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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 1: Summary of Utah FORGE Wells 16A(78)-32, 56-32, 78B-32 and 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

SAIL-Isotopologue (SAIL-iso) Field Campaign Report

The SAIL-iso project took place from November 19, 2022 to June 3, 2023 as part of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Surface Atmosphere Integrated Field Laboratory (SAIL) project at the main project site in Gothic, Colorado. The project sought to use measurements of the stable isotopic composition of water vapor to better understand cloud-aerosol interactions in a remote mountainous watershed. Water vapor isotopologue and humidity measurements were determined using a Los Gatos Research Triple Water Vapor Isotope Analyzer (TWVIA). The instrument was deployed in the guest van and consists of three main components: a water vapor isotope analyzer, a Los Gatos Research Water Vapor Isotope Standard Source (WVISS), and a Los Gatos Research Dry Air Source. Ambient air samples were collected through an inlet located about one meter above roof of the shipping container. Samples were then delivered to the analyzer through Teflon tubing by use of an external pump to minimize the transport time between the inlet and analyzer. The inlet tubing was surrounded by a Briskheat heat trace and insulating material to ensure ambient air samples did not fall below the dew point and result in condensation. The analyzer uses cavity ringdown spectroscopy to report isotopic ratios of ambient air samples at a frequency of 1 Hz. The calibration unit of the instrument was used in conjunction with the dry air source to periodically measure the δ values of standard waters. It uses a nebulizer to push small water droplets into a hot chamber that vaporizes the water without fractionation. This vapor was then transported to the analyzer using a built-in compressor and the dry air source, which allows each standard with known δ values to be measured at a wide range of humidity values for post-measurement calibration of ambient air samples. Multiple sources of uncertainty are introduced at different stages during the data collection and processing. These include (1) instrument precision, (2) uncertainty in the secondary standards, (3) humidity-correction uncertainty, and (4) Vienna Standard Mean Ocean Water – Standard Light Antarctic Precipitation (VSMOW-SLAP) calibration uncertainty. Uncertainty from each step is propagated in quadrature to calculate a total uncertainty of each isotopologue. Uncertainty was determined to be 2.3‰ for δD and 0.4‰ for δ 18 O.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Water vapor isotopic composition during TRACER IOP

The TRACER-Iso project took place from June 1 to September 30, 2022 as part of the intensive observation period (IOP) of the Tracking Aerosol Convection Interactions (TRACER) project at the main project site at the La Porte, Texas, municipal airport. The project sought to use measurements of the stable isotopic composition of water vapor to better understand cloud-aerosol interactions in deep convection in a polluted urban setting. Water vapor isotopologue and humidity measurements were determined using a Picarro L2130 Water Vapor Isotope Analyzer. The instrument was deployed in the guest van and consists of three main components: a water vapor isotope analyzer, and a Los Gatos Research Water Vapor Isotope Standard Source (WVISS), and a Los Gatos Research Dry Air Source. Ambient air samples were collected through an inlet located about one meter above roof of the shipping container. Samples were then delivered to the analyzer through teflon tubing by use of an external pump to minimize the transport time between the inlet and analyzer. The inlet tubing was surrounded by a Briskheat heat trace and insulating material to ensure ambient air samples did not fall below the dew point and result in condensation. The analyzer uses cavity ringdown spectroscopy to report isotopic ratios of ambient air samples at a frequency of 1 Hz. The calibration unit of the instrument was used in conjunction with the dry air source to periodically measure the δ values of standard waters. It uses a nebulizer to push small water droplets into a hot chamber that vaporizes the water without fractionation. This vapor was then transported to the analyzer using a built-in compressor and the dry air source, which allows each standard with known δ values to be measured at a wide range of humidity values for post-measurement calibration of ambient air samples. There are multiple sources of uncertainty introduced at different stages during the data collection and processing. These include (1) instrument precision, (2) uncertainty in the secondary standards, (3) humidity-correction uncertainty, and (4) VSMOW-SLAP calibration uncertainty. Uncertainty from each step is propagated in quadrature to calculate a total uncertainty of each isotopologue. Uncertainty was determined to be 2.3‰ for δD and 0.4‰ for δ18O.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2023

Buoy - California - Wind Sentinel (120), Humboldt - Processed Data

The purpose of the dataset is to provide preliminary filtered, averaged lidar data and standardize the data format of various datastreams from the buoy into NetCDF. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.

