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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Filterbank Multicarrier Communications for High Mobility Underwater Acoustic Links

This paper presents a new method of designing prototype filters that are robust against time and frequency dispersive (i.e., doubly dispersive) underwater acoustic (UWA) channels. The proposed design uses a modified form of Slepian sequences as a basis set to arrive at a design that is maximally compact along both time and frequency/Doppler dimensions. This keeps the inter-symbol interference confined to a small number of adjacent data symbols, both across time and frequency dimensions. It also minimizes the ramp-up (at the beginning) and ramp-down (at the end) of each synthesized data packet. Here, we test our design by making use of the BELLHOP/VirTex channel simulator and compare its performance with that of the widely used orthogonal frequency division multiplexing (OFDM) method. Our emphasis is on scenarios where the channel impulse response extends to a few hundreds of milli-seconds and the relative speed of communicating vehicles can be as large as 10 m/s. We find that while OFDM fails in majority of these scenarios, the proposed design continues to work well in all the scenarios that we simulate.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Modeling and Simulation of MC-Small Design

In order to design a lightweight, highly portable neutron multiplicity counter for use in higher risk regions by mobile field teams, a redesign of the MC-15 is being performed, with the goal of producing a detector below 30 lbs for ease of transport. To achieve this, multiple MCNP® simulations were run, evaluating the number and positioning of the He-3 tubes in the detector as well as the amount of high-density polyethylene (HDPE) surrounding the tubes. Based on these simulations, the proposed redesign of the MC-15 (aka MC-Small) possesses 11 tubes and is 13 in wide, 3.21 in thick and 13 in tall. Including the changes to the positioning of the tubes and the removal of HDPE from less sensitive regions of the detectors (i.e. bases of tubes, back of detector), this resulted in an efficiency of 0.56% when measuring a bare Cf-252 source 19.69 in away, only 28% less efficient (relatively) than the MC-15 while weighing 18 lbs, 12 lbs less than the weight of the MC-15.

42 ENGINEERING↗
DOE OSTI2023

Agilent CRADA (Abstract)

The CRADA between Agilent Technologies Inc. and Battelle will focus on five software components as listed below: Prototype 4D Feature Finding functionality with a particular focus on recovering low level features and extending the bottom end dynamic range of IM-MS technology. Compare and contrast developments to current 4D Feature Finding capabilities. Highlight important algorithmic aspects employed. Implement the PNNL saturation correction algorithm. Agilent will give PNNL the needed data file access API and assistance in understanding it implementation and any needed instrumental aspects. Supported high resolution products to include Agilent’s TOF, QTOF and IM-QTOF mass spectrometers. PNNL will then work with Agilent to benchmark performance. Implementation of the PNNL Hadamard de-multiplexing algorithm. Agilent will give provide PNNL the needed date file access API access and as needed assistance in understanding the current Agilent multiplexed IM offering. PNNL will then work with Agilent on benchmark performance. Add ion mobility collision cross sections to existing and new metabolomic libraries for data analysis with Agilent’s informatics program MPP/ID Browser. PNNL will work with Agilent to create a software pipeline that takes data from chemical and metabolic standards and properly formats it for inclusion in MPP accessible libraries, using the collision cross section as a new separation dimension. Improvements of MPP multidimensional matching to identify metabolomic features using multiple characteristics beyond retention time and accurate mass. Most significantly matching will include analyte collision cross section with proposed support for sample fraction or RapidFire cartridge and fragmentation spectra. PNNL will work with Agilent to modify and improve the current MPP analysis pipeline to allow for creating, aligning, and identifying MS features defined by accurate mass, collision cross section and chromatographic retention time. As additional criteria such as fraction or RapidFire cartridge type are supported in the identification process, then they also will become part of the automation workflow. This includes the automation of said system to work with command line program (i.e. not a GUI) sufficient for programmatic execution in a pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Tools for Large-Scale Mobile Malware Analysis

