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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Affordable Solid Panel "Perfect Wall" System

The SPS is an innovative interpretation of the “perfect wall” concept, in which environmental control layers are located on the exterior side of the structural components, as opposed to traditional cavity insulated, stud-framed walls. The primary objective of this study is to validate the SPS technology in terms of its constructability, cost, and performance. Specifically for this project, we partnered with two affordable housing nonprofits in Minnesota—Twin Cities Habitat for Humanity and Urban Homeworks—to build five new houses using SPS walls, as well as two high-performance stud-framed comparison homes. We also reviewed cost and performance data from 13 SPS homes built prior to this project by MonoPath and Spero Environmental Builders. Reviewing the outcomes of these 20 homes total, we find promising results in terms of constructability, cost, and performance, although more structural performance data are needed before this new technology can see widespread adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Winnett Public School District Energy Improvements

The Winnett Public School District Energy Improvement Project was funded through the U.S. Department of Energy's Renew America's Schools Program, which supports rural and underserved school districts in upgrading aging facilities and improving energy performance. The completed improvements directly advance the program's objectives by replacing the District's outdated coal-fired heating system with a modern, high-efficiency propane boiler system, while also enhancing ventilation systems and building envelope performance throughout the school. These upgrades provide a more reliable heating system and improved indoor air quality, creating a healthier and more comfortable environment for students and staff. In addition, replacing the aging boiler system has reduced long-term maintenance demands for one of Montana's most rural school districts, allowing the District to redirect limited financial resources toward other critical needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Joseph C Coughlan↗
NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Guillaume P Brat↗
DOE OSTI2020

Global Experiences with HPC Operational Data Measurement, Collection and Analysis

As we move into the exascale era, supercomputers grow larger, denser, more heterogeneous, and ever more complex. Operating such machines reliably and efficiently requires deep insight into the operational parameters of the machine itself as well as its supporting infrastructure. To fulfill this need, early adopter sites have started the development and deployment of Operational Data Analytics (ODA) frameworks allowing the continuous monitoring, archiving, and analysis of near realtime performance data from the machine and infrastructure levels, providing immediately actionable information for multiple operational uses. To understand their ODA goals, requirements, and use cases, we have conducted a survey among eight early adopter sites from the US, Europe, and Japan that operate top 50 high-performance computing systems. We have assessed the technologies leveraged to build their ODA frameworks, identified use cases and other push and pull factors that drive the sites' ODA activities, and report on their operational lessons.

Ott, Michael↗
DOE OSTI2024

Developing a Database of Bio-based Materials for Building Envelope Applications

Oak Ridge National Laboratory (ORNL) has been funded by the Department of Energy (DOE) to help accelerate the introduction of building envelope materials that would reduce the carbon footprint of the buildings sector. The DOE’s Building Technologies Office has historically sought to resolve the knowledge gaps regarding the energy efficiency and moisture durability of building envelope systems and to develop the data, guidance, and tools needed to facilitate rapid industry adoption of high-performance, moisture-managed envelope systems. This project will help accelerate the widespread acceptance of a new generation of building materials developed specifically with the intent of reducing the carbon footprint of buildings. We have produced a database of hygrothermal transport properties on low embodied carbon building materials that can be added to energy and durability simulation tools. Properties that were measured include density, heat capacity, thermal conductivity as a function of temperature and relative humidity, moisture dependent permeance, and sorption isotherms as a function of relative humidity. These data sets were measured following consensus national standards using state-of-the-art facilities. The data has been compiled and is being made available to building designers who require these data to assess these new materials in their designs. We will publish the data and seek its addition to reference databases such as the ASHRAE Handbook of Fundamentals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1997

Integrated Requirements Analysis and Technology Roadmaps

In fiscal year 1997, Strategic Insight performed analytical studies for NASA's Highly Reusable Space Transportation (HRST) program, creating program documents which illuminated technical requirements and critical research opportunities. Studies were performed to structure and confirm HRST's evolving technical requirements, building on Marshall's Phase 1 work, which defined HRST system concepts, analytical tools and high-level issues for assessment in Phase 2. Specifically, Strategic Insight: (1) Performed a requirements analysis to update HRST: An Advanced Concepts Study, Study Guidelines, Version 2.0 of January 22, 1996; only minor changes were recommended for the given parameters of interest to concept designers; (2) Conducted mini-workshops during HRST Working Group meetings on April 14-15, 1997 and July 22-24, 1997; and (3) Created structures for technology road maps of candidate HRST concepts, both subsystem and end-to-end concepts, emerging from the 13 cooperative agreement projects.

