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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Performance Portable, Fully Implicit Landau Collision Operator with Batched Linear Solvers

Modern accelerators use hierarchical parallel programming models that enable massive multithreading within a processing element (PE), with multiple PEs per device driven by traditional processes. Batching is a technique for exposing PE-level parallelism in algorithms that have traditionally run on MPI processes or multiple threads within a single process. Opportunities for batching arise in, for example, kinetic discretizations of magnetized plasmas where collisions are advanced in velocity space at each spatial point independently. This paper builds on previous work on a high-performance, fully nonlinear, Landau collision operator by batching the linear solver, as well as batching the spatial point problems and adding new support for multiple grids for multiscale, multispecies problems. An anisotropic relaxation verification test that agrees well with previously published results and analytical models is presented. The performance results from NVIDIA A100 and AMD MI250X nodes are presented with hardware utilization analysis for each architecture. Finally, the entire implicit Landau operator time advance is implemented in Kokkos for performance portability, running entirely on the device and is available in the PETSc numerical library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Salt Hydrate Eutectic Thermal Energy Storage for Building Thermal Regulation (Final Technical Report)

Thermal energy storage is anticipated to play an important role in developing the power grid of the future - a power grid that meets increasing demands of users, is resistant to disruptions, but also allows for greater penetration of renewable resources. Specifically, thermal energy storage materials can be integrated into HVAC systems and building envelopes, where they can be used to shift power demands for building climate control from periods of peak demand to periods of low demand. Phase change materials (PCMs) are compelling as low-cost, high energy density thermal energy storage materials for building thermal management. However, there is a lack of high performance low-cost PCMs within the specific temperature ranges which would most effectively allow for power load shifting. Inorganic salt hydrates represent a promising class of PCMs, but their inherent limitations cause them to be currently unavailable for reliable building applications. The overarching goals of this research effort are to: 1) Discover low-cost, high volumetric density salt hydrate eutectic PCMs to store low-quality heat (10 to 40 °C); 2) Introduce a high thermal conductivity matrix to reduce the time constant for energy storage to ~0.1 to 1 hr, incorporate nucleation catalysts to decrease undercooling, and utilize microencapsulation and shape stabilization approaches, minimizing moisture loss/gain, mitigating phase separation, and maintaining stable melting behavior over the lifetime of the compounds; 3) Evaluate the impact these systems have on peak load shifting, and the potential for overall energy savings under different climatic scenarios and building configurations. These goals will be achieved by an integrated research program consisting of six cohesive research subtasks: 1) Materials discovery of eutectic salt hydrate PCMs by using computationally predicted thermodynamic equilibria, coupled with high-throughput experimental validation, 2) Rapid experimental screening of nucleation catalysts identified through robust computational databases, 3) Embedding salt hydrate PCM into a low cost and scalable high conductivity matrix, 4) Microencapsulation of salt hydrate microspheres using hybrid inorganic-polymer microencapsulation approach, 5) Shape stabilization by thermoreversible salt hydrate salogels, and 6) Analysis of end-use using thermal simulations, and characterization of mock-up energy storage finished components.

25 ENERGY STORAGE↗
DOE OSTI2022

Case Study: NREL Campus Chilled Water Storage Potential: Benchmark Datasets Development and Applications, Task 4 - Use Case Demonstration

The Benchmark Datasets Development and Applications project is a three-year collaboration between the National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Lawrence Berkeley National Laboratory. The project seeks to collect and curate high-resolution, well-calibrated time series of building operational and indoor/outdoor environmental data, which are crucial to understanding and optimizing building energy efficiency performance and demand flexibility capabilities as well as benchmarking energy algorithms. Project outcomes include approximately twelve high-fidelity building datasets, enhanced data representation tools, and four case studies to illustrate example applications. The goal of these case studies is to define and execute analyses that demonstrate how one or more datasets collected through this project can address a data gap or challenge historically faced by building stakeholders. This technical paper summarizes the findings of one of these case studies, in which we studied the operational efficiencies of the central cooling system at NREL. We looked at three years of data from the three chillers in the Field Test Laboratory Building (FTLB), from 2019 to 2021, to compare equipment operation and demand throughout the time period. Our analysis indicates that all three chillers are operating at or below the optimal loading conditions for most of the operation time, and thus there was no efficiency drop due to loading of the chillers at full capacity. Our recommendation is that no chiller capacity increase is needed; instead, the central plant could benefit from adopting advanced control logics for optimal sequencing of chillers during part load operations. Analysis of adding chilled water thermal storage to the central plant indicated 34% savings in demand cost and 24.5% savings in total cost (energy consumption and demand charge cost). The payback period is estimated to be 11-22 years with an assumed TES cost of $\$$100-$200 per ton. This case study shows how a selected dataset is used to solve a practical building problem - learning the operational status of its components, analyzing the effectiveness of a proposed new technique, and aiding decision-making for the building operations and maintenance team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1996

