented to calculate the equilibrium power and temperature distributions in the steady-state critical core condition. The converged steady-state model is then used in PERSENT to calculate the four reactivity feedback temperature coefficients (Doppler, fuel density, coolant density, and core radial expansion) and kinetic parameters that are needed in SAM to model the temperature feedback effects in transient simulations. Within the fully enclosed liquid fuel pins, natural convection is the dominant heat transfer mechanism. The STAR-CCM+ model of the fuel pin considers conjugate heat transfer from the liquid fuel salt to the pin cladding and external reactor coolant. The CFD results of the axial and radial temperature profiles are used to empirically determine an effective fuel salt thermal conductivity in the SAM fuel pin model so that the temperatures predicted by the SAM model match as closely as possible the CFD results. In the central region of the fuel pin, the effective thermal conductivity is as high as similar to 60 times the physical fuel salt thermal conductivity. The whole-plant SAM model is then used to simulate an unprotected station blackout transient. The results of this simulation showed that the large negative fuel axial expansion reactivity feedback reduces fission power to similar to 2.4% nominal power. The core is cooled by natural circulation, which removes heat in the core to the emergency heat removal system, and ultimately, to the ambient. However, peak fuel salt and cladding temperatures can potentially reach as high as 1500 K, albeit briefly, if the shutdown mechanism fails to operate.

stable salt reactor; transient simulations; system↗
DOE OSTI2021

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2022

Generative Design for Resilience of Interdependent Network Systems

Abstract Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria while the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on synthetic supply chain networks and power systems from the IEEE dataset have illustrated the applicability of the developed method for designing resilient interdependent network systems.

Engineering↗
DOE OSTI2024

Successive Procedure for Solution Verification Based on User Needs

This paper discusses a revised solution verification procedure for computational fluid dynamics simulations to estimate the uncertainties in the quantities of interest based on discretization error models. This proposed procedure builds upon current procedures described in ASME V&V 20 but provides more guidance in determining the necessary number of mesh levels to build reliable discretization error models. Such guidance is particularly useful for practicing engineers without prior experience in solution verification. The key features of this proposed solution verification procedure are the ability to determine the need for additional mesh levels iteratively and the seamless treatment for underdetermined, exact, and overdetermined solutions of the power series approximation to the discretization error models. This study applies the proposed procedure to a set of synthetic examples to demonstrate the revised procedure’s clarity in determining the number of mesh solutions required for a reliable estimate of the discretization error in computational fluid dynamics settings. Additionally, this proposed procedure prevents a potential pathway in the current procedure in ASME V&V 20 that may lead to unreasonably small discretization errors.

Weinmeister, Justin↗
DOE OSTI2023

Improved Fast Reactor Capability of Griffin in FY23

Griffin is a MOOSE based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory under the DOE-NE NEAMS program. In FY23, we enhanced capabilities required for fast reactor analysis. This effort included primarily updating the cross-section generation workflow using MC2-3 for various reactor configurations, such as homogeneous, duct-heterogeneous, ring-heterogeneous, and fully-heterogeneous geometries. In addition, we initiated the implementation of a multi-cycle depletion and shuffling capability. To support fast reactor simulation capabilities, we significantly improved the performance of the DFEM-SN-based R-Z transport solver to efficiently solve ultrafine group (over 1000 groups) transport problems. Additionally, the performance of HFEM-PN was improved by introducing red-black iteration, the cmfd acceleration technique, and various optimizations. We also completed the pin power reconstruction capability to support multiphysics simulations while identifying and addressing issues associated with SPH equivalence parameter approach. These enhanced capabilities for fast reactor core simulations, specially HFEM-PN and pin power reconstruction features, were applied to benchmark problems involving ABTR and ABR-1000. These applications showcased excellent agreement with Monte Carlo and other code solutions in terms of eigenvalue, control rod worth, and assembly and pin powers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A measurement of $H_0$ from DESI DR1 using energy densities

We present a new measurement of the Hubble constant, independent of standard rulers and robust to pre-recombination modifications such as Early Dark Energy (EDE), obtained by calibrating the total energy density of the Universe. We start using the present-day photon density as an anchor, and use the baryon-to-photon ratio from Big Bang Nucleosynthesis based measurements and the baryon-to-matter ratio from the baryons' imprint on galaxy clustering to translate to a physical matter density at present day. We then compare this to measurements of the ratio of the matter density to the critical density ($Ω_{\mathrm{m}}$), calculated using the relative positions of the baryon acoustic oscillations, to measure the critical density of the universe and hence $H_0$. The important measurements of the evolution of the energy density all happen at low redshift, so we consider this a low-redshift measurement. We validate our method both on a suite of $N$-body mocks and on noiseless theory vectors generated across a wide range of Hubble parameters in both $Λ$CDM and EDE cosmologies. Using DESI DR1 data combined with the angular CMB acoustic scale and the latest BBN constraints, we find $H_0 = 69.0 \pm 2.5$ km s$^{-1}$ Mpc$^{-1}$, consistent with existing early and late-time determinations of the Hubble constant. We consider the impact of non-standard dark energy evolution on our measurement. Future data, including that from further iterations of DESI and from Euclid, will add to these results providing a powerful test of the Hubble tension.

