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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Building capacity for Health Impact Assessment: Training outcomes from the United States

Background: Despite the continued growth of Health Impact Assessment (HIA) in the US, there is little research on HIA capacity-building. A comprehensive study of longer-term training outcomes may reveal opportunities for improving capacity building activities and HIA practice. Methods: We conducted in-depth interviews with HIA trainees in the United States to assess their outcomes and needs. Using a training evaluation framework, we measured outcomes across a spectrum of reaction, learning, behavior and results. Results: From 2006 to 2012, four organizations trained over 2200 people in at least 75 in-person HIA trainings in 29 states. We interviewed 48 trainees, selected both randomly and purposefully. The mean duration between training and interview was 3.4 years. Trainees reported that their training objectives were met, especially when relevant case-studies were used. They established new collaborations at the trainings and maintained them. Training appeared to catalyze more holistic thinking and practice, including a range of HIA-related activities. Many trainees disseminated what they learned and engaged in components of HIA, even without dedicated funding. Going forward, trainees need assistance with quantitative methods, project management, community engagement, framing recommendations, and evaluation. Conclusions: The research revealed opportunities for a range of HIA stakeholders to refine and coordinate training resources, apply a competency framework and leverage complimentary workforce development efforts, and sensitize and build the capacity of communities. - Highlights: • We interviewed HIA trainees in the United States to assess longer-term outcomes. • Training appeared to catalyze a range of beneficial partnerships and activities. • Trainees reported outstanding needs for specific skills and competencies. • There are various opportunities to improve training and capacity-building.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Testing and demonstration of model predictive control applied to a radiant slab cooling system in a building test facility

Radiant slab systems have the potential to significantly reduce energy consumption in buildings. However, control of radiant slab systems is challenging. Classical feedback control is inadequate due to the large thermal inertia of the systems and heuristic feed-forward control often leads to unacceptable indoor comfort and may not achieve the full energy savings potential. Model predictive control (MPC) is now attracting increasing interest in the building industry and holds promise for radiant systems. However, an often-cited barrier to its implementation in the building industry is the high computational cost and complexity relative to the feedback controls used in conventional systems. The objectives of this study were to (i) verify the correct operation of an open source MPC toolchain developed for radiant slab systems, and (ii) demonstrate its efficacy in a test facility. A matched pair of cells in the FLEXLAB building test facility at the Lawrence Berkeley National Laboratory was used in the study. The proposed MPC toolchain was implemented in one cell and the performance compared to that of the other cell, which used a conventional heuristic control strategy. Finally, the results showed that the simplified MPC approach applied in the toolchain worked as expected and realized energy savings over the conventional control strategy. The MPC yielded 42% chilled water pump power reduction and 16% cooling thermal energy savings, while maintaining equal or better indoor comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Using non-invasive MEMS pressure sensors for measuring building envelope air leakage

Micro-electromechanical systems (MEMS)-based sensors have seen significant improvements in accuracy and cost over the past several years, and due to the need for altitude sensing in unmanned aerial vehicles and wearable technologies, absolute pressure sensors have shown the most dramatic improvements. This paper investigates applications of MEMS sensors in commercial buildings, including pairing two absolute pressure sensors to determine differential pressures between different locations in and around buildings. Differential pressure measurements are commonly made in commercial buildings for a number of applications, including static pressure measurements for variable-speed supply fan control, air balancing in hospitals, and for envelope and duct air leakage testing. This paper focuses specifically on envelope air-leakage testing applications of these sensors. The use of continuous pressure measurements in addition to one-time non-intrusive outdoor airflow measurements of rooftop packaged units is investigated. A comparison with standardized leakage testing indicated that continuous measurements with MEMS pressure-sensors were able to estimate envelope leakage to within 3–16% of the standardized test result. In addition, the magnitude and stability of differential pressure differences that can be resolved with absolute pressure sensors are also investigated in the laboratory and the field. Results show that the sensors can be effectively used for pressures seen in low rise buildings with rooftop units.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation

This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Good practices for documenting AI-based studies on energy and buildings

Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.

