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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 OSTI2021

EpiCast: Simulating Epidemics with Extreme Detail

In early 2020, COVID-19 swept the globe. Governments attempted to “flatten the curve” through business shutdowns and stay-at-home orders, but the United States was hit hard. By the end of March, mere months after the virus first emerged in humans 7,000 miles away, the U.S. had recorded 192,300 cases and 5,300 deaths. While this unprecedented disaster sent shockwaves through every level of society and clouded an uncertain future, state and local governments turned to computational and mathematical epidemiology researchers to help formulate intervention strategies to limit the spread of the disease. Traditional forecasting models provided a reasonable understanding of how the near future was likely to look, but local policy makers and public health communities still struggled to understand how potential mitigations ought to be implemented. Decision makers needed a way to measure the impact of their policy choices—they needed better technology. EpiCast answered the call, bringing urgently needed answers to policymakers grappling with how to adjust school and business schedules. EpiCast is modeling software that generates a synthetic, representative population to simulate infectious disease transmission in the United States with extreme detail and granularity. The software models human behavior combined with community-specific information to provide a fine-grained preview of the effect of potential mitigation strategies for decision makers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A Pseudoreversible Normalizing Flow for Stochastic Dynamical Systems with Various Initial Distributions

Here, we present a pseudoreversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with various initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDEs, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE’s final state without simulating trajectories. The existing normalizing flow model for SDEs depends on the initial distribution, meaning the model needs to be retrained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. Additionally, we propose to use a pseudoreversible network architecture to define the normalizing flow model, which has sufficient expressive power and training efficiency for a variety of SDEs in science and engineering, e.g., in particle physics. We provide a rigorous convergence analysis of the pseudoreversible normalizing flow model to the target probability density function in the Kullback–Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Generation Plant Cost of Operations and Cycling Optimization Model (Final Technical Report)

Modern coal plants are a masterpiece of engineering, having been refined and improved over more than a century. As they evolved, they have grown more efficient and cleaner. At the same time, they have grown much larger and increasingly designed to operate on very specific fuels at or near the maximum capacity, providing baseload power. In recent decades, however, they have been called on to operate at reduced capacity (cycled) at a loss of efficiency and possibly accelerated wear and tear. The purpose of this project was to develop a model to accurately estimate the cost of cycling large coal plants so that they can be operated efficiently as part of a comprehensive strategy for generation planning and dispatch. The final goal is a model which is commercial-ready that can be “tuned” to different plants for widespread use.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Towards an open model intercomparison platform for integrated assessment models scenarios

The majority of scenarios in the IPCC database are generated by integrated assessment models (IAMs) and come from model intercomparison projects. However, the way in which the current model intercomparison projects are organized is not open to all IAM teams worldwide. Here we propose a transparent and inclusive platform that is open to anyone with an IAM regarding protocols development, scenario submissions and results evaluation. We discuss the challenges of this approach, particularly human resources and financial support. Here, we identify diversity in the level of model capability and quality of model output as possibly critical issues. Despite such challenges, the IAM community and its scientific activities can improve and benefit from the proposed platform, ultimately contributing to better climate policymaking.

IAM↗
DOE OSTI2025

Real-Twin

Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.

Wang, Chieh (Ross) [Oak Ridge National Laboratory ↗
DOE OSTI2024

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

StaNdaRT: a repository of standardised test models and outputs for supernova radiative transfer

We present the first results of a comprehensive supernova (SN) radiative-transfer (RT) code-comparison initiative (StaNdaRT), where the emission from the same set of standardised test models is simulated by currently used RT codes. We ran a total of ten codes on a set of four benchmark ejecta models of Type Ia SNe. We consider two sub-Chandrasekhar-mass (M tot = 1.0 M ⊙ ) toy models with analytic density and composition profiles and two Chandrasekhar-mass delayed-detonation models that are outcomes of hydrodynamical simulations. We adopt spherical symmetry for all four models. The results of the different codes, including the light curves, spectra, and the evolution of several physical properties as a function of radius and time are provided in electronic form in a standard format via a public repository. We also include the detailed test model profiles and several Python scripts for accessing and presenting the input and output files. We also provide the code used to generate the toy models studied here. In this paper, we describe the test models, radiative-transfer codes, and output formats in detail, and provide access to the repository. We present example results of several key diagnostic features.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

