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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Improving Thermal Management Strategies for Data Centers: A Physical Testbed Incorporating Small Modular Reactor and Microreactor Technology

This study aims to accelerate the demonstration of various thermal management systems for data centers using nuclear-generated heat to enhance energy and grid reliability. Utilizing mobile containerized and stationary test beds at INL's High Performance Computing (HPC) facility, this project integrates with various nuclear-related energy systems testing facilities. Key components include immersion cooling apparatus, absorption chillers, and adjustable thermal management simulators. Tasks involve acquiring necessary hardware, sensors, and cooling apparatus, engaging with data center industry stakeholders, and providing a testing platform for algorithms, models, tools, and software. The objective is to expedite the deployment of nuclear-powered data centers, thereby improving energy reliability and affordability.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Numerical modeling of hydrogen mixing in a direct-injection engine fueled with gaseous hydrogen

Hydrogen is considered as one of the most promising options to achieve effective decarbonization of the energy and transportation sectors. As such, it has recently been receiving increasing attention because of its promising potential as an energy carrier for advanced energy and propulsion systems. With a focus on internal combustion engines, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential for high engine efficiency and specific power while reducing the risk of backfiring and pre-ignition issues. Therefore, many experimental and numerical efforts have recently been dedicated to understanding the physical and chemical behaviors of hydrogen in engine during mixing and combustion. This study focuses on computational fluid dynamics (CFD) modeling of the hydrogen DI process in a hydrogen optical research engine. Under the conditions studied, gaseous hydrogen is injected into the combustion chamber via a centrally located single-hole injector at a pressure of 100 bar. Two configurations, namely low-and high-tumble, are investigated to understand the impact of different in-cylinder flow patterns on the fuel-air mixture preparation. Simulations are carried out using the commercial CFD software CONVERGE. Here, the in-cylinder turbulence is modeled with an unsteady Reynolds-averaged Navier-Stokes (URANS) formulation closed by the renormalization group (RNG) k-ε model. Several numerical methods and model constants, including but not limited to turbulent Schmidt number, are evaluated. The numerical results are systematically compared against experimental measurements of velocity and hydrogen concentration fields on the vertical center plane to assess the performance of the CFD model, unveil the physics of hydrogen mixing, and establish best practices for modeling hydrogen DI under relatively high injection pressure conditions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Aerial Inspection Planner

SAND2025-04581O Aerial Inspection Planner is a software tool that creates aerial trajectories for full coverage of a 3D object at minimum cost. It can be used for inspection of aircraft, buildings, ships, wind turbines, etc. It uses a 3D model of the object, and given the coverage constraints, outputs an efficient flight path for a UAV to follow.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.

Moon, Brady↗
DOE OSTI2022

CAS Operator Training Simulator

SAND2022-1525 O The Central Alarm System (CAS) training simulator is a standalone application that will work on any Alarm Control and Display (AC&D) System. Designed to be used by an instructor in a classroom environment, this software plays back scenario alarms and videos usually exported out of Scribe3D or any software that can produce scenario-based videos. Users can select from an array of different scenarios to play. The alarms are sent to an AC&D controller and the videos are sent to a Video Management Server (VMS). Both alarms and associated videos displayed by the intended AC&D system. 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.

Le, Tam↗
DOE OSTI2021

Autonomous Discovery in Science and Engineering

The Center for Advanced Mathematics for Energy Research Applications (CAMERA) is an integrated, cross-disciplinary center aimed at inventing, developing, and delivering the fundamental new mathematics required to capitalize on experimental investigations at scientific facilities. Jointly funded by the Office of Advanced Scientific Computing Research (ASCR) and the Office of Basic Energy Sciences (BES) within the US Department of Energy’s Office of Science, CAMERA identifies areas in experimental science that can be aided by new mathematical insights, develops the needed algorithmic tools, and delivers them as user-friendly software to the experimental community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Controlled dehumidification to extract clean water from a multicomponent gaseous mixture of organic contaminants

