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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

User's manual for the BROOM decision support system

The Building Restoration Operations Optimization Model (BROOM) software system, developed by Sandia National Laboratories, is a comprehensive system designed to improve the efficiency and quality of sample collection, data management, and interpretation. Currently, BROOM is capable of supporting sampling efforts at facilities contaminated with chemical and/or biological agents. BROOM provides advanced visualization, analysis, and sampling optimization functions, which speed up the sampling process and lower the associated sampling costs. BROOM enables the user to develop a defensible sampling plan based on statistical analysis of sample results. BROOM consists of two applications: one that runs on a hand-held PDA and one that runs on a Windows desktop/laptop. This document covers the installation and basic operation of both components and provides examples of the application of these tools during and following an actual field exercise.

Knowlton, Robert G.↗
NASA NTRS1978

Solar heating and cooling system for an office building at Reedy Creek Utilities

The solar energy system installed in a two story office building at a utilities company, which provides utility service to Walt Disney World, is described. The solar energy system application is 100 percent heating, 80 percent cooling, and 100 percent hot water. The storage medium is water with a capacity of 10,000 gallons hot and 10,000 gallons chilled water. Performance to date has equaled or exceeded design criteria.

Source record↗
DOE OSTI2023

Challenges and opportunities of machine learning control in building operations

Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. Here this paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Impacts of Electric Space Heating in Multifamily Apartments in Juneau, Alaska [Slides]

The study objective was to understand the change in electrical consumption and cost after the addition of a ductless mini-split air-source heat pump (ASHP) across rental apartments heated by electrical resistance heating systems in multifamily buildings in Juneau, Alaska. Alaska Electric Light and Power Company (AELP), the utility in Juneau, Alaska, funded installation of 19 heat pumps across four buildings. Using electrical meter interval data, including the total apartment consumption and submeter data for each heating appliance (electric resistance baseboard and heat pump), the National Laboratory of the Rockies (NLR) calculated energy use metrics before and after heat pump installation and visualized results. Through this research, NLR compares electrical use (total and specific to heating appliances), adjusted for weather and apartment size, and electrical demand in the year before and after installation of an ASHP across study units. The results will inform the utility and residents of documented energy and cost savings from transitioning building heating systems to a higher-efficiency electricity-based appliance, deliver a basis for building energy modeling across other multifamily housing dwellings, and enable estimates of potential electricity savings to AELP grid under widespread adoption scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Tool coupling for the design and operation of building energy and control systems based on the Functional Mock-up Interface standard

This paper describes software tools developed at the Lawrence Berkeley National Laboratory (LBNL) that can be coupled through the Functional Mock-up Interface standard in support of the design and operation of building energy and control systems. These tools have been developed to address the gaps and limitations encountered in legacy simulation tools. These tools were originally designed for the analysis of individual domains of buildings, and have been difficult to integrate with other tools for runtime data exchange. The coupling has been realized by use of the Functional Mock-up Interface for co-simulation, which standardizes an application programming interface for simulator interoperability that has been adopted in a variety of industrial domains. As a variety of coupling scenarios are possible, this paper provides users with guidance on what coupling may be best suited for their application. Furthermore, the paper illustrates how tools can be integrated into a building management system to support the operation of buildings. These tools may be a design model that is used for real-time performance monitoring, a fault detection and diagnostics algorithm, or a control sequence, each of which may be exported as a Functional Mock-up Unit and made available in a building management system as an input/output block. We anticipate that this capability can contribute to bridging the observed performance gap between design and operational energy use of buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Design and Performance of an Image Processing Occupancy Sensor