Hong, Tianzhen [Lawrence Berkeley National Laborat↗
DOE OSTI2020

Experimental study on a novel three-phase absorption thermal battery with high energy density applied to buildings

Buildings contribute to 75% of total electricity consumption and 80% of peak electricity demand in the United States. A significant portion of building electricity consumption goes to space conditioning. Therefore, using thermal energy storage (TES) to decouple the building electricity consumption and the fluctuating space-conditioning load has significant potential to shift peak electricity demand and improve the stability of the grid. To provide effective TES within the spatial constraints in buildings, a three-phase absorption thermal battery (TATB) system with very high energy storage density was designed and studied. The TATB stores low-temperature heat in hydrate crystals of salts and supports space conditioning through absorption systems. In this study, a benchtop TATB using lithium chloride (LiCl) hydrate crystals as the storage material was tested under practical operating conditions for its energy storage density and discharge rate. We report the TATB successfully generated LiCl hydrate crystals during charging mode and then dissolved the crystals during discharging mode. A material-based energy storage density of 300 kWh/m 3 (specific energy of 903 kJ/kg) was achieved with a discharge rate of up to 1.3 kW thermal energy. The test results verified the high performance of the TATB and encouraged further development of this technology.

25 ENERGY STORAGE↗
DOE OSTI2025

Urban morphology from a landscape perspective: How building morphology distribution land models (BMDLM) emulate pattern and process

Urban form (e.g., building morphology such as height or footprint) can be used to predict environmental footprints, such as energy/water consumption and carbon emissions. Although progress has been made in predicting building characteristics to fill gaps in observation or derive 3-D representations, the relationships between morphology and other variables such as land use and population are poorly understood. Understanding these relationships may enable projections for how cities will evolve with landscapes in the future. A suite of random forest models, the Building Morphology Distribution Land Models (BMDLM), was developed to determine how well building morphology for two distinct statistical measures (central tendency and frequency) can be predicted using land use (e.g., zoning) and population at different resolutions. Clark County, Nevada and Los Angeles County, California are explored as case studies. Generally, 1-km models outperformed 30-m models. Frequency distribution models had the best performance, especially in LA County. Frequency models significantly outperformed spatial autocorrelative models using inverse distance weighting (IDW). BMDLM offers a new take on modeling urban form in which generalized landscape patterns are characterized to understand the influence of population and zoning on urban development, as described by urban scaling theory.

Sturtevant, Jillian [Baylor Univ., Waco, TX (Unite↗
DOE OSTI2025

Comparative life cycle assessment of a modular cross-laminated timber residential building designed for disassembly and reuse versus traditional wood frame construction

There is a need for affordable housing across the U.S., with high-performance modular and prefabricated buildings providing a logical avenue for meeting some of this demand. However, there is a need to balance high performance construction – including low emissions – with affordability. To provide a proof-of-concept in meeting these goals, the Circular Home is a cross-laminated timber (CLT)-based deconstructible and reconfigurable single-family residence that meets high performance targets in moisture, energy, design, economics, and life cycle assessment (LCA). This study focuses on the LCA, presenting a cradle-to-cradle whole-building life cycle assessment (WBLCA) for the Circular Home and a functionally equivalent Baseline Home constructed with traditional materials and methods. The functional unit is 1 m 2 of gross floor area across 60 years. Revit building information models (BIM) provided material quantities and Tally LCA was utilized for impact data (inclusive of biogenic carbon sequestration), supplemented with manufacturer environmental product declarations (EPDs). The Circular Home outperforms the baseline residence in most measured impact categories, including global warming potential (GWP), producing −2.73 kgCO 2 eq/m 2 in embodied emissions, whereas the modeled baseline has an embodied GWP of 428 kgCO 2 eq/m 2 . The careful material selection and advanced building design optimizes performance, with the Circular Home containing only −0.006 times the embodied emissions and −0.02 times the operational emissions of its traditional counterpart. Finally, the unique contribution of this work is in the environmental impact comparison of a high-performance modular CLT structure that can be affordably scaled and mass produced in a U.S. market, compared to typical single family home construction.

