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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 OSTI2023

Conceptual Design of Integrated Energy Systems via Multiscale Market Simulations and Surrogate Models for Market Interactions

This final report describes novel capabilities developed as part of DISPATCHES, Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems, for designing hybrid energy systems (HES) a.k.a. integrated energy systems (IES) in the context of a larger electricity market. Capabilities are demonstrated on case studies for nuclear and renewable power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Indra: a public computationally accessible suite of cosmological N -body simulations

ABSTRACT Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 10243 dark matter particles in a box of length 1 h−1 Gpc, 64 snapshots of particle data and halo catalogues, and 505 time-steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large data sets public and computationally accessible.

Falck, Bridget↗

DOE OSTI2025

Quantum Electrodynamics Coupled-Cluster at Scale: High-Performance Implementation for Complex Systems

Coupled-cluster theory (CC) is a highly accurate and versatile method for simulating complex interactions within quantum systems. The extension of CC theory to model mixed electron-photon processes with quantum electrodynamics (QED) has improved our capability to predict cavity-modified chemistry, a field where photons are used as cost-effective and eco-friendly alternatives to catalyze/inhibit chemical reactions. However, calculations with CC methods, even without incorporating QED effects, are often prohibitively expensive. Simulations of larger systems require scalable infrastructures that exist for traditional CC methods but not for QED-CC methods. As such, we present a GPU-enabled, high-performance, open-source implementation of the quantum electrodynamics coupled-cluster method with single and double excitations (QED-CCSD) within the ExaChem quantum chemistry software package. ExaChem relies on the Tensor Algebra for Many-body Methods (TAMM) infrastructure: a parallel heterogeneous tensor library designed to achieve scalable performance on modern heterogeneous supercomputing platforms. Furthermore, we discuss theoretical foundations, algorithmic details, and numerical benchmarks to showcase the larger systems that ExaChem can simulate and how the integration of photonic degrees-of-freedom alters their ground-state properties.

Basis sets↗

DOE OSTI2026

Design of Premoderated Pulsed-Neutron Die-Away Experiments for Validating Beryllium Thermal Neutron Scattering Laws

Accurate thermal scattering laws (TSLs) are essential for reliable neutron transport simulations, particularly for systems with low-energy neutrons. Pulsed-neutron die-away (PNDA) experiments provide a highly sensitive platform for TSL validation but face challenges from fast neutron leakage and material constraints in nonhydrogenous targets such as beryllium. This work investigates the use of high-density polyethylene (HDPE) as a premoderator to reduce leakage, improve thermalization, and shorten experimental run times while preserving sensitivity to the Be TSL. Here, three premoderated configurations, encapsulation, slab, and interstitial, were evaluated using MCNP6.3® simulations with ENDF/B-VIII.0 nuclear data. The effects of the Be TSL was quantified by comparing decay constants from simulations with and without 𝑆⁡(𝛼,𝛽) treatments for both beryllium and HDPE. The results show that premoderation enables the use of as little as 5.20% of the beryllium volume required for an unreflected geometry, achieving uncertainties below 0.5% and reducing run time by up to 72.2% in the studied configurations. The interstitial configuration achieved the best balance between low statistical uncertainty and high TSL sensitivity, outperforming the slab and encapsulation designs. These findings demonstrate that carefully optimized premoderation can significantly enhance the efficiency, feasibility, and precision of PNDA experiments for Be TSL validation.

Integral Experiments↗

DOE OSTI2024

Mechanical properties of freestanding few-layer graphene/boron nitride/polymer heterostacks investigated with local and non-local techniques

van der Waals two-dimensional materials and heterostructures combined with polymer films continue to attract research attention to elucidate their functionality and potential applications. This study presents the fabrication and mechanical testing of 2D material heterostacks, consisting of few-layer boron nitride and graphene heterostructures synthesized via chemical vapor deposition, capped with a polymethyl methacrylate layer and suspended across ∼200 μm wide trenches using a combined wet–dry transfer method. The mechanical characterization of the heterostacks was performed using two independent approaches: (a) non-local testing with a custom-built tensile testing platform and (b) local load–displacement testing employing atomic force microscopy probes, complemented by finite element simulations. Both approaches provided new results, which are in good agreement with each other. Overall, our findings offer new insights into a combined load capacity in complex multi-material two-dimensional systems, and can contribute to advancing micro and nano-scale device designs and implementations.

