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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

National Energy Education Development Project (NEED Project) (CRADA Final Report)

The U.S. Department of Energy Building Technologies Office (BTO) funds student competitions that introduce students to careers in the building sciences and increase public awareness around high-performance buildings to support the goal of developing, demonstrating, and accelerating the adoption of cost-effective technologies, techniques, tools, and services that enable high-performing, energy-efficient and demand-flexible residential and commercial buildings in both the new and existing buildings markets. The U.S. Department of Energy Solar Decathlon® (DOE/SD) is a flagship, high-visibility international competition started in 2002 that advances the goals of BTO by introducing students to building science careers; educating students and the public about the latest technologies and materials in high-performance buildings; encouraging student-led projects and research centered around building science; and demonstrating to the public the comfort and savings of homes that combine energy-efficient construction, home systems, appliances and innovative design with onsite renewable energy production. SD is a collegiate competition, comprising 10 contests, that challenges student teams to design and build highly efficient and innovative buildings powered by renewable energy. The winners will be those teams that best blend architectural and engineering excellence with innovation, market potential, building efficiency, and smart energy production. Solar Decathlon is comprised of two Challenges – Design Challenge (annual) and Build Challenge (biennial). The National Renewable Energy Laboratory (NREL) provides competition management for Solar Decathlon. NREL and Participant establish this CRADA to enable the success of the overall Solar Decathlon program by managing sponsorship funds and creating a K12 education program. Participant is to act as an Education Partner to Solar Decathlon, which includes: 1) accepting and dispersing sponsorship funds for DOE/SD; and 2) providing K12 education program to support Solar Decathlon Competition Events in April each year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Effects of a Small Fraction of Norbornene Comonomers on the Frontal Ring Opening Metathesis Polymerization of Dicyclopentadiene and Dihydrofuran

Frontal ring-opening metathesis polymerization (FROMP) is an energy-efficient process for the construction of high performance polydicyclopentadiene (pDCPD) thermosets. A small fraction of norbornene (NBE) comonomer is often added to DCPD to liquify the resin, but their effects on the resin chemistry and frontal curing characteristics are not quantitatively understood, especially for recently developed deconstructable DCPD resins containing cleavable comonomers. 2,3-Dihydrofuran (DHF) is a commercially available, effective cleavable comonomer for ROMP of norbornenes, and it has been recently demonstrated to enable full deconstruction of pDCPD thermosets at 5% loading. Herein, we investigated deconstructable DCPD resins containing 5% of different (oxa)NBE derivatives and 5% DHF. We report the effects of such a small fraction of (oxa)NBE comonomers on the resin pot life (room temperature resin reactivity), front curing characteristics, degree of cure, crosslinking density, comonomer distribution, and deconstruction kinetics of the resulting pDCPD thermosets. These effects can be correlated to the metathesis reactivity of these comonomers. As a result, this study provides quantitative understanding that will guide the future development of new deconstructable FROMP resins with desired construction and deconstruction characteristics.

Luo, Xuyi [Stanford University, CA (United States)↗
DOE OSTI2025

Deep Point Cloud Building Envelope Segmentation (DeeP-CuBES) using Deep Learning

Building Information Modeling (BIM) plays an important role in building design and construction, particularly for achieving energy-efficient retrofits. Building envelope retrofits using panelized prefabricated system, such as those popularized by the Energiesprong program, need accurate as-built dimensions of facade features (windows, doors, etc.) to achieve the desired thermal and air tightness. Traditionally, building surveying is done manually, resulting in a time-consuming and labor-intensive process. Recently, 3D point clouds from terrestrial LiDAR have been used to automate the generation of as-built dimensions of existing buildings. However, automated BIM using LiDAR relies on solving the point cloud semantic segmentation (PCSS) problem. In this work, we propose a robust pipeline for solving the PCSS problem using deep neural networks, focusing on overcoming challenges posed by imbalanced datasets and complex architectural features. We introduce the first high-density, labeled, and validated building envelope point cloud dataset derived from multiple building scans, specifically curated to tackle challenges in facade-level segmentation. Results from the trained neural networks show that advanced attention-based architectures and incorporating radiometry (light intensity and RGB) features significantly boost segmentation accuracy for windows and doors.

