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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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DOE OSTI2021

A Computationally Efficient Algorithm for Computing Convex Hull Prices

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may execute side payments when a participant’s cost is not covered through energy sale settlements from locational marginal pricing schemes, or when a participant incurs lost opportunity costs to follow the dispatch signal. Convex hull pricing minimizes these and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. However, computing convex hull prices involves solving either a large-scale linear program - which in turn requires explicit descriptions of market participants’ convex hulls -or the Lagrangian dual of the corresponding non-convex scheduling problem. Here, we propose a computationally feasible and industrially scalable Benders decomposition approach to computing convex hull prices at least an order of magnitude faster than the current state-of-the-art while leveraging recent advances in convex hull formulations for thermal generating units.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2026

A Decomposition-Based Learn-To-Optimize Approach with Feasibility Layer Assistance for Sub-Hourly Unit Commitment

Sub-hourly unit commitment (UC) with 15-min intervals is gaining significant attention as a way to respond rapidly to the fluctuations in electricity supply and demand introduced by renewable resources. However, the increased temporal resolution and complex inter-temporal dependencies pose substantial computational challenges for traditional optimization methods. To this end, this paper explores a decomposition-based learn-to-optimize approach. Building on recent advances in machine learning, our method revisits the long- overlooked Lagrangian relaxation framework, which is a classical decomposition technique that enables tractable subproblem solving. These smaller subproblems are inherently well-suited for machine learning, as their reduced dimensionality and structural regularity allow predictive models to efficiently learn and generalize solution patterns. We thus propose a generic predictive model, which embeds Gated Recurrent Units (GRUs) and Attention in the encoder-decoder structure, and integrate a rule-based feasibility layer to capture temporal dependencies, reduce training effort, and improve feasibility w.r.t. unit-level constraints. Our method has been validated on the IEEE 118-bus system, demonstrating promising performance in solving sub-hourly UC problems efficiently and feasibly.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Numerical investigation of supersonic turbulent boundary layers with high wall temperature

A direct numerical approach has been developed to simulate supersonic turbulent boundary layers. The mean flow quantities are obtained by solving the parabolized Reynolds-averaged Navier-Stokes equations (globally). Fluctuating quantities are computed locally with a temporal direct numerical simulation approach, in which nonparallel effects of boundary layers are partially modeled. Preliminary numerical results obtained at the free-stream Mach numbers 3, 4.5, and 6 with hot-wall conditions are presented. Approximately 5 million grid points are used in all three cases. The numerical results indicate that compressibility effects on turbulent kinetic energy, in terms of dilatational dissipation and pressure-dilatation correlation, are small. Due to the hot-wall conditions the results show significant low Reynolds number effects and large streamwise streaks. Further simulations with a bigger computational box or a cold-wall condition are desirable.

Guo, Y.↗

NASA NTRS1997

Finite Element Aircraft Simulation of Turbulence

A turbulence model has been developed for realtime aircraft simulation that accommodates stochastic turbulence and distributed discrete gusts as a function of the terrain. This model is applicable to conventional aircraft, V/STOL aircraft, and disc rotor model helicopter simulations. Vehicle angular activity in response to turbulence is computed from geometrical and temporal relationships rather than by using the conventional continuum approximations that assume uniform gust immersion and low frequency responses. By using techniques similar to those recently developed for blade-element rotor models, the angular-rate filters of conventional turbulence models are not required. The model produces rotational rates as well as air mass translational velocities in response to both stochastic and deterministic disturbances, where the discrete gusts and turbulence magnitudes may be correlated with significant terrain features or ship models. Assuming isotropy, a two-dimensional vertical turbulence field is created. A novel Gaussian interpolation technique is used to distribute vertical turbulence on the wing span or lateral rotor disc, and this distribution is used to compute roll responses. Air mass velocities are applied at significant centers of pressure in the computation of the aircraft's pitch and roll responses.

McFarland, R. E.↗

DOE OSTI2019

Visualization of Simulation and TAP Interaction

The Temporal Analysis of Products (TAP) Reactor applies transient kinetics to analyze sophisticated chemical reaction mechanisms, rapidly changing temperature, flow rate, pressure or concentration and then monitoring the system’s time dependent response. A simulation tool exists for the TAP reactor, but it requires a visualization platform to interface effectively with researchers. This graphical user interface remedies this problem, fixing many of the difficulties caused by users interacting directly with the simulation code by providing an intuitive web application that satisfies the simulation input parameters for fast generation of data. This tool will ensure robust and repeatable simulation results, expedite the data generating process and enhance the overall ability of TAP to produce significant findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Robust Importance Sampling for Bayesian Model Calibration with Spatio-Temporal Data

