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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 OSTI2019

Native Plasmid-Encoded Mercury Resistance Genes Are Functional and Demonstrate Natural Transformation in Environmental Bacterial Isolates

Plasmid-mediated horizontal gene transfer (HGT) is a major driver of genetic diversity in bacteria. We experimentally validated the function of a putative mercury resistance operon present on an abundant 8-kbp native plasmid found in groundwater samples without detectable levels of mercury. Phylogenetic analyses of the plasmid-encoded mercury reductases from the studied groundwater site show them to be distinct from those reported in proximal metal-contaminated sites. We synthesized the entire native plasmid and demonstrated that the plasmid was sufficient to confer functional mercury resistance in Escherichia coli . Given the possibility that natural transformation is a prevalent HGT mechanism in the low-cell-density environments of groundwaters, we also assayed bacterial strains from this environment for competence. We used the native plasmid-encoded metal resistance to design a screen and identified 17 strains positive for natural transformation. We selected 2 of the positive strains along with a model bacterium to fully confirm HGT via natural transformation. From an ecological perspective, the role of the native plasmid population in providing advantageous traits combined with the microbiome’s capacity to take up environmental DNA enables rapid adaptation to environmental stresses. Horizontal transfer of mobile genetic elements via natural transformation has been poorly understood in environmental microbes. Here, we confirm the functionality of a native plasmid-encoded mercury resistance operon in a model microbe and then query for the dissemination of this resistance trait via natural transformation into environmental bacterial isolates. We identified 17 strains including Gram-positive and Gram-negative bacteria to be naturally competent. These strains were able to successfully take up the plasmid DNA and obtain a clear growth advantage in the presence of mercury. Our study provides important insights into gene dissemination via natural transformation enabling rapid adaptation to dynamic stresses in groundwater environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Numerical methods for the weakly compressible Generalized Langevin Model in Eulerian reference frame

A well established approach for the computation of turbulent flow without resolving all turbulent flow scales is to solve a filtered or averaged set of equations, and to model non-resolved scales by closures derived from transported probability density functions (PDF) for velocity fluctuations. Effective numerical methods for PDF transport employ the equivalence between the Fokker–Planck equation for the PDF and a Generalized Langevin Model (GLM), and compute the PDF by transporting a set of sampling particles by GLM (Pope (1985) [1]). The natural representation of GLM is a system of stochastic differential equations in a Lagrangian reference frame, typically solved by particle methods. A representation in a Eulerian reference frame, however, has the potential to significantly reduce computational effort and to allow for the seamless integration into a Eulerian-frame numerical flow solver. GLM in a Eulerian frame (GLMEF) formally corresponds to the nonlinear fluctuating hydrodynamic equations derived by Nakamura and Yoshimori (2009) [12]. Unlike the more common Landau–Lifshitz Navier–Stokes (LLNS) equations these equations are derived from the underdamped Langevin equation and are not based on a local equilibrium assumption. Similarly to LLNS equations the numerical solution of GLMEF requires special considerations. In this paper we investigate different numerical approaches to solving GLMEF with respect to the correct representation of stochastic properties of the solution. We find that a discretely conservative staggered finite-difference scheme, adapted from a scheme originally proposed for turbulent incompressible flow, in conjunction with a strongly stable (for non-stochastic PDE) Runge–Kutta method performs better for GLMEF than schemes adopted from those proposed previously for the LLNS. We show that equilibrium stochastic fluctuations are correctly reproduced.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

