Search NASA⌕ Search

SEARCH · Search NASA

Results for “spatial data visualization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

WE-A-BRD-00: MR Basics II

Ever since its introduction as a diagnostic imaging modality over 30 years ago, the radiation therapy community has acknowledged the utility of MR imaging as a tool for not only improved visualization of the target volume but also for demarcation of adjacent organs at risk. However, the adaptation of MR imaging in radiation oncology has, until recently been slow due in large part to the inability to image radiation therapy patients in their treatment position. With the introduction of so-called wide bore high field MR scanners, multi element flexible receive only RF coils, high performance imaging gradients and a range of volumetric imaging sequences it is now possible to obtain both high resolution and high signal-to-noise ratio images of in-treatment radiation therapy patients within clinically feasible imaging times. As a Result, there is renewed interest in the use of MR imaging for radiation oncology treatment planning that is being translated into physical siting and integration of these systems into radiation oncology departments. As MR imaging expands into the radiation oncology domain there is a significant and unmet need for radiation therapy physicists to become educated regarding the strengths, limitations and technical challenges associated with MR imaging. The purpose of this presentation is to address this need by providing an educational overview of the techniques and challenges associated with MR imaging of patients for radiation therapy treatment planning. As such this presentation will: 1) describe the fundamental differences between imaging of patients for diagnostic and therapeutic purposes (i.e. radiation therapy planning), 2) describe most commonly used imaging sequences and contrasts for identification of disease for radiation planning, 3) identify the most common sources of image distortion and techniques to reduce their effect on spatial fidelity of the MR data, 4) describe the effects of motion and methods to quantify/correct it, and 5) identify emergent techniques for performing MR only treatment simulation. Upon completion attendees will have a working understanding of the basic methodologies associated with MR imaging in radiation oncology, the unique technical challenges imposed by MR imaging in the treatment position and techniques to address these. Learning Objectives: 1. Understand the differences between MR imaging for diagnostic imaging and for radiation therapy planning. 2. Identify the most common sources of distortion and artifacts and simple methods to correct them. 3. Understand the challenges with MR imaging in the therapy treatment position and appropriate techniques to address them.

60 APPLIED LIFE SCIENCES↗

WE-A-BRD-01: MR Imaging for Treatment Planning: What Every Physicist Should Know

Ever since its introduction as a diagnostic imaging modality over 30 years ago, the radiation therapy community has acknowledged the utility of MR imaging as a tool for not only improved visualization of the target volume but also for demarcation of adjacent organs at risk. However, the adaptation of MR imaging in radiation oncology has, until recently been slow due in large part to the inability to image radiation therapy patients in their treatment position. With the introduction of so-called wide bore high field MR scanners, multi element flexible receive only RF coils, high performance imaging gradients and a range of volumetric imaging sequences it is now possible to obtain both high resolution and high signal-to-noise ratio images of in-treatment radiation therapy patients within clinically feasible imaging times. As a Result, there is renewed interest in the use of MR imaging for radiation oncology treatment planning that is being translated into physical siting and integration of these systems into radiation oncology departments. As MR imaging expands into the radiation oncology domain there is a significant and unmet need for radiation therapy physicists to become educated regarding the strengths, limitations and technical challenges associated with MR imaging. The purpose of this presentation is to address this need by providing an educational overview of the techniques and challenges associated with MR imaging of patients for radiation therapy treatment planning. As such this presentation will: 1) describe the fundamental differences between imaging of patients for diagnostic and therapeutic purposes (i.e. radiation therapy planning), 2) describe most commonly used imaging sequences and contrasts for identification of disease for radiation planning, 3) identify the most common sources of image distortion and techniques to reduce their effect on spatial fidelity of the MR data, 4) describe the effects of motion and methods to quantify/correct it, and 5) identify emergent techniques for performing MR only treatment simulation. Upon completion attendees will have a working understanding of the basic methodologies associated with MR imaging in radiation oncology, the unique technical challenges imposed by MR imaging in the treatment position and techniques to address these. Learning Objectives: 1. Understand the differences between MR imaging for diagnostic imaging and for radiation therapy planning. 2. Identify the most common sources of distortion and artifacts and simple methods to correct them. 3. Understand the challenges with MR imaging in the therapy treatment position and appropriate techniques to address them.

