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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Algorithmic design for 3D printing at building scale

Here, this paper addresses the use of algorithmic design paired with additive manufacturing and their potential impact on architectural design and fabrication of a full-sized building, as demonstrated with the AMIE project. AMIE (Additive Manufacturing and Integrated Energy) was collaboration to 3d print a building and vehicle. Both the car and building were designed to generate, store and share energy in an effort to reduce or eliminate reliability on the power grid. This paper is intended to outline our methodology in successfully designing for these innovative strategies, with a focus on the use of computational design tools as a catalyst for design optimization, integrated project delivery, rapid prototyping and fabrication of building elements using additive manufacturing.

3D printing↗
DOE OSTI2017

Toward large-scale building detection with convolutional neural network using remote sensing images

Establishing up-to-date nationwide building maps is essential to understand urban dynamics, such as estimating population and urban planning and many other applications. However, an efficient and effective solution is yet to be developed. In this paper, for the first time we evaluate three state-of-the-art CNNs for detecting buildings across entire United States using aerial images. The three CNN architectures, fully convolutional neural network, conditional random field as recurrent neural network, and SegNet, support semantic pixel-wise labeling and focus on capturing textural information at multi-scale. We use 1-meter resolution NAIP images as the test data set, and compare the detection results across the three methods. In addition, we propose to combine signed distance function labels with SegNet, which is the preferred CNN architecture identified by our extensive evaluations. The results are further improved in terms of precision, recall rate and the number of building detected. On average, model inference on test images is less than one minute for an area of size $\sim56$ $km^2$. With these promising results and the time required to process images, the framework offers great potential toward country scale building mapping with remote sensing imagery.

Yang, Lexie↗
DOE OSTI2017

Evaluation of Embedded VIPs for Use in the Building Envelope

An objective for expanded use of vacuum insulation panel (VIP) products in buildings will be assisted by an understanding of the relationship between small-scale laboratory thermal measurements and large-scale tests of building components. Thermal resistances of a typical element of VIP embedded in closed-cell cellular plastic (foam) insulation obtained with a heat flow meter (HFM) apparatus are compared with results obtained with a hot-box facility for a building component. Small-scale samples of the foam-VIP composites were measured in single- and multi-transducer HFMs. The multi-transducer HFM was used to determine the heat flow distribution across the different sections of the VIP, i.e. center-of-panel vs. edges and joints. These measurements enable validation of numerical models, which can be utilized to model and evaluate large-scale building components containing VIPs.

Biswas, Kaushik↗
DOE OSTI2018

Model-free Control of Building HVAC Systems to Accommodate Solar Photovoltaic Energy

With the increasing demand for energy in day-today life, the need for integrating renewable energy is growing. In this paper, we take a step in addressing the issue by employing solar photovoltaic (PV) energy to maintain indoor temperatures in buildings. The objective we aim to meet is to use as much of the generated PV energy as possible while maintaining the indoor comfort temperatures. We set this objective in order to reduce wastage of renewable energy and cut down the need for storage devices. We formulate a thermal model of the building HVAC system and employ the model-free control (MFC) technique to achieve the defined objective. Although recently introduced, MFC has shown promising results in various domains and it is more appealing due to its light computational complexity. Furthermore, we analyze the relationship between the generated PV energy and the total energy consumption of the buildings. The results show that the aforementioned relationship is linear in nature as the number of buildings increases.

Telsang, Bhagyashri↗
DOE OSTI2018

Automatic Building Energy Model Creation (AutoBEM) for Urban-Scale Energy Modeling and Assessment of Value Propositions for Electric Utilities

Residential and commercial buildings in the United States consume 73% of all electricity generated. Demand charges (i.e. “peak usage”) can account for 30-70% of a utility bill and continue to grow even as energy prices are decreasing, due largely in part to adoption of intermittent renewables and less efficient, more costly generation assets which must be brought online during peak demand. In order to quantitatively assess business value and either enhance existing or propose new programs for utilities, the ability to detect, generate, and simulate a building energy model has been applied to a 1,385 km2 utility district involving over 160,000 buildings in a smart grid backed by a fiber optic network. The organization operating the electrical distribution and fiber optic networks has prioritized and guided the creation of a virtual utility in order to assess a prioritized list of potential revenue streams and determine the most cost-effective programs for their organization and their rate-payers. We provide an overview of the technical capabilities developed for generally-applicable urban-scale building energy modeling to any geographical area and the utility-defined use cases being assessed.

