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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Atomic density effects on temperature characteristics and thermal transport at grain boundaries through a proper bin size selection

This study focuses on the proper characterization of temperature profiles across grain boundaries (GBs) in order to calculate the correct interfacial thermal resistance (ITR) and reveal the influence of GB geometries onto thermal transport. The solid-solid interfaces resulting from the orientation difference between the (001), (011), and (111) copper surfaces were investigated. Temperature discontinuities were observed at the boundary of grains due to the phonon mismatch, phonon backscattering, and atomic forces between dissimilar structures at the GBs. We observed that the temperature decreases gradually in the GB area rather than a sharp drop at the interface. As a result, three distinct temperature gradients developed at the GB which were different than the one observed in the bulk solid. This behavior extends a couple molecular diameters into both sides of the interface where we defined a thickness at GB based on the measured temperature profiles for characterization. Results showed dependence on the selection of the bin size used to average the temperature data from the molecular dynamics system. The bin size on the order of the crystal layer spacing was found to present an accurate temperature profile through the GB. We further calculated the GB thickness of various cases by using potential energy (PE) distributions which showed agreement with direct measurements from the temperature profile and validated the proper binning. The variation of grain crystal orientation developed different molecular densities which were characterized by the average atomic surface density (ASD) definition. Our results revealed that the ASD is the primary factor affecting the structural disorders and heat transfer at the solid-solid interfaces. Using a system in which the planes are highly close-packed can enhance the probability of interactions and the degree of overlap between vibrational density of states (VDOS) of atoms forming at interfaces, leading to a reduced ITR. Thus, an accurate understanding of thermal characteristics at the GB can be formulated by selecting a proper bin size.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Microstrain in tetragonal lead-zirconate-titanate: The effect of pressure on the ionic displacements

Piezoelectric materials respond to external stimuli by adjusting atomic positions. In solid-solutions, the changes occurring in atomic scale are very complex since the short- and long-range order are different. Standard methods used in diffraction data analysis fail to model the short-range order accurately. Pressure-induced cation displacements in ferroelectric Pb(Zr{sub 0.45}Ti{sub 0.55})O{sub 3} perovskite oxide are modeled by starting from a short-range order. We show that the model gives the average structure correctly and properly describes the local structure. The origin of the microstrain in lead zirconate titanate is the spatially varying Zr and Ti concentration and atomic distances, which is taken into account in the simulation. High-pressure neutron powder diffraction and simulation techniques are applied for the determination of atomic positions and bond-valences as a function of pressure. Under hydrostatic pressure, the material loses its piezoelectric properties far before the transition to the cubic phase takes place. The total cation valence +6 is preserved up to 3.31 GPa by compensating the increasing B-cation valence by decreasing Pb-displacement from the high-symmetry position. At 3.31 GPa, Pb-displacement is zero and the material is no more ferroelectric. This is also the pressure at which the Pb-valence is minimized. The average structure is still tetragonal. The model for microstrain predicts that the transition occurs over a finite pressure range: Pb-displacements are spatially varying and follow the distribution of Zr and Ti ions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

First-Principles Simulations of CuCl in High-Temperature Water Vapor

Experimental data suggest that the solubility of copper in high-temperature water vapor is controlled by the formation of hydrated clusters of the form CuCl(H 2 O) n , where the average number of water molecules in the cluster generally increases with increasing density [Migdisov, A. A.; et al. Geochim. Cosmochim. Acta2014, 129, 33–53]. However, the precise nature of these clusters is difficult to probe experimentally. Moreover, there are some discrepancies between experimental estimates of average cluster size and prior simulation work [Mei, Y. Geofluids2018, 2018, 4279124]. Here, we have performed first-principles Monte Carlo (MC) and molecular dynamics (MD) simulations to explore these clusters in finer detail. We find that molecular dynamics is not the most appropriate technique for studying aggregation in vapor phases, even at relatively high temperatures. Specifically, our MD simulations exhibit substantial problems in adequately sampling the equilibrium cluster size distribution. In contrast, MC simulations with specialized cluster moves are able to accurately sample the phase space of hydrogen-bonding vapors. At all densities, we find a stable, slightly distorted linear H 2 O–Cu–Cl structure, which is in agreement with the earlier simulations, surrounded by a variable number of water molecules. The surrounding water molecules do not form a well-defined second solvation shell but rather a loose network of hydrogen-bonded water with molecular CuCl on the outside edge of the water cluster. We also find a broad distribution of hydration numbers, especially at higher densities. In contrast to previous simulation work but in agreement with experimental data, we find that the average hydration number substantially increases with increasing density. Moreover, the value of the hydration number depends on the choice of cluster definition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Network analysis of earthquake ground motion spatial correlation: a case study with the San Jacinto seismic nodal array