60 APPLIED LIFE SCIENCES↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗

Neutron tomography of axially symmetric objects using 14 MeV neutrons from a portable neutron generator

In nuclear boiling water reactor cores, the distribution of water and steam (void) is essential for both safety and efficiency reasons. In order to enhance predictive capabilities, void distribution assessment is performed in two-phase test-loops under reactor-relevant conditions. This article proposes the novel technique of fast-neutron tomography using a portable deuterium-tritium neutron generator to determine the time-averaged void distribution in these loops. Fast neutrons have the advantage of high transmission through the metallic structures and pipes typically concealing a thermal-hydraulic test loop, while still being fairly sensitive to the water/void content. However, commercially available fast-neutron generators also have the disadvantage of a relatively low yield and fast-neutron detection also suffers from relatively low detection efficiency. Fortunately, some loops are axially symmetric, a property which can be exploited to reduce the amount of data needed for tomographic measurement, thus limiting the interrogation time needed. In this article, three axially symmetric test objects depicting a thermal-hydraulic test loop have been examined; steel pipes with outer diameter 24 mm, thickness 1.5 mm, and with three different distributions of the plastic material POM inside the pipes. Data recorded with the FANTOM fast-neutron tomography instrument have been used to perform tomographic reconstructions to assess their radial material distribution. Here, a dedicated tomographic algorithm that exploits the symmetry of these objects has been applied, which is described in the paper. Results are demonstrated in 20 rixel (radial pixel) reconstructions of the interior constitution and 2D visualization of the pipe interior is demonstrated. The local POM attenuation coefficients in the rixels were measured with errors (RMS) of 0.025, 0.020, and 0.022 cm{sup −1}, solid POM attenuation coefficient. The accuracy and precision is high enough to provide a useful indication on the flow mode, and a visualization of the radial material distribution can be obtained. A benefit of this system is its potential to be mounted at any axial height of a two-phase test section without requirements for pre-fabricated entrances or windows. This could mean a significant increase in flexibility of the void distribution assessment capability at many existing two-phase test loops.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

AGR-3/4 Irradiation Test Train Disassembly and Component Metrology First Look Report

The AGR-3/4 experiment was designed to study fission product transport within graphitic matrix material and nuclear-grade graphite. To this end, this experiment consisted of 12 capsules, each fueled with 4 compacts containing UCO TRISO particles as driver fuel and 20 UCO designed-to-fail (DTF) fuel particles in each compact. The DTF fuel was fabricated with a thin pyrocarbon layer which was intended to fail during irradiation and provide a source of fission products. These fission products could then migrate through the compact and into the surrounding concentric rings of graphitic matrix material and/or nuclear graphite. Through post-irradiation examination (PIE) of the rings (including physical sampling and gamma scanning) fission product concentration profiles within the rings can be determined. These data can be used to elucidate fission product transport parameters (e.g. diffusion coefficients within the test materials) which will be used to inform and refine models of fission product transport. After irradiation in the Advanced Test Reactor (ATR) had been completed in April 2014, the AGR-3/4 experiment was shipped to the Hot Fuel Examination Facility (HFEF) at the Materials and Fuels Complex (MFC) for inspection, disassembly, and metrology. The AGR-3/4 test train was received at MFC in two separate shipments between February and April 2015. Visual examinations of the test train exterior did not indicate dimensional distortion, and only two small discolored areas were observed at the bottom of Capsules 8 and 9. No corresponding discoloration was found on the inside of these capsules, however. Prior to disassembly, the two test train sections were subject to analysis via the Precision Gamma Scanner (PGS), which did not indicate that any gross fuel relocation had occurred. A series of specialized tools (including clamps, cutters, and drills) had been designed and fabricated in order to carry out test train disassembly and recovery of capsule components (graphite rings and fuel compacts). This equipment performed well for separating each capsule in the test train and extracting the capsule components. Only a few problems were encountered. In one case, the outermost ring (the sink ring) was cracked during removal of the capsule through tubes. Although the sink ring will be analyzed in order to obtain a mass balance of fission products in the experiment, these cracks do not pose a major concern because the sink ring will not be analyzed in detail to obtain the spatial distribution of fission products. In Capsules 4 and 5, the compacts could not be removed from the inner rings. Strategies for removing the compacts are being evaluated. Sampling the inner rings with the compacts in-place is also an option. Dimensional measurements were made on the compacts, inner rings, outer rings, and sink rings. The diameters of all compacts decreased by 0.5 to 2.0 %. Generally, the extent of diametric shrinkage increased linearly with increasing neutron fluence. Most compact lengths also decreased. Compact lengths decreased with increasing fluence, reaching maximum shrinkage of about 0.9 % at a fast fluence of 4.0x10 25 n/m 2 E > 0.18 MeV. Above this fluence, the extent of length shrinkage appeared to decrease with fluence, and two compacts from Capsule 7 were found to have slightly increased in length (< 0.1 %) after a fluence of 5.2x10 25 n/m 2 .

