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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamic Building Load Control to Facilitate High Penetration of Solar Photovoltaic Generation (Final Technical Report)

Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.

14 SOLAR ENERGY↗
DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗
DOE OSTI2026

From models to reality: a systematic review on simulated and measured residential heat pump energy savings

High-performance HVAC solutions are central to residential energy management. A substantial share of these are electric, reversible-cycle systems, with heat pumps representing the largest portion of current and near-term adoption. This review synthesizes peer-reviewed and grey literature on residential space heating and cooling heat pumps. The academic literature is dominated by modeling (73.8%), with limited field measurement (13.1%). Grey literature from United States serve as a supplemental resource providing measured savings. Conversions from electric-resistance heating consistently show the largest site energy reductions, while oil/propane baselines yield moderate savings, and gas baseline scenario often deliver small and region-dependent savings. This study cross-checks the grey literature measured data with simulation data filtered from the ResStock dataset. The comparison indicates a discrepancy between simulations and measured data: simulated site EUIs are typically lower than measured EUIs, but percentage energy savings fall in similar ranges, implying simulations capture directional effects while underestimating energy use. Factors associated with variability and model–measurement differences include system characterization and control representation (e.g., backup heat engagement, thermostat/setpoint strategies, commissioning/installation quality), occupant behavior, weather normalization, metering scope, and envelope characterization. This paper also outlines the proposed methodology for comparing simulation and measured data for heat pumps. It emphasizes the metrics used for comparison and units harmonization, building characteristics matching, and compact metadata are needed for simulations to match measured data. The proposed methodology is expected to improve the credibility of simulated savings as measured evidence grows.

Yu, Lili↗
DOE OSTI2020

Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytics goals in this domain.

Kandakatla, Akshith R.↗
DOE OSTI2020

Evaluating Implementations of the Immersed Boundary Method in the Weather Research and Forecasting Model

The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact analysis of heating electrification in US buildings with geothermal heat pumps

Few studies have investigated the impacts of large-scale deployment of geothermal heat pumps (GHPs, also called ground source heat pumps) on the electric grid. GHPs utilize the ground as a heat source to warm buildings more efficiently than other space-heating systems. The coupling with the ground offers seasonal thermal storage so that GHPs can also cool buildings in summer more efficiently than other space-cooling systems. This study simulated the performance of GHP systems for various commercial and residential buildings in 15 climate zones in the United States. Combined with the latest End-Use Load Profiles of the US building stock and grid modeling, this study aims to assess the impacts of a national deployment of GHP systems on the US electric grid in terms of energy consumption, emissions, and operational resilience. The preliminary results show that the GHP deployment can save 429 billion kWh of electricity (a 19% reduction from baseline) and reduce carbon emissions by 496 million tons per year (a 31 % reduction from baseline). A geographical view of the results indicates that retrofitting existing HVAC systems with new GHP systems can lead to further reductions in annual electricity consumption and peak electricity demand in the southern regions of the United States than in other parts of the country. On the other hand, GHP retrofits result in higher percentages of site energy savings and carbon emission reduction in the north (cold climates) than in the south (warm climates).

Liu, Xiaobing↗
DOE OSTI2020

Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Synthesis of precipitation-strengthened Al-Sc, Al-Zr and Al-Sc-Zr alloys via selective laser melting of elemental powder blends

Selective laser melting is used to create Al-1.5Sc, Al-1.5Zr and Al-0.75Sc-0.75Zr (at.%) alloys from blends of elemental Al, Sc, and Zr powders. This study investigates elemental alloying elements (Sc and Zr) which are high-melting and highly reactive, unlike previous work which focused on more concentrated elemental additions of lower-melting, lower-reactivity Cu and Si to aluminum. High-speed in situ synchrotron x-ray imaging and diffraction show that the 20–30 μm Al, Sc, and Zr powders fully melt and sufficiently mix in the molten state to create, on solidification, a homogeneous distribution of primary, micron-size L12 precipitates (Al 3 Sc, Al 3 Zr, and Al 3 (Sc,Zr), respectively) and nucleate micron-size Al matrix grains, as confirmed by SEM imaging of cross-sections. Here, a second laser pass, simulating a realistic additive-manufacturing build condition, fully remelts the initial volume which shows, after solidification, the same Al 3 (Sc,Zr) L1 2 primary micro-precipitates and very fine Al grains. After aging at 300–400°C, the alloys show large increases in hardness, consistent with an exceptionally high number density (1.4 × 10 24 m –3 ) and volume fraction (2.5%) of secondary Al 3 (Sc,Zr) nano-precipitates with a Sc-rich core and Zr-rich shell, as measured via atom-probe tomography.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Regional suitability of heating tower heat pumps based on technical and economic performance maps

