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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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DOE OSTI2023

Psychrometric Analysis for New NuMI Evaporator Weather Station

Tritium (H-3) is a weakly radioactive isotope of hydrogen and is a byproduct of accelerator operations at Fermi National Accelerator Laboratory (Fermilab). The Neutrinos at the Main Injector (NuMI) target produced tritiated condensate exhausted via the MI-65 building evaporators. Re-condensation of tritium evaporate was observed on the MI-65 rooftop, on nearby buildings, and on the surrounding grounds due to inefficiency of the existing evaporator system. A new evaporator system is being designed to eliminate re-condensation, and psychrometric data (temperature, humidity and dew point) plays a vital role in determining the location of a weather station to control the new evaporator system.

Alvarado-Anderson, Chase↗
DOE OSTI2021

EarthScope Seismic Station A21K (A21K-6) Field Campaign Report

Seismic station A21K has been successfully operating over the last five years. The station was constructed on 4/24/2014 by creating a borehole using an auger drill and inserting a PVC pipe casing. Roughly one month later, on 6/2/2014, the seismometer, weather station (Vaisala WXT520 meteorological sensor), enclosure, and electronics were installed. Collected data is being reported in real-time and is publicly available. On rare occasion, the station has required in-person service/maintenance visits, occurring on 4/29/2015 (fix faulty cable), 4/5/2016 (swap VIE), and 8/4/2017 (general check-up visit while in the area). This station is one in a 280-station seismic array/grid that make up the EarthScope: Transportable Array (USArray) project, funded by the National Science Foundation. USArray is operated by the Incorporated Research Institutions for Seismology (IRIS), a nonprofit 503(c) Corporation founded in 1984, that is a consortium of over 100 US universities dedicated to the operation of science facilities for the acquisition, management, and distribution of seismological data.

North Slope of Alaska, seismology, Earth system↗
DOE OSTI2021

Weather and Random Forest-based Load Profiling Approximation Models and its Transferability across Climate Zones

This study is to provide predictive understanding of the associations of various weather attributes with residential and commercial load profiles, for a variety of climate zones and seasons. In this work, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked/screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons).

load composite, Random Forest, regression tree, lo↗
DOE OSTI2025

Atmospheric and Soil Parameters in Five Urban Sites in Knoxville, Tennessee. 2024

This dataset, which contains ten csv files, reports several meteorological conditions such as temperature, wind speed, solar radiation, and net radiation measured in several urban parks in Knoxville, Tennessee, USA: Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West View Park (WVP), Victor Ashe Park (VAP), and West Hills Park (WHP). Also, hourly average of soil parameters such soil temperature, moisture, and matric potential measured in the five sites are included in this dataset. Air temperature, wind speed and direction, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Soil moisture and temperature parameters were measured the METER Teros 12 probes embedded to a depth of 5 cm into the soil, while soil matric potential was measured with a METER Teros 21 probe. Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sounding System / Raw Data

This dataset contains twice or four times daily radiosonde observations of the atmosphere at the Woods Hole weather station. Variables include atmospheric pressure, temperature, relative humidity, wind speed, wind direction, north wind speed, and east wind speed. Note that variables are a function of height.

17 WIND ENERGY↗
DOE OSTI2024

Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications

To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

Array↗
DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗
DOE OSTI2021

Update of Erosion Conditions and Assessment of Mitigation Options for the L-Bar Site

The L-Bar former uranium milling site was remediated and closed per regulations and is now under the long-term stewardship of the U.S. Department of Energy, Office of Legacy Management (DOE/LM). Since assuming stewardship in 2004, DOE/LM has observed erosion and sedimentation processes at the L-Bar Site that will challenge meeting the design closure criteria in the long term, and therefore erosion control structures were installed in several areas. Portions of these structures were subsequently damaged by summer convective storm events and they are being redesigned. At the request of DOE/LM, Desert Research Institute (DRI) reviewed the proposed redesign plans of the erosion control structures. The purpose of this review is to support DOE/LM decisions regarding mitigation of ongoing erosion processes, including assessing possible repair of the existing erosion control structures, as well as plan for effective long-term site management. This design review is performed in reference to the Uranium Mill Tailings Radiation Control Act (UMTRCA) standards contained in 40 CFR 192.02 that require containment of residual radioactive material be effective for up to 1,000 years (if achievable, but certainly for 200 years), limit radon release, protect groundwater, and minimize future maintenance. The erosion control structures under review are not subject to the UMTRCA standard directly, but they contribute to the performance of the overall containment system by virtue of their relationship with the large diversion channels bounding the disposal cell. The 11 design drawings included in “Erosion Control Alternatives Analysis, L-Bar, New Mexico, Disposal Site, LTS-111-0043-08-002,” dated January 11, 2019, and listed in Appendix A, and supporting calculations in Weston (2019) are the focus of the review. Additionally, DOE/LM provided supporting data such as a 2018 photogrammetry-based digital elevation model of the site, precipitation records collected from an on-site weather station, and sedimentation records collected at an on-site basin. This assessment includes review of the proposed design to mitigate current site conditions but also addresses site changes between the initial erosion assessment report (Miller et al., 2012) and current conditions. The analysis is at a predesign engineering level and does not include development of hydraulic or sediment transport models. Site precipitation data are included in Miller et al. (2012). The natural conditions of the site, the design of site diversion systems, observed erosion at the site, and predesign engineering analysis are used to develop a holistic view of the challenges to L-Bar Site stability and possible responsive actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Integrative investigation of dust emissions by dust storms and dust devils in North Africa

