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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

A Tool to Incorporate Non-Energy Impacts in Energy Efficiency Investment Decision Making for Firms

Energy efficiency is a key demand-side strategy for sustainability, recently identified by the United States Department of Energy as a pillar of industrial decarbonization. The increased focus on decarbonization and the requirement for efficiency to enable electrification, another decarbonization pillar, due to the spark spread between natural gas and electricity prices, make energy efficiency increasingly relevant. Still, industries face challenges in adopting energy efficiency measures. Researchers have long found a gap in adoption of even those measures with a profitable net present value, attributed to lack of strategic value among other barriers (see for rigorous exploration and taxonomy). One solution to facilitate energy efficiency projects is the inclusion of non-energy impacts, as this has been shown to double potential deployment of such projects at system level. Energy efficiency can provide valuable benefits outside of simple operating cost reductions, from decreased pollution to enhanced productivity. The inclusion of these benefits in decision making assessments faces hurdles due to inconsistency of ancillary benefits across projects, difficulties in quantifying impacts and the need for additional measurement to quantify them. The decision-making tools to support this have been designed primarily for the European context. We begin with a stakeholder engagement process to better characterize the U.S. decision making process surrounding adoption of energy efficiency investments. Characterization of non-energy impacts has developed substantially over recent decades. Cagno et al. provided a framework for studying the applicability of these impacts to energy efficiency projects, listing 120 key performance indicators focused mainly on reductions of costs/harms. Other researchers have included impacts on the strategic and revenue side that can be merged into this framework as well. We seek a tractable set of impacts that can be included in a decision-making tool in the US, and as such are well suited to US industry, management and decision making processes. We also seek to understand how to best quantify or characterize these impacts. This work will demonstrate the results of a survey conducted among US manufacturing industry decision makers to assess the decision making landscape of stakeholders as well as the most relevant performance indicators for energy efficiency projects.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2024

Multi-operation tool for photovoltaic cell processing

Multi-operation tools for photovoltaic cell processing are described. In an example, a multi-operation tool includes a conveyor system to move a photovoltaic (PV) cell continuously along a conveyor path through a laser scribing station and an adhesive printing station. Furthermore, the PV cell may be aligned to a laser head of the laser scribing station and a printer head of the adhesive printing station in a single alignment operation prior to being laser scribed and printed with an adhesive in a continuous process.

Beckett, Nathan Phillips↗
DOE OSTI2025

Evaluating Economic Impact: An Investment Tool for Large Language Model Integration in Workweek Management

This paper explores the development and application of an investment tool designed to quantify the costs and potential savings associated with integrating large language models (LLMs) into work week management optimization (WMO) within the nuclear industry. LLMs, with their advanced natural language processing capabilities, can significantly enhance various aspects of work management, such as problem identification, prioritization, planning, scheduling, information retrieval, and information summary. Our investment tool focuses on evaluating the return on investment (ROI) for LLM applications in WMO by considering four pivotal decision factors: model selection, application, user training, and hosting options. This paper details the development and implementation of the ROI model and illustrates its application through multiple case studies, analyzing the impact of different variables, such as work time saved, number of requests, and model performance, on the computed ROI over two years. The computed ROI is also compared over different hosting solutions. Our findings indicate that ROI increases with enhanced work time savings and optimal request load but can decline with high request volumes or increased model costs. This model aids decision-makers in the nuclear industry by providing a structured approach to assessing the economic viability and potential savings from integrating LLMs into WMO processes.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluating Economic Impact: An Investment Tool for Large Language Model Integration in Workweek Management

This paper explores the development and application of an investment tool designed to quantify the costs and potential savings associated with integrating large language models (LLMs) into work week management optimization (WMO) within the nuclear industry. LLMs, with their advanced natural language processing capabilities, can significantly enhance various aspects of work management, such as problem identification, prioritization, planning, scheduling, information retrieval, and information summary. Our investment tool focuses on evaluating the return on investment (ROI) for LLM applications in WMO by considering four pivotal decision factors: model selection, application, user training, and hosting options. This paper details the development and implementation of the ROI model and illustrates its application through multiple case studies, analyzing the impact of different variables, such as work time saved, number of requests, and model performance, on the computed ROI over two years. The computed ROI is also compared over different hosting solutions. Our findings indicate that ROI increases with enhanced work time savings and optimal request load but can decline with high request volumes or increased model costs. This model aids decision-makers in the nuclear industry by providing a structured approach to assessing the economic viability and potential savings from integrating LLMs into WMO processes.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

GSAS Tools

SAND2023-06684O GSAS Tools is a web application that manages user access to modeling and simulation tools and promotional material. This software, which is a spiking neural network (SNN) simulator, represents an SNN as a graph of stochastic differential equations and simulates the time-evolution of these equations. It has the capability of reading inputs from file and writing outputs to file, and generally supports experimentation, analysis, and algorithm development using SNNs. 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.