TH-AB-209-04: 3D Light Sheet Luminescence Imaging with Cherenkov Radiation

Purpose: To recover a three-dimensional density distribution of luminescent molecular probes located several centimeters deep within a highly scattering tissue. Methods: We developed a novel sheet beam Cherenkov-excited luminescence scanned imaging (CELSI) methodology. The sample was irradiated by a horizontally oriented, vertically scanned 6 MV X-ray sheet beam (200mm × 5mm, 0.2mm vertical step) from a radiotherapy linear accelerator. The resulting Cherenkov light emission – and thus luminescent probe excitation – occurred exclusively along the irradiation plane due to a short diffusion path of secondary particles and Cherenkov photons. Cherenkov-excited luminescence was detected orthogonally to the sheet beam by gated, intensified charge coupled device camera. Analogously to light sheet microscopy, a series of luminescence images was taken for varied axial positions (depths) of the Cherenkov light sheet in sample. Knowledge of the excitation plane position allowed a 3D image stack deconvolution and depth-variant attenuation correction. The 3D image post-processing yielded a true spatial density distribution of luminescent molecules in highly scattering tissue. Results: We recovered a three-dimensional shape and position of 400 µL lesion-mimicking phantom tubes containing 25 µM solution of PtG4 molecular probe from 3 centimeter deep tissue-like media. The high sensitivity of CELSI also allowed resolving 100 micron capillaries of test solution. Functional information of partial oxygen pressure at the site of PtG4 molecular probe was recovered from luminescence lifetime CELSI. Finally, in-vivo sheet beam CELSI localized milimeter-sized PtG4-labelled tumor phantoms in multiple biological objects (hairless mice) from single scan. Conclusion: Presented sheet beam CELSI technique greatly extended the useful depth range of luminescence molecular imaging. More importantly, the light sheet microscopy approach was successfully adapted to CELSI, providing means to recover a completely attenuation-corrected 3D image of luminescent probe distribution. Gated CELSI acquisition yielded functional information of a spatially resolved oxygen concentration map of deep lying targets. This work was supported by NIH research grant R01CA109558 and R21EB017559, as well as by Pilot Grant Funds from the Norris Cotton Cancer Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

CHARACTERIZING THE COOL KOIs. II. THE M DWARF KOI-254 AND ITS HOT JUPITER

We report the confirmation and characterization of a transiting gas giant planet orbiting the M dwarf KOI-254 every 2.455239 days, which was originally discovered by the Kepler mission. We use radial velocity measurements, adaptive optics imaging, and near-infrared spectroscopy to confirm the planetary nature of the transit events. KOI-254 b is the first hot Jupiter discovered around an M-type dwarf star. We also present a new model-independent method of using broadband photometry to estimate the mass and metallicity of an M dwarf without relying on a direct distance measurement. Included in this methodology is a new photometric metallicity calibration based on J - K colors. We use this technique to measure the physical properties of KOI-254 and its planet. We measure a planet mass of M{sub P} = 0.505 M{sub Jup}, radius R{sub P} = 0.96 R{sub Jup}, and semimajor axis a = 0.030 AU, based on our measured stellar mass M{sub *} = 0.59 M{sub Sun} and radius R{sub *} = 0.55 R{sub Sun }. We also find that the host star is metal-rich, which is consistent with the sample of M-type stars known to harbor giant planets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
DOE OSTI2018

Thermoacoustic range verification in the presence of acoustic heterogeneity and soundspeed errors - Robustness relative to ultrasound image of underlying anatomy

Purpose: To demonstrate robustness of thermooacoustic range verification to acoustic heterogeneity and discrepancies between assumed and true propagation speed, i.e., soundspeed errors. Methods: A beam sweeper was used to deliver 250 ns pulses that deposited 0.26 Gy of 16 MeV protons and 2.3 Gy of 60 MeV helium ions into water and oil targets, respectively. Thermoacoustic signals were detected by a 96-channel ultrasound array with a 1-4 MHz sensitivity band (-6 dB), bandpass filtered and backprojected to create thermoacoustic images in the plane of the ultrasound array. The same soundspeed and transducer array were used to estimate range and generate the ultrasound images onto which Bragg peak locations were overlaid. An air-gap phantom that displaced the Bragg peak by 6.5 mm demonstrated accuracy. Robustness to soundspeed errors was demonstrated in a waterbath as the assumed propagation speed scanner setting was altered by +/- 5%. Tissue-mimicking gelatin and a bone sample were introduced to demonstrate robustness to acoustic heterogeneity relative to ultrasound images of the underlying morphology. Results: Single ion pulse measurements sufficed during the helium run, but signal averaging was required for protons. Moreover, range and entry point into the target were estimated from data collected by transducers placed at least 6 cm distal to the Bragg peak. When ultrasound images depicted the air-target interface where the beam enters, estimates of the entry point agreed with ultrasound images and range estimates agreed with Monte Carlo simulations to within 300 mu m, even when thermoacoustic emissions traveled through a strongly scattering bone sample. Estimated Bragg peak locations were translated 6.5 mm by the air-gap phantom and correctly identified scenarios when the beam stopped inside the bone. Conclusions: Soundspeed errors dilate and acoustic heterogeneities deform ultrasound images. When thermoacoustic receivers are co-located with the ultrasound imaging array, the same transformations shift thermoacoustic range estimates. Hence, thermoacoustic range verification is robust relative to ultrasound images of underlying anatomy. When the treatment target is visible in ultrasound, e.g., prostate, online thermoacoustic range estimates could verify that the treatment spot is inside the target.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗
DOE OSTI2014