The design and performance of a novel image-processing occupancy sensor (IPOS) based on production-grade embedded hardware are described. The IPOS overcomes several issues related to traditional passive infrared (PIR) and ultrasonic-based motion sensors. It also provides a rich set of feedback signals that more sophisticated applications can use for occupant-optimized lighting, daylighting,temperature,and ventilation control, as well as with building security systems. The IPOS was tested as part of an integrated system consisting of a building energy management system (EMS) alongside a pair of PIR sensors produced by different manufacturers. IPOS output was fed to the EMS via the building automation and controls network (BACnet) and logged alongside the PIR outputs in an officeenvironment over several days. Tests suggest the IPOS device identifies occupancy events better than two commercial PIR sensors (94.1% accuracy compared to 73.1% on average). Also, IPOS could correctly identify stationary occupants whereas the PIR sensors could not.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Modeling the Air Flow in the 3410 Building Filtered Exhaust Stack System

Additional ventilation capacity has been designed for the 3410 Building filtered exhaust stack system. The updated system will increase the number of fans from two to three and will include ductwork to incorporate the new fan into the existing stack. Stack operations will involve running various two-fan combinations at any given time. The air monitoring system of the existing two-fan stack was previously found to be in compliance with the ANSI/HPS N13.1-1999 standard, however it is not known if the modified (three-fan) system will comply. Subsequently, a full-scale three-dimensional (3-D) computational fluid dynamics (CFD) model of the modified stack system has been created to examine the sampling location for compliance with the standard. The CFD modeling results show good agreement with testing data collected from the existing 3410 Building stack and suggest that velocity uniformity and flow angles will remain well within acceptance criteria when the third fan and associated ductwork is installed. This includes two-fan flow rates up to 31,840 cfm for any of the two-fan combinations. For simulation cases in which tracer gas and particles are introduced in the main duct, the model predicts that both particle and tracer gas coefficients of variance (COVs) may be larger than the acceptable 20 percent criterion of the ANSI/HPS N13.1-1999 standard for each of the two-fan, 31,840 cfm combinations. Simulations in which the tracers are introduced near the fans result in improved, though marginally acceptable, COV values for the tracers. Due to the remaining uncertainty that the stack will qualify with the addition of the third fan and high flow rates, a stationary air blender from Blender Products, Inc. is considered for inclusion in the stack system. A model of the air blender has been developed and incorporated into the CFD model. Simulation results from the CFD model that includes the air blender show striking improvements in tracer gas mixing and tracer particle dispersion. The results of these simulations suggest the air blender should be included in the stack system to ensure qualification of the stack.

3410 Building↗
NASA NTRS1980

Solar heating and hot water system installed at office building, One Solar Place, Dallas, Texas

A solar heating on cooling system is described which is designed to provide 87 percent of the space heating needs, 100 percent of the potable hot water needs and is sized for future absorption cooling. The collection subsystem consists of 28 solargenics, series 76, flat plate collectors with a total area of 1,596 square feet. The solar loop circulates an ethylene glyco water solution through the collectors into a hot water system exchanger. The water storage subsystem consists of a heat exchanger, two 2,300 gallon concrete hot water storage tanks with built in heat exchangers and a back-up electric boiler. The domestic hot water subsystem sends hot water to the 10,200 square feet floor area office building hot water water fixtures. The building cold water system provides make up to the solar loop, the heating loop, and the hot water concrete storage tanks. The design, construction, cost analysis, operation and maintenance of the solar system are described.

Source record↗
DOE OSTI2022

Numerical modeling and parametric study of a dual purpose underground thermal battery

We report integrating thermal energy storage with building energy systems can enable flexible building electric demands at buildings to help mitigate the mismatch between electricity supply and demand. A novel building heating and cooling system that integrates a dual-source heat pump with hybrid thermal storage named dual-purpose underground thermal battery (DPUTB) has been developed for reshaping building electric demands. The proposed DPUTB integrated geothermal heat pump system is an original innovation that enables Grid-interactive Energy Efficient buildings. This paper focuses on the study of the novel DPUTB. The DPUTB works as both a thermal storage tank (an inner tank) and a ground heat exchanger (an outer tank separated from the inner tank by the insulation material). High fidelity and computationally effective models are needed to predict the performance of the novel DPUTB. This study has developed a simplified dynamic model for the DPUTB according to heat transfer and energy conservation principles and validated it by using experimental data obtained from testing a small-scale DPUTB apparatus. A parametric study was conducted to identify a design that can achieve the target thermal storage performance of load shift and energy efficiency. The parametric study results show that the inner tank shell thermal conductivity and the phase change material's melting point are the two most influencing factors on the performance of the DPUTB. One single full-size DPUTB with the identified design could provide 1-ton cooling (3.51 kW) with the supply temperature lower than 11 °C for 4 hours in summer after being fully charged in 8 hours. The inner tank filled with phase change material is for cooling thermal storage as a latent tank in the design. However, its capacity can be as high as 60 MJ as a sensible water tank for heating storage in winter. In the future study, the DPUTB model will be incorporated into the dual-source heat pump system for evaluating the overall system performance of demand side management in the long term.

