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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings

As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.

Chen, Zhelun↗
DOE OSTI2018

Optimisation of curvilinear external shading of windows in cellular offices

Shading of windows influences building cooling and heating loads through control of solar heat gains, and lighting load through access to available daylight. Shading shape thus presents an important factor both in building energy analysis and building aesthetics. Curvilinearity of solar paths suggests that the optimal shading shape may be curvilinear as well, and our aim here is to test this expectation. To accommodate curvilinearity of shading shape, outer edges of shading, which consists of overhang, western and eastern fins, are modeled as non-uniform rational basis spline (NURBS) curves, a widely accepted representation standard for curves in design industry. As a case study, a cellular office is considered in the Pacific Northwest National Laboratory (PNNL) office building model, with its overhang lined up by seven control points, and the fins lined up by five control points each, with two ending control points joint for the overhang and the fins. With control points allowed to take on nine different alternative depths, genetic optimisation is employed for 16 representative USA climates with respect to total equivalent source energy for heating, cooling and lighting loads. The main finding is that in a very close proximity to optimal shadings found by genetic optimisation there exist shadings with much simpler control point structure, obtained by identifying depths of successive control points, that have nearly rectangular overhangs. Since the difference between these simpler shadings and the optimal ones is less than 0.24%, this partially rejects the expectation that the optimal shading shape should be curvilinear. Structure of these near-optimal shadings also suggests a new way to partition shadings into independent regions: the lower and the upper parts of the western fin, joints of the overhang with the western and the eastern fin, the interior part of the overhang and the rest of the eastern fin.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Case Study: Integrate Nuclear Water Desalination - Regional Potable Water in Arizona

The present study analyzes the economic viability of an Integrated Energy System (IES) that couples a Reverse Osmosis (RO) water desalination facility with a Nuclear Power Plant (NPP). The case study is conducted in collaboration with Arizona Public Service (APS), the operating owner of the Palo Verde Generating Station (PVGS) NPP. Cooling water for the reactor steam cycle is derived from treated effluent from municipal wastewater treatment plants. APS has established a long-term water resources program to effect a substantial reduction in plant cooling costs through advanced treatment and cooling technologies, and through the use of alternative water sources to replace the increasingly expensive effluent. One possible option is to replace some amount of the annual volume of effluent with brackish groundwater from a local regional aquifer. Although much less expensive than the municipal effluent, the quantity of brackish groundwater that could be used for plant cooling is limited as a result of the salinity and its impact on plant operation. Consequently, supplemental treatment could be required such that a greater amount of brackish groundwater could be used to reduce the demand on effluent. A study was conducted in 2018 by Idaho National Laboratory (INL) to investigate the economics of a PVGS onsite RO desalination plant that would reduce the salinity of a municipal effluent and brackish water blend to an acceptable level. One of the main findings of that study was that the overall economics of water desalination can be greatly improved if, in addition to cooling water for PVGS, potable water could also be produced and sold for profit. In fact, the study concluded that only producing cooling water for PVGS via RO desalination is not economically viable. The present report investigates the economic impact of a large, regional RO desalination plant that could provide potable water for the region, considering the conclusions from the 2018 scoping study. The study looks in particular at the water-market situation for the developing municipalities in the west valley of Phoenix. In addition to providing potable water for the municipalities, the existing infrastructure that conveys effluent to the Palo Verde plant and onsite water treatment facilities could be used to manage the RO concentrate. The processed concentrate could lead to a cost reduction in plant cooling by replacing some amount of effluent. The analysis reported here considers two cases (for various scenarios). First, the Base Case considers that neither the regional nor the onsite RO is built. The 2018 INL study showed that some brackish water can be blended with the municipal effluent water without having to build the onsite RO. That correspond to the maximum economically profitable option. The Base Case is where APS pumps the maximum volume of less-expensive brackish water (limited by water chemistry in the circulating water system), i.e. the case for which cooling water acquisition and treatment cost are lowest (without RO). Second, the proposed RO Case includes two RO plants, one onsite at PVGS and another larger, regional RO plant close to the brackish water wells. The regional RO produces potable water that is sold to the regional municipalities, while the PVGS onsite RO treats (part of) the regional ROs' concentrate and brackish water blend. The desalinated water from the PVGS RO is used in the circulating water system at PVGS. The analysis evaluates the difference in economics, using the Net Present Value (NPV) and Internal Rate of Return (IRR), between the cases. By comparing the two cases, in addition to evaluating the economics of the regional RO, we can also assess the impact of the regional RO on PVGS operational costs and, consequently, APS economics. The study shows that there exist combinations of regional RO and PVGS RO sizes for which the total blowdown and water chemistry limits at PVGS (including regional RO concentrate treatment at PVGS) are satisfied. However, such combinations only exist if no additional brackish water is directly mixed in the tertiary water system at PVGS. This leads to higher Levelized Costs of Potable Water (LCOPW) compared to cases where additional brackish water is injected (but violate the physical constraints). Additional studies are suggested to investigate the benefit of additional brackish water injection (lowers cooling water cost) versus the cost of lifting the physical constraints, e.g. building additional evaporation ponds. Staying in the case of no additional brackish water and satisfying the existing PVGS constraints, the lowest LCOPW (~0.5 $/m 3 ) can be achieved with a size of the regional RO of ~1.4e7 m 3 /yr (11000 AF/yr) capacity, which leads to ~8.63e6 m 3 /yr (7000 AF/yr) of potable water while no onsite RO at PVGS is built. Considering the vii residential water demand model developed for the Phoenix west valley, the NPV for this regional RO would be ~$100 million if all municipalities in the vicinity participate from the beginning of the project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data

