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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 OSTI2025

Floquet engineering with spatially nonuniform driving fields

In Floquet engineering, we apply a time-periodic modulation to change the effective behavior of a wave system. In this work, we generalize Floquet engineering to more fully exploit spatial degrees of freedom, expanding the scope of effective behaviors we can access. We develop a perturbative procedure to engineer space-time dependent driving forces that effectively transform broad classes of tight-binding systems into one another. We demonstrate several applications, including removing disorder, undoing Anderson localization, and enhancing localization to an extreme in spatially modulated waveguides. This procedure straightforwardly extends to other types of physical systems and different Floquet driving field implementations.

42 ENGINEERING↗
DOE OSTI2025

Numerical Investigation of Ammonia/n-Heptane Dual-Fuel Spray Flames Using Large Eddy Simulations

Diesel engines are extensively used in heavy-duty transportation, power generation, and marine vehicles due to their superior thermal efficiency and extended high-load operability compared to spark ignition (SI) engines. However, combustion in diesel engines is generally characterized by locally rich fuel–air mixtures and high combustion temperatures, causing significant amounts of soot and NO x emissions from these engines. Utilizing carbon-free alternative fuels and enhancing fuel efficiency represent promising strategies to mitigate greenhouse gas (GHG) and other emissions in the heavy-duty transportation sector. In this context, ammonia (NH 3 ), as a hydrogen carrier, has received significant attention as a viable substitute for hydrocarbon fuels due to its carbon-free composition, relatively high energy density, and well-established infrastructure. Many previous studies have considered combustion and emission characteristics of ammonia-hydrocarbon fuel blends in engines and simplified flames. But, detailed investigations on the effects of ammonia on the performance of hydrocarbon fuels under engine conditions are lacking. In the present study, we perform large eddy simulations (LES) of the ignition and flame processes in a constant-volume combustion reactor, where n-heptane is injected in an ammonia/air ambient mixture in a diesel-like environment. A detailed and validated reaction mechanism containing 302 species and 1981 reactions is employed. The Engine Combustion Network Spray H experimental data is used to validate the spray model under both non-reacting and reacting conditions. Dual-fuel combustion is simulated using the well-stirred reactor (WSR) approach. Results are presented for two spray cases: (1) single fuel (SF) with n-heptane injected into a mixture of air and combustion products and (2) dual-fuel (DF) with the injection of n-heptane in a mixture of air, ammonia, and combustion products. It is observed that the presence of ammonia has a significant effect on the ignition and flame development processes. With ammonia addition, both the first- and second-stage ignition delay times increase, but the effect of ammonia on the second-stage ignition is significantly more prominent. In addition, the ignition kernel size and growth rate decrease noticeably. For SF spray, the main ignition is characterized by multiple ignition kernels near the spray tip, whereas for DF spray, a single relatively small ignition kernel forms and grows slowly in the downstream direction. The flame development and the final quasi-steady flame structure are also modified due to ammonia. Here, the outcome of this research would enable a better understanding of ammonia–diesel dual-fuel spray flame behavior and guide the development of associated engine combustion strategies.

ammonia↗
DOE OSTI2026

Comparison of URANS and LES predictions for the open phase of the OECD NEA CSNI fluid structure interaction CFD benchmark

The OECD NEA CSNI WGAMA CFD Task Group ran a benchmark in 2020 and 2021 to assess the predictive capabilities of coupled fluid structure interaction (FSI) CFD analysis methods. This paper presents the predictions made for the open phase of the benchmark using URANS and LES turbulence modelling approaches, and a comparison of the results to the experimental data. The benchmark comprised a channel containing two inline cylinders in cross-flow. The cylinders were fixed at one end, free at the other, and had measured resonant frequencies and damping properties. The URANS modelling used ANSYS Fluent 2-way coupled to ANSYS Mechanical. The LES modelling used Nek5000, 1-way coupled to Diablo. Comparisons with cross-channel velocity profiles are presented, both for the mean flow and its RMS. Comparisons are also made to the frequency spectra for point measurements of fluid velocity and pressure, and for the accelerations of the free end of each cylinder. URANS predicts the average velocity profiles relatively well, and is able to predict the velocity and acceleration spectra at the shedding frequency. However, the frequency content at the 4th harmonic of the shedding frequency is low in the URANS flow fields, and so does not excite accelerations at the resonant frequency of the cylinders. LES makes better predictions of the average profiles, and the velocity spectra agree well at both the shedding frequency and at higher frequencies. In conclusion, the 1-way coupled LES results show good agreement for acceleration spectra.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Rheology and engine performance of very low sulfur fuel oil blended with 10% fast pyrolysis and hydrothermal liquefaction oils in a 2-stroke crosshead engine