Noel, Todd↗
DOE OSTI2020

Low-Income Energy Affordability Data (LEAD) Tool

This resource summary covers the Low-Income Energy Affordability Data (LEAD) Tool, which helps stakeholders make data-driven decisions on energy goals and program planning by improving their understanding of low-income and moderate-income household energy characteristics.

Low-Income Energy Affordability Data Tool, LEAD To↗
DOE OSTI2024

Development of a Computational Framework for Multiphysics Multiphase Species Tracking using NEAMS Tools

This report implements a high-fidelity multiphysics modeling framework using the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program tools to track isotopic species in Molten Salt Reactors (MSRs), with a specific focus on the 91-depletion chain within the Molten Salt Reactor Experiment (MSRE). The model integrates neutronics, thermal-hydraulics, depletion, and thermochemistry to simulate the production, transport, and phase transitions of isotopes under steady-state and transient conditions. The main findings reveal that isotopes such as bromine-91 largely remain in the liquid phase, while others, including krypton-91and yttrium-91, transition to the gas phase, significantly influencing the reactor’s radiological source term. The study also shows that during transients, like a reactivity insertion transient, rapid void formation and the expansion of the liquid-gas interface led to substantial transfers of dissolved isotopes into the gas phase, altering isotope distribution and largely increasing the source term in the off-gas system. Additionally, the research highlights that short-lived isotopes dominate the initial off-gas response during transients, while longer-lived isotopes determine the equilibrium state, underscoring the necessity of dynamic simulations for accurate species tracking and reactor safety analysis. The developed methodology will be applied in the future to the tracking of a larger number of species and introduce other species tracking mechanisms, such as deposition and plating.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Additive manufacturing of AISI M2 tool steel by binder jetting (BJ): Investigation of microstructural and mechanical properties

The presented research demonstrates for the first time the successful processing of AISI M2 tool steel by binder jetting, a promising additive manufacturing technique capable of producing complex shapes with minimal residual stresses and isotropic properties. The optimal printing parameters were explored by varying processing parameters such as the binder saturation (45 %–105 %), binder set time (0 to 10 s), targeted bed temperature (50–60 °C), oscillator (2600–2750 rpm), recoater (20–28 mm/s), and roller speeds (200–300 rpm). Microstructural characterization and evaluation of mechanical properties of binder jetted parts were performed using x-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) to study their chemical composition, powder morphology, microstructure, carbide morphologies, relative density, hardness, compressive strength, and ductility. Two powder sizes (5 and 10 μm) were used, and sintering was performed at varying temperatures (1270, 1280, and 1300 °C) and durations (60 and 120 min), followed by a furnace, air, and water cooling. An optimum hardness of ~970 HV was obtained when parts were sintered at 1270 °C for 60 min, followed by water quenching. Impressive compressive strength of ~ 3580 MPa was observed in the sample sintered at 1280 °C for 60 min duration, followed by air cooling. Furnace-cooled parts showed the highest density of ~95 %, whereas the relative density of air- and water-cooled parts varied between ~91 to 93.50 %, respectively. The microstructure of sintered samples revealed the formation of M 6 C stable carbide, M 2 C metastable carbide, MC as a secondary carbide, and a-Fe matrix, which contributed to the observed increase in mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Binder-jetted AISI M2 tool steel during hot isostatic pressing: Densification and carbide transformation

Binder jetting (BJ) enables fabrication of complex components from high-alloy steels such as AISI M2; however, residual porosity after sintering limits mechanical performance. This study investigates the coupled effects of binder chemistry, sintering conditions, cooling rate, and subsequent hot isostatic pressing (HIP) on densification, microstructure, and mechanical response in binder-jetted M2 tool steel. HIP increased relative density from ∼93 to 95% to >99% and improved compressive strength by ∼40-70%. Densification was governed by initial pore morphology, where closed porosity was effectively eliminated, while interconnected porosity limited full consolidation. Microstructural analysis (XRD, EBSD, SEM) shows that HIP promotes dissolution of metastable carbides and redistribution of alloying elements (W, Mo, V), transforming heterogeneous carbide networks into finer and more uniformly distributed M 2 C, MC, and M 6 C phases through diffusion-assisted homogenization. Among the investigated conditions, the FluidFuse binder combined with sintering at 1270 °C for 60 min and furnace cooling produced the most balanced response, achieving ∼99.7% density, ∼855 HV hardness, ∼4130 MPa compressive strength, and ∼24% strain. In contrast, higher sintering temperatures promoted carbide coarsening, reducing ductility despite high density. HIP reduces microstructural heterogeneity and drives the system toward a near-equilibrium state with reduced sensitivity to prior processing history. A comparative assessment with conventional and additive manufacturing routes (LPBF, DED, EBM, FFF) shows that the BJ-HIP approach achieves competitive densification and mechanical performance. These findings provide a mechanistic basis for controlling densification and microstructure in high-alloy steels processed via BJAM.