Lespasio, Marcus↗

DOE OSTI2022

Electron Weibel instability induced magnetic fields in optical-field ionized plasmas

Generation and amplification of magnetic fields in plasmas is a long-standing topic that is of great interest to both plasma and space physics. The electron Weibel instability is a well-known mechanism responsible for self-generating magnetic fields in plasmas with temperature anisotropy and has been extensively investigated in both theory and simulations, yet experimental verification of this instability has been challenging. Recently, we demonstrated a new experimental platform that enables controlled initialization of highly nonthermal and/or anisotropic plasma electron velocity distributions via optical-field ionization. Using an external electron probe bunch from a linear accelerator, the onset, saturation, and decay of the self-generated magnetic fields due to electron Weibel instability were measured for the first time to our knowledge. In this paper, we will first present experimental results on time-resolved measurements of the Weibel magnetic fields in non-relativistic plasmas produced by Ti:Sapphire laser pulses (0.8 μm) and then discuss the feasibility of extending the study to a quasi-relativistic regime by using intense CO 2 (e.g., 9.2 μm) lasers to produce much hotter plasmas.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Modeling light signals using data from the first pulsed neutron source program at the DUNE vertical drift ColdBox test facility at the CERN Neutrino Platform

In this paper, we present a first quantitative test of detected light signals produced in a pulsed neutron source run in a small vertical drift LArTPC at the CERN Neutrino Platform ColdBox test facility. The ColdBox cryostat, detectors, neutron sources, and particle interactions are modeled and simulated using Fluka. We demonstrate the ability to identify the contribution from neutron interactions using X-ARAPUCA photodetectors, and show first comparisons of data to simulation, which indicate reasonable agreement. A time constant is also fitted from the neutron-beam-off light signal spectrum and found consistent between data and simulation. Several important systematic effects are discussed and serve as guides for future runs at larger LArTPCs.

Detector modelling and simulations I (interaction ↗

DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗

DOE OSTI2025

Plant Reload Optimization (prlo)

The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.

Kim, Junyung [Idaho National Laboratory] (00090005↗

DOE OSTI2021

A population data-driven workflow for COVID-19 modeling and learning

CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces, and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of protective behaviors of individuals on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county, and state stakeholders in response to evolving decision-making priorities, while incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible, and performant analytical platform for planning and crisis response.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Preliminary Simulations of Commercial Drying Cycles Using the Advanced Drying Cycle Simulator

The purpose of this report is to document updates on the apparatus to simulate commercial vacuum drying procedures at the Nuclear Energy Work Complex at Sandia National Laboratories. Validation of the extent of water removal in a dry spent nuclear fuel storage system based on drying procedures used at nuclear power plants is needed to close existing technical gaps. Operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system during subsequent storage and disposal. A general lack of data suitable for model validation of commercial nuclear canister drying processes necessitates well-designed investigations of drying process efficacy and water retention. Scaled tests that incorporate relevant physics and well-controlled boundary conditions are essential to provide insight and guidance to the simulation of prototypic systems undergoing drying processes. This report documents fiscal year 2023 (FY23) updates on the Advanced Drying Cycle Simulator (ADCS). This apparatus was built to simulate commercial drying procedures and quantify the amount of residual water remaining in a pressurized water reactor (PWR) fuel assembly after drying. The ADCS was constructed with a prototypic 17×17 PWR fuel skeleton and waterproof heater rods to simulate decay heat. These waterproof heaters are the next generation design to heater rods developed and tested at Sandia National Laboratories in FY20. In FY23, a series of four simulated commercial drying tests was completed. This report presents the temperature and pressure histories of the drying tests as well as axial temperature profiles that can be compared to data from the Electric Power Research Institute (EPRI) High Burnup Demonstration TN-32B cask. Water content measurements and dew point calculations from a Hiden Analytical HPR-30 mass spectrometer are also presented in this report. Due to familiarization with this first-of-a-kind system, refinements to equipment calibration and test procedures have been identified to better match commercial drying cycles for future simulated tests. However, the presented data demonstrate the successful construction and operation of a viable research platform for quantifying residual water content closely approaching that expected in dry storage canisters during commercial drying procedures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Xyce™ Parallel Electronic Simulator Version 7.8 Release Notes

The Xyce™ Parallel Electronic Simulator has been written to support the simulation needs of Sandia National Laboratories’ electrical designers. Xyce™ is a SPICE-compatible simulator with the ability to solve extremely large circuit problems on large-scale parallel computing platforms, but also includes support for most popular parallel and serial computers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Xyce™ Parallel Electronic Simulator (V.7.9) Release Notes

42 ENGINEERING↗

DOE OSTI2025

Xyce™ Parallel Electronic Simulator Version 7.10 Release Notes

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Hydrodynamic Modelling for Tidal Energy Kites: Cooperative Research and Development Final Report, CRADA Number CRD-24-30123

The primary goal of the work is to improve accuracy of hydrodynamic loads acting on the lifting surface of the kite turbine due to complex air-water-lifting body interaction in a low-order numerical model using high-fidelity numerical input. The result of this study will help the Participant better design control algorithms for the kite turbine so that the turbine can harvest more energy while maintaining stability of the platform as well as its reliability. The use of hydrodynamic coefficients/load components from computational fluid dynamic simulation will inform better inputs for a medium-fidelity dynamic model (specifically an OrcaFlex model) which is intended for use in rapid design iterations in the design process.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because it can aid in both cybersecurity analysis and the evaluation of other cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

cyber-physical systems↗

DOE OSTI2024

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because they can aid in both cybersecurity analysis and the evaluation of cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

alfalfa↗