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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

or-topas: Operations Research Toolkit for Pyomo Alternative Solutions

SAND2026-16702O OR-TOPAS: Operations Research Toolkit for Pyomo Alternative Solutions is a tool that enhances optimization applications defined by the Pyomo modeling library. It offers functions to generate optimal or near-optimal solutions, operating independently of Pyomo’s solver interface. Users can configure these functions with specific solver names and options, resulting in a custom solution object that returns a list of solutions. The OR-TOPAS library includes methods tailored to the properties of the model, such as binary integer programs versus linear programs, and specific solver interfaces like Gurobi. It does not provide models for specific applications, but it is applicable to a wide range of Pyomo optimization models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Siirola, John [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Change Agent: Energy Storage as a Driver of Regulatory Evolution

Purpose of Review Dividing the electric grid into the functions of generation, transmission, anddistribution enabled the drawing of jurisdictional lines and the application of the U.S.Constitution’s federalist system to energy regulation. Energy storage technologies,which can be placed throughout the grid to increase flexibility, can provide serviceacross all three of those functions. But the jurisdictional boundaries that have beendrawn around those functions have created barriers that restrict energy storagetechnologies from achieving their full potential. This review analyzes regulatory changes made to reduce barriers to storage deployment and their broader impacts on energy regulation in the U.S. Recent Findings Major energy regulations promulgated at the state and federal levels have generally focused on liberalizing the U.S. electric system through deregulation and increased competition. Paradoxically, however, these efforts have erected strict regulatory barriers that prevent energy storage technologies from providing service across multiple functions. A new wave of regulations in recent years has endeavored to reduce and remove those barriers. Summary Energy regulations adopted in recent years to reduce barriers to energy storage functionality in recent years have had deep and far-reaching impacts on U.S. electric regulation. These impacts go beyond storage and affect all energy technologies. This paper traces the development of energy regulation in U.S., the functional barriers that they created that impede energy storage functionality, recent efforts to remove those barriers, and the broader effects of those efforts. It concludes with a brief discussion of remaining barriers that prevent energy storage from reaching their full potential on the U.S. electric grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Measurements of the z > 5 Lyman-α forest flux autocorrelation functions from the extended XQR-30 data set

We present the first observational measurements of the Lyman-α (Ly α) forest flux autocorrelation functions in ten redshift bins from 5.1 ≤ z ≤ 6.0. We use a sample of 35 quasar sightlines at z > 5.7 from the extended XQR-30 data set; these data have signal-to-noise ratios of >20 per spectral pixel. We carefully account for systematic errors in continuum reconstruction, instrumentation, and contamination by damped Ly α systems. With these measurements, we introduce software tools to generate autocorrelation function measurements from any simulation. Our measurements of the smallest bin of the autocorrelation function increase with redshift when normalizing by the mean flux, $\langle{F}\rangle$. This increase may come from decreasing $\langle{F}\rangle$ or increasing mean free path of hydrogen-ionizing photons, λmfp. Recent work has shown that the autocorrelation function from simulations at z > 5 is sensitive to λmfp, a quantity that contains vital information on the ending of reionization. For an initial comparison, we show our autocorrelation measurements with simulation models for recently measured λmfp values and find good agreements. Further work in modelling and understanding the covariance matrices of the data is necessary to get robust measurements of λmfp from this data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The lowdown on breakdown: Open questions in plant proteolysis

Abstract Proteolysis, including post-translational proteolytic processing as well as protein degradation and amino acid recycling, is an essential component of the growth and development of living organisms. In this article, experts in plant proteolysis pose and discuss compelling open questions in their areas of research. Topics covered include the role of proteolysis in the cell cycle, DNA damage response, mitochondrial function, the generation of N-terminal signals (degrons) that mark many proteins for degradation (N-terminal acetylation, the Arg/N-degron pathway, and the chloroplast N-degron pathway), developmental and metabolic signaling (photomorphogenesis, abscisic acid and strigolactone signaling, sugar metabolism, and postharvest regulation), plant responses to environmental signals (endoplasmic-reticulum-associated degradation, chloroplast-associated degradation, drought tolerance, and the growth-defense trade-off), and the functional diversification of peptidases. We hope these thought-provoking discussions help to stimulate further research.

Eckardt, Nancy A. (ORCID:0000000316581412)↗
DOE OSTI2025

Performance of Diffusion Monte Carlo Calculations for Predicting the Relative Energies of Quinoidal and Nonquinoidal Species

Coupled cluster singles and doubles with perturbative triples [CCSD(T)] and single determinant fixed-node diffusion Monte Carlo (SD-DMC) have emerged as two of the most useful methods for providing benchmark reaction and interaction energies of chemical systems without strong static correlation. The errors in DMC energies are dominated by an inexact description of the nodal surfaces for electron exchange. One of the main approaches to addressing the fixed-node error is to use multideterminant (MD) trial wave functions. We consider here the energy differences between pairs of related molecules with aromatic and quinoidal structures as well as between quinoidal isomers. Quinoidal systems tend to have some diradical character, leading one to anticipate that SD-DMC calculations may face challenges in accurately describing their energetics. The MD trial wave functions were generated from the complete active space calculations. A comparison is made with the predictions of well-converged CCSD(T) calculations.