Circularity↗
DOE OSTI2020

Build orientation, surface roughness, and scan path influence on the microstructure, mechanical properties, and flexural fatigue behavior of Ti–6Al–4V fabricated by electron beam melting

In the present work, microstructure and fatigue behavior of Electron Beam Melted (EBM) Ti–6Al–4V have been systematically studied. Fatigue behavior was investigated using four-point bend tests of rectangular bars with internal features fabricated with different build orientations, scan paths, and surface conditions. Microstructural variations were evaluated by optical microscopy and Vickers micro-hardness indentations, both of which were found to be consistent throughout the build and within ranges reported in the literature. The fatigue performance of the EBM material was comparable to wrought Ti–6Al–4V when the loading direction was parallel to the build direction, but severely limited when loaded perpendicular to the build direction or if surface roughness defects were present. Scanning electron microscopy and interrupted fatigue tests were utilized to characterize fracture surfaces, failure mechanisms, crack initiation, and propagation behavior. Crack initiation sites in the vertically-oriented specimens were identified to be melt-related defects formed by insufficient melting.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Cognitive barriers during monitoring-based commissioning of buildings

Monitoring-based commissioning (MBCx) is a continuous building energy management process used to optimize energy performance in buildings. Although monitoring-based commissioning (MBCx) can reduce energy waste by up to 20%, many buildings still underperform due to issues such as unnoticed system faults and inefficient operational procedures. While there are technical barriers that impede the MBCx process, such as data quality, the focuses of this paper are the non-technical, behavioral and organizational, barriers that contribute to issues initiating and implementing MBCx. In particular, this paper discusses cognitive biases, which can lead to suboptimal outcomes in energy efficiency decisions, resulting in missed opportunities for energy savings. This paper provides evidence of cognitive biases in decisions during the MBCx process using qualitative data from over 40 public and private sector organizations. The results describe barriers resulting from cognitive biases, listed in descending order of occurrence, including: risk aversion, social norms, choice overload, status quo bias, information overload, professional bias, and temporal discounting. Building practitioners can use these results to better understand potential cognitive biases, in turn allowing them to establish best practices and make more informed decisions. Finally, researchers can use these results to empirically test specific decision interventions and facilitate more energy efficient decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Assembly of Building Blocks by Double-End-Anchored Polymers in the Dilute Regime Mediated by Hydrophobic Interactions at Controlled Distances

Hierarchical assembly of building blocks via competing, orthogonal interactions is a hallmark of many of nature’s composite materials that do not require highly specific ligand-receptor interactions. To mimic this assembly mechanism requires the development of building blocks capable of tunable interactions. In the present work, we explored the interplay between repulsive (steric and electrostatic) and attractive hydrophobic forces. The designed building blocks allow hydrophobic forces to effectively act at controlled, large distances, to create and tune the assembly of membrane-based building blocks under dilute conditions and affect their interactions with cellular membranes via physical cross-bridges. Specifically, we employed double-end-anchored poly(ethylene glycol)s (DEA-PEGs)—hydrophilic PEG tethers with hydrophobic tails on both ends. Using differential-interference-contrast optical microscopy, synchrotron small angle X-ray scattering (SAXS), and cryogenic electron microscopy, we investigated the ability of DEA-PEGs to mediate assembly in the dilute regime on multiple length scales and on practical time scales. The PEG length, anchor hydrophobicity, and molar fraction of DEA-PEG molecules within a membrane strongly affect the assembly properties. Additional tuning of the intermembrane interactions can be achieved by adding repulsive interactions via PEG-lipids (steric) or cationic lipids to the DEA-PEG-mediated attractions. Further, while the optical and electron microscope imaging methods provided qualitative evidence of the ability of DEA-PEGs to assemble liposomes, the SAXS measurements and quantitative line-shape analysis in dilute preparations demonstrated that the ensemble average of loosely organized liposomal assemblies maintains DEA-PEG concentration-dependent tethering on defined nanometer length scales. For cationic liposome–DNA nanoparticles (CL–DNA NPs), aggregation induced by DEA-PEGs decreased internalization of NPs by cells, but tuning the DEA-PEG-induced attractions by adding repulsive steric interactions via PEG-lipids limited aggregation and increased NP uptake. However, confocal microscopy imaging together with colocalization studies with Rab11 and LysoTracker as markers of intracellular pathways showed that modifying CL–DNA NPs with DEA-PEGs alters their interactions with the plasma and endosomal membranes.