Analyzing mobile applications for malicious behavior is an important area of re- search, and is made di cult, in part, by the increasingly large number of appli- cations available for the major operating systems. There are currently over 1.2 million apps available in both the Google Play and Apple App stores (the respec- tive o cial marketplaces for the Android and iOS operating systems)[1, 2]. Our research provides two large-scale analysis tools to aid in the detection and analysis of mobile malware. The rst tool we present, Andlantis, is a scalable dynamic analysis system capa- ble of processing over 3000 Android applications per hour. Traditionally, Android dynamic analysis techniques have been relatively limited in scale due to the compu- tational resources required to emulate the full Android system to achieve accurate execution. Andlantis is the most scalable Android dynamic analysis framework to date, and is able to collect valuable forensic data, which helps reverse-engineers and malware researchers identify and understand anomalous application behavior. We discuss the results of running 1261 malware samples through the system, and provide examples of malware analysis performed with the resulting data. While techniques exist to perform static analysis on a large number of appli- cations, large-scale analysis of iOS applications has been relatively small scale due to the closed nature of the iOS ecosystem, and the di culty of acquiring appli- cations for analysis. The second tool we present, iClone, addresses the challenges associated with iOS research in order to detect application clones within a dataset of over 20,000 iOS applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Aerial Lidar and Geomorphic Change Detection Software- Tools for Monitoring Canyon Sediments at LANL - 18668

The Los Alamos National Laboratory Environmental Remediation program has conducted extensive geomorphic surveys of sediment deposits in the canyon bottoms within and surrounding the facility. The goal of the geomorphic surveys was to provide basic information needed to characterize chemical inventories of the sediments downstream of historical release sites. Sediment units were mapped based on depositional setting and age. Representative characterization samples were collected for each unit and submitted for chemical analysis. Chemical inventories were estimated by calculating the total volume of each depositional unit and multiplying by average chemical concentration. This method of characterization provides a spatial distribution of chemical data based on fluvial processes. In general, for the canyon bottom sediments, the higher concentrations of industrial chemicals were associated with fine-grained floodplain deposits closest to the source. Since initial characterization, many of the reaches have undergone changes in response to post-wildfire floods and severe rain events. These events impacted the channel and floodplain deposits. Much of the elevated concentrations are confined to the over-bank deposits distal from the primary channel. In order to ensure that the sediment units containing elevated chemical concentrations are stable, repeat aerial light detection and ranging (LiDAR) surveys are conducted and changes are assessed using the Geomorphic Change Detection (GCD) plug-in for ArcGIS. These tools provide high confidence uncertainty estimates and extensive areal coverage. This is a valuable method for long-term monitoring of surface sediments with low-level but widespread contaminant distributions. All of the monitored canyon bottom reaches pass risk assessment and present technically infeasible sediment removal options. Following the 2014 monsoon season, aerial LiDAR data has been collected annually and the resulting digital elevation models (DEMs) processed using the GCD method. Changes are identified using GCD and are verified by field walk downs. If erosion is identified in areas which are vulnerable to mobilizing contaminated sediment, enhanced controls can be installed to stabilize banks or other areas with high erosion potential. Annual sediment monitoring samples are collected where there is evidence for flow and deposition within the past year. Since 2014, no severe floods have been catalogued using the LiDAR surveys and GCD software. The recent rainfall/runoff events have not caused major changes to the canyon bottom channels and floodplains. Geomorphic processes identified in the monitoring results include channel aggradation and incision that over the course of a monsoon season results in minor changes to the system. Other active processes contributing to observed changes are minor slides, flows, slumps, and falls of unconsolidated sediment on steep bedrock or soil surfaces. A comprehensive baseline has been established by geomorphic mapping and characterization sampling. Periodic monitoring consists of LiDAR surveys and GCD analysis which offers an effective and defensible methodology for documenting stability of surface sediments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Microstructure and Property Evolution in Advanced Cladding and Duct Materials Under Long-Term and Elevated Temperature Irradiation: Modeling and Experimental Investigation

