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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Experimental and Modeled Assessment of Interventions to Reduce PM2.5 in a Residence during a Wildfire Event

Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be used to estimate impacts to large numbers of homes based on high-level housing characteristics. In addition to reviewing literature sources, we performed an experiment to evaluate interventions to mitigate degraded indoor air quality. We instrumented one residence for one week during an extreme wildfire event in the Pacific Northwest. Outdoor ambient concentrations of PM2.5 reached historic levels, sustained at over 200 μg/m3 for multiple days. Outdoor and indoor PM2.5 were monitored, and data regarding building characteristics, infiltration, and mechanical system operation were gathered to be consistent with the type of information commonly known for residential energy models. Two conditions were studied: a high-capture minimum efficiency rated value (MERV 13) filter integrated into a central forced air (CFA) system, and a CFA with MERV 13 filtration operating with a portable air cleaner (PAC). With intermittent CFA operation and no PAC, indoor corrected concentrations of PM2.5 reached 280 μg/m3, and indoor/outdoor (I/O) ratios reached a mean of 0.55. The measured I/O ratio was reduced to a mean of 0.22 when both intermittent CFA and the PAC were in operation. Data gathered from the test home were used in a modeling exercise to assess expected I/O ratios from both interventions. The mean modeled I/O ratio for the CFA with an MERV 13 filter was 0.48, and 0.28 when the PAC was added. The model overpredicted the MERV 13 performance and underpredicted the CFA with an MERV 13 filter plus a PAC, though both conditions were predicted within 0.15 standard deviation. The results illustrate the ways that models can be used to estimate indoor PM2.5 concentrations in residences during extreme wildfire smoke events.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

EGS-Collab Experiment 1: Time-lapse ERT Data and E4D Inversion Files for 10-24-2018 through 11-07-2018 Flow Test

This data submission includes the raw time-lapse ERT (electrical resistivity tomography) monitoring data, flow system data, operator logs, E4D (https://e4d.pnnl.gov) inversion files, and meta-data necessary to reproduce the 4D ERT inversion for the Oct. 24 through Nov. 7 2018 post-stimulation flow test in test bed 1. The tests were done at the Sanford Underground Research Facility at Homestake Mine in South Dakota.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

A Refined Method to Translate Solar Data Quality Assessment Flags to Estimated Measurement Uncertainty

Integrating solar resource uncertainties due to radiometer measurement performance and operational data quality assessment can provide improved estimates of economic bankability, system design performance, and compliance of solar energy conversion systems. Estimating radiometer measurement uncertainty is an established procedure consistent with recognized best practices and international guidelines. SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the fourth of six for the Data Quality and Uncertainty Integration Project, presents a refined algorithm description for software to translate solar resource data quality assessment results into estimated uncertainty values in a Solar Resource Operational Uncertainty Integrator (SROUI) application. This algorithm requires three-component solar irradiance measurements - global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance - collected at 1- to 60-minute intervals, as described in the previous deliverables. The development of this report as Deliverable 6.4 was an iterative process that included reviews and feedback from the project team on initial drafts designed to refine how the new software could best support determining solar resource data uncertainty. The results of this effort will contribute to the final software system development by National Renewable Energy Laboratory staff.

14 SOLAR ENERGY↗
DOE OSTI2023

Form EIA-923 Data Reformatted

Form EIA-923 data collection provides a centralized and comprehensive source for monthly operating data about the high-voltage bulk electric power grid in the Lower 48 states.

17 WIND ENERGY↗
DOE OSTI2023

Form EIA-930 Data Reformatted

Form EIA-930 data collection provides a centralized and comprehensive source for hourly operating data about the high-voltage bulk electric power grid in the Lower 48 states.

17 WIND ENERGY↗
DOE OSTI2024

Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses

Neural operators, which can act as implicit solution operators of hidden governing equations, have recently become popular tools for learning the responses of complex real-world physical systems. Nevertheless, most neural operator applications have thus far been data-driven and neglect the intrinsic preservation of fundamental physical laws in data. Here, in this work, we introduce a novel integral neural operator architecture called the Peridynamic Neural Operator (PNO) that learns a nonlocal constitutive law from data. This neural operator provides a forward model in the form of state-based peridynamics, with objectivity and momentum balance laws automatically guaranteed. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental data sets. We also compare the performances with baseline models that use predefined constitutive laws. We show that, owing to its ability to capture complex responses, our learned neural operator achieves improved accuracy and efficiency. Moreover, by preserving the essential physical laws within the neural network architecture, the PNO is robust in treating noisy data. The method shows generalizability to different domain configurations, external loadings, and discretizations.