- x-ray scattering↗
DOE OSTI2019

Self-Assembly of Two-Dimensional Perovskite Nanosheet Building Blocks into Ordered Ruddlesden–Popper Perovskite Phase

The self-assembly of nanoparticles, a process whereby nanocrystal building blocks organize into even more ordered superstructures, is of great interest to nanoscience. In this work, we report the layer-by-layer assembly of 2D perovskite nanosheet building blocks. Structural analysis reveals that the assembled superlattice nanocrystals match with the layered Ruddlesden-Popper perovskite phase. This assembly proves reversible, as these superlattice nanocrystals can be reversibly exfoliated back into their building blocks via sonication. This study demonstrates the opportunity to further understand and exploit thermodynamics to increase order in a system of nanoparticles and to study emergent optical properties of a superlattice from 2D, weakly attracted, perovskite building blocks.

42 ENGINEERING↗
DOE OSTI2021

Design of multi-scale protein complexes by hierarchical building block fusion

A systematic and robust approach to generating complex protein nanomaterials would have broad utility. We develop a hierarchical approach to designing multi-component protein assemblies from two classes of modular building blocks: designed helical repeat proteins (DHRs) and helical bundle oligomers (HBs). We first rigidly fuse DHRs to HBs to generate a large library of oligomeric building blocks. We then generate assemblies with cyclic, dihedral, and point group symmetries from these building blocks using architecture guided rigid helical fusion with new software named WORMS. X-ray crystallography and cryo-electron microscopy characterization show that the hierarchical design approach can accurately generate a wide range of assemblies, including a 43 nm diameter icosahedral nanocage. The computational methods and building block sets described here provide a very general route to de novo designed protein nanomaterials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Highly transparent silanized cellulose aerogels for boosting energy efficiency of glazing in buildings

Abstract To maintain comfortable indoor conditions, buildings consume ~40% of the energy generated globally. In terms of passively isolating building interiors from cold or hot outdoors, windows and skylights are the least-efficient parts of the building envelope because achieving simultaneously high transparency and thermal insulation of glazing remains a challenge. Here we describe highly transparent aerogels fabricated from cellulose, an Earth-abundant biopolymer, by utilizing approaches such as colloidal self assembly and procedures compatible with roll-to-roll processing. The aerogels have visible-range light transmission of 97–99% (better than glass), haze of ~1% and thermal conductivity lower than that of still air. These lightweight materials can be used as panes inside multi-pane insulating glass units and to retrofit existing windows. We demonstrate how aerogels boost energy efficiency and may enable advanced technical solutions for insulating glass units, skylights, daylighting and facade glazing, potentially increasing the role of glazing in building envelopes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Building fault detection data to aid diagnostic algorithm creation and performance testing

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ORBITaL-Net: A labeled training library for large-scale building feature extraction

Over the course of several years, nearly 1.5 million building outlines have been created from approximately 128,000 training tiles covering roughly 7,000 km 2 of very high-resolution multispectral overhead imagery, primarily dated between 2010 and 2020. This dataset, dubbed the Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is designed for machine learning applications and is global in scope, with samples drawn from 72 countries across North America, South America, Africa, Europe, and Asia. ORBITaL-Net captures a great diversity in geographic setting, structural characteristics, land use (urban and rural), terrain, and imagery conditions. While the labeled building outlines are themselves valuable, the dataset’s true strength lies in the pairing of these labels with corresponding reference imagery, which is being released for open source use. Similar to SpaceNet and Replicable AI For Microplanning (ramp), this building outline dataset will allow the larger computer vision community from academia, government, and industry the opportunity to develop robust, scalable, and generalizable geospatial machine learning techniques. Unlike SpaceNet and ramp, which offer high resolution labels and imagery primarily for large urban cities, ORBITaL-Net is not focused on training samples from heavily populated areas but instead aims to capture the innate variability of conditions present in both the physical environment and imagery collections.

Geography↗