SUMMARY The spatial correlation of earthquake ground motion intensity can be measured from strong motion data; however, the data used in past studies is sparsely sampled in space, and only the interstation distance was considered as a correlation variable. These limitations mean that we have only weak constraints on the true correlation structure of ground motion and that potentially important aspects of spatial correlation are unconstrained. In this study, we combine a large-N seismic array and graph analytics to explore this issue at a local scale using small local and regional earthquakes. Our result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements, of seismic wavefields. Aftershock sequences of major earthquakes would provide particularly data-rich targets of opportunity.

58 GEOSCIENCES↗
DOE OSTI2023

Assessment of Sodium Thermal Stratification Models Utilizing the TSTF Benchmark

As a result of certain transient scenarios, a thermally stratified layer of liquid sodium can develop in the bulk coolant volumes of a sodium-cooled fast reactor (SFR). In addition to the effects a stratification layer has on the temperature of the heat transport system, a stratification layer can also influence the transition to and establishment of natural circulation flow, which plays an important role in passive cooling and the inherent safety of a pool-type SFR. Therefore, the ability to accurately capture thermal stratification phenomena is important when demonstrating the safety basis of a pool-type SFR during transient sequences. The present work assesses various computational models with different fidelities in their ability to predict thermal stratification in the upper plenum of an SFR. Each computational model will be assessed using the data generated at the Thermal Stratification Test Facility (TSTF) located at the University of Wisconsin-Madison. Using measured flow rate and inlet temperature data, the measured temperature distributions of the tests are compared to the predictions of the lumped volume-based models in SAS4A/SASSYS-1, a 1D-based model in SAM, and a 3-D computational fluid dynamics (CFD) model using STAR-CCM+. The relative performance of the various computational methods is assessed with respect to key metrics such as bulk coolant temperature distribution and plenum exit temperature. A total of eight tests are analyzed, covering different combinations of flow rates (3 and 10 GPM) and upper internal structure (UIS) configurations (none, solid, porous, and open) The perfect mixing model of SAS4A/SASSYS-1 provides the highest accuracy when the flow rate is high and there is no UIS in the test vessel, as high flow rate injection promotes thermal mixing of the sodium in the test vessel. For most of the analyzed tests, the stratified volume model of SAS4A/SASSYS-1 is able to predict the delay in the outlet temperature drop and temperature distribution in the test vessel by a small number of layers to represent thermal stratification. However, the stratified volume model can only simulate a maximum of three temperature layers within a volume and when a layer approaches the elevation of the outlet, the predicted outlet temperature can demonstrate rapid, non-physical changes. The 1-D axial mixing model of SAM provides results that agree reasonably well with the measured data in the prediction of the temporal evolution of the outlet temperature with the exception of the case with a high flow rate and no UIS. The SAM 1-D model has a similar level of accuracy to CFD results when it comes to predicting the outlet temperature. CFD shows overall good agreement in predicting the temperature distribution in the test vessel and outlet temperature. As CFD can model the test vessel geometry in detail, it performs well in the cases of complex geometries such as tests that included a UIS and internal flow through the UIS resulting in active mixing of the coolant in the test vessel. Each of the models discussed in the present work has the potential to be useful during the various stages of reactor design, analysis, and licensing. The lumped-volume approach can be applied for fast turnaround safety calculations to obtain overall reactor behavior during transients. The 1-D models provide improved accuracy when stratification is expected for a relatively low increase in the computational cost. The CFD model can be utilized for confirmatory analysis of the 1-D model, when experimental measurements are not available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Machine-learning prediction for quasiparton distribution function matrix elements