The heating tower heat pump (HTHP) technology is evolving rapidly due to its high performance in summer and frost-free characteristic in winter. However, lacking comprehensive comparisons between the HTHP and conventional methods, such as air-source heat pump (ASHP), and chiller and boiler, greatly limits its applications. To address this problem, this paper carries out technological and economic performance analyses of the three systems in 353 locations over China by the processes of building load calculation, system sizing, annual simulation, performance comparison, and demonstration. Energy saving principle and potential of the HTHP are presented and explained. Based on that, economic performance comparison is conducted by considering the energy conversion efficiency and prices. The results show that the HTHP has advantage over the ASHP in most locations in China. The improvement in annual coefficient of performance (COP) is about 10% to 20%. For the locations in southwest China like Chongqing, the improvement is also less than 10% due to the large regeneration penalization in winter and small difference between dry-bulb and wet-bulb temperature in summer. The annual COP of the HTHP is 10% to 23% higher than that of the chiller and boiler in hot summer and cold winter zone, and cold zone. However, the HTHP shows no advantage over the chiller and boiler in severe cold zone because of the low efficiency of heat pump in winter, neither in hot summer and warm winter zone due to the small heating load. Based on the results, technical and economic performance maps are developed to guide the application of the HTHP in different climate regions.

heating tower heat pump, air-source heat pump, chi↗
DOE OSTI2024

Game Theoretic Orchestration for Cooperation among Power Distribution System Applications

The evolving transformation with the proliferation of distributed energy resources and advanced metering, necessitates advanced distribution systems to integrate and orchestrate a large number of grid-edge devices while also serving multiple system-level objectives such as resilience, decarbonization, equity and other system mandates. The parallel deployment and control of resources towards achieving diverse objectives may lead to conflicts between applications that want to control overlapping sets of device setpoints, potentially leading to oscillatory behavior and suboptimal performance. This work aims at leveraging game theoretic framework to drive cooperative behavior among competitive applications. The work proposes a weighted-consensus based game design to facilitate conflict resolution through consensus-building iterations for modular platform. Simulation-based evaluation on a sample test system demonstrates the performance the proposed deconfliction strategy in resolving operational conflicts and achieving close-to-optimal trade off among the applications. Results also compare the proposed strategy with a distribution optimization approach and illustrate it effectiveness in diverse apps regardless of their design while also incentivizing apps with flexible design.

Advanced distribution operations, cooperation, app↗
DOE OSTI2026

Energy Impact of Radiative Cooling Paints in Warehouses Under Various United States Climates

Although radiative cooling research is widely found in the literature, no comprehensive study has yet been conducted on the impact of novel radiant cooling (>0.91 reflectance) on the energy efficiency of warehouses. Here, in this work, we develop three building models based on a Department of Energy prototype warehouse model using trnsys, representing a typical warehouse with a black roof, a typical warehouse with a white roof, and a warehouse with novel radiative cooling (RC) paint on its roof. These models are run for 15 different cities, each representative of a different ASHRAE climate zone, to better understand the impact of RC in many different climates. It was found that an RC-coated roof in a warehouse could reduce the building's annual heating, ventilation, and air conditioning (HVAC) loads by up to 14.11 kWh/m 2 of the roof area compared to a black roof, resulting in a maximum reduction in energy costs of 0.55 $\$$/m 2 or $\$$2646/year for a large 4835 m 2 warehouse. Similarly, replacing the typical white roof coating with an RC coating could reduce the warehouse's energy consumption by up to 8.17 kWh/ m 2 of roof area, thus reducing energy costs by as much as 0.29 $\$$/m 2 or $\$$1386/year for a 4835 m 2 warehouse. In addition, applying RC paint to an unconditioned warehouse could reduce the building's ASHRAE Standard 55 indoor temperature exceedance by up to 1330 h/year compared to a black roof and up to 532 h/year compared to a white roof.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A geo-visual analysis for exploring the socioeconomic benefits of the heating electrification using geothermal energy