Source record↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Transformative Efficiency and Automation in Modular Homes (TEAMH)

This report documents the Transformative Efficiency and Automation in Modular Homes (TEAMH) project, which evaluates the integration of advanced building envelope technologies and automation-assisted modular construction to improve residential energy performance and construction efficiency. The study investigates high-performance insulation systems, including vacuum insulation panels (VIPs), combined with light gauge steel (LGS) modular construction and factory automation. Laboratory testing, whole-building energy modeling across multiple climate zones, and factory demonstrations were conducted to assess thermal performance, energy savings, and production efficiency. Results indicate that upgraded envelope assemblies can achieve up to ~50% heating and ~34% cooling energy savings relative to IECC 2018 code-compliant homes, while automation-assisted construction can reduce wall assembly time by 24%–46% compared to conventional wood framing. The findings demonstrate the potential for scalable, high-performance modular homes that deliver significant energy savings with competitive projected costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1976

Initial operation of a solar heating and cooling system in a full-scale solar building test facility

The Solar Building Test Facility (SBTF) located at Hampton, Virginia became operational in early summer of 1976. This facility is a joint effort by NASA-Lewis and NASA-Langley to advance the technology for heating and cooling of office buildings with solar energy. Its purposes are to (1) test system components which include high-performing collectors, (2) test performance of complete solar heating and cooling system, (3) investigate component interactions and (4) investigate durability, maintenance and reliability of components. The SBTF consists of a 50,000 square foot office building modified to accept solar heated water for operation of an absorption air conditioner and for the baseboard heating system. A 12,666 square foot solar collector field with a 30,000 gallon storage tank provides the solar heated water. A description of the system and the collectors selected is given here, along with the objectives, test approach, expected system performance and some preliminary results.

Knoll, R. H.↗
NASA NTRS2024

Ultra-Light, Strong, and Self-Reprogrammable Mechanical Metamaterials

Versatile programmable materials have long been envisioned that can reconfigure themselves to adapt to changing use cases in adaptive infrastructure, space exploration, disaster response, and more. We introduce a robotic structural system as an implementation of programmable matter, with mechanical performance and scale on par with conventional high-performance materials and truss systems. Fiber reinforced composite truss-like building-blocks form strong, stiff, and lightweight lattice structures as mechanical metamaterials. Two types of mobile robots operate over the exterior surface and through the interior of the system, performing transport, placement, and reversible fastening using the intrinsic lattice periodicity for indexing and metrology. Leveraging programmable matter algorithms to achieve scalability in size and complexity, this system design enables robust collective automated assembly and reconfiguration of large structures with simple robots. We describe the system design and experimental results from a 256-unit cell assembly demonstration and lattice mechanical testing, as well as demonstration of disassembly and reconfiguration. The assembled structural lattice material exhibits ultra-light mass density (0.0103 g/cc) with high strength and stiffness for its weight (0.01117 MPa and 1.1129 MPa, respectively), a material performance realm appropriate for applications like space structures. With simple robots and structure, high mass-specific structural performance, and competitive throughput, this system demonstrates potential for self-reconfiguring autonomous metamaterials for diverse applications.

Robotics↗
NASA NTRS1976

Initial operation of a solar heating and cooling system in a full-scale solar building test facility

The Solar Building Test Facility (SBTF) was constructed to advance the technology for heating and cooling of office buildings with solar energy. Its purposes are to (1) test system components which include high-performing collectors, (2) test the performance of a complete solar heating and cooling system, (3) investigate component interactions, and (4) investigate durability, maintenance and reliability of components. The SBTF consists of a 50,000 square foot office building modified to accept solar heated water for operation of an absorption air conditioner and for the baseboard heating system. A 12,666 square foot solar collector field with a 30,000 gallon storage tank provides the solar heated water. A description of the system and the collectors selected is printed along with the objectives, test approach, expected system performance, and some preliminary results.

Knoll, R. H.↗
DOE OSTI2024

Hardware-in-the-loop Laboratory Performance Verification of Flexible Building Equipment in a Typical Commercial Building

This project aims to develop high-resolution equipment performance and occupant data that quantifies demand flexibility in typical commercial buildings. The dataset documented in this report includes comprehensive time-series measurements from hardware-in-the-loop (HIL) experiments conducted across multiple testbeds designed to simulate realistic operational environments for HVAC systems. Specifically, it captures minute-by-minute high-resolution data on the performance of various typical HVAC systems, including a variable-air-volume (VAV) air handling unit (AHU) system with chillers and an ice tank in the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST), a two-stage air-source heat pump (ASHP) at the NIST, and a water-source heat pump (WSHP) at Texas A&M University (TAMU). The data were generated under a range of controlled conditions reflecting different grid scenarios and climatic influences, as well as various control strategies, building types, occupancy patterns, and occupant behaviors. This dataset provides DE-EE0009153 Final Report 5 detailed insights into the demand flexibility of these systems, including their response to grid signals, occupant behaviors, energy consumption patterns, and operational efficiency under different conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Energy Systems Integration Facility Stewardship Summary: Fiscal Year 2023