The performance and emissions for a downscaled single-cylinder 2-stroke crosshead engine were determined for a very low sulfur fuel oil (VLSFO) when blended with 10 wt.% fast pyrolysis (FP) or hydrothermal liquefaction (HTL) bio-intermediates. The FP and HTL oils were derived from biomass and were observed to contain lower molecular weight (MW) hydrocarbons than neat VLSFO (which was evaluated as a baseline comparison). The addition of either biofuel reduced the overall viscosity of the VLSFO. Aging tests at 50, 90, and 120°C showed that the dynamic viscosity of VLSFO increased with exposure time up to two weeks. Similar trends were observed for the FP and HTL blends, but a pronounced spike in viscosity occurred for these fuels during the early period of exposure. None of the viscosity increases exceeded the operational limits of fuel system pumps. Engine performance studies were conducted under low, medium and high load operational settings. The relative performance of the test fuels was highly dependent on operating condition. In general, the engine results for the three test fuels were similar, but modest improvements in brake thermal efficiency and brake specific fuel consumption were observed, which may be attributed to the heightened reactivity of low molecular weight fraction of the FP and HTL oils.

09 BIOMASS FUELS↗
DOE OSTI2026

Survey of emerging nuclear data needs for nonproliferation applications with advanced reactors

Nuclear science plays a key role in non-proliferation activities supporting advanced reactor technologies. Nuclear data underpin predictions and interpretations of nuclear material behavior and signatures in reactor fuel production, use, transport, and storage. Advanced reactors provide new challenges compared to the current fleet of thermal fission reactors. This report consolidates reported nuclear data needs from representative workshops, conferences, and publications, identifying six themes for recommended future investments supporting non-proliferation and safeguards applications. While also identified as data needs, major fission product evaluations and (α,n) reactions were omitted as there are ongoing activities producing new data under NA22/Objective O. Each theme is summarized below with example data and association with the nonproliferation mission for advanced fuels and reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗
DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2022

Pyomo.DOE: An open-source package for model-based design of experiments in Python

Predictive mathematical models are a cornerstone of science and engineering. Yet selecting, calibrating, and validating said science-based models often remains an art in practice. Model-based design of experiments (MBDoE) provides a systematic framework to maximize information gain from experiments while minimizing time and resource costs. But MBDoE remains limited to niche application areas, in part because practitioners must integrate expertise in statistics, computational optimization, and modeling. To help reduce this barrier, we introduce Pyomo.DOE, an open-source package for MBDoE. Pyomo.DOE uses a nonlinear sensitivity analysis code k_aug to quickly approximate the Fisher information matrix and leverages a new stochastic programming abstraction. We demonstrate Pyomo.DOE with the first application of MBDoE to fixed-bed breakthrough experiments, which highlights the power of Pyomo.DOE to quantify the value of experimental modifications a priori for large-scale partial differential-algebraic equation (PDAE) models. Here we also provide a mathematical primer on MBDoE targeted at general chemical engineers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ion-beam radiation-induced Eshelby transformations: The mean and variance in hydrostatic and shear residual stresses

