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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 OSTI2015

MO-F-CAMPUS-T-01: Radiosurgery of Multiple Brain Metastases with Single-Isocenter VMAT: Optimizing Treatment Geometry to Reduce Normal Brain Dose

Purpose: To develop an optimization algorithm to reduce normal brain dose by optimizing couch and collimator angles for single isocenter multiple targets treatment of stereotactic radiosurgery. Methods: Three metastatic brain lesions were retrospectively planned using single-isocenter volumetric modulated arc therapy (VMAT). Three matrices were developed to calculate the projection of each lesion on Beam’s Eye View (BEV) by the rotating couch, collimator and gantry respectively. The island blocking problem was addressed by computing the total area of open space between any two lesions with shared MLC leaf pairs. The couch and collimator angles resulting in the smallest open areas were the optimized angles for each treatment arc. Two treatment plans with and without couch and collimator angle optimization were developed using the same objective functions and to achieve 99% of each target volume receiving full prescription dose of 18Gy. Plan quality was evaluated by calculating each target’s Conformity Index (CI), Gradient Index (GI), and Homogeneity index (HI), and absolute volume of normal brain V8Gy, V10Gy, V12Gy, and V14Gy. Results: Using the new couch/collimator optimization strategy, dose to normal brain tissue was reduced substantially. V8, V10, V12, and V14 decreased by 2.3%, 3.6%, 3.5%, and 6%, respectively. There were no significant differences in the conformity index, gradient index, and homogeneity index between two treatment plans with and without the new optimization algorithm. Conclusion: We have developed a solution to the island blocking problem in delivering radiation to multiple brain metastases with shared isocenter. Significant reduction in dose to normal brain was achieved by using optimal couch and collimator angles that minimize total area of open space between any of the two lesions with shared MLC leaf pairs. This technique has been integrated into Eclipse treatment system using scripting API.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A METHOD TO SEARCH FOR BULK MOTIONS IN THE ICM WITH CHANDRA CCD SPECTRA: APPLICATION TO THE BULLET CLUSTER

We propose a strategy to search for bulk motions in the intracluster medium (ICM) of merging clusters based on Chandra CCD data. Our goal is to derive robust measurements of the average redshift of projected ICM regions obtained from the centroid of the K{sub α} line emission. We thoroughly explore the effect of the unknown temperature structure along the line of sight to accurately evaluate the systematic uncertainties on the ICM redshift. We apply our method to the “Bullet cluster” (1E 0657-56). We directly identify 23 independent regions on the basis of the surface brightness contours, and measure the redshift of the ICM averaged along the line of sight in each. We find that the redshift distribution across these regions is marginally inconsistent with the null hypothesis of a constant redshift or no bulk motion in the ICM, at a confidence level of about 2σ. We tentatively identify the regions most likely affected by bulk motions and find a maximum velocity gradient of about (46 ± 13) km s{sup −1} kpc{sup −1} along the line of sight on a scale of ∼260 kpc along the path of the “bullet.” We interpret this as the possible signature of a significant mass of ICM pushed away along a direction perpendicular to the merging. This preliminary result is promising for a systematic search for bulk motions in bright, moderate-redshift clusters based on spatially resolved spectral analysis of Chandra CCD data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Explaining machine-learning models for gamma-ray detection and identification

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Gradient boosting machine for modeling the energy consumption of commercial buildings

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Demonstration of a Piston Plug feed System for Feeding Coal/Biomass Mixtures across a Pressure Gradient for Application to a Commercial CBTL System