This paper addresses two challenges in Bayesian calibration: 1) computational speed of existing sampling algorithms, and 2) calibration with spatio-temporal responses. The commonly used Markov Chain Monte Carlo (MCMC) approaches require many sequential model evaluations making the computational expense prohibitive. This paper proposes an efficient sampling algorithm: iterative importance sampling with genetic algorithm (IISGA). While iterative importance sampling enables computational efficiency, the genetic algorithm enables robustness by preventing sample degeneration and avoids getting stuck in multimodal search spaces. An inflated likelihood further enables robustness in high-dimensional parameter spaces by enlarging the target distribution. Spatio-temporal data complicate both surrogate modeling, which is necessary for expensive computational models, and the likelihood estimation. In this work, singular value decomposition is investigated for reducing the high-dimensional field data to a lower-dimensional space prior to Bayesian calibration. Then the likelihood is formulated and Bayesian inference is performed in the lower-dimension, latent space. An illustrative example is provided to demonstrate IISGA relative to existing sampling methods, and then IISGA is employed to calibrate a thermal battery model with 26 uncertain calibration parameters and spatio-temporal response data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Groundwater flow and heat transport for systems undergoing freeze-thaw: Intercomparison of numerical simulators for 2D test cases

In high-elevation, boreal and arctic regions, hydrological processes and associated water bodies can be strongly influenced by the distribution of permafrost. Recent field and modeling studies indicate that a fully-coupled multidimensional thermo-hydraulic approach is required to accurately model the evolution of these permafrost-impacted landscapes and groundwater systems. However, the relatively new and complex numerical codes being developed for coupled non-linear freeze-thaw systems require verification. Here in this paper, this issue is addressed by means of an intercomparison of thirteen numerical codes for two-dimensional test cases with several performance metrics (PMs). These codes comprise a wide range of numerical approaches, spatial and temporal discretization strategies, and computational efficiencies. Results suggest that the codes provide robust results for the test cases considered and that minor discrepancies are explained by computational precision. However, larger discrepancies are observed for some PMs resulting from differences in the governing equations, discretization issues, or in the freezing curve used by some codes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗

DOE OSTI2026

A cell-centered AMR-ALE framework for 3D multi-material hydrodynamics. Part II: linesweep ALE rezoning for nonconformal block-structured AMR meshes

The simulation of flows presenting contact discontinuities, vorticity, and large variations in spatial scales can be performed in a framework coupling Arbitrary Lagrangian Eulerian (ALE) algorithms and Adaptive Mesh Refinement (AMR). This coupling requires adaptation of ALE rezoning techniques to meshes containing nonconformal nodes arising from both the AMR topology and the junction of mesh blocks. Here, in this paper, we present an ALE rezoning strategy that is compatible with such meshes, and that can also act as a disentangling algorithm. Emphasis is put on an algorithm that respects intrinsic Lagrangian mesh properties in order to preserve accuracy around discontinuities. To that end, we adapt the weighted linesweep algorithm to nonconformal block-structured AMR meshes. Then, we present control parameters introduced in the method for it to be applicable in practical situations. Notably, the method is coupled to a specific metric optimization in order to palliate some shortcomings of the linesweep method. Finally, numerical test cases are presented that feature the capabilities of the ALE-AMR algorithm for flows that present discontinuities, vorticity, and a variety of scales. Notably, we show that our ALE-AMR algorithm gives results at least similar to Euler-AMR, but provides better accuracy in cases where discontinuities are involved, thanks to a method that respects the Lagrangian features of the mesh. Additionally, it enables Euler-AMR-like computations on domains with temporally varying domain boundaries.

Adaptive mesh refinement↗

DOE OSTI2025

Initial validation of ALFRED: A Monte Carlo code built on Geant4 for TREAT energy deposition

Predicting the energy deposited in the specimen during an experiment in the Transient Reactor Test (TREAT) Facility is a complex problem due to the nature of the transients occurring in the reactor. In addition, the many particles contributing to energy deposition have different behavior in time and space. ALFRED, a new Geant4 based code, is developed to transport and simulate each particle generated in the core. This code is verified against OpenMC (Open Monte Carlo) on the Godiva benchmark and a simple TREAT model. Next, the energy deposition in TREAT is calculated: 181.05 ± 0.01 MeV for the “instantaneous” energy deposition (which accounts for the energy deposited within 1 s after neutron emission) in fuel and 189.90 ± 0.01 MeV for the total energy deposition in fuel. We discuss these results in this paper with previous calculations and experimental evaluations. This work demonstrates ALFRED’s potential as a high-fidelity tool for computing the spatial and temporal energy deposition in TREAT paving the way for a better understanding of the energy coupling factors in TREAT.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗

DOE OSTI2024

Hydrogen-induced tunable remanent polarization in a perovskite nickelate

Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO 3 into an insulating phase with both metastable dipolar polarization and space-charge polarization. We then demonstrate transient negative differential capacitance in thin film capacitors. The space-charge polarization caused by long-range movement and trapping of protons dominates when the electric field exceeds the threshold value. First-principles calculations suggest the polarization originates from the polar structure created by H doping. We find that polarization decays within ~1 second which is an interesting temporal regime for neuromorphic computing hardware design, and we implement the transient characteristics in a neural network to demonstrate unsupervised learning. These discoveries open new avenues for designing ferroelectric materials and electrets using light-ion doping.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II - Clustering Extremely High-Dimensional Grid-Based Data

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Ensemble-Averaging Resolves Rapid Atmospheric Response to the 2017 Total Solar Eclipse [Rapid Resolution of the Atmospheric Response to the 2017 Total Solar Eclipse]

Rapid changes in solar radiative forcing influence heat, scalar and momentum fluxes and thereby shift the trajectory of near-surface atmospheric transitions. Surface fluxes are difficult to obtain during atmospheric transitions by either bulk or eddy-covariance methods because both techniques assume quasi-stationarity in an atmospheric state and require sufficiently long blocks of data, typically on the order of 10–30 min, to obtain statistically significant results. These computational requirements limit the temporal resolution of atmospheric processes that researchers can examine using traditional measurement techniques. In this paper, we present a novel observational approach to calculate surface fluxes at sub-minute temporal resolutions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1984

Linear analysis of ion cyclotron interaction in a multicomponent plasma

The mechanism by which hot anisotropic protons generate electromagnetic ion cyclotron waves in a plasma containing cold H(+) and He(+) ions is quantitatively studied. Linear growth rates (both temporal and spatial) are computed for different plasma parameters: concentration, temperature,and anisotropy of cold He(+) ions and of hot protons. It is shown that: (1) for parameters typical of the geostationary altitude the maximum growth rates are not drastically changed when a small proportion (about 1 to 20 percent) of cold He(+) ions is present; (2) because of the important cyclotron absorption by thermal He(+) ions in the vicinity of the He(+) gyrofrequency, waves which could resonate with the bulk of the He(+) distribution cannot be generated. Therefore quasi-linear effects, in a homogeneous medium at least, cannot be responsible for the heating of He(+) ions which is often observed in conjunction with ion cyclotron waves. The variation of growth rate versus wave number is also studied for its importance in selecting suitable parameters in numerical simulation experiments.

Gendrin, R.↗

NASA NTRS1990

The inviscid stability of supersonic flow past axisymmetric bodies

The supersonic flow past a sharp cone is studied. The associated boundary layer flow (i.e., the velocity and temperature field) is computed. The inviscid linear temporal stability of axisymmetric boundary layers in general is considered, and in particular, a so-called 'triply generalized' inflection condition for 'subsonic' nonaxisymmetric neutral modes is presented. Preliminary numerical results for the stability of the cone boundary layer are presented for a freestream Mach number of 3.8. In particular, a new inviscid mode of instability is seen to occur in certain regimes, and this is shown to be related to a viscous mode found by Duck and Hall (1988).

Duck, Peter W.↗

NASA NTRS1992

Premixed shock-induced combustion studies in the hypulse facility

Engine performance calculations strongly suggest that the oblique detonation-wave engine can outperform the diffusive-burning scramjet engine in the high hypervelocity flight regime; i.e., at flight Mach numbers exceeding about 14. Research is described that examines and characterizes the behavior of oblique detonation waves (ODW). The effort is both computational and experimental, the latter utilizing the NASA Hypulse expansion tube. The experimental work is hampered by preignition of the hydrogen-oxygen-helium mixtures in either the intermediate or acceleration sections of the facility. The experiments are described and possible causes of this preignition are discussed. Initial results obtained from a parallel computation effort indicate that temporally steady ODW are achievable in the Hypulse facility.

Chinitz, W.↗

NASA NTRS1997

On spurious behavior of CFD simulations

Spurious behavior in underresolved grids and/or semi-implicit temporal discretizations for four computational fluid dynamics (CFD) simulations are studied. The numerical simulations consist of (a) a 1-D chemically relaxed nonequilibrium model, (b) the direct numerical simulation (DNS) of 2-D incompressible flow over a backward facing step, (c) a loosely-coupled approach for a 2-D fluid-structure interaction, and (d) a 3-D compressible unsteady flow simulation of vortex breakdown in delta wings. Using knowledge from dynamical systems theory, various types of spurious behaviors that are numerical artifacts were systematically identified. These studies revealed the various possible dangers of misinterpreting numerical simulation of realistic complex flows that are constrained by the available computing power. In large scale computations underresolved grids, semi-implicit procedures, loosely-coupled implicit procedures, and insufficiently long time integration in DNS are most often unavoidable. Consequently, care must be taken in both computation and in interpretation of the numerical data. The results presented confirm the important role that dynamical systems theory can play in the understanding of the nonlinear behavior of numerical algorithms and in aiding the identification of the sources of numerical uncertainties in CFD.

Numerical Solution↗