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

AGR-3/4 Irradiation Test Train Disassembly and Component Metrology First Look Report

The AGR-3/4 experiment was designed to study fission product transport within graphitic matrix material and nuclear-grade graphite. To this end, this experiment consisted of 12 capsules, each fueled with 4 compacts containing UCO TRISO particles as driver fuel and 20 UCO designed-to-fail (DTF) fuel particles in each compact. The DTF fuel was fabricated with a thin pyrocarbon layer which was intended to fail during irradiation and provide a source of fission products. These fission products could then migrate through the compact and into the surrounding concentric rings of graphitic matrix material and/or nuclear graphite. Through post-irradiation examination (PIE) of the rings (including physical sampling and gamma scanning) fission product concentration profiles within the rings can be determined. These data can be used to elucidate fission product transport parameters (e.g. diffusion coefficients within the test materials) which will be used to inform and refine models of fission product transport. After irradiation in the Advanced Test Reactor (ATR) had been completed in April 2014, the AGR-3/4 experiment was shipped to the Hot Fuel Examination Facility (HFEF) at the Materials and Fuels Complex (MFC) for inspection, disassembly, and metrology. The AGR-3/4 test train was received at MFC in two separate shipments between February and April 2015. Visual examinations of the test train exterior did not indicate dimensional distortion, and only two small discolored areas were observed at the bottom of Capsules 8 and 9. No corresponding discoloration was found on the inside of these capsules, however. Prior to disassembly, the two test train sections were subject to analysis via the Precision Gamma Scanner (PGS), which did not indicate that any gross fuel relocation had occurred. A series of specialized tools (including clamps, cutters, and drills) had been designed and fabricated in order to carry out test train disassembly and recovery of capsule components (graphite rings and fuel compacts). This equipment performed well for separating each capsule in the test train and extracting the capsule components. Only a few problems were encountered. In one case, the outermost ring (the sink ring) was cracked during removal of the capsule through tubes. Although the sink ring will be analyzed in order to obtain a mass balance of fission products in the experiment, these cracks do not pose a major concern because the sink ring will not be analyzed in detail to obtain the spatial distribution of fission products. In Capsules 4 and 5, the compacts could not be removed from the inner rings. Strategies for removing the compacts are being evaluated. Sampling the inner rings with the compacts in-place is also an option. Dimensional measurements were made on the compacts, inner rings, outer rings, and sink rings. The diameters of all compacts decreased by 0.5 to 2.0 %. Generally, the extent of diametric shrinkage increased linearly with increasing neutron fluence. Most compact lengths also decreased. Compact lengths decreased with increasing fluence, reaching maximum shrinkage of about 0.9 % at a fast fluence of 4.0x1025 n/m2 E > 0.18 MeV. Above this fluence, the extent of length shrinkage appeared to decrease with fluence, and two compacts from Capsule 7 were found to have slightly increased in length (< 0.1 %) after a fluence of 5.2x1025 n/m2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Competing Pathways for Nucleation of the Double Perovskite Structure in the Epitaxial Synthesis of La 2 MnNiO 6

Over the past decades a confluence of advanced synthesis techniques, data-driven characterization, and rapid increases in computing power has sparked a renaissance in materials engineering. The emerging “materials-by-design” approach, while radically transforming the development of multicomponent systems, has tended to overlook the complex kinetic pathways that define materials synthesis. Although we are able to envision almost limitless materials combinations, we are unable to synthesize all of them in practice, since existing characterization and modeling approaches often fail to capture the inherent complexity of such systems. There is currently a disconnect between highly local structural characterization and macroscale properties measurements, resulting in oversimplified or incomplete structure-property models. Here we describe a multi-technique approach that combines aberration-corrected transmission electron microscopy with emerging oxide atom probe tomography to measure chemical ordering and extended defects in the model complex oxide, La2MnNiO6. We visualize cation ordering, as well as a three-dimensional network of secondary phases, which we describe in terms of ab initio structure calculations. We propose a defect model in which these phases alter their surrounding octahedral environment, severely disrupting cation superexchange. Through this array of experimental and theoretical techniques, we uncover fundamental structure-property relationships and illustrate a new approach to engineer complex, multicomponent systems.

atom probe tomography↗

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

X-ray nano-imaging of defects in thin film catalysts via cluster analysis

Functional properties of transition-metal oxides strongly depend on crystallographic defects; crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Yet, localized lattice distortions remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here, in this study, we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SrIrO 3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO 3 , and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments.