Dust aerosols in North Africa account for >50% of the global total; however dust emission areas are still unclear. Based on the analysis of dust storms simulated with the numerical Weather Research and Forecast (WRF) model, satellite aerosol index (AI), and the dust data observed at 300 meteorological stations over 20 years, the spatio-temporal distribution characteristics of dust storm, dust devil and AI are compared and analyzed. From this study, the results show that: 1) There are two dust emission mechanisms: the dynamically-dominated dust storm and thermally-dominated dust devil; 2) Dust storms occur most frequently in Spring and are concentrated in the areas of Grand Erg Occidental Desert to the Erg Chech-Adrar Desert, the northern part of Grand Erg Oriental, the Atouila Desert to the Ouarane Desert, the Mediterranean coast, the eastern side of Nubian Desert and Bodélé Depression; 3) Dust devils occur most frequently from April to August and are mainly concentrated in the central part of North Africa, especially in the southwest of Hoggar Mountains to the west of Air Mountains, the border area of Egypt - Sudan - Libya and the vicinity of Tibesti Plateau; 4) The spatio-temporal distribution of AI is correlated more with the dust devils emission whereas the annual average contributions by dust storms and dust devils are 61.3% and 38.7%, respectively. This study discovers a new area of dust emissions by dust devils, and provides a better explanation for the spatio-temporal distribution of AI in North Africa.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Northern Region Coal Prospects

This presentation was delivered to the audience attending the U.S. DOE/USEA Northern Regional Coal Workshop in Bismarck, ND on March 11, 2026. The presentation highlights the opportunity for coal plants in the region to provide power to military installations and data centers. There is also an analysis on how coal-fired power stations maintain regional supply during extreme cold weather events. Finally, a summary slide is presented that highlights a recent business case analysis on diverting captured carbon dioxide for use in enhanced oil recovery in the Bakken.

Bakken↗
DOE OSTI2021

Urban microclimate and its impact on building performance: A case study of San Francisco

Urban microclimate exerts an increasing influence on urban buildings, energy, and sustainability. This study uses 10-year measured hourly weather data at 27 sites in San Francisco, California, to (1) analyze and visualize the urban microclimate patterns and urban heat island effect; (2) simulate annual energy use and peak electricity demand of typical large office buildings and large hotels to investigate the influence of urban microclimate on building performance; (3) simulate indoor air temperature of a single-family house without air-conditioning during the record three-day heatwave of 2017, to quantify the divergence of climate resilience due to urban microclimate effect. Results show significant microclimate effects in San Francisco with up to 11 °C outdoor air temperature difference between the coastal and downtown areas on September 1, 2017, during the record three-day heatwave. The simulated energy results of the prototype large office and large hotel buildings using the 2017 weather data show over 100% difference in annual heating energy use and 65% difference in annual cooling energy use across different stations; as well as up to 30% difference in peak cooling electricity demand. The impacts on annual site or source energy use are minimal (less than 5%) as cooling and heating in a mild climate are a relatively small portion of overall building energy use in San Francisco. Results also show the microclimate effects influence indoor air temperature of unconditioned homes by up to 5 °C. Newer buildings and homes are much less affected by microclimate effects due to more stringent performance requirements of the building envelope and energy systems. Furthermore, these findings inform that San Francisco microclimate variations should be considered in urban energy planning, building energy codes and standards, as well as heat resilience policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Soil Temperature Sensor Data, 2025, Five sites in Knoxville, Tennessee

This dataset contains surface soil temperature measurements from five urban parks in Knoxville, Tennessee: Cumberland Estates Park (CE), Socially Equal Energy Efficient Development (SD), West View Park (WV), Victor Ashe Park (VA), and West Hills Park (WH). The dataset includes 16 CSV files documenting soil temperature measurements recorded by HOBO Pendant MX Water Temperature Data Loggers. Data collection for all sites began on January 1, 2025. The end time for each sensor is provided in the End Time_2025.csv file. Each logger was installed at a depth of 10 inches and positioned approximately 3 to 6 feet from the weather station at each site. This dataset is part of a broader study examining the effects of soil moisture and plant evapotranspiration on ambient temperature and relative humidity across multiple urban parks in Knoxville.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗
DOE OSTI2024

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

Farguell, Angel (ORCID:000000032395220X)↗
DOE OSTI2020

Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure

Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. Quality volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume on freeways are typically collected through continuous count stations installed by state DOTs, while there is lack of traffic volume observability on off-freeway roads. The National Renewable Energy Laboratory (NREL), in Collaboration with the I-95 Corridor Coalition and the University of Maryland, extended its research into estimating volumes anywhere anytime from industry probe based data for off-freeway roads. NREL combined vehicle probe count data with several other data sets (speed, whether, roadway geometry, time-of-day, day-of-week, etc.) to estimate hourly volumes as well as AADTs. The research validated and demonstrated the machine learning model, namely XGBoost, using data collected from Pennsylvania, North Carolina, and Tennessee.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