There have been rapid developments in the direct calculation in lattice QCD (LQCD) of the Bjorken-x dependence of hadron structure through large-momentum effective theory (LaMET). LaMET overcomes the previous limitation of LQCD to moments (that is, integrals over Bjorken x) of hadron structure, allowing LQCD to directly provide the kinematic regions where the experimental values are least known. LaMET requires large-momentum hadron states to minimize its systematics and allow us to reach small- x reliably. This means that very fine lattice spacing to minimize lattice artifacts at order $(P_za)^n$ will become crucial for next-generation LaMET-like structure calculations. Furthermore, such calculations require operators with long Wilson-link displacements, especially in finer lattice units, increasing the communication costs relative to that of the propagator inversion. In this work, we explore whether machine-learning algorithms can make predictions of correlators to reduce the computational cost of these LQCD calculations. We consider two algorithms, gradient-boosting decision tree and linear models, applied to LaMET data, the matrix elements needed to determine the kaon and $η_s$ unpolarized parton distribution functions (PDFs), meson distribution amplitude (DA), and the nucleon gluon PDF. We find that both algorithms can reliably predict the target observables with different prediction accuracy and systematic errors. The predictions from smaller displacement z to larger ones work better than those for momentum p due to the higher correlation among the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Quantification of Organic Porosity and Water Accessibility in Marcellus Shale Using Neutron Scattering

Pores within organic matter (OM) are a significant contributor to the total pore system in gas shales. These pores contribute most of the storage capacity in gas shales. Here we present a novel approach to characterize the OM pore structure (including the porosity, specific surface area, pore size distribution, and water accessibility) in Marcellus shale. By using ultrasmall and small-angle neutron scattering, and by exploiting the contrast matching of the shale matrix with suitable mixtures of deuterated and protonated water, both total and water-accessible porosity were measured on centimeter-sized samples from two boreholes from the nanometer to micrometer scale with good statistical coverage. Samples were also measured after combustion at 450 °C. Analysis of scattering data from these procedures allowed quantification of OM porosity and water accessibility. OM hosts 24–47% of the total porosity for both organic-rich and -poor samples. This porosity occupies as much as 29% of the OM volume. In contrast to the current paradigm in the literature that OM porosity is organophilic and therefore not likely to contain water, our results demonstrate that OM pores with widths >20 nm exhibit the characteristics of water accessibility. In conclusion, our approach reveals the complex structure and wetting behavior of the OM porosity at scales that are hard to interrogate using other techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Influence of nonstatistical properties in nuclear structure on emission of prompt fission neutrons

We report the Hauser-Feshbach fission-fragment decay (HF 3 D) model is extended to calculate the prompt fission neutron spectrum (PFNS) for the thermal-neutron-induced fission on 235 U, where the evaporated neutrons from all possible fission-fragment pairs are aggregated. By studying model parameter sensitivities on the calculated PFNS as well as nonstatistical behavior of low-lying discrete level spin distribution, we conclude that discrepancies between the aggregation calculation and the experimental PFNS seen at higher neutron emission energies can be attributed to both the primary fission-fragment yield distribution and the possible high spin states that are not predicted by the statistical theory of nuclear structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Yang, Yi↗
DOE OSTI2015

United polarizable multipole water model for molecular mechanics simulation

We report the development of a united AMOEBA (uAMOEBA) polarizable water model, which is computationally 3–5 times more efficient than the three-site AMOEBA03 model in molecular dynamics simulations while providing comparable accuracy for gas-phase and liquid properties. In this coarse-grained polarizable water model, both electrostatic (permanent and induced) and van der Waals representations have been reduced to a single site located at the oxygen atom. The permanent charge distribution is described via the molecular dipole and quadrupole moments and the many-body polarization via an isotropic molecular polarizability, all located at the oxygen center. Similarly, a single van der Waals interaction site is used for each water molecule. Hydrogen atoms are retained only for the purpose of defining local frames for the molecular multipole moments and intramolecular vibrational modes. The parameters have been derived based on a combination of ab initio quantum mechanical and experimental data set containing gas-phase cluster structures and energies, and liquid thermodynamic properties. For validation, additional properties including dimer interaction energy, liquid structures, self-diffusion coefficient, and shear viscosity have been evaluated. The results demonstrate good transferability from the gas to the liquid phase over a wide range of temperatures, and from nonpolar to polar environments, due to the presence of molecular polarizability. The water coordination, hydrogen-bonding structure, and dynamic properties given by uAMOEBA are similar to those derived from the all-atom AMOEBA03 model and experiments. Thus, the current model is an accurate and efficient alternative for modeling water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Cosmological constraints from the tomographic cross-correlation of DESI Luminous Red Galaxies and Planck CMB lensing