The in-service degradation of reactor core materials is related to underlying changes in the irradiated microstructure. During reactor operation, structural components and cladding experience displacement of atoms by collisions with neutrons at temperatures at which the radiation-induced defects are mobile, leading to microstructure evolution under irradiation that can degrade material properties. At the doses and temperatures relevant to fast reactor operation, the microstructure evolves by dislocation loop formation and growth, microchemistry changes due to radiation-induced segregation, radiation-induced precipitation, destabilization of the existing precipitate structure, and in some cases, void formation and growth. These processes do not occur independently; rather, their evolution is highly interlinked. Radiation-induced segregation of Cr and existing chromium carbide coverage in irradiated alloy T91 track each other closely. The radiation-induced precipitation of Ni-Si precipitates and RIS of Ni and Si in alloys T91 and HCM12A are likely related. Neither the evolution of these processes nor their coupling is understood under the conditions required for materials performance in fast reactors (temperature range 300-600°C and doses beyond 200 dpa). Further, predictive modeling is not yet possible as models for microstructure evolution must be developed along with experiments to characterize these key processes and provide tools for extrapolation. To extend the range of operation of nuclear fuel cladding and structural materials in advanced nuclear energy and transmutation systems to that required for the fast reactor, the irradiation-induced evolution of the microstructure, microchemistry, and the associated mechanical properties at relevant temperatures and doses must be understood. Predictive modeling relies on an understanding of the physical processes and also on the development of microstructure and microchemical models to describe their evolution under irradiation. This project will focus on modeling microstructural and microchemical evolution of irradiated alloys by performing detailed modeling of such microstructure evolution processes coupled with well-designed in situ experiments that can provide validation and benchmarking to the computer codes. The broad scientific and technical objectives of this proposal are to evaluate the microstructure and microchemical evolution in advanced ferritic/martensitic and oxide dispersion strengthened (ODS) alloys for cladding and duct reactor materials under long-term and elevated temperature irradiation, leading to improved ability to model structural materials performance and lifetime. Specifically, we propose four research thrusts, namely Thrust 1: Identify the formation mechanism and evolution for dislocation loops with Burgers vector of a<100> and determine whether the defect microstructure (predominately dislocation loop/dislocation density) saturates at high dose. Thrust 2: Identify whether a threshold irradiation temperature or dose exists for the nucleation of growing voids that mark the beginning of irradiation-induced swelling, and begin to probe the limits of thermal stability of the tempered Martensitic structure under irradiation. Thrust 3: Evaluate the stability of nanometer sized Y- Ti-O based oxide dispersion strengthened (ODS) particles at high fluence/temperature. Thrust 4: Evaluate the extent to which precipitates form and/or dissolve as a function of irradiation temperature and dose, and how these changes are driven by radiation induced segregation and microchemical evolutions and determined by the initial microstructure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Implementing Mixed Reality Tools to Support Mission Delivery at Hanford - 20520

Mission Support Alliance's (MSA) Public Works and Information Systems organizations have been working with Microsoft Corporation to develop and implement mixed reality operational solutions using their HoloLens technology. This collaborative effort is intended to drive innovative solutions and significantly improve MSA's efficiency in performing work at the Hanford Site. HoloLens, a mixed reality tool, is a commercial off-the-shelf technology that combines a head-mounted viewing screen with applications to help people and organizations learn, communicate, and collaborate more effectively through the use of mixed reality. https://microsoft.com/en-us/hololens MSA's initial pilot successfully demonstrated its ability to use mixed reality utilizing HoloLens' advanced features in a variety of ways, illustrating the potential efficiency gains previously stated as goals of this pilot. These achievements include the development of proprietary technology that allows synchronization between a HoloLens device and a mobile device or alternate global positioning system (GPS) device. This synchronization enables the HoloLens device to access real time GPS location data anywhere on the Hanford Site. This real time location data, combined with MSA's improved Hanford Geographical Information System (GIS) data, enables the mixed reality pilot application to identify underground utility systems as well as related utility attributes. This ability will aid utility workers in several areas, including excavation activities, the placement of large cranes, identifying interactions and conflicts with future underground utility placements and, eventually, recognizing and de-conflicting tank waste transfer system valve alignments. The HoloLens mixed reality application is also able to provide full or small scale holographic images of a facility's digital twin. This feature will allow MSA's Water and Sewer Utilities team to train and validate procedures associated with the new water treatment facility virtually, before it is constructed. This reduces the amount of time it takes to complete the training and procedure validation, and accelerates the overall construction schedule. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Materials for Ultra‐Coherent, Mobile, Electron‐Spin Qubits