Selvakumar, Balaji [ORNL]↗
DOE OSTI2025

Decarbonizing residential buildings in the United States: A comparative analysis of households and construction professionals

In this study, we present a comparative analysis of surveys distributed to home occupants and construction professionals in the U.S., focused on energy upgrades and electrification retrofits that support residential building decarbonization. The surveys were executed by separate research groups and combined for this study. The study examines the decision-making, sentiments, perceptions, experiences, and practices of both groups by analyzing data from three separate surveys. These surveys assess technologies, attitudes, awareness, motivations, barriers, and opportunities related to energy retrofits and electrification. The analysis highlights key differences in the perceptions and behaviors of households and construction professionals, revealing substantial barriers to achieving decarbonization goals. For example, households cite climate change and sustainability as key motivators for pursuing energy retrofits (89%), while construction industry professionals view these themes as less important for their clients (44%). This suggests an opportunity for the construction industry to align its messaging with the values that households prioritize, helping to advance residential decarbonization. Overall, the study identifies challenges faced by both groups, factors influencing the adoption of energy-efficient practices, and inconsistencies between occupant and construction industry professionals' views. These insights contribute to the development of targeted strategies and policies to accelerate the decarbonization of residential buildings in the U.S.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A review of prefabricated overclad panels for building energy retrofits

Over 50% of existing buildings in the US were constructed before 1980 and lack modern, energy-efficient technologies. Despite the large number of old buildings, less than 2% of buildings are retrofitted each year with many of those retrofits consisting of small incremental improvements that often ignore the building envelope. The opaque building envelope affects 25% of building energy use equating to 10% of total US primary energy use. To meet decarbonization goals by 2035, the US must increase the rate of retrofit of existing building envelopes. The process of prefabrication is one potential opportunity to increase the rate of envelope retrofits and reduce costs. Prefabrication is the process of manufacturing construction components in a controlled environment before shipping them on-site to perform assembly of the construction. The process of prefabrication has been demonstrated to increase production efficiency, quality control, and rate of production. Prefabricated overclad panels, consisting of a cladding material and insulation designed to be installed over the existing envelope, have been successfully used to retrofit existing buildings in countries other than the US; however, the technology still has some barriers to major market adoption within the US. This paper will present a review of current implementations and demonstrations of prefabricated overclad panels. The review will include identification of panel types, materials, and applications and aims to identify barriers to retrofit market entry and widespread adoption.

Hayes, Nolan [ORNL] (ORCID:0000000332245718)↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2025

AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing

The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.

Gautam, Ashish [ORNL]↗
DOE OSTI2026

Flexible AI Models for Grid Resilience

The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
DOE OSTI2026

Synaptic Functionality and Neuromorphic Information Processing in Membrane Ion Channel Junctions

The human brain performs complex memory and computational tasks with high energy efficiency by regulating ion transport through membrane channels. These signaling mechanisms have been inspiring the development of nanofluidic memristors that emulate synaptic behavior. Here, in this study, we describe a membrane ion channel synapse (MICS), constructed from aqueous droplets linked by gramicidin A channels, that achieves neuromorphic functionality. MICS exhibits memristive ion transport with hysteretic current–voltage behavior arising from voltage-dependent channel formation and ion transport dynamics. MICS emulates a range of synaptic behaviors including associative learning. We further demonstrate its application in reservoir computing by performing handwritten digit classification and tic-tac-toe game and explore the system parameters that improve the computational performance. This droplet-based biomimetic synapse offers a potentially scalable and energy-efficient platform for next-generation neuromorphic computing systems.

Droplet interface bilayer↗
DOE OSTI2025

A Safe Response to Renewable Energy Hazards

The International Association of Fire Fighters (IAFF) and Underwriters Laboratories, LLC (UL) in conjunction with UL Solutions initiated a joint project in 2022 under an agreement with the United States Department of Energy-Office of Energy Efficiency and Renewable Energy (DOE-EERE). This project focused on two separate and important initiatives related to energy efficiency in residential buildings. Initiative 1: Fire Performance on Energy Efficient Exterior Walls. Initiative 2: Firefighting Tactics in Residential Properties with Building Energy Storage Systems (BESS). The project’s first initiative addresses concerns surrounding new technologies with enhanced, energy-efficient exterior walls installed on residential properties. The concerns of fire rapidly traveling vertically up the exterior of these walls were examined. This addressed a growing concern from the first responder community that many times, the fires on the exterior of residential buildings have already evolved into an attic fire by the time of arrival – making it problematic to address the fire scenario. The test plan for Initiative 1 incorporated a modified version of an American Society for Testing and Materials (ASTM) test method, ASTM E2707, Standard Test Method for Determining Fire Penetration of Exterior Wall Assemblies Using a Direct Flame Impingement Exposure, as the foundation of the research. The test method involved a wall structure intended to represent retrofit construction to evaluate how fire would spread vertically or laterally. The second aspect of the UL-IAFF Project focuses on the fire service response to Residential Battery Energy Storage System (RBESS) incidents. These simulation tests were constructed in the large-scale fire test facility at UL Solutions’ Northbrook, IL campus. A baseline test was conducted that involved a test structure with no batteries—shelving units populated with standardized commodities, representing a typical garage with cellulosic and plastic contents. Three additional tests have been conducted to generate data with the contribution of energy storage system (ESS) batteries to compare fire and explosion hazards against the baseline test. Through this work, fire service tactical considerations can be explored. From the data, the team can determine 1) the visual indicators of a residential fire that has involved an RBESS (or, potentially, other large batteries) and 2) the impact of fire service-initiated ventilation of the structure on the fire conditions and explosion risks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs

Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo 6+ , V 5+ ) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.

organic↗
DOE OSTI2026

Experimental and data-driven characterization of window-induced air leakage in residential buildings

Windows contributes up to 40% of envelope heat losses and around 9% of total building energy consumption due to air leakage. In the U.S., 48 million homes still use single-pane windows. Although the U.S. has an estimated 1.4 billion windows in its building stock and about 24 million windows are installed annually, only around 29 million individual window replacements (∼2%) occur each year. To address this gap, this study generates empirical evidence by (1) evaluating the contribution of windows to whole-building air leakage in 20 residential buildings using blower door tests before and after window replacement and (2) assessing whether building and window characteristics influence the measured change. Most simulation studies assume that replacing windows not only lowers the U-factor but also reduces air leakage by 10–20%. However, this assumption lacks empirical validation, highlighting the need for experimental analysis of air leakage specifically associated with windows. Using blower door tests in accordance with ASTM E779–19, the results indicated an average reduction in air infiltration of 6.1% within the range of 0.5–19.30% across all buildings and no significant correlations were found between air leakage improvements and any building/window characteristics. This research aims to help homeowners, and energy modelers to provide empirical data on importance of upgrading to more energy-efficient windows, supporting energy-efficient building standards.

Air leakage↗
DOE OSTI2025

Investigation of the Cyanothece nitrogenase cluster in Synechocystis : a blueprint for engineering nitrogen-fixing photoautotrophs

ABSTRACT The nitrogenase gene cluster of unicellular diazotrophic cyanobacteria, such asCyanothece, is frequently selected by nature for nitrogen-fixing partnerships with eukaryotic phototrophs. The essential cluster components that confer an advantage in such partnerships remain underexplored. To use this cluster for the development of synthetic, phototrophic nitrogen-fixing systems, a thorough and systematic analysis of its constituent genes is necessary. An initial effort to assess the possibility of engineering this cluster into non-diazotrophic phototrophs led to the generation of aSynechocystis6803 strain with significant nitrogenase activity. In the current study, a refactoring approach was taken to determine the dispensability of the non-structural genes in the cluster and define a minimal gene set for constructing a functional nitrogenase for phototrophs. Using a bottom-up strategy, thenifgenes fromCyanothece51142 were re-organized to form new operons. The genes were then seamlessly removed to determine their essentiality in the nitrogen fixation process. We demonstrate that besides the structural genesnifHDK,nifBSUENPVZTXW, as well ashesAB, are important for optimal nitrogenase function in a phototroph. We also show that optimal expression of these genes is crucial for efficient nitrogenase activity. Our findings provide a solid foundation for generating synthetic systems that will facilitate solar-powered conversion of atmospheric nitrogen into nitrogen-rich compounds, a stride toward a greener world. IMPORTANCE Integrating nitrogen fixation genes into various photosynthetic organisms is an exciting strategy for converting atmospheric nitrogen into nitrogen-rich products in a green and energy-efficient way. In order to facilitate this process, it is essential that we understand the fundamentals of the functioning of a prokaryotic nitrogen-fixing machinery in a non-diazotrophic, photoautotrophic cell. This study examines a nitrogenase gene cluster that has been naturally selected on multiple occasions for a nitrogen-fixing partnership by eukaryotic photoautotrophs and provides a basic blueprint for designing a photosynthetic organism with nitrogen-fixing ability.