Krolewski, Alex [U. Waterloo (main); Waterloo U., ↗
DOE OSTI2024

A Transactive Approach for Service Restoration Utilizing Customer Load Flexibility and Grid-Edge Resources: Preprint

This paper develops a transactive energy system model to restore electricity to customers in an isolated distribution system after an outage. The model engages a variety of customer types -- prosumers, flexible loads, critical/noncritical customers, and distributed generators -- as active participants in the restoration process. Unlike many existing transactive approaches, the proposed model is developed for service restoration and accounts for various customer types and their \textit{autonomy} and \textit{privacy} through an iterative approach to determine the optimal market price, while maintaining system-level power flow and voltage constraints. The advantages of the proposed approach are numerically validated on a modified IEEE 123-bus test system.

distributed energy resources↗
DOE OSTI2024

A Transactive Approach for Service Restoration Utilizing Customer Load Flexibility and Grid-Edge Resources

This paper develops a transactive energy system model to restore electricity to customers in an isolated distribution system after an outage. The model engages a variety of customer types -- prosumers, flexible loads, critical/noncritical customers, and distributed generators -- as active participants in the restoration process. Unlike many existing transactive approaches, the proposed model is developed for service restoration and accounts for various customer types and their autonomy and privacy through an iterative approach to determine the optimal market price, while maintaining system-level power flow and voltage constraints. The advantages of the proposed approach are numerically validated on a modified IEEE 123-bus test system.

distributed energy resources↗
DOE OSTI2021

The updated ITPA global H-mode confinement database: description and analysis

The multi-machine ITPA Global H-mode Confinement Database has been upgraded with new data from JET with the ITER-like wall and ASDEX Upgrade with the full tungsten wall. This paper describes the new database and presents results of regression analysis to estimate the global energy confinement scaling in H-mode plasmas using a standard power law. Various subsets of the database are considered, focusing on type of wall and divertor materials, confinement regime (all H-modes, ELMy H or ELM-free) and ITER-like constraints. Apart from ordinary least squares, two other, robust regression techniques are applied, which take into account uncertainty on all variables. Regression on data from individual devices shows that, generally, the confinement dependence on density and the power degradation are weakest in the fully metallic devices. Using the multi-machine scalings, predictions are made of the confinement time in a standard ELMy H-mode scenario in ITER. The uncertainty on the scaling parameters is discussed with a view to practically useful error bars on the parameters and predictions. One of the derived scalings for ELMy H-modes on an ITER-like subset is studied in particular and compared to the IPB98(y,2) confinement scaling in engineering and dimensionless form. Transformation of this new scaling from engineering variables to dimensionless quantities is shown to result in large error bars on the dimensionless scaling. Regression analysis in the space of dimensionless variables is therefore proposed as an alternative, yielding acceptable estimates for the dimensionless scaling. The new scaling, which is dimensionally correct within the uncertainties, suggests that some dependencies of confinement in the multi- machine database can be reconciled with parameter scans in individual devices. This includes vanishingly small dependence of confinement on line-averaged density and normalized plasma pressure (β), as well as a noticeable, positive dependence on effective atomic mass and plasma triangularity. Extrapolation of this scaling to ITER yields a somewhat lower confinement time compared to the IPB98(y, 2) prediction, possibly related to the considerably weaker dependence on major radius in the new scaling (slightly above linear). Further studies are needed to compare more flexible regression models with the power law used here. In addition, data from more devices concerning possible ‘hidden variables’ could help to determine their influence on confinement, while adding data in sparsely populated areas of the parameter space may contribute to further disentangling some of the global confinement dependencies in tokamak plasmas.