This research project has had the goal of gaining a better understanding of the physics of electrons bound to the surface of superfluid helium from both experimental and theoretical perspectives. It has particularly been aimed at two areas which had not been well studied: the relaxation and decoherence of the spin of the electrons on the helium surface and how the properties of underlying metallic layers affect the behavior of the electrons when the helium covering the metal is thin. This work is motivated in part by interest in using the spin of these electrons as a quantum bit, or qubit. Low levels of decoherence are advantageous for qubits, and moving the electrons, as one might do in a quantum processor, will be easiest if thin helium films can be employed. It had been suggested that spin decoherence should be very weak for electrons bound to superfluid He, but before this work there have been no quantitative studies of spin relaxation and decoherence. It is especially important to know how moving the electrons across the helium surface would affect their spin coherence. Calculations performed as part of this project show that the Rashba effective magnetic field, the mechanism which limits the spin coherence of mobile electrons in silicon-based devices (an actively pursued qubit technology), is exceptionally weak for electrons bound to helium. This project has identified other decoherence mechanisms which are stronger, but still weak compared to analogous silicon-based structures. Calculated spin coherence times for mobile electrons approach one day, as compared to microseconds in silicon. With coherence times of this magnitude, the spin qubit errors on helium will be completely dominated by errors in the quantum gates. In related work, the possibility of using an artificial spin-orbit interaction (a gradient magnetic field) for quantum operations on the electrons spins was considered. The calculations show that a moderate gradient field, small enough to be generated by a narrow superconducting wire, will enable high-fidelity quantum operations on electrons held in lithographically-defined quantum dots by driving them with a microwave electric field. The spin and motional coherence of the electrons is sufficient to allow high-fidelity 2-qubit quantum operations between electrons in neighboring quantum dots. As an outgrowth of experiments aiming to measure electron spin coherence it was discovered that very high densities of electrons can be stably supported on thin helium films coating ultra-smooth amorphous metallic layers. The measured densities are high enough that the electron system has almost certainly transitioned from an ordered array of electrons, known as a Wigner crystal (ordered by the electrons’ mutual repulsion), to a quantum fluid known as a Fermi liquid. This transition has been a subject of intense interest for over 40 years, since the electron Wigner crystal was first observed with electrons bound to superfluid helium, but it has never been unambiguously observed. Experiments are still underway in these new structures to definitively determine whether true quantum melting of the Wigner crystal has been demonstrated. This work has also catalyzed the development of a new approach for measuring the transport of electrons across very thin helium films, as will be needed for some of the quantum computing applications. The high electron density experiments as well as experiments with electrons bound in quantum dots have led to new techniques which may enable spin coherence measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Final report for LDRD project 11-0783 : directed robots for increased military manpower effectiveness.

The purpose of this LDRD is to develop technology allowing warfighters to provide high-level commands to their unmanned assets, freeing them to command a group of them or commit the bulk of their attention elsewhere. To this end, a brain-emulating cognition and control architecture (BECCA) was developed, incorporating novel and uniquely capable feature creation and reinforcement learning algorithms. BECCA was demonstrated on both a mobile manipulator platform and on a seven degree of freedom serial link robot arm. Existing military ground robots are almost universally teleoperated and occupy the complete attention of an operator. They may remove a soldier from harm's way, but they do not necessarily reduce manpower requirements. Current research efforts to solve the problem of autonomous operation in an unstructured, dynamic environment fall short of the desired performance. In order to increase the effectiveness of unmanned vehicle (UV) operators, we proposed to develop robots that can be 'directed' rather than remote-controlled. They are instructed and trained by human operators, rather than driven. The technical approach is modeled closely on psychological and neuroscientific models of human learning. Two Sandia-developed models are utilized in this effort: the Sandia Cognitive Framework (SCF), a cognitive psychology-based model of human processes, and BECCA, a psychophysical-based model of learning, motor control, and conceptualization. Together, these models span the functional space from perceptuo-motor abilities, to high-level motivational and attentional processes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements

Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Here, our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above.

42 ENGINEERING↗
DOE OSTI2022

liionpack: A Python package for simulating packs of batteries with PyBaMM

Electrification of transport and other energy intensive activities is of growing importance as it provides an underpinning method to reduce carbon emissions. With an increase in reliance on renewable sources of energy and a reduction in the use of more predictable fossil fuels in both stationary and mobile applications, energy storage will play a pivotal role and batteries are currently the most widely adopted and versatile form. Therefore, understanding how batteries work, how they degrade, and how to optimize and manage their operation at large scales is critical to achieving emission reduction targets. The electric vehicle (EV) industry requires a considerable number of batteries even for a single vehicle, sometimes numbering in the thousands if smaller cells are used, and the dynamics and degradation of these systems, as well as large stationary power systems, is not that well understood. As increases in the efficiency of a single battery become diminishing for standard commercially available chemistries, gains made at the system level become more important and can potentially be realised more quickly compared with developing new chemistries. Mathematical models and simulations provide a way to address these challenging questions and can aid the engineer and designers of batteries and battery management systems to provide longer lasting and more efficient energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2018

