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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Low-E Storm Windows Gain Acceptance as a Home Weatherization Measure

This article for Home Energy Magazine describes work by the U.S. Department of Energy to develop low-emissivity storm windows as an energy efficiency-retrofit option for existing homes. The article describes the low-emissivity invisible silver metal coatings on the glass, which reflect heat back into the home in winter or back outside in summer and the benefits of low-e storm windows including insulation, air sealing, noise blocking, protection of antique windows, etc. The article also describes Pacific Northwest National Laboratory's efforts on behalf of DOE to overcome market barriers to adoption of the technology, including performance validation studies in the PNNL Lab Homes, cost effectiveness analysis, production of reports, brochures, how-to guides on low-e storm window installation for the Building America Solution Center, and a video posted on YouTube. PNNL's efforts were reviewed by the Pacific Northwest Regional Technical Forum (RTF), which serves as the advisory board to the Pacific Northwest Electric Power Planning Council and Bonneville Power Administration. In late July 2015, the RTF approved the low-e storm window measure’s savings and specifications, a critical step in integrating low-e storm windows into energy-efficiency planning and utility weatherization and incentive programs. PNNL estimates that more than 90 million homes in the United States with single-pane or low-performing double-pane windows would benefit from the technology. Low-e storm windows are suitable not only for private residences but also for small commercial buildings, historic properties, and facilities that house residents, such as nursing homes, dormitories, and in-patient facilities. To further assist in the market transformation of low-e storm windows and other high-efficiency window attachments, DOE helped found the window Attachment Energy Rating Council (AERC) in 2015. AERC is an independent, public interest, non-profit organization whose mission is to rate, label, and certify the performance of window attachments.

Building Technologies Office↗
DOE OSTI2022

It's All About the Envelope: Prioritizing Envelope Upgrades for Electrification of Cold Climate Homes

Building decarbonization via electrification on a clean grid is the most promising climate solution proposed to date for the building sector. In cold climate zones, building electrification will be driven in large part by moving from natural gas space heating to cold climate heat pumps (CCHPs). CCHPs are commercially available today, including economical cold climate air source heat pumps (ccASHPs). But there's one big problem - wide-scale adoption of ccASHPs will dramatically increase winter peak electricity demand, even with the highest efficiency ccASHP products. Furthermore, cold climate space heating loads will drive unprecedented electric system peaks during the lowest periods of renewable generation and are likely to overwhelm existing distribution systems. This scenario is avoidable by coupling electrification with building envelope upgrades to reduce peak heating loads. This paper presents a model, built from home energy audit and research data sets, that quantifies the above challenges. Results demonstrate how weatherization efforts coupled with additional high-performance envelope upgrade measures can prepare the building stock for electrification and show the benefit these measures can bring to future utility operations. Much of this envelope upgrade work is cost-effective, according to conservative cost-benefit testing and program successes to date, and is coupled with substantial non-energy benefits. However, persistent market barriers have made scaling of envelope retrofit work challenging for decades, suggesting additional policy support is required. Lessons learned from previous policy experience, combined with new technology and administrative support, create exciting potential for this decarbonization climate solution.

air sealing↗
DOE OSTI2023

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗
DOE OSTI2023

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗
DOE OSTI2022

Electrical Switching of Nematic Plasmonic Nanocolloids for Infrared Solar Gain Control

Controlling solar gain is essential to reduce the energy consumption by residential and commercial buildings, which are responsible for ≈40% of all energy generated globally. However, the dynamic control of solar gain in the near-infrared that corresponds to ≈50% of the overall Sun's energy at the Earth's surface, separately from the visible spectral range has been a challenge, albeit commercial coatings provide passive control while retaining highly visible transmission. To address this problem, in this paper switchable silver colloids (SSCs) are demonstrated, colloidal analogs of the common silver coatings used for solar gain control. When dispersed in a nematic liquid crystal, orientations of such plasmonic silver nanoplates are controlled by ≈1 V low-voltage electric fields, allowing for sub-second switching of the near-infrared-based solar gain. Installed and retrofit products made from thin films of such nanoplate dispersions confined between glass and plastic substrates, respectively, exhibit electrically controlled infrared-based solar gain while retaining high visible-range transparency, low haze, and high color rendering index. Furthermore, this study's findings reveal a great potential of soft-matter systems in addressing energy-related problems.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents

Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the Autotune research which employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.