Database↗
DOE OSTI2023

Turbulence and E × B flow correlations across the L-H transition in DIII-D deuterium and hydrogen plasmas

The isotope dependence of the low- to high confinement-mode (L- to H-mode) transition power threshold P LH presents significant challenges for the initial (non-nuclear) hydrogen operations phase of the international thermonuclear experimental reactor (ITER). Here, we examine the isotope dependence of turbulence and E × B flow correlation properties in the L-mode edge plasma, leading up to the L-H transition. At marginal auxiliary power (near P LH ), turbulence is initially suppressed periodically during limit cycle oscillations (LCO) that precede the transition to sustained H-mode confinement. Here, we present evidence that the long-range (toroidal) correlation of the E × B edge plasma flow across the LCO phase is much weaker in hydrogen than in deuterium in the DIII-D tokamak, congruent with the higher threshold power P LH required to access LCO and H-mode in hydrogen. Concomitantly, the time required to initially quench edge turbulence via localized edge E × B flow shear is significantly longer in hydrogen (1–1.5 ms) than in deuterium (approx. 100 µs). No toroidal long-range correlation of the turbulence amplitude is observed, in agreement with expectations based on the relatively short poloidal turbulence correlation length. Radial edge turbulence and flow correlation lengths are longer in deuterium than in hydrogen plasmas as one would expect from ‘naïve’ gyro-Bohm isotope transport scaling, despite the substantially higher thermal flux across the last closed flux surface in hydrogen before the L-H transition. This article is part of a discussion meeting issue 'H-mode transition and pedestal studies in fusion plasmas'.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Autoregressive long-horizon prediction of plasma edge dynamics *

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

autoregressive deep learning↗
DOE OSTI2024

TunIO: An AI-powered Framework for Optimizing HPC I/O

I/O operations are a known performance bottleneck of HPC applications. To achieve good performance, users often employ an iterative multistage tuning process to find an optimal I/O stack configuration. However, an I/O stack contains multiple layers, such as high-level I/O libraries, I/O middleware, and parallel file systems, and each layer has many parameters. These parameters and layers are entangled and influenced by each other. The tuning process is time-consuming and complex. In this work, we present TunIO, an AI-powered I/O tuning framework that implements several techniques to balance the tuning cost and performance gain, including tuning the high-impact parameters first. Furthermore, TunIO analyzes the application source code to extract its I/O kernel while retaining all statements necessary to perform I/O. It utilizes a smart selection of high-impact configuration parameters of the given tuning objective. Finally, it uses a novel Reinforcement Learning (RL)-driven early stopping mechanism to balance the cost and performance gain. Experimental results show that TunIO leads to a reduction of up to ≈73% in tuning time while achieving the same performance gain when compared to H5Tuner. It achieves a significant performance gain/cost of 208.4 MBps/min (I/O bandwidth for each minute spent in tuning) over existing approaches under our testing.

Rajesh, Neeraj↗
DOE OSTI2025

Impact of nitrogen molecular breakup on divertor conditions in JET L-mode plasmas using SOLPS-ITER

SOLPS-ITER simulations of nitrogen-seeded, low-confinement mode plasmas in the Joint European Torus (JET) predict that the electron temperature in the low-field side (LFS) divertor leg is reduced locally by up to an order of magnitude when nitrogen is assumed to recycle as molecules (N 2 ) instead of atoms using a fixed nitrogen injection rate. The LFS divertor temperature reduction under the assumption of molecular recycling occurs due to a three-step mechanism: (1) the plasma penetration of nitrogen atoms is increased due to the strong triple bond of the N 2 molecule and the kinetic energy release in the dissociation event, both mechanisms contributing equally, (2) the abundance of (particularly multiply-charged) nitrogen ions in the divertor is increased and (3) the electron temperature is reduced due to the increase in radiation (by up to a factor of 4) from nitrogen ions. Setting the volume-integrated nitrogen radiated power to a constant value (0.6 MW) instead of the nitrogen injection rate, SOLPS-ITER predicts under the molecular nitrogen recycling assumption that the peak line-integrated N II, N III and N IV intensities in the LFS divertor are approximately within 15%, 35% and 5%, respectively, of the reference atomic nitrogen recycling case. The predicted peak N II, N III and N IV intensities under either assumption are within 30%, 65% and 5%, respectively, of measurements using the vertically viewing mirror-link divertor spectrometer (Meigs et al., 2010) in nitrogen-seeded JET L-mode plasmas (Lomanowski et al., 2019). ERO2.0 simulations using a constant nitrogen seeding rate on static background plasma solutions from EDGE2D-EIRENE (previously presented in Mäenpää et al., (2022), revised here to include fast reflections) predict that N II to N IV line emission is increased by 20% to 30% when nitrogen is assumed to recycle as molecules, demonstrating the importance of considering the effect of molecular dissociation reactions on the divertor plasma in a self-consistent manner.