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been applications to the search for new materials that draw upon big-data classification problems. Though, when it comes to physical systems governed by conservation laws, the role of machine learning has been more limited. Here, we present our recent work in exploring the role of machine learning methods in discovering, or aiding, the search for physics. Specifically, we focus on using machine learning algorithms to represent high-dimensional free energy surfaces with the goal of identifying precipitate morphologies in alloy systems. Traditionally, this problem has been approached by combining phase field models, which impose first-order dynamics, with elasticity, to traverse a free energy landscape in search of minima. Equilibrium precipitate morphologies occur at these minima. Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization. This combination of data-driven methods offers an alternative to the imposition of first-order dynamics via phase field methods, and represents one aspect of applying machine learning to materials physics.

42 ENGINEERING↗
DOE OSTI2013

Diffusion of Small He Clusters in Bulk and Grain Boundaries in α-Fe

The diffusion properties of He interstitials and He clusters in the bulk and grain boundaries (GBs) of α-Fe have been studied using molecular dynamics with a newly developed Fe-He potential. The low migration energy barrier for a single He interstitial in the bulk is consistent with that obtained using ab initio methods. Small He clusters can migrate at low temperatures, but at higher temperatures they will kick out a self-interstitial atom (SIA) and become trapped by the vacancy, forming an He-vacancy complex. It is of great interest to note that small Henvacancy clusters (n<5) in the bulk are able to absorb an SIA, and the clusters become mobile again. Trapping and de-trapping of He clusters by emitting and absorbing an SIA represent an important dynamic process that provides a mechanism for the diffusion of He clusters and the nucleation of He bubbles in bulk Fe, particularly under irradiation in which numerous SIAs and vacancies are constantly being produced. A single He interstitial can migrate one-dimensionally or two-dimensionally within GBs, depending on the GB structure. Small interstitial Hen clusters 2 (n ~ 1 - 10) can easily kick out an SIA, and become trapped by the vacancy, while the SIA quickly diffuses away from the clusters, disappearing into the GB, such that de-trapping of the He clusters by absorbing an SIA is less likely to occur. This suggests that small He clusters may be treated as relatively immobile defects in GBs. The different behavior of He clusters in the bulk compared to their behavior in GBs may explain the different He bubble sizes experimentally observed in the bulk and in GBs in reduced activation ferritic/martensitic steels that have been simultaneously neutron irradiated and He implanted.

Computer simulations↗
DOE OSTI2011

Variably Saturated Flow and Multicomponent Biogeochemical Reactive Transport Modeling of a Uranium Bioremediation Field Experiment