We use luminous red galaxies selected from the imaging surveys that are being used for targeting by the Dark Energy Spectroscopic Instrument (DESI) in combination with CMB lensing maps from the Planck collaboration to probe the amplitude of large-scale structure over 0.4 ≤ z ≤ 1. Our galaxy sample, with an angular number density of approximately 500 deg -2 over 18,000 sq.deg., is divided into 4 tomographic bins by photometric redshift and the redshift distributions are calibrated using spectroscopy from DESI. We fit the galaxy autospectra and galaxy-convergence cross-spectra using models based on cosmological perturbation theory, restricting to large scales that are expected to be well described by such models. Within the context of ΛCDM, combining all 4 samples and using priors on the background cosmology from supernova and baryon acoustic oscillation measurements, we find S 8 = σ 8 (Ωm/0.3) 0.5 = 0.73 ± 0.03. This result is lower than the prediction of the ΛCDM model conditioned on the Planck data. Furthermore, our data prefer a slower growth of structure at low redshift than the model predictions, though at only modest significance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

4D Imaging of ZnO-Coated Nanoporous Al 2 O 3 Aerogels by Chemically Sensitive Ptychographic Tomography: Implications for Designer Catalysts

The 3D chemical structure (4D spectromicroscopy) of nanoporous Al 2 O 3 aerogels coated with ZnO by atomic layer deposition (ALD) was studied by multienergy scanning transmission X-ray microscopy. These materials are representative of a class of designer catalysts in which the nanoporous support is prepared separately from the active catalyst material, which is subsequently introduced by ALD, thereby allowing independent optimization of the morphology, chemistry, and spatial distribution of the support and catalyst. The samples studied were prepared by Ga ion and Xe plasma focused ion beam (FIB) milling as well as drop casting from water suspension. Zn L and Al K edge spectra of six samples with three different ZnO loadings were measured to investigate how loading and different sample preparation methods affect the 3D distribution of the ZnO and Al 2 O 3 . Scanning transmission X-ray microscopy (STXM) and ptychographic imaging at two energies each at the Zn L 3 and Al K edge were measured. The ptychography data were analyzed by using the SHARP reconstruction code to generate quantitative 2D chemical maps of the Al 2 O 3 and the ZnO. The STXM and ptychography maps were then measured at a sequence of tilt angles, covering up to 160° of rotation. The 3D structure of the ZnO and Al 2 O 3 was derived from the tilt series data by tomographic reconstruction using a compressed sensing algorithm. A twodimensional spatial resolution (half-period) of 6 nm, measured by Fourier ring correlation, and a 3D spatial resolution (half-period) of 9 nm, measured by Fourier shell correlation, were achieved when using the COSMIC beamline at the Advanced Light Source (ALS). The results show that for all of the ZnO loadings studied there is nonuniform coverage of the ZnO on the Al 2 O 3 aerogel framework. In addition, we found that both FIB methods create sample artifacts, although the distortion was less with Xe plasma than Ga ion FIB.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Application and Certification of Comparative Vacuum Monitoring Sensors for Structural Health Monitoring of 737 Wing Box Fittings

Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft and space vehicles. The use of in-situ sensors for real-time health monitoring of aircraft structures are a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems are able to detect incipient damage before catastrophic failures occurs. Condition-based maintenance practices could be substituted for the current time-based maintenance approach. Other advantages of on-board distributed sensor systems are that they can eliminate costly, and potentially damaging, disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time- consuming manual inspections. This report presents a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Boeing, Delta Air Lines, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp. and the Federal Aviation Administration (FAA) carried out a certification program to formally introduce Comparative Vacuum Monitoring (CVM) as a structural health monitoring solution to a specific aircraft wing box application. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM into an airline maintenance program and the document revisions necessary to allow for routine use of CVM as an alternate means of performing periodic structural inspects. To carry out the validation process, knowledge of aircraft maintenance practices was coupled with an unbiased, independent evaluation. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically-relevant experimental effort to quantify the reliability of the CVM system applied to the Boeing 737 Wing Box fitting application. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for CVM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and also is acceptable to both the aviation industry and regulatory bodies. This report presents the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discusses how they can converge to produce a confident quantification of SHM performance An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system. This report describes the major elements of an SHM validation approach and differentiates the SHM elements from those found in NDI validation. The activities conducted in this program demonstrated the feasibility of routine SHM usage in general and CVM in particular for the application selected. They also helped establish an optimum OEM-airline-regulator process and determined how to safely adopt SHM solutions. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM technology. It will also streamline the regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.