Ion beam plays a pivotal role in ion implantations and the fabrication of nanostructures. However, there lacks a quantitative model to describe the residual stresses associated with the ion-beam radiation. Radiation-induced residual stress/transformation strain have been mostly recognized in the hydrostatic sub strain space. Here, in this study, we use molecular dynamics (MD) simulations to show that the response of a material to irradiation is generally anisotropic that depends on the ion-beam direction, and should be described using tensorial quantities. We demonstrate that accelerator-based ion beam irradiation, combined with the intrinsic lattice anisotropy and externally induced anisotropy (such as anisotropic mechanical loadings), causes radiation-actuated shear transformation strains in addition to hydrostatic expansion. We map out these complex correlations for several materials. Radiation-induced defects are shown to be responsible for residual shear stresses in the manner of Eshelby inclusion transformation. We propose such tensorial response model should be considered for accurate nanoscale fabrication using ion-beam irradiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Review of the General-Purpose Heat Source Module Reduction and Monitoring Processing at INL

A thorough review of the Module Reduction and Monitoring (MRM) process was conducted to evaluate the efficacy of processing. The degree of reduction was calculated using the mass balance of oxygen contained in carbon monoxide (CO) and carbon dioxide (CO 2 ) removed from all fuel processed between January 2014 through August 2019. The plutonia was slightly reduced from O/Pu ratio of 2.0 to a range of 1.991 to 1.999. The review evaluated publications generated when MRM processing was developed and performed at the Mound Laboratory. A similar degree of reduction was measured at Mound in the 1982 paper authored by Ernie Johnson as well as MRM processing of IHS-60 and some Cassini-era fuel. The original 1982 paper documenting the reduction process reported a mass balance reduction to 1.9931 yet reported a total reduction to ~1.98 (Johnson 1990). The IHS-60 and Cassini reductions were calculated at 1.995 and 1.997 using the number of moles of CO and CO 2 removed as published in a clad pressurization investigation report (Merten, et all 1995). MRM process changes are recommended to improve the accuracy of the CO partial pressure calcualtion.

42 ENGINEERING↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Radial burnup characteristics of spent fuel pellets by zirconium analysis

Resonance ionization mass spectrometry is a powerful tool for in-situ measurements of elemental ratios in micrometer-sized spent fuel samples. By measuring the concentration of the fission product Zr we provide a framework for determining fuel burnup while simultaneously considering the 239 Pu fission fraction. Here, the analyzed spent fuel samples were taken from different fuels and distances from the fuel pellets’ edge. Results show a burnup enhancement at the edge of a fuel pellet of 180–220% compared to the center of the pellet. By comparing the concentration of the anthropogenic 93 Zr to the naturally occurring Zr isotopes, any influence of non-fissiogenic Zr or disturbing neutron capture reactions can be ruled out. This established 93 Zr as a new tool to measure fuel burnup.

Burnup↗
DOE OSTI2026

Persistent structural distortion for anticipated improper ferroelectricity in ultrathin h-Lu 1− x Ca x MnO 3 films

Improper ferroelectricity in hexagonal rare-earth manganites (h-RMnO 3 , R = Ho–Lu, Y, Sc) arises from a geometric distortion as the primary order parameter, resilient to depolarizing fields and promising for ultrathin ferroelectric devices. However, the substrate-induced interface clamping effect, which suppresses the geometric distortion in the sub-nanometer regime, has thus far hindered the realization of two-dimensional improper ferroelectrics. This study demonstrates that doping with calcium can enhance ferroelectric structural distortion in h-LuMnO 3 thin films. Compressively strained h-Lu 1−x Ca x MnO 3 (x = 0.1, 0.2, 0.3, 0.4, 0.5) epitaxial thin films were stabilized on sapphire substrates using an h-ScFeO 3 buffer layer. We have found that the interface clamping effect is entirely overcome when the doping concentration reaches x ⩾ 0.2, establishing a potential quasi-2D ferroelectric system with a remarkably high estimated structural transition temperature of larger than 1200 K inferred indirectly from temperature-resolved reflection high-energy electron diffraction. This finding suggests a general strain engineering strategy to enhance improper ferroelectricity in hexagonal manganites.

hexagonal manganites↗
DOE OSTI2026

Integrated Neutronics Modeling for Inertial Fusion Energy Systems: Development and Application to LD-FIRST