Producing liquid transportation fuels and power via coal and biomass to liquids (CBTL) and integrated gasification combined cycle (IGCC) processes can significantly improve the nation's energy security. The Energy Independence and Security Act of 2007 mandates increasing renewable fuels nearly 10-fold to >2.3 million barrels per day by 2022. Coal is abundantly available and coal to liquids (CTL) plants can be deployed today, but they will not become sustainable without large scale CO 2 capture and storage. Co-processing of coal and biomass in CBTL processes in a 60 to 40 ratio is an attractive option that has the potential to produce 4 million barrels of transportation fuels per day by 2020 at the same level of CO 2 emission as petroleum. In this work, Southern Research Institute (Southern) has made an attempt to address one of the major barriers to the development of large scale CBTL processes - cost effective/reliable dry-feeding of coal-biomass mixtures into a high pressure vessel representative of commercial entrained-flow gasifiers. Present method for dry coal feeding involves the use of pressurized lock-hopper arrangements that are not only very expensive with large space requirements but also have not been proven for reliably feeding coal-biomass mixtures without the potential problems of segregation and bridging. The project involved the development of a pilot-scale 250 lb/h high pressure dry coal-biomass mixture feeder provided by TKEnergi and proven for feeding biomass at a scale up to 6 ton/day. The aim of this project is to demonstrate cost effective feeding of coal-biomass mixtures (50:50 to 70:30) made from a variety of coals (bituminous, lignite) and biomass (wood, corn stover, switch grass). The feeder uses a hydraulic piston-based approach to produce a series of plugs of the mixture that act as a seal against high back-pressure of the gasification vessel in to which the mixture is being fed. The plugs are then fed one by one via a plug breaker into the high pressure gasification vessel. A number of runs involving the feeding of coal and biomass mixtures containing 50 to 70 weight % coal into a high pressure gasification vessel simulator have shown that plugs of sufficient density can be formed to provide a seal against pressures up to 450 psig if homogeneity of the mixture can be maintained. However, the in-homogeneity of coal-biomass mixtures can occur during the mixing process because of density, particle size and moisture differences. Also, the much lower compressibility of coal as opposed to biomass can contribute to non-uniform plug formation which can result in weak plugs. Based on present information, the piston plug feeder offered marginal economic advantages over lock-hoppers. The results suggest a modification to the piston feeder that can potentially seal against pressure without the need for forming plugs. This modified design could result in lower power requirements and potentially better economics.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2012

UV patternable thin film chemistry for shape and functionally versatile self-oscillating gels

We describe a new planar processing chemistry that allows the synthesis and patterning of dynamically self-actuating gels of diverse form. The chemical reaction covalently incorporates a methacrylate-modified ruthenium trisbipyridine monomer within a poly acrylamide (PAAm) gel, modified to provide a flexible chemistry for UV-curable, self-oscillating Belousov–Zhabotinsky (BZ) gels. The photoinitiated polymerization offers significant advantages over previously reported BZ gel chemistries – fast polymerization rates, broad flexibility in the structural and functional attributes that impact the BZ reaction driven mechanics, and a facility for net shape fabrication via projection mode photo lithographic exposure. The facility of the synthetic method enabled an opportunity to broadly explore the structure/property and structure/rate correlations characterizing the dynamics of these gels and develop means to both control and predict the detailed features of the self-oscillating chemical waves they embed. The PAAm systems initiate under the control of transport-dominated influences weighted heavily by the diffusion/depletion of reagents present in the ambient contacting fluid medium. These initiating modes of oscillation are generally unstable temporally, showing shape- and property-sensitive conversions to oscillating modes controlled by the attributes of the gel-phase environment. The present work establishes the nature of these sensitivities, developing insights related to such features in patterned gel objects or films as composition, size, thickness, shape, degree of crosslinking, and gradient forms of structure within the gel. We observed in this work that, across a broad design-rule and compositional space, the PAAm based BZ gel systems exhibit contractions in dimension for regions in which the Ru catalyst is present in the +3 oxidation state (PNIPAAm BZ gels expand in this state). The chemomechanics of the PAAm system are further differentiated from those of PNIPAAm in the generally (and relatively) fast oscillatory rates it can sustain. Furthermore, this work suggests the dynamics in the PAAm gels do not reflect a simple osmotic force driven by differences in the solvation of Ru(II/III) centers. Computational modeling supports a mechanism in which the charge state of Ru centers mediate a differential degree of intra/inter-chain segmental interactions – dynamics that mediate a temporally varying degree of reversibly formed association crosslinks within the gel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

SU-F-BRB-10: A Statistical Voxel Based Normal Organ Dose Prediction Model for Coplanar and Non-Coplanar Prostate Radiotherapy