The SINS/zC-SINF survey of z ∼ 2 galaxy kinematics: Evidence for gravitational quenching

As part of the SINS/zC-SINF surveys of high-z galaxy kinematics, we derive the radial distributions of Hα surface brightness, stellar mass surface density, and dynamical mass at ∼2 kpc resolution in 19 z ∼ 2 star-forming disks with deep SINFONI adaptive optics spectroscopy at the ESO Very Large Telescope. From these data we infer the radial distribution of the Toomre Q-parameter for these main-sequence star-forming galaxies (SFGs), covering almost two decades of stellar mass (10{sup 9.6}-10{sup 11.5} M {sub ☉}). In more than half of our SFGs, the Hα distributions cannot be fit by a centrally peaked distribution, such as an exponential, but are better described by a ring, or the combination of a ring and an exponential. At the same time the kinematic data indicate the presence of a mass distribution more centrally concentrated than a single exponential distribution for 5 of the 19 galaxies. The resulting Q-distributions are centrally peaked for all, and significantly exceed unity there for three-quarters of the SFGs. The occurrence of Hα rings and of large nuclear Q-values appears to be more common for the more massive SFGs. While our sample is small and biased to larger SFGs, and there remain uncertainties and caveats, our observations are consistent with a scenario in which cloud fragmentation and global star formation are secularly suppressed in gas-rich high-z disks from the inside out, as the central stellar mass density of the disks grows.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2018

Fast Ultrasonic Imaging with Total Focusing Method (TFM) for Inspection of Additively Manufactured Polymer Composite Component

Ultrasonic adaptive imaging based on the phased-array technology and the synthetic focusing algorithm Total Focusing Method (TFM) is proposed for fast ultrasonic nondestructive evaluation of additively manufactured composite materials. Nondestructive evaluation and inspection of additively manufactured parts and components are necessary for quality assurance of the materials. Effective detection of defects and anomalies in manufactured parts prevents extra cost and production time. However, there are several limitations encountered when using conventional methods of nondestructive inspection for as built additively manufactured parts due to surface conditions and geometrical complexity. Post-processing operations, such as machining, are usually needed to detect possible defects such as layer disband, micro-cracks, and voids in the composite parts. The capability of aperture focusing in phased array ultrasonic methods provides the opportunity for the adjustment of the delay laws in ultrasonic beams and reduces the noise and beam distortion for better imaging. An additively manufactured carbon fiber reinforced Acrylonitrile Butadiene Styrene (ABS) composite sample with artificial side and bottom-drilled holes of different sizes, used to represent defects, was successfully inspected using the Total Focusing Method (TFM). Unlike conventional ultrasonic techniques, results show a promising way to provide inspection of as built additively manufactured composite parts with rough surface finishes. The proposed method helps to decrease inspection time and eliminate extra machining and preparation costs.

Taheri, Hossein↗
DOE OSTI2020

A High Angular Resolution Survey of Massive Stars in Cygnus OB2: JHK Adaptive Optics Results from the Gemini Near-Infrared Imager

We present results of a high angular resolution survey of massive OB stars in the Cygnus OB2 association that we conducted with the Near-Infrared Imager camera and ALTAIR adaptive optics system of the Gemini North telescope. We observed 74 O- and early-B-type stars in Cyg OB2 in the JHK infrared bands in order to detect binary and multiple companions. The observations are sensitive to equal-brightness pairs at separations as small as [[Formula]], and progressively fainter companions are detectable out to △K=9 mag at a separation of 2″. This faint contrast limit due to read noise continues out to 10″ near the edge of the detector. We assigned a simple probability of chance alignment to each companion based upon its separation and magnitude difference from the central target star and upon areal star counts for the general star field of Cyg OB2. Companion stars with a field membership probability of less than 1% are assumed to be physical companions. This assessment indicates that 47% of the targets have at least one resolved companion that is probably gravitationally bound. Including known spectroscopic binaries, our sample includes 27 binary, 12 triple, and 9 systems with 4 or more components. These results confirm studies of high-mass stars in other environments that find that massive stars are born with a high-multiplicity fraction. The results are important for the placement of the stars in the Hertzsprung–Russell diagram, the interpretation of their spectroscopic analyses, and for future mass determinations through measurement of orbital motion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