25 ENERGY STORAGE↗
DOE OSTI2014

Co-Simulation of Detailed Whole Building with the Power System to Study Smart Grid Applications

Modernization of the power system in a way that ensures a sustainable energy system is arguably one of the most pressing concerns of our time. Buildings are important components in the power system. First, they are the main consumers of electricity and secondly, they do not have constant energy demand. Conventionally, electricity has been difficult to store and should be consumed as it is generated. Therefore, maintaining the demand and supply is critical in the power system. However, to reduce the complexity of power models, buildings (i.e., end-use loads) are traditionally modeled and represented as aggregated “dumb” nodes in the power system. This means we lack effective detailed whole building energy models that can support requirements and emerging technologies of the smart power grid. To gain greater insight into the relationship between building energy demand and power system performance, it is important to constitute a co-simulation framework to support detailed building energy modeling and simulation within the power system to study capabilities promised by the modern power grid. This paper discusses ongoing work at Pacific Northwest National Laboratory and presents underlying tools and framework needed to enable co-simulation of building, building energy systems and their control in the power system to study applications such as demand response, grid-based HVAC control, and deployment of buildings for ancillary services. The optimal goal is to develop an integrated modeling and simulation platform that is flexible, reusable, and scalable. Results of this work will contribute to future building and power system studies, especially those related to the integrated ‘smart grid’. Results are also expected to advance power resiliency and local (micro) scale grid studies where several building and renewable energy systems transact energy directly. This paper also reviews some applications that can be supported and studied using the framework introduced to understand their implications before they can be successfully implemented in the power system.

Makhmalbaf, Atefe↗
DOE OSTI2017

Transactive Control of Commercial Buildings for Demand Response

Transactive control is a type of distributed control strategy that uses market mechanism to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building Heating, Ventilation, and Air- Conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from Systems Engineering Building (SEB) located on our Pacific Northwest National Laboratory (PNNL) campus. We next present a transactive control market structure for commercial building HVAC system, and describe its agent bidding and market clearing strategies. Several case studies are performed in a simulation environment using Building Control Virtual Test Bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak clipping, load shifting, and strategic conservation for commercial building HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Understanding the U.S. Commercial Building Stock: Building Envelope and HVAC Systems Characteristics Using 2012 CBECS Data Analysis

The U.S. Department of Energy (DOE) and its national labs developed a series of commercial reference building models in 2007. Over the past decade, the Pacific Northwest National Laboratory, in support of DOE’s Building Energy Codes Program, expanded these prototype building models to comply with multiple editions of national model codes and standards, including both ASHRAE Standard 90.1 and International Energy Conservation Code. The prototype building models established a credible foundation to develop new requirements in building codes and standards, track progress of codes and standards, and evaluate the energy saving potential of new technologies or systems in the commercial building sector. The 2003 Commercial Building Energy Consumption Survey (CBECS) was the primary data source used to develop the original models. Given the recent release of 2012 CBECS data, this paper characterizes the most representative design choices for building envelope and HVAC systems in recently-constructed commercial buildings. The results from this analysis provide actionable insight for future commercial building research in reflection of recent construction practices.

energy efficiency, building energy codes, CBECS, b↗
DOE OSTI2024

Mapping use cases and dataset needs for benchmarking buildings data

A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