42 ENGINEERING↗

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗

The Influence of Soil Properties on Sea-Breeze Circulations in the Southeast U.S.

Sea-breeze circulations (SBCs) are common weather phenomena at and near coastal regions. They form because of a thermal gradient between the land surface at the coast and the sea surface. In a mid-day regime, a “thermal low” generated at the warm coast will lead to rising air motion, creating a wind shift coming from the sea near the surface displacing the coastal air. A “return flow” moving back towards the sea is generated by upper-level divergence because of the rising motion from the thermal low. SBCs propagate and serve as a method of urban pollutant dispersion in the Los Angeles region of California and are constrained to the coast due to the topography of surrounding mountains serving as a boundary for further inland propagation. Within the northeast U.S. SBCs are seen in the warm season but tend to remain coastally bound due to Coriolis distortion over long distances. Within the southeast U.S. (SEUS), the paradigmatic example of SBCs occurs over the Florida peninsula, where thunderstorms form on a nearly daily occurrence due to the convergence of SBCs from the east and west sides of the peninsula. However, there are further examples of sea-breezes in the SEUS that warrant study. Within the region bordering the SEUS and the Mid-Atlantic, just east of the southern Appalachian Mountains, warm-season SBCs form at the coast of Georgia and the Carolinas. Relatively flat topography ~150-200km inland allows for mostly unimpeded inland SBC propagation. Through visual analysis, Viner et al. catalogued several SBCs that propagated as far inland as the Central Savannah River Area surrounding Augusta, Georgia. Wermter et al. found that while the land-sea thermal gradient at the coast can influence coastal SBC genesis, the inland-coastal thermal gradient over the land is the primary influencer on the speed and depth of inland propagation of SBCs in this region. Additionally, soil moisture itself is a known correlative factor to sea-breeze formation, as it influences the soil temperature and the thermal gradient needed for SBC genesis and inland penetration. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Conversely, drier soil enhances the thermal gradient and promotes SBC formation. However, while there is an inverse relationship between soil moisture and SBCs, higher soil moisture can actually promote more convective rainfall following a SBC if it does not significantly impact the thermal gradient. While the relationship between soil moisture and SBC formation has been conceptually explored and modeled numerically, there is a research gap in observed connections. The Soil Moisture Active Passive (SMAP) satellite mission has been operational since 2015 and has been used to create high-resolution re-analytical Level 4 (L4) datasets of soil moisture and soil temperature at different soil depths: the surface (0-5cm) and rootzone (0-1m). The surface soil temperature effectively acts as the “skin temperature” of the surface at these levels, and a spatial map of the land-sea as well as the coastal-inland thermal gradients can be represented. SMAP data are also assimilated in some atmospheric models such at the High Resolution Rapid Refresh (HRRR) mesoscale model. We propose leveraging the use of SMAP products to fill in spatial gaps left by weather and mesonet stations within the SEUS region, as well as assessing the effectiveness of utilizing SMAP products towards SBC forecasting in both deterministic and machine learning (ML) models.