17 WIND ENERGY↗
DOE OSTI2023

Buoy - California - Wind Sentinel (130), Morro Bay - Processed Data

The purpose of the dataset is to provide preliminary filtered, averaged buoy data and standardize the data format of various data streams from the buoy into NetCDF. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.

17 WIND ENERGY↗
DOE OSTI2024

Hawaii - Wind Sentinel (120), Oahu, Hawaii / Processed Data

The purpose of the dataset is to provide preliminary filtered, averaged lidar data and standardize the data format of various datastreams from the buoy into NetCDF. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.

17 WIND ENERGY↗
DOE OSTI2023

Lidar - California - Leosphere Windcube 866 (130), Morro Bay - Processed Data

The purpose of this dataset is to provide preliminary filtered, averaged lidar data and standardize the data format of various data streams from the buoy into NetCDF. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.

17 WIND ENERGY↗
DOE OSTI2024

Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at $\sqrt{s} = 13$ TeV at the CMS experiment

The LHC has provided an unprecedented amount of proton–proton collision data, bringing forth exciting opportunities to address fundamental open questions in particle physics. These questions can potentially be answered by performing searches for very rare processes predicted by models that attempt to extend the standard model of particle physics. The data collected by the CMS experiment in 2015–2018 at a center-of-mass energy of 13 TeV can be used to test the standard model with high precision and potentially uncover evidence for new particles or interactions. An interesting possibility is the existence of new fermions with masses ranging from the MeV to the TeV scale. Such new particles appear in many possible extensions of the standard model and are well motivated theoretically. New fermions may explain the appearance of three generations of leptons and quarks, the mass hierarchy across these generations, and the nonzero neutrino masses. In this report, the results of searches targeting vectorlike quarks, vector-like leptons, and heavy neutral leptons at the CMS experiment are summarized. The complementarity of current searches for each type of new fermion is discussed, and combinations of several searches for vector-like quarks are presented. The discovery potential for some of these searches at the High-Luminosity LHC is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the CMS experiment

The LHC has provided an unprecedented amount of proton-proton collision data, bringing forth exciting opportunities to address fundamental open questions in particle physics. These questions can potentially be answered by performing searches for very rare processes predicted by models that attempt to extend the standard model of particle physics. The data collected by the CMS experiment in 2015-2018 at a center-of-mass energy of 13 TeV can be used to test the standard model with high precision and potentially uncover evidence for new particles or interactions. An interesting possibility is the existence of new fermions with masses ranging from the MeV to the TeV scale. Such new particles appear in many possible extensions of the standard model and are well motivated theoretically. New fermions may explain the appearance of three generations of leptons and quarks, the mass hierarchy across the generations, and the nonzero neutrino masses. In this report, the results of searches targeting vector-like quarks, vector-like leptons, and heavy neutral leptons at the CMS experiment are summarized. The complementarity of current searches for each type of new fermion is discussed, and combinations of several searches for vector-like quarks are presented. The discovery potential for some of these searches at the High-Luminosity LHC is also discussed.

