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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effects of small marine energy deployments on oceanographic systems

The placement and operation of marine energy deployments in the ocean have the potential to change flow patterns, decrease wave heights, and/or remove energy from the oceanographic system. Changes in oceanographic systems resulting from harvesting marine energy, particularly tidal and wave energy, may be of concern. These changes include alterations in nearfield and farfield physical processes, as well as potential secondary environmental effects such as changes in sediment transport patterns, biological processes, or coastal erosion. Knowledge of changes in oceanographic systems associated with marine energy is primarily available from numerical modeling studies, informed by some laboratory tests and very few field measurements. A literature review was conducted using the Tethys knowledge base and other online sources, building on conclusions from the Ocean Energy Systems-Environmental State of the Science report. Potential changes in oceanographic systems that may be caused by marine energy differ between tidal and wave devices because of different extraction mechanisms and siting locations. Numerical models show that tidal extraction on the order of hundreds of megawatts or with significant channel blockage is required to create changes in oceanographic processes that exceed natural variability. Effects from wave energy extraction in arrays are localized and dependent on array spacing and proximity to the shore. Here the available evidence supports the conclusion that the risk of significant environmental effects from such changes could be retired (i.e., less investigation required for every project) for small deployments—those representative of the state of the industry in 2021. Determining changes in oceanographic systems to be low risk for small deployments can thereby streamline environmental consenting by reducing monitoring needs at this early stage in the industry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

GNET2: an R package for constructing gene regulatory networks from transcriptomic data

Abstract Motivation The Gene Network Estimation Tool (GNET) is designed to build gene regulatory networks (GRNs) from transcriptomic gene expression data with a probabilistic graphical model. The data preprocessing, model construction and visualization modules of the original GNET software were developed on different programming platforms, which were inconvenient for users to deploy and use. Results Here, we present GNET2, an improved implementation of GNET as an integrated R package. GNET2 provides more flexibility for parameter initialization and regulatory module construction based on the core iterative modeling process of the original algorithm. The data exchange interface of GNET2 is handled within an R session automatically. Given the growing demand for regulatory network reconstruction from transcriptomic data, GNET2 offers a convenient option for GRN inference on large datasets. Availability and implementation The source code of GNET2 is available at https://github.com/jianlin-cheng/GNET2. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
DOE OSTI2025

Grid Value Analysis of Geothermal Systems for End-Use Applications

Fuel based end-uses for residential, commercial, and industrial consumers require a technology change to achieve economy-wide decarbonization. Space heating accounts for 42% of residential and 32% of commercial energy demand, much of which is currently met through carbon emitting fuels. Industrial energy use is heavily fuel based with electricity currently representing 13% of energy demand. Geothermal heat pumps (GHPs) and geothermal direct use can eliminate the need for CO2 emitting and simultaneously allow for more efficient electrification of end uses. Past work has assessed the impact on total energy costs and generation investments but did not identify specific grid services benefited. Energy usage in residential and commercial structures was assessed by leveraging data from ComStock and ResStock models. These models utilize housing attributes, occupancy patterns, weather data, and sophisticated energy simulations to generate hourly load profiles for individual buildings identified by unique IDs associated with their locations. Industrial sector energy use was evaluated using information from the Manufacturing Energy Consumption Survey (MECS) as well as plant utilization data from the US Census to estimate hourly plant operations. The change in end-use demand for electricity, natural gas, and other fuels was calculated for different technologies that could meet this need. Using the ReEDS capacity expansion model, we produce regional price profiles that capture the grid benefit associated with the amount and timing of energy shifts in the power system from the adoption of geothermal systems relative to other technologies that could meet space heating, space cooling, and process heat requirements. We find that geothermal systems for meeting end-use demand add value to the energy system. In buildings where geothermal systems increase grid costs, these values are offset by reduced fuel costs and benefits to externalities, including emissions and health impacts.

decarbonization↗
DOE OSTI2022

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.

42 ENGINEERING↗
DOE OSTI2021

Archparse

Archparse is a Python package that holds the purpose and capability of converting the contents of a text file to a functioning neural network based on the Tensorflow 2.X framework. This is able to be done with minimal written Python code and next to no knowledge of how to build models with Tensorflow directly. More specifically Archparse parses a text file with extension ".arch" which contains neural network architecture information that corresponds to either the Tensorflow 2.X API or custom code written with the Tensorflow 2.X API. Included in the initial version is the capacity to easily produce sequential autoencoders and sequential neural networks.