Physics-based simulation of energy use in buildings is typically widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO 2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO 2 concentration given other easy-to-measure model parameters are known.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City

Active transportation modes--walk and bicycle--are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed an agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a high spatial resolution (e.g., block group level). The agent-based model (ABM) utilizes data from a synthetic population simulator generating agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling (e.g., sidewalk width, bike lane length) into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who live and work at the same geographic locations. Finally, GIS-based maps are developed at block group resolution that allows exploring the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. In addition, the level of impact varies with geographic locations--different boroughs of New York City will have different impacts. Lastly, social promotions resulting in higher social interaction among agents can reinforce the impacts of infrastructure changes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Fault-tolerant building-block computer study

Ultra-reliable core computers are required for improving the reliability of complex military systems. Such computers can provide reliable fault diagnosis, failure circumvention, and, in some cases serve as an automated repairman for their host systems. A small set of building-block circuits which can be implemented as single very large integration devices, and which can be used with off-the-shelf microprocessors and memories to build self checking computer modules (SCCM) is described. Each SCCM is a microcomputer which is capable of detecting its own faults during normal operation and is described to communicate with other identical modules over one or more Mil Standard 1553A buses. Several SCCMs can be connected into a network with backup spares to provide fault-tolerant operation, i.e. automated recovery from faults. Alternative fault-tolerant SCCM configurations are discussed along with the cost and reliability associated with their implementation.

Rennels, D. A.↗
DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗
DOE OSTI2014

Geothermal Case Studies

The US Geological Survey (USGS) resource assessment (Williams et al., 2009) outlined a mean 30GWe of undiscovered hydrothermal resource in the western US. One goal of the Geothermal Technologies Office (GTO) is to accelerate the development of this undiscovered resource. The Geothermal Technologies Program (GTP) Blue Ribbon Panel (GTO, 2011) recommended that DOE focus efforts on helping industry identify hidden geothermal resources to increase geothermal capacity in the near term. Increased exploration activity will produce more prospects, more discoveries, and more readily developable resources. Detailed exploration case studies akin to those found in oil and gas (e.g. Beaumont, et al, 1990) will give operators a single point of information to gather clean, unbiased information on which to build geothermal drilling prospects. To support this effort, the National Renewable Energy laboratory (NREL) has been working with the Department of Energy (DOE) to develop a template for geothermal case studies on the Geothermal Gateway on OpenEI. In fiscal year 2013, the template was developed and tested with two case studies: Raft River Geothermal Area (http://en.openei.org/wiki/Raft_River_Geothermal_Area) and Coso Geothermal Area (http://en.openei.org/wiki/Coso_Geothermal_Area). In fiscal year 2014, ten additional case studies were completed, and additional features were added to the template to allow for more data and the direct citations of data. The template allows for: Data - a variety of data can be collected for each area, including power production information, well field information, geologic information, reservoir information, and geochemistry information. Narratives ? general (e.g. area overview, history and infrastructure), technical (e.g. exploration history, well field description, R&D activities) and geologic narratives (e.g. area geology, hydrothermal system, heat source, geochemistry.) Exploration Activity Catalog - catalog of exploration activities conducted in the area (with dates and references.) NEPA Analysis ? a query of NEPA analyses conducted in the area (that have been catalogued in the OpenEI NEPA database.) In fiscal year 2015, NREL is working with universities to populate additional case studies on OpenEI. The goal is to provide a large enough dataset to start conducting analyses of exploration programs to identify correlations between successful exploration plans for areas with similar geologic occurrence models.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Intercomparison of flood inundation models across land use types and hydrological flood stages