FOS: Physical sciences↗
DOE OSTI2023

Process-dependent anisotropic thermal conductivity of laser powder bed fusion AlSi10Mg: impact of microstructure and aluminum-silicon interfaces

Purpose AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity. Design/methodology/approach The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity. Findings The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm 3 and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm 3 . The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains. Practical implications The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications. Originality/value To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

Engineering↗
DOE OSTI2025

ENnUI : Exemplar Navigator Using Inertial Sensors

SAND2025-04794O ENnUI: Exemplar Navigator Using Inertial Sensors helps users understand and compare different navigation algorithms that rely on inertial sensors. This reference library offers a collection of reference mechanization equations and methods for estimating the position and movement of vehicles over time. Rather than aiming for the highest precision or performance, ENnUI focuses on creating a user-friendly framework that allows users to benchmark their own algorithms against a standard set of tools. It is useful for post-processing data and real-time navigation solutions. 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.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗
DOE OSTI2021

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Lignin molecular weights of Populus trichocarpa residues after CELF pretreatment

Here we present a dataset of molecular weights of lignin from a woody energy crop (Populus trichocarpa) residues after a series of co-solvent enhanced lignocellulosic fractionation (CELF) pretreatment. The natural poplar variant GW-9947 from the Center for Bioenergy Innovation (CBI) was used. The poplar was knife milled and passed through a 1 mm sieve and CELF pretreatment was performed in a Parr autoclave reactor with 7.5 wt % solids loading, 0.5 wt% H2SO4 as catalyst at 150°C with various time. Tetrahydrofuran was added in a 1:1 mass ratio with water as the pretreatment solvent. Lignin was isolated from the pretreated samples after ball-milling in a porcelain jar with ceramic balls via Retsch PM 200 at 580 rpm for 2.5 h followed by enzymatic hydrolysis in acetate buffer (pH 4.8, 50 °C) for 48 h. The solid residue was isolated by centrifugation and hydrolyzed again with freshly added buffer and enzymes for another 48 h. After filtration, the solid residue was extracted twice with 96% (v/v) 1,4-dioxane/water mixture at room temperature overnight. The extracts were combined, rotary evaporated, and freeze-dried to recover lignin. The lignin samples were then derivatized in an acetic anhydride/pyridine (1:1, v/v) mixture and stirred at room temperature for 24 h. Ethanol was added to the reaction mixture, left for 30 min and then removed with a rotary evaporator. The addition and removal of ethanol was repeated at least 3 times until all traces of acetic acid were removed. Acetylated lignin samples were then dissolved in tetrahydrofuran (THF) at a concentration of 1.0 mg/mL. The molecular weight of acetylated lignin was measured by a gel permeation chromatography (GPC) on a PSS-Polymer Standards Service (Warwick, RI, USA) GPC SECurity 1200 system featuring Agilent HPLC 1200 components equipped with four Waters Styragel columns (HR1, HR2, HR4 and HR6) and an UV detector (270 nm). Tetrahydrofuran was used as the mobile phase and flow rate was 0.3 mL/min. The Polymer Standards Service WinGPC Unity software (Build 6807) was used for data processing for all the samples. The data provides information about the effects of CELF pretreatment time at 150 ºC on lignin molecular weights.

09 BIOMASS FUELS↗
DOE OSTI2023

Ice nucleation measurements from DRUM impactors during TRACER campaign in the Houston TX region from July to September 2022