Automated Decomposition of Model-based Learning Problems

A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

Williams, Brian C.↗
DOE OSTI2021

Integrated Models for Electrical Distribution Network Planning and District-Scale Building Energy Use

The increase of greenhouse emissions caused by a rise in global energy consumption is pushing the scientific community to develop tools that address key environmental, techno-economic and social issues. One specific area of focus is to create tools that enable the design of high-performance energy districts, including grid-interactive efficient buildings and electrical distribution infrastructure. This paper outlines the approach of integrating a synthetic distribution network tool (RNM-US) with URBANopt™, a modular and customizable software development kit for thermal and electrical modelling of buildings and energy systems at a district scale. First, we describe the algorithms implemented to integrate RNM-US with URBANopt to automatically generate the distribution network for the district considered. Following, we present a case study for the potential uses of the RNM-US integrations with URBANopt, highlighting the capabilities for economic and technical analysis of the distribution network built in relation to the electricity needs of the modelled buildings.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Moving Zero Energy Office Buildings to the Mainstream: Establishing Design Guidelines and Energy Targets: Preprint

Office buildings represent a significant portion of the commercial sector and are ideal candidates to lead the market towards achieving zero energy or zero energy ready buildings. The variety of sizes and uses for office buildings make them challenging to determine set design guidance that can be deployed across the market. A project committee for ASHRAE’s Zero Energy - Advanced Energy Design Guide (ZE-AEDG) for small and medium office buildings , comprised of members from five major national associations with market-pull in commercial buildings, created design guidance for achieving zero energy office buildings for new construction and major retrofit. This design guidance focuses on the process, providing a pathway for both office owners and design teams to achieve zero energy status. Energy use intensity targets range from 16 kBtu/ft²·yr to 36 kBtu/ft²·yr for the different climate zones were established such that office buildings can be “zero-ready,” that is, having low energy consumption that can easily be offset with renewables, preferably on-site. The design guidance was rooted in extensive energy modeling, as well as case studies of actual office buildings that have achieved high levels of energy performance and met zero energy or zero energy ready status. A unique aspect of the zero-energy guide is that it is not compared against a baseline but focuses on achieving an absolute target. The paper presents process of creating the targets, the selection of strategies to achieve the targets, and sample case studies that show how the cost-sensitive solutions are achievable for the design community. It also presents outreach strategies to deploy market achievable zero energy measures.

buildings↗
DOE OSTI2024

GASNet-EX Specification Collection (Rev. 2024.5.0)

GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in emerging exascale systems. It provides network-independent, high-performance communication primitives including Remote Memory Access (RMA) and Active Messages (AM). GASNet-EX is an evolution of the popular GASNet communication system, building upon over 20 years of lessons learned, and the primary goals are high performance, interface portability, and expressiveness. The library has been used to implement parallel programming models and libraries such as UPC, UPC++, Fortran coarrays, Legion, Chapel, and many others. This anthology collects together the four separate volumes that currently comprise the GASNet-EX specification, as of the 2024.5.0 release of GASNet-EX.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.

Ruby, Max↗
DOE OSTI2023

Unraveling the core of fuel cell performance: engineering the ionomer/catalyst interface