Purpose: Knowledge learned from previous plans can be used to guide future treatment planning. Existing knowledge-based treatment planning methods study the correlation between organ geometry and dose volume histogram (DVH), which is a lossy representation of the complete dose distribution. A statistical voxel dose learning (SVDL) model was developed that includes the complete dose volume information. Its accuracy of predicting volumetric-modulated arc therapy (VMAT) and non-coplanar 4π radiotherapy was quantified. SVDL provided more isotropic dose gradients and may improve knowledge-based planning. Methods: 12 prostate SBRT patients originally treated using two full-arc VMAT techniques were re-planned with 4π using 20 intensity-modulated non-coplanar fields to a prescription dose of 40 Gy. The bladder and rectum voxels were binned based on their distances to the PTV. The dose distribution in each bin was resampled by convolving to a Gaussian kernel, resulting in 1000 data points in each bin that predicted the statistical dose information of a voxel with unknown dose in a new patient without triaging information that may be collectively important to a particular patient. We used this method to predict the DVHs, mean and max doses in a leave-one-out cross validation (LOOCV) test and compared its performance against lossy estimators including mean, median, mode, Poisson and Rayleigh of the voxelized dose distributions. Results: SVDL predicted the bladder and rectum doses more accurately than other estimators, giving mean percentile errors ranging from 13.35–19.46%, 4.81–19.47%, 22.49–28.69%, 23.35–30.5%, 21.05–53.93% for predicting mean, max dose, V20, V35, and V40 respectively, to OARs in both planning techniques. The prediction errors were generally lower for 4π than VMAT. Conclusion: By employing all dose volume information in the SVDL model, the OAR doses were more accurately predicted. 4π plans are better suited for knowledge-based planning than the VMAT plans that are strongly biased in its dose gradient orientation. This project is supported by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022)

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Final Technical Report for Department of Energy Award DE-SC0006625, “Predictability of the carbon-climate system on seasonal to decadal time scales.”

The project aims to investigate the feasibility of advancing our understanding of the carbon cycle, using a carbon-weather data assimilation system that updates the modeled carbon dioxide concentration and atmospheric circulation every six hours using CO 2 data (from the OCO 2 satellite) and weather data. At the core of the system is the DOE-NCAR-CAM5fv global circulation model coupled to the National Center for Atmospheric Research's Data Assimilation Testbed, running an ensemble of 30 models. This combination provides realistic vertical carbon dioxide gradients and conservation of dry air mass. A global four-dimensional distribution of atmospheric CO 2 concentration is produced. Our results show (1) that OCO 2 total precipitable water data are reliable and provide valuable uncertainty information for the OCO 2 data assimilation; and (2) that our approach is a promising method for monitoring national carbon dioxide emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Commuting projections on graphs

For a given (connected) graph, we consider vector spaces of (discrete) functions defined on its vertices and its edges. These two spaces are related by a discrete gradient operator, Grad and its adjoint, ₋Div, referred to as (negative) discrete divergence. We also consider a coarse graph obtained by aggregation of vertices of the original one. Then a coarse vertex space is identified with the subspace of piecewise constant functions over the aggregates. We consider the ℓ 2 -projection Q H onto the space of these piecewise constants. In the present paper, our main result is the construction of a projection π H from the original edge-space onto a properly constructed coarse edge-space associated with the edges of the coarse graph. The projections π H and Q H commute with the discrete divergence operator, i.e., we have div π H = Q H div. The respective pair of coarse edge-space and coarse vertexspace offer the potential to construct two-level, and by recursion, multilevel methods for the mixed formulation of the graph Laplacian which utilizes the discrete divergence operator. The performance of one two-level method with overlapping Schwarz smoothing and correction based on the constructed coarse spaces for solving such mixed graph Laplacian systems is illustrated on a number of graph examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

EXCHANGE Campaign 1: A Community-Driven Baseline Characterization of Soils, Sediments, and Water Across Coastal Gradients

The EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) program is a consortium of scientists working together to improve our understanding of how the two-way exchange of water between estuaries or large lake lacustuaries and the terrestrial landscape influence the state and function of ecosystems across the coastal interface. EXCHANGE Campaign 1 (EC1) focuses on the spatial variation in biogeochemical structure and function at the coastal terrestrial-aquatic interface (TAI). In the Fall of 2021, the EXCHANGE Consortium gathered samples from 52 TAIs. Samples collected from EC1 were analyzed for bulk geochemical parameters, bulk physicochemical parameters, organic matter characteristics, and redox-sensitive elements.Please download ec1_README.pdf for a complete list of available data in each .zip folder, package version history, and detailed information about the project. This README will serve as the central place for EC1 Data Package updates. Experimental setup and v1 methods are documented in Myers-Pigg and Pennington et al., 2023 (https://doi.org/10.1038/s41597-023-02548-7).EC1 Data Package Structure:ec1_README.pdfec1_methods.pdfec1_metadata_v3.zip...ec1_dd.csv...ec1_flmd.csv...ec1_sample_catalog.csv...ec1_metadata_kitlevel.csv...ec1_metadata_collectionlevel.csv...ec1_data_collectionlevel.csv...ec1_igsn_metadata.csvec1_soil_v3.zipec1_sediment_v3.zipec1_water_v3.zipec1_processingscripts_v3.zipThis data package is on v3 and was originally published May 2023 (v1). Subsequent updates will be published here with new version numbers. Please see the Change History section in ec1_README.pdf for detailed changes.---Acknowledging EXCHANGE: General Support and Data Product UseWe ask that users of EXCHANGE data add the following acknowledgement when publishing data in scholarly articles and data repositories:"This research is based on work supported by COMPASS-FME, a multi-institutional project supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research as part of the Environmental System Science Program."

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Dyer, Alec S. (ORCID:0000000219813904)↗
DOE OSTI2014

Utilization of the St. Peter Sandstone in the Illinois Basin for CO2 Sequestration

This project is part of a larger project co-funded by the United States Department of Energy (US DOE) under cooperative agreement DE-FE0002068 from 12/08/2009 through 9/31/2014. The study is to evaluate the potential of formations within the Cambro-Ordovician strata above the Mt. Simon Sandstone as potential targets for carbon dioxide (CO2) sequestration in the Illinois and Michigan Basins. This report evaluates the potential injectivity of the Ordovician St. Peter Sandstone. The evaluation of this formation was accomplished using wireline data, core data, pressure data, and seismic data acquired through funding in this project as well as existing data from two additional, separately funded projects: the US DOE funded Illinois Basin – Decatur Project (IBDP) being conducted by the Midwest Geological Sequestration Consortium (MGSC) in Macon County, Illinois, and the Illinois Industrial Carbon Capture and Sequestration (ICCS) Project funded through the American Recovery and Reinvestment Act (ARRA), which received a phase two award from DOE. This study addresses the question of whether or not the St. Peter Sandstone may serve as a suitable target for CO2 sequestration at locations within the Illinois Basin where it lies at greater depths (below the underground source of drinking water (USDW)) than at the IBDP site. The work performed included numerous improvements to the existing St. Peter reservoir model created in 2010. Model size and spatial resolution were increased resulting in a 3 fold increase in the number of model cells. Seismic data was utilized to inform spatial porosity distribution and an extensive core database was used to develop porosity-permeability relationships. The analysis involved a Base Model representative of the St. Peter at “in-situ” conditions, followed by the creation of two hypothetical models at in-situ + 1,000 feet (ft.) (300 m) and in-situ + 2,000 ft. (600 m) depths through systematic depthdependent adjustment of the Base Model parameters. Properties for the depth shifted models were based on porosity versus depth relationship extracted from the core database followed by application of the porosity-permeability relationship. Each of the three resulting models were used as input to dynamic simulations with the single well injection target of 3.2 million tons per annum (MTPA) for 30 years using an appropriate fracture gradient based bottom hole pressure limit for each injection level. Modeling results are presented in terms of well bottomhole pressure (BHP), injection rate profiles, and three-dimensional (3D) saturation and differential pressure volumes at selected simulation times. Results suggest that the target CO2 injection rate of 3.2 MTPA may be achieved in the St. Peter Sandstone at in-situ conditions and at the in-situ +1,000 ft. (300 m) depth using a single injector well. In the latter case the target injection rate is achieved after a ramp up period which is caused by multi-phase flow effects and thus subject to increased modeling uncertainty. Results confirm that the target rate may not be achieved at the in-situ +2,000 ft. (600 m) level even with multiple wells. These new modeling results for the in-situ case are more optimistic than previous modeling results. This difference is attributed to the difference in methods and data used to develop model permeability distributions. Recommendations for further work include restriction of modeling activity to the in-situ +1,000 ft. (300 m) and shallower depth interval, sensitivity and uncertainty analysis, and refinement of porosity and permeability estimates through depth and area selective querying of the available core database. It is also suggested that further modeling efforts include scope for evaluating project performance in terms of metrics directly related to the Environmental Protection Agency (EPA) Class VI permit requirements for the area of review (AoR) definition and post injection site closure monitoring.