Rapid expansion of the unconventional oil and gas extraction has increased American energy independence, but also led to increased production of large amounts of contaminated water. Wastewater from the oil and gas industry contains a wide range of contaminants. Injecting such contaminated water into disposal wells or discharging it to the environment jeopardizes freshwater resources. Conventional and membrane-based wastewater treatment techniques are often not effective choices to treat highly contaminated wastewater; however, a suitable way to remove contaminants from wastewater is to selectively separate water in a process analogous to humidification-dehumidification (HDH). Only a few studies have investigated the use of HDH for wastewater treatment. In this study, a novel HDH system is introduced for treating highly contaminated water, such as oil and gas flowback and produced water. In this HDH process, a non-condensable gas, such as air, mixes with wastewater vapor to facilitate the separation of contaminants. As a result, clean water condenses from the multicomponent gaseous mixture while air carries organic contaminants out of the dehumidification section. A laboratory apparatus was constructed and experiments were performed to investigate the HDH process for wastewater treatment and to study the effects of flow dynamics including air flow rate on the composition of treated water. Different contaminants including benzene, toluene, 2-propanol and 2-butoxyethanol were tested. Experimental results showed that the system can be successfully applied for removing volatile and/or toxic organic contaminants from wastewater. A representative multicomponent mixture of fracking wastewater was successfully treated using the experimental setup and clean water with quality of 98.3% was obtained. It was revealed that increasing the air-to-vapor mass ratio improves purity of treated water. Quantitative analysis showed that by increasing the air-to-vapor mass ratio from 0.6 to 5.9, the fraction of separated 2-propanol through the air was improved from 43% to more than 96% of the initial amount. ASPEN software was employed to simulate equilibrium conditions. Finally, experimental results were observed to have lower mass fraction of residual contaminant in the treated water compared to equilibrium state.

42 ENGINEERING↗
DOE OSTI2024

Toward Polydisperse Flows With MFIX-EXA

In the presence of large size disparities, single-grid neighbor search algorithms lead to inflated neighbor lists that significantly degrade the performance of Lagrangian particle solvers. If Eulerian–Lagrangian (EL) frameworks are to remain performant when simulating realistic systems, improved neighbor detection approaches must be adopted. To this end, we consider the application of a multigrid neighbor search (MGNS) algorithm in the mfix-exa software package, an exascale EL solver built upon the AMReX library. Here, details regarding the implementation and verification of MGNS are provided along with speedup curves for a bidisperse mixing layer. MGNS is shown to yield up to 15$\times$ speedup on CPU and 6$\times$ speedup on GPU for the problems considered here. The mfix-exa software is then validated for a variety of polydisperse flows. Finally, a brief discussion is given for how dynamic MGNS may be completed, with application to spatially varying particle size distributions.

42 ENGINEERING↗
DOE OSTI2024

Supporting Information for the Integrated Methane Monitoring Platform Design (Final Report)

As the urgency for understanding methane emissions and the number of methane monitoring technologies being deployed have increased in the last two decades, there is an opportunity and a need to integrate the numerous disparate data sources to enable the detection, quantification, contextualization, and reporting of methane emissions along the oil and gas supply chain. Such an integration would enable emissions reductions through early detection of super emitters, data-driven mitigation strategies, and improvedgreenhouse gas inventories. The GTI Energy (“GTI”)project team (“the team”) worked with a multitude of industry experts, stakeholders, and subject matter experts (SMEs) to collect guidance, insights, and information to inform the requirements and subsequent engineering, design, deployment, and operations of an integrated methane monitoring platform (IMMP). This final report describes the results of the team’s effort to execute the Integrated Methane Monitoring Platform Design project, ultimately providing an engineering, design, deployment, and operating plan (EDDOP) for the IMMP. This document provides additional information supporting the findings in the final report.

03 NATURAL GAS↗
DOE OSTI2022

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗
DOE OSTI2021

Artificial Diversity and Defense Security (ADDSec)

Artificial Diversity and Defense Security (ADDSec) machine learning algorithms are used to classify and cluster threats so that an appropriate response can be initiated as a mitigation strategy. The package includes an ensemble of machine learning algorithms such as Support Vector Machines, naïve bayes, logistic regression, and random forest that evolve with the data to recognize anomalous behavior at the host and network levels. Inputs into the machine learning algorithms include end host system calls, system utilization, packet captures, and syslog messages. The machine learning algorithms can be retrained based on user defined intervals or on the number of packets received. ADDSEC's threat responses include Internet Protocol (IP) Address randomization, application port number randomization, and application library randomization. The IP randomization implementation is built on top of a Software Defined Networking (SDN) framework. The SDN controller installs flows on each of the SDN switches with randomized source and destination IP addresses. The application port numbers are randomized using iptables. The application library randomization is created with a LLVM compiler. All randomization schemes are transparent to the endpoints on the network. 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. SAND2021-3379 O