APPLICATION OF VACUUM SALT DISTILLATION TECHNOLOGY FOR THE REMOVAL OF FLUORIDE

Vacuum distillation of chloride salts from plutonium oxide (PuO{sub 2}) and simulant PuO{sub 2} has been previously demonstrated at Department of Energy (DOE) sites using kilogram quantities of chloride salt. The apparatus for vacuum distillation contains a zone heated using a furnace and a zone actively cooled using either recirculated water or compressed air. During a vacuum distillation operation, a sample boat containing the feed material is placed into the apparatus while it is cool, and the system is sealed. The system is evacuated using a vacuum pump. Once a sufficient vacuum is attained, heating begins. Volatile salts distill from the heated zone to the cooled zone where they condense, leaving behind the non-volatile materials in the feed boat. The application of vacuum salt distillation (VSD) is of interest to the HB-Line Facility and the MOX Fuel Fabrication Facility (MFFF) at the Savannah River Site (SRS). Both facilities are involved in efforts to disposition excess fissile materials. Many of these materials contain chloride and fluoride salt concentrations which make them unsuitable for dissolution without prior removal of the chloride and fluoride salts. Between September 2009 and January 2011, the Savannah River National Laboratory (SRNL) and HB-Line designed, developed, tested, and successfully deployed a system for the distillation of chloride salts. Subsequent efforts are attempting to adapt the technology for the removal of fluoride. Fluoride salts of interest are less-volatile than the corresponding chloride salts. Consequently, an alternate approach is required for the removal of fluoride without significantly increasing the operating temperature. HB-Line Engineering requested SRNL to evaluate and demonstrate the feasibility of an alternate approach using both non-radioactive simulants and plutonium-bearing materials. Whereas the earlier developments targeted the removal of sodium chloride (NaCl) and potassium chloride (KCl), the current activities are concerned with the removal of the halide ions associated with plutonium trifluoride (PuF{sub 3}), plutonium tetrafluoride (PuF{sub 4}), calcium fluoride (CaF{sub 2}), and calcium chloride (CaCl{sub 2}). This report discusses non-radioactive testing of small-scale and pilot-scale systems and radioactive testing of a small-scale system. Experiments focused on demonstrating the chemistry for halide removal and addressing the primary engineering questions associated with a change in the process chemistry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2011

HIGH-CONTRAST STELLAR OBSERVATIONS WITHIN THE DIFFRACTION LIMIT AT THE PALOMAR HALE TELESCOPE

We report on high-accuracy high-resolution (<20 mas) stellar observations obtained with the Palomar Fiber Nuller (PFN), a near-infrared ({approx_equal}2.2 {mu}m) interferometric coronagraph installed at the Palomar Hale telescope. The PFN uses destructive interference between two elliptical (3 m Multiplication-Sign 1.5 m) sub-apertures of the primary to reach high dynamic range inside the diffraction limit of the full telescope. In order to validate the PFN's instrumental approach and its data reduction strategy, based on the newly developed 'Null Self-Calibration' (NSC) method, we observed a sample of eight well-characterized bright giants and supergiants. The quantity measured is the source astrophysical null depth, or equivalently the object's visibility at the PFN 3.2 m interferometric baseline. For the bare stars {alpha} Boo, {alpha} Her, {beta} And, and {alpha} Aur, PFN measurements are in excellent agreement with previous stellar photosphere measurements from long baseline interferometry. For the mass-losing stars {beta} Peg, {alpha} Ori, {rho} Per, and {chi} Cyg, circumstellar emission and/or asymmetries are detected. Overall, these early observations demonstrate the PFN's ability to measure astrophysical null depths below 10{sup -2} (limited by stellar diameters), with 1 {sigma} uncertainties as low as a few 10{sup -4}. Such visibility accuracy is unmatched at this spatial resolution in the near-infrared and translates into a contrast better than 10{sup -3} within the diffraction limit. With further improvements anticipated in 2011/2012, a state-of-the-art infrared science camera and a new extreme adaptive optics system, the PFN should provide a unique tool for the detection of hot debris disks and young self-luminous sub-stellar companions in the immediate vicinity of nearby stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