Will, Robert↗
DOE OSTI2012

Theoretical investigations of the physical properties of zircon-type YVO{sub 4}

The crystal structure, electronic properties, elastic properties, hardness and thermodynamic properties of the laser host material zircon-type YVO{sub 4} are studied using the pseudopotential plane wave method within the local density approximation (LDA) and generalized gradient approximation (GGA). The calculated ground state values such as lattice parameter, bulk modulus and its pressure derivative, the band structure and densities of states were in favorable agreement with previous works and the existed experimental data. The elastic constants C{sub ij}, the aggregate elastic moduli (B, G, E), Poisson's ratio and elastic anisotropy have been investigated. In YVO{sub 4}, V-O bonds with shorter bond length and larger Mulliken population make great contribution to hardness than Y-O bonds. Using quasi-harmonic Debye model considering the phonon effects, bulk modulus, heat capacity and thermal expansion coefficient of YVO{sub 4} are calculated within a range of 0-6 GPa and 0-1200 K. - Graphical Abstract: (a) Directional dependence of Young's modulus in zircon-type YVO{sub 4} and (b) projections of the directional dependent Young's modulus in different planes for zircon-type YVO{sub 4}. The units are in GPa. Highlights: Black-Right-Pointing-Pointer This paper systematically studied the physical properties of zircon-type YVO{sub 4} from first-principles calculations. Black-Right-Pointing-Pointer Zircon-type YVO{sub 4} is mechanically stable and it is ductile for B/G>1.75 and v>0.26. Black-Right-Pointing-Pointer Universal elastic anisotropy index A{sup U} for zircon-type YVO{sub 4} is 2.41, so YVO{sub 4} is anisotropic. Black-Right-Pointing-Pointer V-O bonds with shorter bond length and larger Mulliken population make greater contribution to the hardness of YVO{sub 4}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The LSST AGN Data Challenge: Selection Methods

Abstract Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

MO-DE-207A-09: Low-Dose CT Image Reconstruction Via Learning From Different Patient Normal-Dose Images

Purpose: To investigate a novel low-dose CT (LdCT) image reconstruction strategy for lung CT imaging in radiation therapy. Methods: The proposed approach consists of four steps: (1) use the traditional filtered back-projection (FBP) method to reconstruct the LdCT image; (2) calculate structure similarity (SSIM) index between the FBP-reconstructed LdCT image and a set of normal-dose CT (NdCT) images, and select the NdCT image with the highest SSIM as the learning source; (3) segment the NdCT source image into lung and outside tissue regions via simple thresholding, and adopt multiple linear regression to learn high-order Markov random field (MRF) pattern for each tissue region in the NdCT source image; (4) segment the FBP-reconstructed LdCT image into lung and outside regions as well, and apply the learnt MRF prior in each tissue region for statistical iterative reconstruction of the LdCT image following the penalized weighted least squares (PWLS) framework. Quantitative evaluation of the reconstructed images was based on the signal-to-noise ratio (SNR), local binary pattern (LBP) and histogram of oriented gradients (HOG) metrics. Results: It was observed that lung and outside tissue regions have different MRF patterns predicted from the NdCT. Visual inspection showed that our method obviously outperformed the traditional FBP method. Comparing with the region-smoothing PWLS method, our method has, in average, 13% increase in SNR, 15% decrease in LBP difference, and 12% decrease in HOG difference from reference standard for all regions of interest, which indicated the superior performance of the proposed method in terms of image resolution and texture preservation. Conclusion: We proposed a novel LdCT image reconstruction method by learning similar image characteristics from a set of NdCT images, and the to-be-learnt NdCT image does not need to be scans from the same subject. This approach is particularly important for enhancing image quality in radiation therapy.

60 APPLIED LIFE SCIENCES↗