AISI M2 tool steel↗
DOE OSTI2024

Machine tool cross beam design, fabrication, and testing using metal big area additive manufacturing

This paper describes the application of metal Big Area Additive Manufacturing (mBAAM) to the fabrication of a machine tool cross beam. The replacement of a traditional box design weldment with a new design printed by wire arc additive manufacturing using the MedUSA system at Oak Ridge National Laboratory (ORNL) is detailed. This requires a new design strategy based on the unique mBAAM capabilities. The intent of the new design is to reduce mass, while maintaining the dynamic stiffness. To compare the two designs, the natural frequencies and mode shapes are measured using impact testing and predicted using finite element analysis. It is confirmed that the printed structure dynamics agreed with the numerical model predictions, which demonstrates that it is feasible to model a large-scale mBAAM part and understand its behavior prior to printing. Another notable outcome of this study is that the significant residual stress and distortion in the print indicate that knowledge gaps remain for widespread implementation of mBAAM.

42 ENGINEERING↗
DOE OSTI2020

Process design tools and techno-economic analysis for capacitive deionization

Capacitive deionization (CDI) devices use cyclical electrosorption on porous electrode surfaces to achieve water desalination. Process modeling and design of CDI systems requires accurate treatment of the coupling among input electrical forcing, input flow rates, and system responses including salt removal dynamics, water recovery, energy storage, and dissipation. Techno-economic analyses of CDI further require a method to calculate and compare between a produced commodity (e.g. desalted water) versus capital and operational costs of the system. In this work, we demonstrate a new modeling and analysis tool for CDI developed as an installable Matlab program that allows direct numerical simulation of CDI dynamics and calculation of key performance and cost parameters. The program is provided for free and is used to run open-source Simulink models. The Simulink environment sends information to the program and allows for a drag and drop design space where users can connect CDI cells to relevant periphery blocks such as grid energy, battery, solar panel, waste disposal, and maintenance/labor cost streams. The program allows for simulation of arbitrary current forcing and arbitrary flow rate forcing of one or more CDI cells. We employ validated well-mixed reactor formulations together with a non-linear circuit model formulation that can accommodate a variety of electric double layer sub-models (e.g. for charge efficiency). The program includes a graphical user interface (GUI) to specify CDI plant parameters, specify operating conditions, run individual tests or parameter batch-mode simulations, and plot relevant results. The techno-economic models convert among dimensional streams of species (e.g. feed, desalted water, and brine), energy, and cost and enable a variety of economic estimates including levelized water costs.

42 ENGINEERING↗
DOE OSTI2020

Handbook: Water Evaluation Tools

This document provides general instructions on performing a walk-through water survey to collect the required data for a comprehensive water evaluation and entering data into the online Water Balance Tool.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Federal Automotive Statistical Tool: FY 2022 Federal Fleet Trends and Data Quality [Slides]

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2022 and discusses quality of the submitted data. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Heracles: Predictive Tools for Opioid Crisis Intervention - m/q Initiative Project Report

The opioid crisis in the United States is being fueled primarily by fentanyl and its molecular analogs, which can be anywhere from 50 to 1,000 times more potent than morphine. Fentanyl itself is straightforward to synthesize; furthermore, the structure is such that fentanyl’s flexible, rotatable side chains are easy to modify to create new analogs. Reference-free computational techniques to predict and identify new fentanyls have the potential to provide a desperately needed preemptive advantage to regulatory stakeholders and toxicologists. The computational pipeline Heracles was developed with this preemptive advantage in mind. Heracles has two primary components: 1) the creation of an in silico library of putative fentanyl analogs, and 2) a downselection pipeline to prioritize generated fentanyl analogs predicted to be potent and easy to synthesize. Experimental observables were also predicted for prioritized analogs, with validation of the observables begun. Heracles has demonstrated potential to aid in the advancement of reference-free paradigms while providing new tools to first responders and other stakeholders attempting to mitigate the opioid crisis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Developing tools and process controls to manufacture energy-efficient powders for additive manufacturing feedstocks: Computational analysis of metal powder manufacturing via machining

Traditionally, metal powders have been produced through methods such as grinding, atomization, and electrolysis. In contrast to these techniques, Metal Powder Works, Inc. has pioneered a methodology based on metal cutting. This innovative approach utilizes a vibrating cutting tool to machine metal particles, in the form of chips, from a workpiece. This technique allows for control of powder particle size, morphology, and avoids any thermally induced material changes. This collaboration aims to elucidate metal cutting characteristics and assess performance on tough materials like Inconel alloys. Computational models, using FEA and SPH techniques, will be developed initially, focusing on aluminum alloy (Al 7075-T6) for studying mesh sensitivity, cutting forces, and chip morphology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Procurement Analysis Tool (PAT) Informational Webinar

This is a slide deck for the public PAT Informational Webinar scheduled for July 22, 2025. This the first webinar for PAT after the new tool was launched on nrel.gov on June 16, 2025.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