In parallel to population growth and climate change, the rapid pace of urbanization worldwide has led to an enormous increase in energy demand and costs in urban areas. The subsequent energy burden has become an increasing concern for many households in the U.S. Previous studies have revealed that geothermal resources can effectively lower the electricity demand and carbon emissions in large cities. In this paper, we focus on the socioeconomic impacts of geothermal energy on urban systems by presenting an interactive visual analytics dashboard. The dashboard allows urban planners to spatially examine geothermal energy's practical benefits on energy affordability, urban livability, and resilience across the U.S. We compiled a list of socioeconomic metrics by integrating the simulation results from multiple geothermal and building models with multi-domain urban datasets (socioeconomic, demographic, and electricity utility). These metrics are created to characterize the benefits of the heating electrification of buildings using Geothermal Heat Pumps (GHP) for lowering the energy burden of middle-and low-income households nationwide. The visual dashboard employs a combination of multivariate, glyph-based, and geospatial visualization to reveal the variability and patterns in our metrics. We present a pilot study to demonstrate the GHPs' potential as a renewable and affordable solution for increasing the economic and energy grid resilience in U.S cities.

Xu, Haowen↗
DOE OSTI2020

Spitfire

Spitfire is a Python and C++ code for solving canonical flame structure problems to build tabulated chemistry models for the simulation of large-scale fires. Spitfire is a modular software with performance critical code written in C++, abstract numerical methods provided in Python, and chemistry tabulation techniques made convenient for high-level use by non-expert users. It facilitates both high-level usage and novel research in tabulated chemistry approaches. SAND2019-11791 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Armstrong, Elizabeth↗
DOE OSTI2022

Network Defense Workforce Development System

The software implements the methodology of cybersecurity training architecture and is leveraged to build a turn-key based simulated cyberattack game in which the player assumes the facility manager's role and must defend the OT network from adaptive cyberattacks by directly configuring the physical network environment and implementing security policies.

Gourisetti, Sri Nikhil Gupta↗
DOE OSTI2025

NOODLES Grid [SWR-25-93]

NOODLES Grid is a real-time visualization server for power system simulation data. It loads precomputed datasets, builds optimized instance renderings, and streams live interactive scenes to connected clients. It is built for high scalability, flexible visualization, and fast interaction. NOODLES is a cross-platform/device/tool protocol for collaborative visualization. NOODLES was Developed at the National Renewable Energy Laboratory (NREL) as a capability of the Insight Center https://www.nrel.gov/computational-science/insight-center.html

Brunhart-Lupo, Nicholas [National Renewable Energy↗
DOE OSTI2021

Investigating the dielectric properties of barium titanate nanocomposites using transmission electron microscopy image processing

Abstract Barium titanate (BTO) is a ferroelectric perovskite material used in energy storage applications because of its high dielectric constant. A previous study showed that the dielectric constant for BTO nanoparticles drastically increases to over 15,000 at a particle size of 70 nm. This result is highly contested, but its implications to energy storage motivated our investigation into the dielectric constants of BTO nanoparticles that are incorporated into a polymer matrix. We developed a novel method of using image processing techniques on transmission electron microscope images of BTO-polymer nanocomposites. Data on the positions, shapes, sizes, and orientations of BTO nanoparticles were used to build more realistic computational models that simulate the dielectric behavior of the nanocomposites. Here, we investigate the relationship between regions of enhanced electric field and the composite dielectric constant.

36 MATERIALS SCIENCE↗