Lawrence Livermore National Laboratory (LLNL) is proposing a new Laser Driven Fusion Integration Research and Science Test Facility (LD-FIRST) with the goal of providing an experimental testbed for future Inertial Fusion Energy (IFE) systems. However, IFE systems require detailed and accurate multiphysics modeling to quantify material damage, thermal loading, and tritium breeding within complex chamber environments. This article presents the first step in an integrated multiphysics framework that couples meshed CAD-based geometry within Monte Carlo neutronic simulations to enable high-fidelity analysis of IFE chamber concepts, with future coupling to external codes. The neutronics workflow utilizes OpenMC and its third-party capability to use CAD-based geometries through DAGMC and tally on unstructured meshes with Libmesh to evaluate neutron transport behavior, geometric fidelity, and material performance under reactor-relevant conditions. The use of tailored tallies on unstructured meshes in this framework allows direct transfer without interpolating to CFD simulation tools. Two IFE chambers were evaluated, both conceived by LLNL: HYLIFE-II and Laser IFE (LIFE). This work produced high-fidelity conformal surface and volumetric meshes of the HYLIFE-II and LIFE chambers with mapped spatial insight into material damage, thermal loading, and tritium breeding. The HYLIFE-II model was built utilizing available resources and used as a test case to verify that the neutronics framework can handle complex geometries. The LIFE chamber CAD was provided by LLNL and was the main focus of this work. This work analyzes multiple ternary alloy breeding materials for the LIFE chamber, across different 6 Li enrichments to produce data relevant to the LD-FIRST project. This work also investigates the level of model fidelity for the LIFE chamber, and results show that inclusion of detailed first wall and coolant structures increased the predicted tritium breeding ratio (TBR) by ~30%, highlighting the sensitivity of tritium breeding and the need for a high-fidelity simulation framework for IFE chambers. These developments provide a scalable toolset for the design and optimization of next-generation IFE chambers, forming a solid foundation for future coupled multiphysics analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The paradox of the tight spiral pass in American football: A simple resolution

An American football is a rotationally symmetric object, which, when well-thrown, spins rapidly around its symmetry axis. In the absence of aerodynamic effects, the football would be a torque-free gyroscope and the symmetry/spin axis would remain pointing in a fixed direction in space as the football moved on its parabolic path. When a pass is well-thrown through the atmosphere, however, the symmetry axis remains—at least approximately—tangent to the path of motion. The rotation of the symmetry axis must be due to aerodynamic torque; yet, that torque, at first glance, would seem to have precisely the opposite effect. In this work, we explain the action of aerodynamics on the ball's orientation at second glance.

42 ENGINEERING↗
DOE OSTI2022

LANL Regular Employee Population and Demographics, FY15-21

This document summarizes an effort to compile and analyze the LANL regular employee population’s demographic attributes and trends between FY15-FY21 using MicroStrategy, a web-based data analytics and visualization tool. LANL’s HR Division collected and prepared data for the Weapons Program’s annual reporting activities for the 2017 through 2023 NNSA Stockpile Stewardship and Management Plan (SSMP). Los Alamos workforce data represent the fiscal year-end snapshot of the permanent employee population who are categorized by the Common Occupational Classification System (COCS). This work enables further analyses, including trends for all workforce attributes presented in the SSMPs as well as a definition of a crosswalk between COCS categorization and the internal laboratory job classification hierarchy. Data for other sites and the federal workforce, as shown in the SSMPs, are included and corresponding figures can be visualized within the MicroStrategy tool. A portable, Excel-based version of the tool was developed and shared with the multi-site workforce working group. This summary highlights selected aspects of the LANL regular workforce. The data analyses and visualizations communicate insights within three major themes regarding permanent LANL employees: 1) demographic transformation, 2) attrition, and 3) skills. Regarding demographic transformation, and consistent with the general trend across the enterprise, early career regular employees are the fastest growing group, with growth ranging between 12-34% year-to-year over the seven-year period analyzed. The data also reflect effects of internal and external factors on attrition; the large spike in separations in FY18 aligns with the contract transition from LANS to Triad and the dip in attrition between FY20-FY21 is likely a result of the global pandemic. The population of regular employees in general management, engineer, and operator COCS categories have the fastest growth rates amongst all the occupational categories. The MicroStrategy and Excel tools provide additional details and trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