42 ENGINEERING↗
DOE OSTI2022

Lowest-energy broad 𝛼-cluster resonances in 19 F

There is strong astrophysical interest in the structure of 19 F states near the α-decay threshold, as their properties are important for understanding the development of α clustering in the 20 Ne region. The emergence of clustered states, and more generally of states that couple strongly to reaction channels near their decay thresholds, is also a topic of current interest in theoretical nuclear physics. In this work, we determine the parameters of broad low-spin states in 19 F near the α-decay threshold and present a theoretical study of these states. The analysis is restricted to ℓ = 0 and 1 resonances in the α + 15 N system close to the α threshold in 19 F. Excitation functions for 15 N(α,α) elastic scattering were measured using the Thick Target Inverse Kinematics (TTIK) method, and these new data, together with older high–energy-resolution measurements, were analyzed within the R-matrix framework. The nuclear structure of 19 F was calculated using configuration-interaction methods with a recently developed effective interaction Hamiltonian. As a result, we identify a sequence of α-cluster resonances in 19 F and map the distribution of clustering strength, which is relevant for astrophysical reaction modeling and for the theoretical understanding of many-body dynamics and the emergence of clustering in loosely bound or unstable nuclei. Furthermore, the work advances theoretical insight into the origins of clustering and highlights open questions for future theoretical and experimental studies.

19F↗
DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗
DOE OSTI2016

SU-F-T-516: Effects of Inter-Fraction Organ Displacement/deformation On the Delivered Doses to the Heart, Esophagus, and Lungs in Patients Receiving Thoracic Radiotherapy

Purpose: To quantitatively assess the effects of inter-fraction changes in organ shape and location on the delivered dose distribution to the organs at risk (OAR) in lung cancer patients. Methods: This study analyzes treatment data of 10 patients, who were treated to 60Gy in 30 fractions. In each fraction a cone beam CT (CBCT) was acquired. Each CBCT was registered with the planning CT using deformable registration tools within MIM Software. The daily setup shifts were used to translate the planned dose distribution on the deformed planning CT. The structures of lungs, esophagus and heart were re-delineated by a physician on each CBCT. The doses delivered to each OAR, reflecting changes in the position and shape variations, were recomputed. Resultant daily dose volume histograms (DVHs) for OARs were computed and compared to those from the planning CT. Results: Based on the findings of two patients and 24 CBCTs analyzed so far, higher doses are delivered to the lungs and esophagus compared to the treatment plan. The dose differences per fraction between the delivered doses and those in the treatment plan are: for patient 1, lung mean dose = 5.3±1.3cGy and esophagus mean dose = 3.4±3.5cGy. For patient 2, lung mean dose = 12.0±3.9cGy and esophagus mean dose = 34.2±7.5cGy. Regarding the maximum dose to heart, the results varied (−18.9±22.0cGy for patient1 and 53.0±62.2cGy for patient2). Conclusion: The dosimetric effects of inter-fractional anatomical variations could be estimated using deformable image registration and manual organ segmentation for each CBCT. A considerable dose distribution variation between fractions was observed for the OARs. These changes are currently not taken into account while treating the patients and these may explain cases with severe side effects even when the treatment plan looks satisfactory. These results suggest the need for automated daily dose tracking and accumulation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Deeply Virtual Pseudoscalar Meson Production at Jefferson Lab and Transversity GPDs

Transverse-momentum dependent parton distribution functions (TMDs) provide a description of nucleon structure in terms of the parton transverse momentum and its transverse spin. At leading twist there are eight TMDs, each offering a unique feature of quarks in a polarized or an unpolarized nucleon. The Sivers distribution is one of the most interesting TMD due to its non-universality. It has been extracted using the data from semi-inclusive deep-inelastic scattering (SIDIS), but there is no data yet from spin-dependent Drell-Yan (DY) process. Such measurement will provide a crucial test of TMD formalism which predicts an equal magnitude and opposite sign for the Sivers function extracted from SIDIS and DY process. We will discuss key future measurements of TMDs using both SIDIS and DY process with a focus on Hall A SoLID SIDIS program at Jefferson Lab.

Kubarovsky, Valery P.↗
DOE OSTI2022

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.

47 OTHER INSTRUMENTATION↗