Cox, RebeccaE.↗
DOE OSTI2021

Q Framework

The Q Formal Analysis Framework is a set of software tools used to formally prove that implementations of high-consequence digital systems meet mathematical specifications. The tools are designed to work together as a set. Specifications are derived directly from high-level requirements and expressed in the QSpec hierarchical state machine format. The toolkit includes QSpec, QRefact, and QReqs. 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. SAND2021-0504 O

Hulette, GeoffreyC.↗
DOE OSTI2025

TalkPipe

SAND2025-11168O TalkPipe is a software tool to help users create and manage complex data analysis tasks involving Large Language Models. Its easy-to-use interface allows users to combine different analytical processes. TalkPipe includes a Python library, a scripting language, and can be run in a Docker container, making it simple to customize and extend. 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.

Bauer, Travis [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2020

Southwest Wind Power R&D Test Site Development at the National Wind Technology Center (CRADA CRD-12-00472 Final Report)

Southwest Windpower, Inc. (SWWP) has been designing and distributing small wind turbines for more than 22 years and is the recognized global leader in the design, manufacturing and distribution of small wind systems (400-3000 watts). The company has been a pioneer in the development of wind technology and has built and shipped more than 170,000 wind turbines to over 120 countries worldwide. Headquartered in Flagstaff AZ, SWWP has sales representatives in over 88 countries. Applications for SWWP systems include residential homes, commercial properties, micro grids, remote cabins, telecom transmitters, offshore platforms, water pumping and sailboats. In addition, U.S. Department of Defense uses SWWP’s products in Forward Operating Bases (FOB’s), USGS uses them for remote monitoring of glacier movements and the CIA uses them to provide power seismic monitoring. Today, SWWP plans development of new technologies to address distributed energy market needs and become even more aggressive in international markets. This strategy includes extensive Research, Development, Demonstration & Deployment (RDD&D) activities to improve existing production line into more simple, reliable, and cost-effective wind turbine systems. Important part of such activities is the field testing of the new systems, which should provide: (i) validation of the reliable operation of hardware and software; (ii) validation of numerical models of the system and its components; (iii) field comparison of various control and optimization strategies; (iv) customized reliability testing; (v) customized monitoring and analysis testing; and (vi) IEC certification testing (vii) filed demonstration of system performance and reliability. Such field testing facility should encompass multiple towers and appropriate data acquisition systems. SWWP is exploring to move part of the engineering department to Colorado, and execute its RDD&D strategies in this new location. Partnership with NREL will provide great insights into SWWP’s RDD&D processes and more efficient commercialization by utilizing NWTC as the field testing site, and working close with NREL personnel. NWTC is ideally suited to strain the design. NREL is very experienced with customized and IEC testing. During this project, Southwest Windpower aims to improve the value proposition of distributed renewable energy, and make it a competitive choice in the energy markets. As of today, more then 170,000 small wind turbines, manufactured in the U.S., are installed in more then 180 countries. With the improved value proposition, Southwest Windpower wants to create and address new market needs, substantially increase its sales domestically and internationally, which should have positive impact on the creation of American jobs, competitiveness of U.S. economy, export or U.S. products, and the use of renewable energy.

17 WIND ENERGY↗
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 OSTI2022

ADAM: A web platform for graph-based modeling and optimization of supply chains

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. Here, in this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Key insights from US Department of Energy Better Plants workforce development bootcamps (2022–2025)

This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.

Energy efficiency↗
DOE OSTI2023

ExoToComsol v.0.1

SAND2024-08641O ExoToComsol models capability-sharing. For example, the software can develop a multiphysics modeling chain using physics modeling capabilities from different research institutions using either SIERRA or COMSOL. The converter program allows exporting user-defined, region-of-interest from a large FE model, which results in a finer mesh of the submodel that can be used for subsequent analysis. 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.

Rahman, Farhan↗
DOE OSTI2024

Defect diffusion graph neural networks (d2gnn)

SAND2025-01004O Defect Diffusion Graph Neural Networks (d2gnn) is a software tool that assists in the discovery of new materials for high-temperature, clean-energy applications. It uses advanced graph neural networks to model the relationship between material structures and their defect properties. The application helps predict how materials will behave under different conditions and accelerates the development of innovative materials. 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.

Witman, Matthew [Sandia National Lab. (SNL-CA), Li↗