42 ENGINEERING↗
DOE OSTI2021

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

BEAM (Battery Ensemble ANN Modeler) [SWR-19-69]

Dynamic losses of batteries can be a nonlinear function of the operational state of the battery. This package trains an Ensemble of Artificial Neural Networks on battery operational data to model the dynamic losses as a function of the operational state of charge and the active power set point. The package can also be used to return the dynamic battery losses which will be incurred as the battery operates if fed some operational profile.

Bryce, Richard↗
DOE OSTI2022

Deep Lynx Data Historian

This software is intended to facilitate the ingestion of data from some data historian into Deep Lynx. A data historian in this instance is any location where sensor and operational data from some live asset is gathered. The data can be either manual retrieved by this software or the data historian source can push to a listening endpoint provided by this software.

Browning, JerenM↗
DOE OSTI2023

Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States

While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (R2: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions.

Qiu, Yin (ORCID:0009000900948794)↗
DOE OSTI2020

A method to generate heating and cooling schedules based on data from connected thermostats

Internet-connected thermostats are a promising new source of temperature and operational data in homes because they record inside temperatures, setpoints, and HVAC runtimes every five minutes. Over 20 million Internet-connected thermostats have already been installed in American homes. Data from about 20,000 connected thermostats were collected and organized by climate zone, number of occupants, floor area, and day type. A method was developed to create up to 40 representative temperature schedules which, together, can more accurately capture the diversity of heating and cooling behaviors. These results are suitable for input into schedules for building energy simulation models. This information enables more realistic simulations of American heating and cooling behavior, leading to more accurate estimates of energy consumption and savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Performance Evaluation of Data-Enhanced Hierarchical Control for Grid Operations

This paper presents a hardware-in-the-loop (HIL) simulation to evaluate the performance of voltage regulation of Data-Enhanced Hierarchical Control (DEHC). This DEHC uses an advanced distribution management system (ADMS) for grid operations to control legacy and grid-edge devices and coordinate with distributed energy management systems (DERMS) to manage high penetrations of photovoltaics (PV) on a utility distribution system. The HIL platform provides realistic laboratory testing, including accurate modeling (legacy devices, grid-edge devices, and PV) of the real-world distribution system from a utility partner, a real controller (ADMS), software controller DERMS, hardware grid-edge devices, and standard communications protocols. The test results demonstrate functionalities of the integrated platform and the performance of voltage regulation of the coordinated control systems. Based on laboratory testing, the utility can set up the same grid-automation system to manage DERs, legacy devices, and grid-edge devices to achieve their system-level control and operation objectives (e.g., voltage regulation), thus de-risking potential issues such as instability for field deployment.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

POWER DATA ANOMALY DETECTION PACKAGE

SF-25-150 Software package for developing anomaly detection models for electric power operational data

PLATHOTTAM, SIBY JOSE [Argonne National Laboratory↗
DOE OSTI2023

Cambium 2022 Scenario Descriptions and Documentation

The National Renewable Energy Laboratory’s (NREL’s) Cambium data sets are annually released sets of simulated hourly emission, cost, and operational data for a range of modeled futures of the U.S. electric sector with metrics designed to be useful for long-term decision making. The 2022 Cambium data set is the third annual release. The data sets are a companion product to NREL’s Standard Scenarios, which are likewise released annually and are a set of projections of how the U.S. electric sector could evolve across a suite of different potential futures, but covering more scenarios with less temporal granularity. In this documentation, we describe Cambium 2022’s scenarios (Section 3), define the metrics (Section 5), and document the Cambium-specific methods for calculating those metrics (Section 6).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Cambium 2023 Scenario Descriptions and Documentation

The National Renewable Energy Laboratory's (NREL's) Cambium data sets are annually released sets of simulated hourly emission, cost, and operational data for a range of modeled futures of the U.S. electric sector with metrics designed to be useful for long-term decision- making. The 2023 Cambium data set is the fourth annual release. The data sets are a companion product to NREL's Standard Scenarios, which are likewise released annually and are a set of projections of how the U.S. electric sector could evolve across a suite of different potential futures, but covering more scenarios with less temporal granularity (Gagnon et al. 2024). Information about Cambium and related publications can be found at https://www.nrel.gov/analysis/cambium.html, and the Cambium data sets can be viewed and downloaded at https://scenarioviewer.nrel.gov/. In this documentation, we describe Cambium 2023's scenarios, define the metrics, and document the Cambium-specific methods for calculating those metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