Flood Inundation Mapping (FIM) model selection is a key operational decision because accurate, rapid mapping underpins early warning and resource allocation. FIM performance is context-dependent and can vary with hydrograph phase, land-use/land-cover (LULC), and the evaluation benchmark. Intercomparison studies typically assess a single near-peak snapshot against one reference dataset. Here, we provide a context-stratified intercomparison across (i) multiple hydrograph phases, (ii) LULC classes, and (iii) benchmark types, for five FIM approaches spanning a wide range of physical complexity and operational cost (TRITON, LISFLOOD-FP, HEC-RAS 2D, ARC-Curve2Flood, and OWP HAND-FIM). We use the Hurricane Matthew flood (2016) in the Neuse River Basin, North Carolina, USA, as a case study. Using high-resolution remote sensing-derived flood inundation maps, hand-labeled points, and building footprints, we assess model skill across two rising and two falling hydrograph limbs and across major LULC types. Results show that model rankings shift systematically across contexts: LISFLOOD-FP ranks highest in three of four flood phases, while TRITON leads during one rising limb phase; LISFLOOD-FP performs best in vegetated areas, whereas HEC-RAS improves relative performance in agricultural and urban areas; and benchmark choice influences conclusions, with LISFLOOD-FP performing best for flooded-building detection in the late falling limb, while TRITON ranks highest against hand-labeled points. We also report representative wall-clock runtimes for each workflow to provide use-case context for operational feasibility. Together, these results offer transferable guidance for model selection and for designing large-scale, benchmark-aware FIM intercomparison studies.

Nikrou, Parvaneh [University of Alabama]↗
DOE OSTI2022

Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control and optimal sizing

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. Finally, this research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Technology Solutions Case Study: Insulated Siding Retrofit in a Cold Climate

In this study, the U.S. Department of Energy’s team Building America Partner¬ship for Improved Residential Construction (BA-PIRC) worked with Kinsley Construction Company to evaluate the real-world performance of insulated sid¬ing when applied to an existing home. A 1960s home was selected for analysis. It is located in a cold climate (zone 6) where the addition of insulated siding and a carefully detailed water-resistive barrier have the potential to offer significant benefits. In particular, the team quantified building airtightness and heating energy use as a function of outdoor temperatures before and after the installa¬tion of the insulated siding.