basis sets↗
DOE OSTI2026

Optimization of Random Phase Approximation Calculations for Improved Energies of Molecules, Solids, and Surfaces

We present an optimized random phase approximation method (optRPA26) that significantly improves upon conventional RPA through an optimized choice of reference orbitals and energy components, rather than a modification of the RPA correlation functional itself. The method employs an empirically constructed hybrid functional to generate DFT orbitals to evaluate the RPA correlation energy, which is then scaled by a constant. Comprehensive benchmarks across molecules, bulk solids, and surface systems demonstrate that optRPA26 consistently achieves high accuracy, with mean absolute errors of 0.05 eV for W4-11-RE reaction energies, 0.07 eV for cohesive energies, 0.09 eV for metal oxide formation energies, 0.11–0.12 eV for adsorption of small molecules on metals, and 0.06 eV for adsorption on oxides. In addition, optRPA26 correctly captures phase stability in metal oxides and magnetic metals. The optRPA26 approach can be run using standard RPA implementations, highlighting its potential as a general-purpose reference method that can accurately capture covalent, ionic, metallic, and van der Waals bonding in molecules, solids, and interfaces.

Adsorption↗
DOE OSTI2026

Transient Multiphysics Simulations with Pin Power Reconstruction in the Griffin Reactor Physics Code

This work introduces the pin power reconstruction capability available in the Griffin reactor physics code. This capability is implemented in an unstructured mesh framework, and the methods introduced are applied to the 2D SIMBA reactor core, which has assemblies and pins arranged in a hexagonal lattice. Since this reactor has a non-Cartesian geometry and also operates in the thermal spectrum, a general approach to pin power reconstruction is adopted, where SPH-based equivalence is leveraged to preserve assembly-wise reaction rates, while computing full-core form functions to preserve pin-wise fission production rates within the fuel pins of the reactor core. In a 2D microreactor benchmark problem, this pin power reconstruction approach was shown to reproduce pin powers compared to the Serpent2 Monte Carlo code for fixed temperature conditions and control drum rotation angles, yielding a core-wide RMS error level of 0.6\% and a maximum absolute pin error of 2.3\%. In addition, a tabulated library of multigroup cross sections, SPH factors, and form functions was generated to demonstrate the applicability of pin power reconstruction to a thermal feedback problem. Finally, a control drum transient was successfully simulated, showcasing the application of pin power reconstruction in a transient multiphysics feedback problem.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Structural dynamics of the renewable energy economy: A longitudinal input-output insights for a resilient transition

As countries accelerate their energy transitions, understanding how renewable energy (RE) systems structurally integrate into national economies is essential. This study presents a longitudinal economic input-output (EIO) analysis of the renewable energy sector in South Korea from 2016 to 2022. We develop a novel EIO-based framework that disaggregates the RE sector both by energy source (thermal, hydro, nuclear and renewable) and by industrial function (manufacturing, generation, and services), allowing for a detailed assessment of production dynamics, value-added creation, and import dependency. By quantifying backward and forward linkages and induced economic effects, the analysis reveals persistent structural vulnerabilities in renewable manufacturing and increasing sectoral interdependencies. Results reveal that while the renewable energy sector's production and value-added shares have increased, critical segments remain highly import-dependent, particularly in equipment manufacturing. The analysis highlights systemic gaps in domestic supply chain resilience and offers sector-specific insights for reducing vulnerability and enhancing energy security. Although applied to South Korea as a case study, the proposed framework is designed to be transferable to other national contexts where renewable energy planning requires economic structural insights. The findings offer policy-relevant guidance for enhancing domestic energy resilience and aligning industrial strategy with long-term decarbonization goals.

Economic linkage↗
DOE OSTI2026

Accelerating LHC event generation with simplified pilot runs and fast PDFs

High-precision calculations are an indispensable ingredient for the success of the LHC physics programme, yet their poor computing efficiency has been a growing cause for concern, threatening to become a paralysing bottleneck in the coming years. We present solutions to eliminate the apprehension by focussing on two major components of generalpurpose Monte Carlo event generators: the evaluation of parton distribution functions, and the generation of perturbative matrix elements. We show that for the cost-driving event samples employed by the ATLAS experiment to model omnipresent, irreducible Standard Model backgrounds, such as weak boson or top-quark pair production in association with jets, these computational components dominate the overall run time by up to 80 %. We demonstrate that a reduction of the computing footprint of LHAPDF and SHERPA by factors of around 40 can be achieved for multi-leg NLO event generation.