Field experiments at a former uranium mill tailings site have identified the potential for stimulating indigenous bacteria to catalyze the conversion of aqueous uranium in the +6 oxidation state to immobile solid-associated uranium in the +4 oxidation state. This effectively removes uranium from solution resulting in groundwater concentrations below actionable standards. Three-dimensional, coupled variably-saturated flow and biogeochemical reactive transport modeling of a 2008 in situ uranium bioremediation field experiment is used to better understand the interplay of transport rates and biogeochemical reaction rates that determine the location and magnitude of key reaction products. A comprehensive reaction network, developed largely through previous 1-D modeling studies, was used to simulate the impacts on uranium behavior of pulsed acetate amendment, seasonal water table variation, spatially-variable physical (hydraulic conductivity, porosity) and geochemical (reactive surface area) material properties. A principal challenge is the mechanistic representation of biologically-mediated terminal electron acceptor process (TEAP) reactions whose products significantly alter geochemical controls on uranium mobility through increases in pH, alkalinity, exchangeable cations, and highly reactive reduction products. In general, these simulations of the 2008 Big Rusty acetate biostimulation field experiment in Rifle, Colorado confirmed previously identified behaviors including (1) initial dominance by iron reducing bacteria that concomitantly reduce aqueous U(VI), (2) sulfate reducing bacteria that become dominant after {approx}30 days and outcompete iron reducers for the acetate electron donor, (3) continuing iron-reducer activity and U(VI) bioreduction during dominantly sulfate reducing conditions, and (4) lower apparent U(VI) removal from groundwater during dominantly sulfate reducing conditions. New knowledge on simultaneously active metal and sulfate reducers has been incorporated into the modeling. In this case, an initially small population of slow growing sulfate reducers is active from the initiation of biostimulation. Three-dimensional, variably saturated flow modeling was used to address impacts of a falling water table during acetate injection. These impacts included a significant reduction in aquifer saturated thickness and isolation of residual reactants and products, as well as unmitigated uranium, in the newly unsaturated vadose zone. High permeability sandy gravel structures resulted in locally high flow rates in the vicinity of injection wells that increased acetate dilution. In downgradient locations, these structures created preferential flow paths for acetate delivery that enhanced local zones of TEAP reactivity and subsidiary reactions. Conversely, smaller transport rates associated with the lower permeability lithofacies (e.g., fine) and vadose zone were shown to limit acetate access and reaction. Once accessed by acetate, however, these same zones limited subsequent acetate dilution and provided longer residence times that resulted in higher concentrations of TEAP products when terminal electron donors and acceptors were not limiting. Finally, facies-based porosity and reactive surface area variations were shown to affect aqueous uranium concentration distributions; however, the ranges were sufficiently small to preserve general trends. Large computer memory and high computational performance were required to simulate the detailed coupled process models for multiple biogeochemical components in highly resolved heterogeneous materials for the 110-day field experiment and 50 days of post-biostimulation behavior. In this case, a highly-scalable subsurface simulator operating on 128 processor cores for 12 hours was used to simulate each realization. An equivalent simulation without parallel processing would have taken 60 days, assuming sufficient memory was available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

A method for deriving a 4D-interpolated balanced planning target for mobile tumor radiotherapy

Purpose: Tumor control and normal tissue toxicity are strongly correlated to the tumor and normal tissue volumes receiving high prescribed dose levels in the course of radiotherapy. Planning target definition is, therefore, crucial to ensure favorable clinical outcomes. This is especially important for stereotactic body radiation therapy of lung cancers, characterized by high fractional doses and steep dose gradients. The shift in recent years from population-based to patient-specific treatment margins, as facilitated by the emergence of 4D medical imaging capabilities, is a major improvement. The commonly used motion-encompassing, or internal-target volume (ITV), target definition approach provides a high likelihood of coverage for the mobile tumor but inevitably exposes healthy tissue to high prescribed dose levels. The goal of this work was to generate an interpolated balanced planning target that takes into account both tumor coverage and normal tissue sparing from high prescribed dose levels, thereby improving on the ITV approach. Methods: For each 4DCT dataset, 4D deformable image registration was used to derive two bounding targets, namely, a 4D-intersection and a 4D-composite target which minimized normal tissue exposure to high prescribed dose levels and maximized tumor coverage, respectively. Through definition of an ''effective overlap volume histogram'' the authors derived an ''interpolated balanced planning target'' intended to balance normal tissue sparing from prescribed doses with tumor coverage. To demonstrate the dosimetric efficacy of the interpolated balanced planning target, the authors performed 4D treatment planning based on deformable image registration of 4D-CT data for five previously treated lung cancer patients. Two 4D plans were generated per patient, one based on the interpolated balanced planning target and the other based on the conventional ITV target. Plans were compared for tumor coverage and the degree of normal tissue sparing resulting from the new approach was quantified. Results: Analysis of the 4D dose distributions from all five patients showed that while achieving tumor coverage comparable to the ITV approach, the new planning target definition resulted in reductions of lung V{sub 10}, V{sub 20}, and V{sub 30} of 6.3% {+-} 1.7%, 10.6% {+-} 3.9%, and 12.9% {+-} 5.5%, respectively, as well as reductions in mean lung dose, mean dose to the GTV-ring and mean heart dose of 8.8% {+-} 2.5%, 7.2% {+-} 2.5%, and 10.6% {+-} 3.6%, respectively. Conclusions: The authors have developed a simple and systematic approach to generate a 4D-interpolated balanced planning target volume that implicitly incorporates the dynamics of respiratory-organ motion without requiring 4D-dose computation or optimization. Preliminary results based on 4D-CT data of five previously treated lung patients showed that this new planning target approach may improve normal tissue sparing without sacrificing tumor coverage.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. Here, the contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