RF sheath induced sputtering on Proto-MPEX. I. Sheath equivalent dielectric layer for modeling the RF sheath

The pulsed linear plasma device Prototype Material Plasma Exposure eXperiment (Proto-MPEX) uses a radio frequency (RF) helicon antenna with an aluminum nitride ceramic window for plasma production. The RF sheath created under the helicon antenna is sufficient to cause ion impact energies to be greater than the sputtering threshold of the AlN helicon window material and for impurities to be created. In this study, we investigate the RF sheath on the inner diameter of the helicon window and its impact on the impurity production rates in Proto-MPEX. Three models—a 3D COMSOL finite element RF model of the Proto-MPEX helicon region, a rectified DC sheath potential model, and the 3D Global Impurity TRansport code—are coupled together to study impurity production and transportation. This novel method of impurity generation and transport modeling spans length scales ranging from the sheath (millimeters or less) up to the full device (meters) and can be applied to other radio frequency sources and antennas in a wide range of plasma physics studies, including basic plasmas, low-temperature processing plasmas, plasma thrusters, and fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Axion baryogenesis puts a new spin on the Hubble tension

We show that a rotating axion field that makes a transition from a matterlike equation of state to a kinationlike equation of state around the epoch of recombination can significantly ameliorate the Hubble tension, i.e., the discrepancy between the determinations of the present-day expansion rate H 0 from observations of the cosmic microwave background on one hand and type Ia supernovae on the other. We consider a specific, UV-complete model of such a rotating axion and find that it can relax the Hubble tension without exacerbating tensions in determinations of other cosmological parameters, in particular the amplitude of matter fluctuations S 8 . We subsequently demonstrate how this rotating axion model can also generate the baryon asymmetry of our Universe, by introducing a coupling of the axion field to right-handed neutrinos. This baryogenesis model predicts heavy neutral leptons that are most naturally within reach of future lepton colliders, but in finely tuned regions of parameter space may also be accessible at the high-luminosity LHC and the beam dump experiment SHiP. Published by the American Physical Society 2024

Co, Raymond T. (ORCID:0000000283957056)↗
DOE OSTI2025

Revealing Defect-Seeded and Interfacial Generation Mechanisms of Photoinduced Coherent Phonons with 4D Ultrafast Electron Microscopy

Ultrafast photoexcitation of coherent phonons is driven by an impulsive, collective displacement of constituent atoms from their average equilibrium lattice positions [1]. Models describing the generation of coherent acoustic modes typically invoke the creation of an anisotropic strain profile arising from the relatively instantaneous absorption of an ultrafast laser pulse [2]. If the skin depth is shallow relative to the specimen thickness, a steep tensile strain gradient perpendicular to the surface ($\frac{∂ε}{∂z}$) results. Initial relaxation occurs via rapid contraction of the surface layers followed by subsequent coherent oscillations of the lattice and launch of a train of coherent elastic strain waves [i.e., coherent acoustic phonons (CAPs)]. Macroscopically, responses are generally well-described by constitutive relations as gleaned from data gathered using ultrasonic methods or ultrafast spectroscopies. Furthermore, at the atomic to nanoscale level, individual lattice discontinuities and their impact on CAP behaviors can be modeled using multiscale methods [3,4]. Further, average unit-cell level responses on ultrafast timescales can be probed using femtosecond electron and X-ray scattering [5,6].

Flannigan, David J. [University of Minnesota, Minn↗
DOE OSTI2020

Performance evaluation of surface riding wave energy converter with linear electric generator

In this work, we devised a new WEC (wave energy converter) called SR-WEC (Surface Riding WEC). The SR-WEC consists of two bodies: the outer cylinder with an armature assembly (body #1) and a magnet assembly (body #2) sliding inside the armature. For the SR-WEC, the relative sliding displacement and velocity are caused by gravity acceleration and the outer cylinder's motions, and they lead to electrical power generation. To evaluate its performance, a numerical simulation tool was developed, which solves the fully-coupled floater-mooring-generator dynamics. During the developing stage, the appropriate hydrodynamics model, sliding mechanics model, mooring dynamics model, and LEG (linear electric generator) electro-magnetic model were independently developed and then fully coupled in time domain to account for the cross-coupling interactions among them. Then, the developed simulation tool was verified component by component against various laboratory tests. Subsequently, systematic parametric studies were conducted with several important design parameters under various wave conditions to enhance power generation. After that, the average output power was evaluated in enlarged operational wave conditions. The present SR-WEC is particularly designed to be efficient at low sea states, which is good since they cover the majority of typical annual sea states.