58 GEOSCIENCES↗

Measurement of rabbit eardrum vibration through stroboscopic digital holography

In this work, we present a setup for high-power single shot stroboscopic digital holography and demonstrate it in an application on rabbit eardrum vibration measurement. The setup is able to make full-field time-resolved measurements of vibrating surfaces with a precision in the nanometer range in a broad frequency range. The height displacement of the measured object is visualized over the entire surface as a function of time. Vibration magnitude and phase maps can be extracted from these data, the latter proving to be very useful to reveal phase delays across the surface. Such deviations from modal motion indicate energy losses due to internal damping, in contrast to purely elastic mechanics. This is of great interest in middle ear mechanics and finite element modelling. In our setup, short laser pulses are fired at selected instants within the surface vibration period and are recorded by a CCD camera. The timing of the pulses and the exposure of the camera are synchronized to the vibration phase by a microprocessor. The high-power frequency-doubled Nd:YAG laser produces pulses containing up to 5 mJ of energy, which is amply sufficient to record single-shot holograms. As the laser pulse length is 8 ns and the smallest time step of the trigger electronics is 1 μs, vibration measurements of frequencies up to 250 kHz are achievable through this method, provided that the maximum vibration amplitude exceeds a few nanometers. In our application, middle ear mechanics, measuring frequencies extend from 5 Hz to 20 kHz. The experimental setup will be presented, as well as results of measurements on a stretched circular rubber membrane and a rabbit's eardrum. Two of the challenges when measuring biological tissues, such as the eardrum, are low reflectivity and fast dehydration. To increase reflectivity, a coating is applied and to counteract the undesirable effects of tissue dehydration, the measurement setup and software have been optimized for speed without compromising on the quality. Results of a repeatability test will be presented as well. Since the method measures the membrane motion as a function of time in small time steps, we do not only measure vibration amplitude like in time-average holography, but we can also measure non-linear elastic and transient behaviour. In conclusion, the combination of good spatial, depth and time resolution with the fast data acquisition and very wide frequency range make our technique applicable in a number of fields, including biological tissue vibrations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

PET optimization for improved assessment and accurate quantification of {sup 90}Y-microsphere biodistribution after radioembolization

Purpose: {sup 90}Y-microspheres are widely used for the radioembolization of metastatic liver cancer or hepatocellular carcinoma and there is a growing interest for imaging {sup 90}Y-microspheres with PET. The aim of this study is to evaluate the performance of a current generation PET/CT scanner for {sup 90}Y imaging and to optimize the PET protocol to improve the assessment and the quantification of {sup 90}Y-microsphere biodistribution after radioembolization. Methods: Data were acquired on a Biograph mCT-TrueV scanner with time of flight (TOF) and point spread function (PSF) modeling. Spatial resolution was measured with a{sup 90}Y point source. Sensitivity was evaluated using the NEMA 70 cm line source filled with {sup 90}Y. To evaluate the count rate performance, {sup 90}Y vials with activity ranging from 3.64 to 0.035 GBq were measured in the center of the field of view (CFOV). The energy spectrum was evaluated. Image quality with different reconstructions was studied using the Jaszczak phantom containing six hollow spheres (diameters: 31.3, 28.1, 21.8, 16.1, 13.3, and 10.5 mm), filled with a 207 kBq/ml {sup 90}Y concentration and a 5:1 sphere-to-background ratio. Acquisition time was adjusted to simulate the quality of a realistic clinical PET acquisition of a patient treated with SIR-Spheres{sup ®}. The developed methodology was applied to ten patients after SIR-Spheres{sup ®} treatment acquiring a 10 min per bed PET. Results: The energy spectrum showed the{sup 90}Y bremsstrahlung radiation. The {sup 90}Y transverse resolution, with filtered backprojection reconstruction, was 4.5 mm in the CFOV and degraded to 5.0 mm at 10 cm off-axis. {sup 90}Y absolute sensitivity was 0.40 kcps/MBq in the center of the field of view. Tendency of true and random rates as a function of the {sup 90}Y activity could be accurately described using linear and quadratic models, respectively. Phantom studies demonstrated that, due to low count statistics in {sup 90}Y PET acquisition, the optimal parameters for the standard OSEM+PSF reconstruction were only one iteration and a postreconstruction filter of 6 mm FWHM, for both TOF and non-TOF reconstructions. Moreover, when TOF is included, the signal to noise ratio increased and visibility achieved 100% by the experienced observers and 93.3% according to the Rose model of statistical detection. In 50% of patients, TOF allowed the visualization of {sup 90}Y radioembolized lesions not seen without TOF, confirming phantom results. Liver activity was accurately quantified, with no significant differences between reconstructed and actual delivered activity to the whole-liver [mean relative difference (10.2 ± 14.7)%]. Conclusions: Qualitative and quantitative{sup 90}Y PET imaging improved with the introduction of TOF in a PET/CT scanner, thereby allowing the visualization of microsphere deposition in lesions not visible in non-TOF images. This technique accurately quantifies the total activity delivered to the liver during radioembolization with {sup 90}Y-microspheres and allows dose estimation.

60 APPLIED LIFE SCIENCES↗

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