Vander Wal, MichaelD.↗
DOE OSTI2026

An Online Tool for Preliminary Design and Techno-Economic Analysis of District Geothermal Heating and Cooling Systems

District geothermal heating and cooling systems (DGHCS) have significant benefits for reducing energy consumption as well as building- and grid-level peak electric demand. Currently, no publicly available tools are available to effectively design and conduct techno-economic analysis of DGHCS. GeoWISE was originally developed for preliminary design and techno-economic analysis of geothermal heating and cooling systems in an individual commercial or residential building. This paper introduces recent upgrades of GeoWISE that allow users to design and conduct techno-economic analysis of DGHCS. Several new features are implemented in GeoWISE to allow selection and specification of multiple new or existing buildings. A database of information for over 125 million existing U.S. buildings was used in GeoWISE that allows users easily locate existing buildings of interest based on street addresses, and optionally edit information of the buildings (e.g., footprint, vintage, principal functions, number of floors, window-to-wall ratio). Unique energy simulation models of the selected buildings are then automatically created using the Automatic Building Energy Modeling (AutoBEM) and EnergyPlus simulations are performed to predict thermal loads of the buildings. A simplified DGHCS is then designed and simulated to predict its energy use. A central borehole heat exchanger (BHE) of the DGHCS is sized using the RowWise algorithm of GHEDesigner to meet the thermal loads within user-specified land areas for installing BHE. The upgraded GeoWISE reports the needed capacity of heating and cooling equipment in each building, design of the central BHE, energy consumption reduction, and energy cost saving resulting from using DGHCS compared with conventional HVAC systems. A case study is showcased using the upgraded GeoWISE to design and conduct techno-economic analysis of a simplified DGHCS.

Prem Anand Jayaprabha, Jyothis Anand [ORNL] (ORCID↗
DOE OSTI2021

Extract useful information from building permits data to profile a city’s building retrofit history

Building retrofit is one of the key strategies for cities to reduce energy use and GHG emissions. The historical information about changes to buildings is crucial to infer the buildings’ current energy system efficiency levels and to identify candidate buildings for retrofit. In general, a building permit is required before the start of any construction activity of a building, such as changing building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Therefore, the permits are a potentially good resource for mining information on the city’s retrofit history. In this study, we use the permit dataset from the city of San Francisco as a case study. Location and time information from the dataset is also used to depict the retrofit timeline of each building and the whole building stock. The type of work of the permit is inferred from the descriptive text by a machine learning model. At last, the limitations of the current permit dataset and potential improvements on the permit data management are discussed to better utilize the information in the future.

Zhang, Wanni↗
DOE OSTI2025

Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Bezier Curve Informed Melt Pool Geometry to Model Additive Manufacturing Microstructures Using SPPARKS

Additive manufacturing is a transformative technology with the potential to manufacture designs which traditional subtractive machining methods cannot. Additive manufacturing offers fast builds at near final desired geometry; however, material properties and variability from part to part remain a challenge for certification and qualification of metallic components. AM induced metallic microstructures are spatially heterogeneous and highly process dependent. Engineering properties such as strength and toughness are significantly affected by microstructure morphologies resulting from the manufacturing process Linking process parameters to microstructures and ultimately to the dynamic response of AM materials is critical to certifying and qualifying AM built parts and components and improving the performance of AM materials. The AM fabrication process is characterized by building parts layer by layer using a selective laser melt process guided by a computer. A laser selectively scans and melts metal according to a designated geometry. As the laser scans, metal melts, fuses, and solidifies forming the final geometry in a layerwise fashion. As the laser heat source moves away, the metal cools and solidifies forming metallic microstructures. This work describes a microstructure modeling application implemented in the SPPARKS kinetic Monte Carlo computational framework for simulating the resulting microstructures. The application uses Bzier curves and surfaces to model the melt pool surface and spatial temperature profile induced by moving the laser heat source; it simulates the melting and fusing of metal at the laser hot spot and microstructure formation and evolution when the laser moves away. The geometry of the melt pool is quite flexible and we explore effects of variances in model parameters on simulated microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The role of information structures in game-theoretic multi-agent learning

Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively from their experience when interacting with other agents in dynamic environments. The outcome of a MAL process is jointly determined by all agents’ decision-making. Hence, each agent needs to think strategically about others’ sequential moves, when planning future actions. The strategic interactions among agents makes MAL go beyond the direct extension of single-agent learning to multiple agents. With the strategic thinking, each agent aims to build a subjective model of others decision-making using its observations. Such modeling is directly influenced by agents’ perception during the learning process, which is called the information structure of the agent’s learning. As it determines the input to MAL processes, information structures play a significant role in the learning mechanisms of the agents. This review creates a taxonomy of MAL and establishes a unified and systematic way to understand MAL from the perspective of information structures. Here we define three fundamental components of MAL: the information structure (i.e., what the agent can observe), the belief generation (i.e., how the agent forms a belief about others based on the observations), as well as the policy generation (i.e., how the agent generates its policy based on its belief). In addition, this taxonomy enables the classification of a wide range of state-of-the-art algorithms into four categories based on the belief-generation mechanisms of the opponents, including stationary, conjectured, calibrated, and sophisticated opponents. We introduce Value of Information (VoI) as a metric to quantify the impact of different information structures on MAL. Finally, we discuss the strengths and limitations of algorithms from different categories and point to promising avenues of future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

NATURF: Urban Building Parameters for Chicago, Illinois, USA at a 100m resolution

132 Urban parameters based on building physical dimensions and location were generated for the city of Chicago at 100m resolution using the NATURF model. To use the binary file with WRF, the binary file and the index file must be placed in their own directory in WRF_GEOG and accessed in the same way NUDAPT44 would be accessed.

Vernon, Chris R [Pacific Northwest National Labora↗
DOE OSTI2022

Design, Construction, and Field Validation of a Blown-In Fiberglass Wall System in a Cold, Wet, and Windy Climate

In the Native Village of Tununak, located on Nelson Island in Southwest Alaska,buildings are exposed to wind-driven rain and snow throughout the year. The Cold Climate Housing Research Center (CCHRC) and Knauf Insulation partnered with the community in 2018 to design a residence for a public safety officer. Construction began in fall 2021 and is scheduled to conclude in summer 2022. The house will use integrated trusses with floor, attic, and wall cavities filled with blown-in fiberglass insulation. Hygrothermal models of the building envelope system demonstrate that the high ambient moisture load will wet the outer edges of the insulation annually regardless of the extent and integrity of exterior weather sealing measures; blown-in fiberglass is expected to be more resilient to this wetting than blown-in cellulose. To verify this, researchers will embed temperature, relative humidity, and moisture sensors in the two windward walls of the house to monitor the ability of the blown-in fiberglass insulation to dry out after wetting events that may have occurred during the transport of the insulation to the building site, storage of the insulation on site, construction process, or after installation in the wall. Sensors will remain in place for two years,documenting the wall's performance over time and verifying the design's potential for use in buildings located in climates where the chance for moisture damage is high. Here, we describe the wall system , design criteria, and the results from the hygrothermal modeling. We share details on the monitoring system's components and the planned management and analysis of data. Finally, we explain how the experiences and data from this project will inform future builds in similar locations.

building design↗
DOE OSTI2025

Increasing Mosquito Abundance Under Global Warming

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence‐based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short‐Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5‐8.5, respectively. We expect that this model can serve as a valuable tool and inform science‐based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development and experimental evaluation of new building air leakage measurement methods: measurement of interior air leaks and comparison to conventional methods

Building air leaks (both through exterior and interior surfaces) can have a significant impact on energy consumption, indoor air quality, fire safety, and moisture accumulation affecting structural durability. Blower door testing has been used to measure leaks in buildings, but commonly used testing methods do not directly measure interior leaks. In this paper, new testing methods (guarded interior test and zonal multipoint pressure testing method) are presented that directly measure these interior leaks, utilizing common blower door equipment for both single and multi-point testing. Furthermore, these new methods are compared to conventional methods in terms of the information provided, limitations and time/effort needed. In addition, building leak measurement results are analyzed to reveal a) coupling between power law model values (exponent and coefficient) for an ensemble of buildings, b) the error in using single point testing when estimating low pressure leakage, and c) how building power law models vary from low to high pressure ranges.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics

For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanical behavior of a material interface undergoing openings, which is critical to understand and predict interfacial failures under complex loadings. However, existing theoretical models have limitations on enough complexity and flexibility to well learn the real-world TSR from experimental observations. A neural network can fit well along with the loading paths but often fails to obey the laws of physics, due to a lack of experimental data and understanding of the hidden physical mechanism. In this paper, we propose a thermodynamic consistent neural network (TCNN) approach to build a data-driven model of the TSR with sparse experimental data. The TCNN leverages recent advances in physics-informed neural networks (PINN) that encode prior physical information into the loss function and efficiently train the neural networks using automatic differentiation. We investigate three thermodynamic consistent principles, i.e., positive energy dissipation, steepest energy dissipation gradient, and energy conservative loading path. All of them are mathematically formulated and embedded into a neural network model with a novel defined loss function. A real-world experiment demonstrates the superior performance of TCNN, and we find that TCNN provides an accurate prediction of the whole TSR surface and significantly reduces the violated prediction against the laws of physics.

Zhang, Jiaxin↗
DOE OSTI2020

Building fault detection data to aid diagnostic algorithm creation and performance testing

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Pahute Mesa-Oasis Valley Hydrostratigraphic Framework Model for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (Rev. 0)

A new, revised three-dimensional (3-D) hydrostratigraphic framework model (HFM) for the Central and Western Pahute Mesa Corrective Action Units 101 and 102 was completed in 2019. The initial (Phase I) model was completed in 2002 and was documented in A Hydrostratigraphic Model and Alternatives for the Groundwater Flow and Contaminant Transport Model of Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (BN, 2002). Subsequent flow and transport modeling revealed the need to collect additional data to reduce uncertainties. The Phase II data collection and characterization effort included extensive well drilling and testing activities, and rebuilding of the Phase I 3-D HFM, resulting in the Phase II HFM (NSTec, 2014). Further drilling (Well ER-20-12), data collection, and interpretive work (e.g., Thirsty Canyon Lineament [Wurtz and Day, 2018]) were incorporated into the Pahute Mesa-Oasis Valley (PM-OV) HFM. The PM-OV HFM incorporates the area of interest from the Phase II HFM and adds additional areas, primarily to the north and west with minor additions to the south and east. The new, expanded area matches the PM-OV groundwater basin as defined in Fenelon et al. (2016) and includes Pahute Mesa, a former nuclear testing area at the Nevada National Security Site; and Oasis Valley, a groundwater discharge area downgradient from contaminant source areas on Pahute Mesa. The data and information necessary to build the PM-OV HFM are provided in Navarro (2019). The PM-OV HFM will be used by hydrologic modelers who are tasked with developing a model to determine how contaminants are transported by groundwater flow in an area of complex geology. The model area is large (more than 6,250 square kilometers) and geologically complex, including Paleozoic- to Mesozoic-age structures (e.g., thrust faults, Thirsty Canyon Lineament), at least seven Tertiary-age calderas, several intrusive bodies, and many relatively recent basin-and-range normal faults. Investigators from the U.S. Geological Survey; National Security Technologies, LLC; National Laboratories; and U.S. Department of Energy contractors have organized the more than 300 volcanic units and other rocks in the model area into 77 hydrostratigraphic units (HSUs). The volcanic rocks are subdivided into 42 aquifers, 11 confining units, and 9 composite units (containing both aquifer and confining unit rocks). The underlying pre-Tertiary rocks were divided into 5 HSUs, comprising 2 aquifers and 3 confining units. The model also includes 1 alluvial aquifer unit, 8 intrusive confining units, and 1 granite confining unit. The model depicts the thickness, extent, and geometric relationships of these HSUs (“layers” in the model) along with all the major structural features that control them, including calderas and faults. The most substantive differences between the Phase II HFM and the PM-OVHFM include (1) addition of the new data and resulting revised interpretations from the additional area encompassed by the PM-OV groundwater basin; (2) incorporation of 16 new faults; (3) 12 boreholes from the northern extension; (4) 2 additional caldera-collapse collars; and (5) correlation of the simulated faulting and hydrostratigraphy in the eastern part of the model domain with the delineations represented in the neighboring Rainier Mesa-Shoshone Mountain HFM-Prime and Yucca Flat-Climax Mine HFM. All PM-OV HFM delineations were based on published and unpublished data from geophysical investigations, surface geologic maps, and drill-hole records. Phase II approached model uncertainty using the model-building software to test model configurations and hydrologic consequences using a range of values for parameters such as fault offset amounts and HSU elevations. The PM-OV HFM expands on this approach to encompass the entire PM-OV groundwater basin.

54 ENVIRONMENTAL SCIENCES↗