The biggest obstacle to the widespread implementation of polymer electrolyte membrane fuel cells (PEMFCs) is their cost, primarily due to the use of platinum catalysts. The high intrinsic catalyst activity exhibited on a rotating disk electrode (RDE) is rarely realized in a membrane electrode assembly (MEA), which is a long-standing challenge for PEMFCs and a cause of low catalyst utilization. To translate the high RDE performance of a catalyst into a MEA, the design of an ideal ionomer/catalyst interface is proposed: a thin, conformal ionomer film covers the maximum surface of a Pt nanoparticle and thus simultaneously maximizes catalyst utilization, (i.e., high mass activity and electrochemically active surface area) and O 2 diffusion rate (i.e., high current density performance) without compromising proton conduction. Building such an interface is a long-standing challenge due to the lack of interaction between the ionomer and catalyst particles, resulting in large ionomer agglomerates and inhomogeneous ionomer coverage over the catalyst nanoparticle, with consequent poor fuel cell performance. In this work, this ionomer/catalyst interface has been engineered, utilizing the electrostatic attraction between positively charged catalyst and negatively charged ionomer particles in a catalyst ink and preserved in a solid catalyst layer. As a result, this interface leads to previously unachieved proton exchange membrane fuel cell performance in terms of both catalyst utilization (75% vs. 45%) and peak/rated power density (i.e., 1.430/0.930 W cm -2 , H 2 /air, cathode Pt loading: 0.1 mgPt cm -2 ) for pure Pt catalysts, even better than those of Pt alloy catalysts. This work demonstrates the formation of an interface in the liquid phase (using ultra-small-angle X-ray scattering in combination with cryo-TEM, isothermal–titration–calorimetry) and the preservation of the interface in the solid catalyst layer (using TEM) and estimates the effective coverage and thickness of the ionomer film (using limiting current density, RDE and fuel cell performance).

25 ENERGY STORAGE↗
DOE OSTI2025

Engineering 2‐Pyrone‐4,6‐Dicarboxylic Acid Production Reveals Metabolic Plasticity of Poplar

Woody biomass is a promising source of fermentable sugars for biofuels and bio-based chemicals, but its industrial use is limited by the costly biorefinery process. A viable strategy to reduce costs involves enhancing both biomass processability and the generation of high-value co-products. Here, we report the implementation of a synthetic metabolic pathway in Populus tremula × P. alba to produce 2-pyrone-4,6-dicarboxylic acid (PDC), a key building block for biodegradable plastics and high-performance materials. This artificial pathway—comprising microbial genes AroG, QsuB, PmdA, PmdB, and PmdC—enabled de novo PDC production in the stems of transgenic poplar. Pathway expression also induced substantial metabolic reprogramming and altered cell wall composition. These include the hyperaccumulation of simple phenolics like protocatechuic acid (PCA) and vanillic acid (VA), alongside reduced levels of p-hydroxybenzoic acid. A large portion of VA was ester-linked to cell wall lignin, while PCA was incorporated into the lignin backbone, forming novel benzodioxane units; concurrently, lignin in transgenic plants exhibited a drastic reduction in guaiacyl- and syringyl-units, with a notable increase in p-hydroxyphenyl-units. Hemicellulose content, particularly xylan, was also significantly increased. Moreover, expression of the PDC-pathway led to the formation of novel VA-derived suberin aromatics, enhancing suberization in bark and roots and improving salt stress tolerance. These changes led to improved saccharification efficiency, with up to 25% more glucose and 2.5 times xylose released from woody biomass. These results demonstrate the metabolic flexibility of poplar and highlight its potential for engineering cost-effective, stress-resilient bioenergy crops with enhanced biorefinery traits.

2-pyrone-4↗
DOE OSTI2024

Impact Analysis of Transitioning to Heat Pump Rooftop Units for the U.S. Commercial Building Stock

Twenty percent (25%) of the energy consumed by the U.S. commercial building sector is from on-site combustion of fossil fuels for space heating. Part of decarbonizing U.S. energy systems to meet climate initiatives will require electrification of space heating equipment, often by transitioning to heat pumps. Rooftop units (RTU) are the most prominent commercial building HVAC system type and should therefore be prioritized for electrification solutions. However, there is limited understanding of the impact on emissions when considering regional electricity generation methods, as well as the impact of ambient temperature on capacity and efficiency, defrost operation, realistic sizing methodologies, and supplementary heating on overall heat pump performance. This study explores the effects of transitioning all installed, existing RTUs to high-performance heat pump RTUs for the U.S. commercial building stock. The analysis is performed using ComStock (TM), the U.S. Department of Energy's calibrated model of the U.S. commercial building stock. Results show 10% and 9% reductions in stock aggregate energy consumption and greenhouse gas emissions, respectively. This analysis will help inform the transition to heat pump RTUs for the U.S. commercial building stock.