Divertor↗
DOE OSTI2024

ITER ECH Transmission Line System Design and Status

The electron cyclotron (EC) heating & current drive (H&CD) system on ITER provides plasma heating by generating, transmitting, and launching high-intensity, high-frequency (170 GHz) electromagnetic wave energy steerable across the plasma cross-section. The transmission line (TL) subsystem connects the Matching Optics Unit (MOU) on each of the 24 gyrotrons to the 32 feed points in the four upper launchers and the 24 feed points in the equatorial launcher. Each TL must be able to operate at up to 1.2 MW of input power for up to 1 hour pulse lengths. The TL system contains 50 mm water-cooled corrugated waveguide, 90o miter bends, 140o miter bends, polarizer miter bend pairs, switches, expansion units, pumpouts, DC breaks, MOU-TL adapters, Radio Frequency (RF) loads, and isolation shutter valves. A detailed finite element analysis has been used to verify the thermo-mechanical performance of each component. The microwave performance has been analyzed using a 2-D electromagnetic code combined with a Monte Carlo code. This approach allows the impact of manufacturing and installation tolerances to be assessed and optimized to provide a high probability of achieving the system performance requirements. Prototypes of the waveguide and TL components have been fabricated and tested at high-power. Production contracts are now being issued for fabrication and delivery of the waveguide and components to ITER.

Hanson, Gregory↗
DOE OSTI2021

Efficient flexible characterization of quantum processors with nested error models

We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a metric of the amount of unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitutes a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy two-qubit processor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Iterative reconstruction excursions for Baryon Acoustic Oscillations and beyond

ABSTRACT The density field reconstruction technique has been widely used for recovering the baryon acoustic oscillation (BAO) feature in galaxy surveys that has been degraded due to non-linearities. Recent studies advocated adopting iterative steps to improve the recovery much beyond that of the standard technique. In this paper, we investigate the performance of a few selected iterative reconstruction techniques focusing on the BAO and the broad-band shape of the two-point clustering. We include redshift-space distortions, halo bias, and shot noise and inspect the components of the reconstructed field in Fourier space and in configuration space using both density field-based reconstruction and displacement field-based reconstruction. We find that the displacement field reconstruction becomes quickly challenging in the presence of non-negligible shot noise and therefore present surrogate methods that can be practically applied to a much more sparse field such as galaxies. For a galaxy field, implementing a debiasing step to remove the Lagrangian bias appears crucial for the displacement field reconstruction. We show that the iterative reconstruction does not substantially improve the BAO feature beyond an aggressively optimized standard reconstruction with a small smoothing kernel. However, we find taking iterative steps allows us to use a small smoothing kernel more ‘stably’, i.e. without causing a substantial deviation from the linear power spectrum on large scales. In one specific example we studied, we find that a deviation of 13 per cent in $P(k\sim 0.1\, h{\rm \,\,Mpc^{-1}})$ with an aggressive standard reconstruction can reduce to 3–4 per cent with iterative steps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Probabilistic predictions and uncertainty estimation for radiological and nuclear effects modeling

Better characterization of meteorological uncertainty is needed to predict the effects of radiological and nuclear releases in the atmosphere. Multi-physics ensembles of atmospheric simulations can estimate uncertainty of physical processes, but they are often limited for computational reasons. Because weather uncertainty is important for emergency response applications, but the conventional way of running multi-physics ensembles is infeasible, we have developed new machine learning-based methods that quickly estimate and identify key sources of meteorological uncertainty. Machine learning accelerates the analysis of weather uncertainty by iteratively learning about model physics options that affect plume predictions. We have demonstrated the power of machine learning by creating a large multi-physics weather and dispersion ensemble dataset containing 1,200 simulations for two hypothetical radiological release scenarios. By training on less than 10% of the ensemble, we can quickly and accurately predict different outcomes, including the mass of radiological material deposited and spatial distribution and extent of contaminated areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Prospective Design Method for Nuclear Power: The Evaluation, Requirements, and Goals Outline for Nuclear (ERGON) Method

Human factors researchers at Idaho National Laboratory (INL) have worked on projects spanning control room modernization, operator support systems, visualization design, and novel system creation. These projects demonstrated the need for an explicit design method for nuclear power. Human factors teams found a high standard in the Human Factors Engineering Program Review Model (NUREG-0711) and needed a design methodology which could be successful in gaining approval. Previous work has been synthesized as the Evaluation, Requirements, and Goals Outline for Nuclear (ERGON) method here. Design tasks are broken into four phases: Context and Orientation, Human Factors Review, Prototyping and Evaluation, Iteration and Improvement. ERGON is intended as a flexible and direct design method for many applications in nuclear power. ERGON has been vetted through collaborative research and development with nuclear utilities and as such, the ERGON method can assist utilities to achieve approval from a NUREG-0711 summative evaluation for HSI implementations.

42 ENGINEERING↗