Microbiology↗
DOE OSTI2025

Novel Electrowinning Reactor for the Energy-Efficient, Low- Cost Production of Rare Earth Metals

This project developed a novel neodymium (Nd) electrowinning reactor for energy-efficient electrowinning of Nd metal. Throughout this project we have developed an alternative chloride based molten salt electrolysis process. Our process lowers the specific electrical energy consumption compared to the state of the art, while producing reusable chlorine gas and eliminating direct CO2 and PFC emissions. Facilities required for implementing the project were setup and designs were finalized, and a standard operating procedure for safe operation of high temperature electrolysis cells was written. The electrowinning reactor was designed and constructed. Electrolysis experiments confirmed the ability to reproducibly electrowin Nd metal on a Mo cathode. The current efficiency for Nd electrowinning was measured as a function of applied current density in the presence of the separator. Successful electrowinning of Nd sponge at high current densities (200 mA/cm2 and above) at a current efficiency >80% was demonstrated using multiple techniques. Stable Nd electrowinning up to 10h at 250 mA/cm2 was demonstrated. All of these design advancements were used to develop a techno-economic and life cycle assessment model that demonstrated that our process could be operated at cost of less than $0.20/kg-Nd (~30% lower compared to state of the art when comparing electrolysis energy cost) with a >20% total reduction in global warming potential compared to the state of the art while generating no direct CO2 or perfluorocarbon emissions.

42 ENGINEERING↗
DOE OSTI2026

Reimagining Heating, Ventilation, and Air Conditioning for New Manufactured Homes

Manufactured homes represent one of the most affordable paths to home ownership for American households, but minimum-efficiency equipment and poor-quality home installation can lead to excess energy use and high operating costs. Slipstream, with partners from the Florida Solar Energy Center (FSEC), Northwest Energy Works, and Washington State University, identified and tested manufactured home HVAC innovations targeted for new construction offering the most promising mix of improved energy performance and likelihood of industry uptake.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Updating the Building Science Advisor (BSA): A Tool to Assist in the Design of Durable Building Envelopes

Predicting the moisture durability of building envelope components remains challenging due to multiple influencing factors, including material selection, assembly positioning, local climate conditions, air tightness, interior environment, and construction quality. Building codes increasingly emphasize energy efficiency through enhanced insulation and tighter envelopes but offer limited guidance on moisture durability considerations. Consequently, builders face uncertainty, particularly as new materials and assemblies enter the market.The Building Science Advisor (BSA) is a free, web-based expert system developed to address these challenges by providing actionable insights into the moisture durability and energy efficiency of both new and retrofit wall designs. Recently updated, we are now providing version 3.0 of the tool. BSA features significant user interface improvements, enhancing navigation and user interaction through a refreshed, intuitive design. Additionally, the tool incorporates a newly developed database containing pre-simulated wall assembly cases, significantly reducing response times and improving the accuracy of moisture durability assessments. Furthermore, the updated BSA includes moisture content as a performance criterion, providing users with a more comprehensive understanding of moisture-related durability risks. These enhancements enable rapid, reliable assessments tailored to specific climate zones and local building practices. BSA continues to offer targeted guidance on wall retrofit scenarios and delivers access to an expanded library of location-specific building science resources.This paper describes these key updates, highlighting the enhanced features, expanded capabilities, and overall improvements to user experience and educational content. The paper includes a demonstration that illustrates how the revised BSA effectively supports practitioners in designing durable, energy-efficient building envelope assemblies.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

The relative importance of building design parameters in reducing energy use and sensible heat release from buildings in light of forecasted future weather data and building coverage ratio

Buildings typically have a 60-to-75-year lifespan before they require significant maintenance or modifications. However, most builders evaluate the performance of their new buildings using whole-building energy simulation tools based on the current typical meteorological year (TMY) file or actual meteorological year. The energy use consumption and sensible heat release pattern observed from buildings could potentially change based on shifting global climates. Therefore, the recommended energy-efficiency design parameters might also change during these periods. In this study, we evaluate the role of different building design parameters, such as material reflectivity, HVAC COP, and insulation values, on building energy usage and sensible heat release from buildings with different building coverage ratios (BCR), based on the current and future weather file TMY (fTMY) for the middle of the century (2040–2060). The role of sensible heat release from buildings is not accounted for accurately while estimating building energy usage in most whole-building energy simulations. The study conducts a series of whole-building energy simulation analyses using EnergyPlus to evaluate the role of different design parameters based on TMY and fTMY weather conditions. The analysis is conducted for two hot desert climatic cities: Phoenix (USA) and Abu Dhabi (UAE). The results show that, for the base case in a future climate, the sensible heat release is reduced by an average of 30% due to the reduced delta T between the surface and ambient air. Further, the results show an increase in total energy consumption by 5% annually. The results also show that, for buildings with traditional coatings, shorter buildings release more heat than taller buildings. On the other hand, for buildings with reflective paints, shorter buildings release less heat than taller buildings. The findings from this study can be used by policymakers, utility companies, and builders to better understand the relative role of different building design parameters while constructing new and retrofitting existing buildings.

Alhazmi, Mansour [King Fahd University of Petroleu↗