A summary of NREL's good stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, infrastructure and capability upgrades, and examples of R&D impact. In fiscal year 2023, ESIF installed its third-generation high-performance computer, expanded capabailities for validating building energy controls, and demonstrated a leap in scale through virtual networking with another national laboratory. ESIF researchers made breakthroughs in cybersecurity for energy systems, efficient high-powered electric vehicle charging, leveraging reinforcement learning for grid resilience, and more.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2026

Lessons learned from the development and implementation of a workforce training curriculum for advanced controls for high performance HVAC systems

Over the past decade, academic research on advanced controls has slowly transitioned into new software platforms, giving rise to various companies developing and deploying these innovative products, including solutions for light commercial HVAC systems. However, the current workforce remains widely unprepared to install, maintain and operate these systems, particularly complex software-based control platforms, as most workforce training programs still focus on traditional building automation for large commercial buildings. This paper presents the development and piloting of curriculum for three key types of professionals: ● Technicians (trade-level): installing and maintaining modern high-performance HVAC systems and controls ● Programmers (undergrad-level): developing and implementing advanced controls ● Engineers and energy professionals (undergrad/grad-level): managing and evaluating system performance We share details of the material developed including training videos, open-source software, instruction manuals. We also present the results of a pilot implementation of the training materials with real students.

Casillas, Armando↗
DOE OSTI2024

CommBench: Micro-Benchmarking Hierarchical Networks with Multi-GPU, Multi-NIC Nodes

Modern high-performance computing systems have multiple GPUs and network interface cards (NICs) per node. The resulting network architectures have multilevel hierarchies of subnetworks with different interconnect and software technologies. These systems offer multiple vendor-provided communication capabilities and library implementations (IPC, MPI, NCCL, RCCL, OneCCL) with APIs providing varying levels of performance across the different levels. Understanding this performance is currently difficult because of the wide range of architectures and programming models (CUDA, HIP, OneAPI). We present CommBench, a library with cross-system portability and a high-level API that enables developers to easily build microbenchmarks relevant to their use cases and gain insight into the performance (bandwidth & latency) of multiple implementation libraries on different networks. We demonstrate CommBench with three sets of microbenchmarks that profile the performance of six systems. Our experimental results reveal the effect of multiple NICs on optimizing the bandwidth across nodes and also present the performance characteristics of four available communication libraries within and across nodes of NVIDIA, AMD, and Intel GPU networks.

Hidayetoglu, Mert↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Automated Air Sealing Demonstration: Denver Federal Center Building 40

This project aimed to demonstrate building airtightness achieved by automated air sealing in a commercial building setting. The automated air sealing demonstrated in this project uses a modified blower door to pressurize and distribute the sealing aerosol to achieve the required building airtightness. To demonstrate this technology, Building 40 at the Denver Federal Center, a federally owned campus under the jurisdiction, custody and control of the U.S. General Services Administration (GSA), was selected for testing (Figure 2). This building is currently undergoing several retrofit projects, including increasing insulation, installing high-performance windows, and adding advanced equipment, control systems, and automated air sealing. This demonstration project involved installing automated air sealing and measuring the improvements in the building's airtightness. The automated air sealing was installed over two days by AeroBarrier, the vendor. An external blower door test contractor measured the airtightness of the demonstration space before and after air sealing. The new airtightness value and the percentage increase in airtightness were used to evaluate the energy savings potential of automated air sealing. These airtightness values were used to compute the energy savings and CO 2 emissions reduction for different climate zones, building types, and initial airtightness conditions. In addition, the heating, ventilation and air conditioning (HVAC) load reduction attributable to the reduced building air leakage was studied. This study included research to determine the cost and time reduction of automated air sealing. Finally, the automated air sealing performance was evaluated qualitatively using a focus group discussion that included GSA and Bristol, the general contractor. The installation has shown the demonstration site, with a floor size of 4,462 ft 2 , air leakage has reduced by more than 50% in less than 7 hours, including preparation, site sealing, and cleanup. The performance objectives were classified as quantitative or qualitative based on the evaluation metrics used to assess the project’s success. The key performance objectives for this project were the level of airtightness achieved, the time and cost required to perform the sealing, and the HVAC load reduction attributable to air sealing. Table 1 shows the quantitative performance objectives.

42 ENGINEERING↗