ACTIVE GALACTIC NUCLEUS FEEDBACK AND ENTROPY INJECTION IN GALAXY CLUSTER CORES

We make the first estimate of non-gravitational energy profiles in galaxy cluster cores (and beyond) based on observational data. Comparing the observed entropy profiles within r{sub 500}, from the Representative XMM-Newton Cluster Structure Survey to simulated base entropy profiles without feedback from both adaptive mesh refinement (AMR) and smoothed particle hydrodynamic (SPH) non-radiative simulations, we estimate the amount of additional non-gravitational energy, E{sub ICM}, contained in the intracluster medium (ICM), as well as the total energy feedback, E{sub Feedback}, from active galactic nuclei (AGNs; the central AGNs in most cases) into the clusters. The total feedback energy scales with the mean spectroscopic temperature as E{sub Feedback}∝T{sub sp}{sup 2.52±0.08} and E{sub Feedback}∝T{sub sp}{sup 2.17±0.11} for the SPH and AMR baseline profiles. The mean non-gravitational energy per particle within r{sub 500} remaining in the ICM after energy lost during cooling is ε{sub ICM} = 2.8 ± 0.8 keV for the SPH theoretical relation and ε{sub ICM} = 1.7 ± 0.9 keV for the AMR theoretical relation. We use the NRAO/VLA Sky Survey source catalog to determine the radio luminosity, L{sub R} , at 1.4 GHz of the central source(s) of our sample. For T{sub sp} > 3 keV, the E{sub Feedback} correlates with L{sub R} , although with different normalization for cool-core and non-cool-core clusters. We show that AGNs could provide a significant portion of the feedback.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-J-211: Scatter Correction for Clinical Cone-Beam CT System Using An Optimized Stationary Beam Blocker with a Single Scan

Purpose: X-ray scatter photons result in significant image quality degradation of cone-beam CT (CBCT). Measurement based algorithms using beam blocker directly acquire the scatter samples and achieve significant improvement on the quality of CBCT image. Within existing algorithms, single-scan and stationary beam blocker proposed previously is promising due to its simplicity and practicability. Although demonstrated effectively on tabletop system, the blocker fails to estimate the scatter distribution on clinical CBCT system mainly due to the gantry wobble. In addition, the uniform distributed blocker strips in our previous design results in primary data loss in the CBCT system and leads to the image artifacts due to data insufficiency. Methods: We investigate the motion behavior of the beam blocker in each projection and design an optimized non-uniform blocker strip distribution which accounts for the data insufficiency issue. An accurate scatter estimation is then achieved from the wobble modeling. Blocker wobble curve is estimated using threshold-based segmentation algorithms in each projection. In the blocker design optimization, the quality of final image is quantified using the number of the primary data loss voxels and the mesh adaptive direct search algorithm is applied to minimize the objective function. Scatter-corrected CT images are obtained using the optimized blocker. Results: The proposed method is evaluated using Catphan@504 phantom and a head patient. On the Catphan©504, our approach reduces the average CT number error from 115 Hounsfield unit (HU) to 11 HU in the selected regions of interest, and improves the image contrast by a factor of 1.45 in the high-contrast regions. On the head patient, the CT number error is reduced from 97 HU to 6 HU in the soft tissue region and image spatial non-uniformity is decreased from 27% to 5% after correction. Conclusion: The proposed optimized blocker design is practical and attractive for CBCT guided radiation therapy. This work is supported by grants from Guangdong Innovative Research Team Program of China (Grant No. 2011S013), National 863 Programs of China (Grant Nos. 2012AA02A604 and 2015AA043203), the National High-tech R&D Program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗
DOE OSTI2018

Stochastic Generation Capacity Expansion Planning with Approximate Dynamic Programming

Failing to consider the many long-term uncertainties that affect the performance of a power generation portfolio can result in suboptimal generation expansion plans. Further, traditional deterministic approaches can omit flexible plans that are able to adapt to future events. In this paper, we explore the use of approximate dynamic programming (ADP) to create forward-looking generation expansion plans. A case study is included with three sequential decision periods; three generation technologies; and four uncertainties: demand growth, natural gas prices, renewable portfolio standards, and the adoption of customer-driven solar generation. The flexible plans found through ADP show a 3 % reduction in total expected cost when compared to myopic planning heuristics while circumventing the computational burdens that accompany high-dimensional dynamic programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