During TRACER, three Davis Rotating Uniform size-cut Monitors (DRUM; DRUMAir 4-DRUM) were used to collect aerosols for ice nucleation measurements in the Brooks laboratory at Texas A&M University. The three instruments were located at AMF1 in La Porte, Ancillary site in Guy (ANC), and onboard the Texas A&M Rapid Onsite Atmospheric Measurements Van (ROAM-V). ROAM-V was deployed to capture airmasses behind (maritime) and ahead (continental) of the passage of the sea-breeze front through Houston. On select sampling days, ROAM-V sampled in the morning/mid-day on the coast and then transited to a second inland site for the afternoon/evening. The suite of instruments deployed on ROAM-V included a Condensation Particle Counter (CPC; GRIMM Model 5.403 CPC), Scanning Mobility Particle Sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A Differential Mobility Analyzer), Cloud Condensation Nuclei counter (Droplet Measurement Technologies CCN Counter), micro pulse lidar (Droplet Measurement Technologies Micro Pulse LiDAR (miniMPL)), and one of the Davis Rotating Uniform size-cut Monitors (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the GPS on the phone application “My Altitude”. Each DRUM sampler was operated at a flow rate of 23 LPM. Each DRUM has four stages where the aerodynamic diameter size cuts are as follows: stage 1 larger than 3 μm, stage 2 from 3 to 1.2 μm, stage 3 from 1.2 to 0.34 μm, and stage 4 from 0.34 to 0.15 μm. Pretreated aluminum foil was used as a substrate on all stages. At AMF1 and ANC sites, the DRUMs were operated on the shared aerosol inlet, with generous support of the DOE ARM site staff. These instruments rotated 24 mm per day and only contained aluminum foil substrates. Substrates were changed weekly and transported to Texas A&M for storage in -80C freezer until analysis. The DRUM onboard ROAM-V was operated at a faster rotation rate of 150 mm per day to clearly separate the multiple deployment locations for ROAM-V. For the ROAM-V DRUM, substrates were changed every deployment and transported to Texas A&M for storage in -80C freezer until analysis. At Texas A&M, ice nucleation experiments were conducted to measure the ice nucleation temperature of the ambient aerosol samples collected from the three DRUMs using our previously established procedures (Alsante et al., 2023; Fornea et al., 2009; Matthews et al., 2023). For ice nucleation, only samples collected on stage 3 were analyzed, given that these are the most relevant size (1.2 to 0.34 μm diameter) for potential ice nucleating particles. For the AMF1 and ANC sites, we cut and analyzed 2 mm (2-hour) samples. We analyzed the time periods of the AMF1 site instrument when the ROAM-V was deployed. A 72-hour period from July 11th at 23:49 through July 15th at 1:49 was analyzed from the ANC site instrument. For the ROAM-V instrument, we separated the daily samples by site location. Between 1- and 6-hour independent samples were analyzed at each location. We also cut the ROAM-V samples in half to allow for compositional analysis of the aerosol on the other half of the substrate. All viable samples from the ROAM-V were analyzed. Analysis was done using a custom-built ice nucleation apparatus, recently updated to include an array of 16 individual samples (Matthews et al., 2023). On our experimental setup, we used 100 μL of Ultra-High-Performance Liquid Chromatography (UHPLC) water (Sigma Aldrich, >99.9% purity) to wash off the aerosol from the DRUM substrate. Then, we micropipetted 2 μL droplet samples into each of the 16 wells of the array, using hydrophobically coated microscope slides. Experiments would cycle 28 times from 10 C to -40C over a 20-hour period. During analysis of the TRACER samples, nine experiments were conducted with UHPLC water process blanks that followed an identical preparation procedure. The average freezing temperature and standard deviation for these process blanks is -27.7±2.4 C. This data was collected for ARM Field Campaign AFC07023 and supported by DOE ASR grant DE-SC0021047. For any further questions, please feel free to contact the instrument PI, Sarah D. Brooks, sbrooks@tamu.edu. Alsante, A. N., Thornton, D. C., & Brooks, S. D. (2023). Ice nucleation catalyzed by the photosynthesis enzyme RuBisCO and other abundant biomolecules. Communications Earth & Environment, 4(1), 51. Fornea, A. P., Brooks, S. D., Dooley, J. B., & Saha, A. (2009). Heterogeneous freezing of ice on atmospheric aerosols containing ash, soot, and soil. Journal of Geophysical Research: Atmospheres, 114(D13). DOI:10.1029/2009JD011958 Matthews, B. H., Alsante, A. N., & Brooks, S. D. (2023). Pollen Emissions of Subpollen Particles and Ice Nucleating Particles. ACS Earth and Space Chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