Sanyal, Jibonananda↗
DOE OSTI2015

Mandating better buildings: a global review of building codes and prospects for improvement in the United States

This paper provides a global overview of the design, implementation, and evolution of building energy codes. Reflecting alternative policy goals, building energy codes differ significantly across the United States, the European Union, and China. This review uncovers numerous innovative practices including greenhouse gas emissions caps per square meter of building space, energy performance certificates with retrofit recommendations, and inclusion of renewable energy to achieve “nearly zero-energy buildings”. These innovations motivated an assessment of an aggressive commercial building code applied to all US states, requiring both new construction and buildings with major modifications to comply with the latest version of the ASHRAE 90.1 Standards. Using the National Energy Modeling System (NEMS), we estimate that by 2035, such building codes in the United States could reduce energy for space heating, cooling, water heating and lighting in commercial buildings by 16%, 15%, 20% and 5%, respectively. Impacts on different fuels and building types, energy rates and bills as well as pollution emission reductions are also examined.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Ways to Improve Russian Coal-Fired Power Plants

Coal is an important fuel for the electric power industry of Russia, especially in Ural and the eastern part of the country. It is fired in boilers of large (200 – 800 MW) condensing power units and in many cogeneration power plants with units rated at 50 – 180 MW. Many coal-fired power plants have been operated for more than 40 – 50 years. Though serviceable, their equipment is obsolete and does not comply with the current efficiency, environmental, staffing, and availability standards. It is urgent to retrofit and upgrade such power plants using advanced equipment, engineering and business ideas. Russian power-plant engineering companies have designed such advanced power units and their equipment such as boilers, turbines, auxiliaries, process and environmental control systems similar to those produced by the world’s leading manufacturers. Their performance and ways of implementation are discussed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Potential Early Markets for Fusion Energy

We examine potential early markets for fusion energy and their projected cost targets, based on analysis and synthesis of many relevant, recent studies and reports. Seeking to provide guidance to ambitious fusion developers aspiring to enable commercial deployment before 2040, we examine cost requirements for fusion-generated electricity, process heat, and hydrogen production based on today’s market prices but with various adjustments relating to possible scenarios in 2035, such as ‘‘business-as-usual,’’ high renewables penetration, and carbon pricing up to 100 $/tCO 2 . Key findings are that fusion developers should consider focusing initially on high-priced global electricity markets and consider including integrated thermal storage, depending on techno-economic factors, in order to maximize revenue and compete in markets with high renewables penetration. Process heat and hydrogen production will be tough early markets for fusion, but may open up to fusion as markets evolve and if fusion’s levelized cost of electricity falls below 50 $/MWh e . Finally, we discuss potential ways for a fusion plant to increase revenue via cogeneration (e.g., desalination, direct air capture, or district heating) and to lower capital costs (e.g., by minimizing construction times and interest or by retrofitting coal plants).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Building simulation: Ten challenges

Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. In conclusion, the formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Enhancing occupant behavior representation for interoperability between building information modeling and building energy modeling