New, Joshua↗
DOE OSTI2018

Liquid-Cooled Server Technology that Dramatically Reduces Electricity in Building-Connected Data Centers and Enables 95% Waste Heat Reuse

Today data centers in the U.S consume about 70 TWh of electricity annually. What if there was an easy way to reduce this electricity use by up to 35%? Even better, what if data centers were located in ordinary buildings without the need for expensive air conditioning and air handing systems? Better still, what if the heat energy produced by the data center's servers could be easily recovered and reused for building heating? This presentation will provide an overview of a unique and innovative form of liquid immersion cooling for data center servers developed by LiquidCool Solutions that can be easily deployed to achieve the benefits described above. As validation of the benefits, the presentation will describe testing of a small-scale version of this immersion cooling system conducted at the National Renewable Energy Laboratory. Testing demonstrated that the system was able to recover 90% to 95% of server heat energy at a temperature high enough for use by conventional building heating systems while eliminating internal server fans and using just 1% of the server power to circulate cooling fluid through the system. The concluding section of the presentation will focus on development of a roadmap for scaling-up and commercializing this innovative cooling technology. This will include proposals for how specific commercial building types could make effective use of this technology to recover and reuse server waste heat and a strategy for overcoming barriers to adoption of such a new and innovative technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about. Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could. Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur. To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

Blum, David↗
DOE OSTI2019

Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

Blum, David↗
DOE OSTI2019

A Preliminary Study of a Novel Heat Pump Integrated Underground Thermal Energy Storage for Shaping Electric Demand of Buildings

A Dual-Purpose Underground Thermal Battery (DPUTB) innovatively integrates a ground heat exchanger with underground thermal energy storage. The DPUTB can be integrated with an Electric-Driven Heat Pump (EDHP) to enable more flexible electric load for meeting the thermal demands of a building (e.g., space cooling and space heating). A one-dimensional numerical model of the DPUTB is developed and validated against measured performance data of a small-scale DPUTB prototype. The validated DPUTB model is integrated with a heat pump model and a building energy simulation model to predict the load shifting performance and energy consumption of an integrated EDHP and DPUTB system. Preliminary simulation results indicate that the integrated system can flatten the electric load profile of a typical residential building and reduce the electric demand during peak hours by 37% on a typical summer day. In addition, the integrated EDHP and DPUTB system also reduces the daily power consumption of the building by 11% compared with a conventional residential space heating and cooling system.

Liu, Xiaobing↗
DOE OSTI2020

Cost Assessment of Building Envelope Retrofits

About 65% of US buildings were constructed before the Department of Energy established the Building Energy Codes Program in 1992; therefore, their envelopes are likely significant contributors to heating and cooling loads. Numerous techniques to improve the thermal, airtightness, and water tightness performance of existing envelopes have been explored; however, these tend to be lengthy and disruptive because most of the material assembly occurs at the job site. Overclad panels that are fabricated offsite and include most of the envelope components are a potential mechanism to reduce construction time and minimize disturbance to building occupants. These benefits have been demonstrated by European programs such as Energiesprong and MORE-CONNECT, as well as a few case studies in the US. Nevertheless, preliminary evaluations appear to indicate that overclad panels may be too costly to be implemented in the US. This paper summarizes cost estimates, strengths and weaknesses from several envelope retrofit techniques; assesses the feasibility of overclad panels in the US; and proposes methods, such as advanced manufacturing techniques, that could decrease the cost of building envelope retrofits.