Bothmann, Enrico [Gottingen U.]↗
DOE OSTI2025

Investigation of oscillations above the acoustic cut-off frequency for a sample of Kepler stars. II. Identifying temporal frequency shifts

ABSTRACT Stellar oscillations with frequencies greater than the acoustic cut-off frequency are not trapped within the star’s interior. Geometric interference between these high-frequency waves produces a peak-like structure in the power spectrum of the star, known as the pseudo-modes. In this study, we aim to analyse 51 stars previously identified (from a sample of 91 Kepler stars) to contain statistically significant evidence for pseudo-modes, to determine if the pseudo-mode frequencies vary in time. We obtained these frequency variations and the uncertainty by the resampled periodogram approach, where periodogram realisations were created from successive, overlapping 90-d time segments, and a cross-correlation function was generated between them. The computed shifts were compared against temporal p-mode frequency shifts and a stellar magnetic activity proxy, $S_{\mathrm{ph}}$. We found that for 16 per cent of the stars pseudo-mode frequency shifts were significantly anticorrelated with p-mode shifts, as is the case for the Sun. However, we also found pseudo-mode and p-mode shifts to be significantly correlated in-phase in 8 per cent of our sample. The remainder showed no significant correlation. We also searched for trends between the maximum variation of pseudo-mode frequency shifts and stellar parameters. Whilst we found no correlation between the maximum variation and stellar photospheric and chromospheric proxies, we found frequency variations to have a greater amplitude for cooler and slower rotating stars, which is in opposition to p-mode frequency shift variation. Temporal changes to pseudo-mode frequencies may be attributable to variations in stellar magnetic activity, and therefore can contribute to our understanding of stellar activity cycles.

Millson, L. J. (ORCID:0009000342542676)↗
DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Exploring Wholesale Energy Price Trends: The Renewables and Wholesale Electricity Prices (ReWEP) tool (Ver. 2024.1)

The Renewables and Wholesale Electricity Prices (ReWEP) visualization tool from Berkeley Lab has been updated with nodal electricity pricing and wind and solar generation data through the end of 2023. ReWEP users can explore trends in wholesale electricity prices and their relationship to wind and solar generation. ReWEP includes nodal pricing trends across locations, regions, and different timeframes. The tool consists of maps, time series, and other interactive figures that provide: (1) a general overview of how average pricing, negative price frequency, and extreme high prices vary over time, and (2) a summary of how pricing patterns are related to wind and solar generation. Interactive functionality allows investigation by year, season, time of day, and region, where region is defined as the Independent System Operators (ISO) or Regional Transmission Organizations (RTO) region. ReWEP also contains prices throughout much of the western United States from the Western Energy Imbalance Market and the Western Energy Imbalance Service Market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Exploring Wholesale Energy Price Trends: The Renewables and Wholesale Electricity Prices (ReWEP) tool (Ver. 2024.1)

The Renewables and Wholesale Electricity Prices (ReWEP) visualization tool from Berkeley Lab has been updated with nodal electricity pricing and wind and solar generation data through the end of 2023. ReWEP users can explore trends in wholesale electricity prices and their relationship to wind and solar generation. ReWEP includes nodal pricing trends across locations, regions, and different timeframes. The tool consists of maps, time series, and other interactive figures that provide: (1) a general overview of how average pricing, negative price frequency, and extreme high prices vary over time, and (2) a summary of how pricing patterns are related to wind and solar generation. Interactive functionality allows investigation by year, season, time of day, and region, where region is defined as the Independent System Operators (ISO) or Regional Transmission Organizations (RTO) region. ReWEP also contains prices throughout much of the western United States from the Western Energy Imbalance Market and the Western Energy Imbalance Service Market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Using Parameter Sweep in WaterTAP to Analyze New Water Treatment Technologies

We describe a powerful and generalized parameter sweep tool in this report that was originally developed to analyze the performance of existing and novel water treatment models being developed in WaterTAP. Since WaterTAP is built upon IDAES and Pyomo, the parameter sweep tool can be used to systematically explore and debug the behavior of most Pyomo and IDAES numerical models. In order to enable meaningful analyses, the parameter sweep tool has been designed with the following features: 1) Model flexibility: The parameter sweep tool does not enforce any restrictions on the types of models that can be used with it. As long as a Pyomo model can be solved and the parameter is active and mutable, the tool only needs functions that describe how to run the model, the sweep parameters, and the output quantities of interest. 2) Flexible sampling: The parameter sweep tool has inbuilt functions to generate samples from a random distribution or a multidimensional Euclidean space. Furthermore, the users have to ability to supply samples generated from a tool of their choice. 3) Multiple sweep types: A user can choose from one of 3 types of parameter sweeps depending on their needs. 4) Detailed outputs: Outputs generated by the parameter sweep tool can be stored in detailed H5 file or user-friendly CSV files for post processing. 5) Parallel computing: The parameter sweep supports shared and distributed memory parallel computing to enable the use of high performance computers (HPC) for large-scale analyses. 6) Modular: The parameter sweep tool is self-contained and can easily be integrated within an outer-loop analysis or as desired by the user. 7) Ease of use: The tool is well documented and a simple sweep can be easily executed by following the online documentation in a few lines of code. We demonstrate the use of the parameter sweep tool on a simple water treatment system from the WaterTAP repository and show its parallel scaling performance on an Apple laptop and NREL's Eagle HPC. The parameter sweep tool is actively being used with models currently being developed within WaterTAP and we expect its use to grow beyond it to other IDAES and Pyomo models.

97 MATHEMATICS AND COMPUTING↗