BA-PIRC↗
DOE OSTI2021

Overall chilled water system energy consumption modeling and optimization

The emergence of increasingly affordable variable-speed drive technology has changed the approach used to control chilled water systems equipped with these drives. The purpose of this research was to develop an integrated chilled water modeling technique that can determine the optimal system setpoints and estimate the energy saving potential of chiller system. The chiller system equipped with Variable Frequency Drives (VFDs) on cooling tower fans and condenser water pumps. To accomplish the objective, physical component models of the centrifugal chiller, cooling tower and condenser water pump were established with the goal of incorporating the system’s condenser water flow rate and cooling tower fan speeds as optimization variables. Furthermore, a cooling load prediction algorithm was developed using a multiple non-linear regression model to approximate the building’s cooling load subject to a range of environmental conditions. The inputs and outputs of the individual component models were linked to estimate how adjusting the cooling tower fan and condenser water pump speed would influence the system’s comprehensive performance. Here, the overall system model was then optimized using a generalized reduced gradient optimization algorithm to determine the potential energy savings through speed control with VFDs and to ascertain a control logic strategy for the building automation system to operate the heating and cooling system. A case-study was performed on a single chiller system at a museum and the model was calibrated according to logged data collected over four months. Results showed that for the system analyzed, the energy saving of optimizing the cooling tower fan system was found to be 12–15%, while the energy saving potential of optimizing the condenser water pump with the cooling tower fan was negligible. Additionally, comparing different cooling tower fan control strategies showed that a wet-bulb approach-based cooling tower control strategy was shown to have the highest correlation to the optimized fan speed with an R 2 of 0.924.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for X-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches offer dramatic improvements in efficiency and help eliminate human bias. To advance this field, we advocate an AI-driven XAS analysis pipeline that features several interconnected key building blocks: benchmarks, workflows, databases, and AI/ML models. Specifically, we present two case studies for XAS ML. In the first study, we demonstrate the importance of reconciling the discrepancies between simulation and experiment using spectral domain mapping (SDM). Our ML model, which is trained solely on simulated spectra, predicts an incorrect oxidation state trend for Ti atoms in a combinatorial zinc titanate film. After transforming the experimental spectra into a simulation-like representation using SDM, the same model successfully recovers the correct oxidation state trend. In the second study, we explore the development of universal XAS ML models that are trained on the entire periodic table, which enables them to leverage common trends across elements. Looking ahead, we envision that an AI-driven pipeline can unlock the potential of real-time XAS analysis to accelerate scientific discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Advanced Energy Retrofit Guide: Practical Ways to Improve Energy Performance, K-12 Schools (Book)

The U.S. Department of Energy developed the K-12 Advanced Energy Retrofit Guide to provide specific methodologies, information, and guidance to help energy managers and other stakeholders plan and execute energy efficiency improvements. We emphasize actionable information, practical methodologies, diverse case studies, and unbiased evaluation of the most promising retrofit measure for each building type. K-12 schools were selected as one of the highest priority building sectors, because schools affect the lives of most Americans. They also represent approximately 8% of the energy use and 10% of the floor area in commercial buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

DOE Zero Energy Ready Home Case Study: Amaris Custom Homes, St. Paul, Minnesota

For this project, Amaris worked with U.S. Department of Energy (DOE) team, NorthernSTAR Building America Partnership, to develop the first Zero Energy Ready Home (ZERH) in Minnesota's cold climate using reasonable, cost-effective, and replicable construction materials and practices. The result is a passive solar, super-efficient 3542-ft2 walkout ranch-style home with all the creature comforts. Along with meeting ZERH standards, Amaris also achieved certifications for Leadership in Energy & Environmental Design for Homes v4, MN Green Path Emerald, and a Builders Association of the Twin Cities Reggie Award of Excellence. The home achieves a HERS score of 41 without photovoltaics; with PV, the home achieves a HERS score of 5.

Building America↗
DOE OSTI2016

Technology Solutions for New Homes Case Study: Multifamily Zero Energy Ready Home Analysis

AvalonBay Communities, which is a large multifamily developer, was developing a three-building complex in Elmsford, New York. The buildings were planned to be certified to the ENERGY STAR® Homes Version 3 program. This plan led to AvalonBay partnering with the Advanced Residential Integrated Solutions (ARIES) collaborative, which is a U.S. Department of Energy Building America team. ARIES worked with AvalonBay to redesign the project to comply with Zero Energy Ready Home (ZERH) criteria.

aries↗
DOE OSTI2016

Technology Solutions for New and Existing Homes Case Study: Addressing Multifamily Piping Losses with Solar Hot Water

Sun Light & Power, a San Francisco Bay Area solar design-build contractor, teamed with the U.S. Department of Energy’s Building America partner the Alliance for Residential Building Innovation (ARBI) to study this heat-loss issue. The team added three-way valves to the solar water heating systems for two 40-unit multifamily buildings. In these systems, when the stored solar hot water is warmer than the recirculated hot water returning from the buildings, the valves divert the returning water to the solar storage tank instead of the water heater. This strategy allows solar-generated heat to be applied to recirculation heat loss in addition to heating water that is consumed by fixtures and appliances.

ARBI↗
DOE OSTI2015

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. As a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.

Apache Spark↗