Salonvaara, Mikael↗
DOE OSTI2020

Optimizing HVAC Operations in Multi-unit Buildings for Grid Demand Response

The thermal inertia of buildings, along with the flexibility associated with thermostatically controlled loads (TCL) allows heating ventilation and cooling (HVAC) systems to be used for grid demand response (DR). In this work, we consider an HVAC system that serves multiple units in a residential building to meet their space heating requirements. We aim to determine the optimal power flow to each unit that minimizes the power costs incurred by the building's occupants while keeping in consideration their thermal comfort. The DR program is assumed to allow the building temperatures to deviate from the set-points up to a maximum limit. Despite the complex, non-linear structure of the problem, we show how the optimal solutions can be obtained efficiently using quadratic programming. Since HVAC systems can run on either electricity or natural gas, we study the efficacy of the DR regime for both hourly electricity prices and flat gas prices over the course of 24 hours. Finally, we run simulations to determine the optimal thermal power and the evolution of unit temperatures for various energy pricing schemes.

Naqvi, Syed A.↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2020

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

It has been shown that model predictive control (MPC) is a promising solution for energy-efficient building operations. However, the deployment of MPC in a large portion of the building stock has not been possible partially because of high installation costs. Every building is unique and requires a tailored MPC solution. The best performing solutions are often based on physics-based modeling, which is, however, computationally expensive and requires dedicated software. A promising direction that tackles this problem is to train a neural network-based optimal control policy to imitate the behavior of physics-based MPC from the simulation data generated offline. The neural networks give control actions that closely approximate those produced by physics-based MPC, but with a fraction of the computational and memory requirements and without the need for licensed software. The main advantage of the proposed approach stems from simple evaluation at execution time, leading to low computational footprints and easy deployment on embedded HW platforms. In the case study, we present the energy savings potential of physics-based MPC applied to an office building in Belgium. We demonstrate how neural network approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the remaining challenges and open research questions.

Drgona, Jan↗
DOE OSTI2022

Healthy Buildings Guide for Small Businesses: A Quick Reference to Improve Indoor Air Quality (Restaurants Excluded)

Indoor air pollutant concentrations can be higher than those outdoors, putting occupants at risk. These pollutants come from a variety of sources: materials used to build and furnish indoor spaces, retail products and services, and occupants themselves. Ventilation, temperature, and humidity also affect indoor air quality. It is important to consider the documented benefits of healthy indoor environments at any time – not just during a public health crisis – and it is key to consider the indoor air quality where we do business as well as at home. Small businesses vary in size, services, products, ownership style, and building mechanical systems, yet all benefit from healthy indoor spaces for employees and customers. This guide provides steps, in conjunction with public health guidance, that small businesses can take to improve and maintain indoor air quality. Small businesses often have unique ownership structures – the building owner and business owner can be different parties – so this guide addresses both building and business owner action items. There are many ways to pursue healthy indoor air, including eliminating, minimizing, and/or controlling the contaminants we bring in, and removing or diluting any existing contaminants through ventilation, filtration, air cleaning, and/or disinfection. Best results are achieved through a comprehensive approach combining various strategies, including those discussed in this guide.

air filtration↗
DOE OSTI2022

Performance analysis of novel thermal storage integrated heat pump system in a residential building at the hot climate for demand flexibility

A novel thermal energy storage integrated heat pump system was proposed to reshape the electricity load profile of residential buildings while maintaining thermal comfort. High-fidelity computer simulations are needed for evaluating the feasibility of the proposed system. This study investigates the annual performance of the proposed system through Modelica-based system simulations. A rule-based control strategy was developed to shift the electric demand of a typical single-family house in Atlanta, GA from peak to off-peak hours to utilize the Time-of-Use electricity rate to lower the energy costs for conditioning the building. For comparison, a conventional air-source heat pump system serving the same building was also developed. Simulation results indicate that the proposed system is capable of shifting around 90% of the building's electricity consumption for meeting the thermal demand from peak to off-peak hours on a daily basis. In addition, the annual power consumption and operating cost for running the HVAC system can be reduced by 6% and 34%, respectively, compared with the conventional air-source heat pump.