Building Performance Simulation (BPS) has been adopted as an essential tool for designing, operating, and retrofitting buildings to optimize energy efficiency throughout the building life cycle. The Green Building XML (gbXML) schema facilitates seamless data exchange between Building Information Modeling (BIM) and Building Energy Modeling (BEM) software tools. However, limited occupant behavior (OB) representation in BIM often leads to inconsistent and inaccurate energy simulation in BEM software. This paper presents 154 systematic enhancements to the existing occupant behavior XML (obXML) schema v1.3.4, initially developed for standardizing OB representation for BEM, to address existing limitations and improve interoperability with BIM models. The enhancements encompass improved integration with BIM models through extended building representations and system operations, expanded support for advanced OB models with additional environmental parameters and mathematical capabilities, and implementation of a standardized model documentation framework. To facilitate seamless data transformation between gbXML and obXML schemas, we developed a publicly available gb-obXML Schema Converter. Three case studies demonstrate the enhanced schema’s capabilities: representation of building information using a two-story office building model, documentation of a window operation behavior model, and validation of the schema converter’s functionality. The enhanced obXML schema v1.4 enables sophisticated modeling of occupant-building interactions while maintaining consistency with industry-standard BIM schemas. The standardized documentation framework facilitates reproducibility and knowledge sharing in the OB research community, while the schema converter automates the integration of building information into OB simulation workflows. These enhancements establish a foundation for more accurate building performance simulation by supporting sophisticated representation of occupant behavior within the BIM-to-BEM simulation workflows.

Chung, Jihoon↗
DOE OSTI2018

Improvement in the Mass Resolution of Single Particle Mass Spectrometry Using Delayed Ion Extraction

A specific delayed ion extraction (DIE) technique, which combines a standard rectangular extraction pulse with an exponential pulse, was introduced for a single particle mass spectrometry (SPMS) instrument, and it can focus ions in a wide mass range and results in a mass resolution improvement for the mass range of the studied ions. The experimental results indicate that the average mass resolution for positive ions is about 1000 when the mass-to-charge ratio (m/z) is greater than 70, and for negative ions, when the m/z is greater than 70, the average resolution can reach 2000. The highest mass resolutions achieved so far are 1260 for positive ions and 2400 for negative ions for SPMS, which are very beneficial for mass peak interpretation and chemical compound identification. The primary applications for atmospheric particle measurements show that the high mass resolution of SPMS with the DIE technique is very beneficial for the analysis of carbon and metallic element containing particles, and {sup 39}K{sup +} with C{sub 3}H{sub 3}{sup +} and {sup 41}K{sup +} and C{sub 3}H{sub 5}{sup +} in organic particles were successfully differentiated using SPMS. The results indicate that SPMS with DIE technique can significantly ease mass peak interpretation and improve the mass assignment ability during analysis. Furthermore, existing SPMS instruments can be improved by a facile retrofitting process to implement the DIE technique. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Neutronic and thermal-hydraulic feasibility studies for High Flux Isotope Reactor conversion to low-enriched uranium silicide dispersion fuel

An iterative design process involving neutronic and thermal-hydraulic modeling and simulation has been employed to assess the feasibility of converting the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) from high-enriched uranium (HEU) to low-enriched uranium (LEU) silicide dispersion fuel. ORNL is funded by the National Nuclear Security Administration to evaluate HFIR conversion. Previous HFIR conversion studies focused on U-10Mo monolithic fuel; however, due to potential fabrication issues with the complex HFIR U-10Mo fuel design, ORNL is evaluating U 3 Si 2 -Al dispersion fuel as an alternative LEU fuel system.Fueled by 10.1 kg of HEU and operated at 85 MW, HFIR provides one of the highest steady-state neutron fluxes of any research reactor in the world. Retrofitting a compact, high-power density, HEU-based core with LEU is a challenging problem to solve, especially when considering the conversion requirements. Neutronic and thermal-hydraulic analyses were performed with Shift and HSSHTC, respectively, to predict reactor performance and thermal safety margins. A number of designs were proposed and evaluated using an iterative approach in an effort to show that reactor performance could match that obtained using HEU fuel and that thermal safety margins were adequate. Furthermore this study concludes that conversion of HFIR with U 3 Si 2 -Al LEU fuel is feasible if, among other requirements, the fuel meat region is centered and symmetric about the fuel plate thickness centerline, the active fuel zone length is increased from 50.80 cm to 55.88 cm, the proposed fabrication tolerances can be met, and the fuel can be qualified for HFIR conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Uncertainty Estimation Improves Energy Measurement and Verification Procedures

Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