42 ENGINEERING↗
DOE OSTI2026

Probing Physics beyond the Standard Model through Combined Analyses of Next-generation Type Ia Supernova, Cosmic Microwave Background, and Baryon Acoustic Oscillation Surveys

Observations of Type Ia supernovae (SNe Ia), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe the intermediate and early epochs, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SN Ia sample expected from the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra (TT/EE/TE/ϕϕ) from the South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4–like experiment. We derive constraints on ΛCDM and its extensions involving the dark energy equation-of-state parameters (w 0 , w a ) and the sum of neutrino masses ∑m ν using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year 3 SN Ia sample can improve upon the DES Year 5 dark energy constraints by a factor of 2−2.5×, with the gains driven primarily by the significantly higher SN Ia density in the LSST sample. Similarly, DESI-DR3 shows up to a 1.8× improvement on dark energy parameters over DR2, driven largely by the substantial increase in the low-redshift sample. Combining CMB with LSST-Y3-SN Ia and DESI-DR3-BAO yields σ(w 0 ) = 0.028 and σ(w a ) = 0.11 for w 0 w a CDM cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10%–30% when freeing ∑m ν and spatial curvature. Moreover, the joint analysis of the three datasets can enable a 2σ–3σ detection of ∑m ν .

Raghunathan, Srinivasan [University of California;↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗
DOE OSTI2022

The Generalized Spallation Model

The Generalized Spallation Model (GSM) event generator is an advanced, robust, and scalable Fortran code developed from an extended and improved version of the earlier event generator codes CEM03.03 and LAQGSM03.03. The GSM implements its algorithms within the context of cascade exciton, particle equilibration, particle evaporation, and particle disintegration models sampled via Monte-Carlo techniques. Coalescence of particles is also considered. The GSM assumes the incident particles has sufficient energy to satisfy the assumptions utilized in the GSM. The GSM calculates reactions induced by hadrons and nuclei incident on nuclear targets at incident energies ranging from ~20 MeV up to several TeV per nucleon. Usage of GSM is recommended however for particles with incident energies exceeding ~100 MeV to ensure model-assumptions are satisfied. The GSM additionally provides its clients with sub-model libraries to allow them to model desired physics within their own physical models.

Juneau, Chase↗
DOE OSTI2022

Geographically aware estimates of remotely sensed water properties for Chesapeake Bay

This software contains the code for a data-driven model to produce high-resolution (< 1 km2) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation Earth Systems Models. Our model fits daily MODIS Aqua reflectance data to surface observations (< 1 m depth) from 2000-2021 in Chesapeake Bay, USA.

Stachelek, Jemma↗
DOE OSTI2025

A Multi-Model, Multi-Scale Research Program in Stressors, Responses, and Coupled Systems Dynamics at the Energy-Water-Land Nexus and for Concentrated, Interdependent Infrastructures: Toward Next Generation Capabilities in Integrated Impacts, Adaptation, and Vulnerability (I-IAV) Modeling and a Community of Practice

The goal of this research program was to build a next generation integrated suite of science-driven modeling and analytic capabilities, and a more expanded and connected community of practice, for analyses of the stressors, impacts, adaptations and vulnerabilities of global and regional change. The emphasis was on understanding energy-water-land interactions and feedbacks and interdependent infrastructures at appropriate regional and temporal scales. Although the scope spans many complex facets of data, modeling, and analysis, as well as scales appropriate for integrated impacts and adaptation research, the focus of this effort was the development of multi-model, multi-scale capabilities spanning the domains of Multi-Sector Dynamics (MSD) models; Impact, Adaptation, and Vulnerability (IAV) models; and Earth System Models (ESMs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geometry-complete diffusion for 3D molecule generation and optimization

Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