Shi, Liang↗
DOE OSTI2022

Design of Phase-Change Thermal Storage Device in a Heat Pump for Building Electric Peak Load Shaving: Preprint

Replacing carbon-intensive fossil fuel heating systems with electric heat pumps powered by renewables is a promising approach to decarbonize the building sector. However, one of the technical barriers of this approach is the large scale of heat demand, which will put excessive stress on the electricity grid. Integrating thermal energy storage (TES) into the heating systems can help alleviate this problem, by shifting thermal load and thus shaving peaks in the building electric load. Therefore, it is critical to understand how to design a thermal storage device in a heat pump for peak load shaving. In this study, we developed a numerical model for a cascaded vapor compression heat pump system integrating a phase change thermal storage device. This novel system can control the net thermal charging and discharging rate of the TES independently from the building's thermal load, which allows for precise control of electric power use. In the current study, we controlled the system to shave building electric peak load during a cold winter morning, and to charge TES during the relatively warm afternoon while still providing space heating. We used the model to evaluate the system performance with and without peak shaving. We also investigated the effect of PCM transition Tt on the peak reduction and electric energy saving potentials. The results show that peak shaving scheme effectively reduces the peak electric power consumption during a predefined discharge time window. When comparing to the no shaving case, for PCM Tt = 10 degrees C, the peak electric load reduction is 23.5%. When comparing to an air-source heat pump with back up electric heater, as Tt increases from 0 to 20 degrees C, the peak reduction increases from 46.1% to 50.9%. Integrating PCM Tt = 10 degrees C with peak shaving leads to the 45.5% of electric energy saving, which is the highest among the three transition temperatures.

electric energy saving↗
DOE OSTI2022

A Scalable Method for Decarbonizing Modular Building Solutions: Preprint

The decarbonization movement emphasizes the shift in focus from energy efficiency to directly reducing global-warming impact. Blokable, LLC, a vertically integrated modular builder with an all-electric portfolio, worked with NREL on a roadmap to decarbonize its high-performance building product at a relative cost advantage by utilizing the learning curves of mass production. Previous decarbonization literature focused on either i) lifecycle assessments, or ii) efficiency measures. These decarbonization exercises were bespoke to individual building projects and did not consider positive feedback loops of builder experience or process repetition. Vertically integrated, prefab builders possess the unique ability to leverage learning and repetition to decarbonize their design-build-operate process. This collaboration between Blokable and NREL resulted in a decarbonization strategy utilizing the company's scaling and production efficiencies, on-site renewable energy and storage, and the projected evolution of building components over time based on trends and emerging legislation. The method developed here encompasses a growing business model, lifecycle carbon assessment, and projected changes in product and grid emissions over time due to existing trends and emerging legislation. This methodology incorporates learning-curve efficiencies gleaned from scaled manufacturing, as well as open-source tools integration for energy and carbon accounting. The output projects and compares cost and carbon savings per modular unit as production increases to 10,000 dwelling units annually over 15 years. The resulting roadmap illustrates a path to roughly 60% carbon savings and beyond-net-zero-energy performance at no incremental cost by 2030. The methodology can be mapped to other integrated or productized builders for methodical decarbonization.

affordable housing↗
DOE OSTI2022

The Future of Energy Efficiency for U.S. Buildings - Drivers and Market Scenarios

This paper identifies likely drivers of building efficiency over the next ten years and expectations for how efficiency markets may evolve over this period. To prepare these predictions, we conducted an extensive literature review, interviewed 22 experts, reviewed legislation and executive orders in 12 states, and implemented a detailed questionnaire completed by 41 efficiency practitioners. The two most important drivers revealed by our research are (1) public policies and regulations, particularly those associated with climate change mitigation and adaptation and (2) the cost of energy relative to the cost of delivering efficiency. Other important drivers are technology changes, economic conditions, social priorities, and industry (including utility) business practices for increasing the uptake of efficiency in buildings. Our research indicates that efficiency markets will increasingly focus on supporting building decarbonization and enabling demand flexibility through the use of controls in grid-interactive efficient buildings and communities. Efficiency improvements for specific technologies (e.g., heat pumps, controls, and windows) and technological advances not specific to energy technologies (e.g., interoperability, artificial intelligence, and universal internet access) will improve the efficacy of efficiency measures and actions. Marketing of efficient products and services will increasingly focus on grid services, decarbonization, non-energy benefits for consumers, and integration with other distributed energy resources (DERs). We anticipate increased investment in disadvantaged and historically underserved communities, recognizing the social, health, and safety benefits of efficient energy usage and remediating historical biases. Lastly, we predict that while state and local government actions will vary, jurisdictions will increase their efficiency goals overall.

Schiller, Steven R↗