commercial building↗
DOE OSTI2023

Impact Analysis of Transitioning to Heat Pump Rooftop Units for the U.S. Commercial Building Stock: Preprint

20% of the U.S. commercial building sector's energy usage is from on-site combustion of fossil fuels for heating. Decarbonization will require electrification of these systems to meet climate goals, often by transitioning to heat pumps. Rooftop units (RTU) are the most prominent commercial building HVAC system type and should therefore be prioritized for decarbonization solutions. However, there is limited understanding of the impact on emissions that considers regional electricity generation methods, as well as the impact of other heat pump intricacies such as the effects of temperature on capacity/efficiency, defrost operation, realistic sizing methodologies, and supplementary heating. This study explores the effects of transitioning all conventional RTUs to high-performance heat pump RTUs for the U.S. commercial building stock. The analysis is performed using ComStock, DOE's calibrated model of the U.S. commercial building stock developed by NREL. Results show 9% and 17% reductions in stock aggregate energy consumption and GHG emissions, respectively. Furthermore, average heat pump operational details are presented by state. This analysis will help inform the transition to heat pump RTUs for the U.S. commercial building stock.

commercial building electrification↗
DOE OSTI2021

An Approach to High-Performance Affordable Housing Using Point-Source Space Conditioning

In cooperation with the Building America Program, The Levy Partnership explored a variety of options to reduce space-conditioning energy consumption, working with partners to design, manu- facture, and build test homes in three states. The Levy Partnership tested the use of minisplit ductless heat pumps in combi- nation with small through-wall transfer fans to provide space conditioning in small single-story homes, including manufactured homes and homes built by Habitat for Humanity. The team set out to achieve 50% less energy consumption, compared to the 2009 International Energy Conservation Code, while maintaining home afford- ability and thermal comfort. The strategy also included a highly efficient thermal envelope to reduce space-conditioning loads.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration

Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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↗
DOE OSTI2022

Towards Superior Software Portability with SHAD and HPX C++ Libraries

As hardware architectures and software stacks complexity grows, development productivity, performance and software portability, quickly evolve from desirable features to actual needs. SHAD, the Scalable High-performance Algorithms and Data-structures C++ library is designed to mitigate these issues: it provides general purpose building blocks as well as high-level custom utilities, and offers a shared-memory programming abstraction which facilitates the programming of complex systems, scaling up to High Performance Computing clusters. SHAD’s portability is achieved through an abstract runtime interface, which decouples the upper layers of the library and hides the low level details of the underlying architecture. This layer enables SHAD to interface with different runtime/threading systems, e.g. Intel TBB and Global Memory and Threading (GMT). However, current backends targeting distributed systems, rely on a centralized controller which may possibly limit scalability up to hundreds of nodes and creates a network hot spot due to all to one communication for synchronization, and possibly resulting in degraded performance at high process counts. In this research, we explore HPX, the C++ standard library for parallelism and concurrency, as an additional backend in support of the SHAD library, and present the methodologies in support of local and remote task executions in SHAD with respect to HPX. Finally, we evaluate the proposed system by comparing against existing backends of SHAD and analyzing their performance on C++ Standard Template Library algorithms.

Wu, Nanmiao↗
DOE OSTI2021

High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling

Surrogate modeling can play a key role in reducing high computational burdens for large-scale physics-based modeling and uncertainty quantification. With the rapid development of large-scale building stock energy modeling, surrogate modeling has also begun to be widely applied in this field; however, most existing surrogate models lack hourly time resolution for regional-scale modeling, which is essential for understanding building demand profiles and grid impacts. Further, there is generally a lack of necessary data and feature engineering frameworks specific to building modeling for efficiently managing large datasets and complex computations. Furthermore, this paper proposes a modeling framework for large-scale (city-/region-scale), high-resolution, high-fidelity surrogate building stock energy models. Our developed framework consists of six modules: (1) building stock energy modeling (ComStock TM and ResStock TM ), (2) data engineering for large simulation data, (3) high performance computing workflow, (4) feature engineering, (5) machine learning model development, and (6) model performance evaluation. Two case studies apply the developed framework in both residential and commercial building stock analysis to demonstrate its computational efficiency and surrogate modeling accuracies. Results show that surrogate models, when efficiently trained using the HPC workflow module, reach a high level of modeling accuracy for two case studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