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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Application of Geant4 Monte Carlo simulation in dose calculations for small radiosurgical fields

The Geant4 toolkit was used to develop a Monte Carlo (MC)-based engine for accurate dose calculations in small radiation field sizes. The Geant4 toolkit (version 10.1.p02) was used to simulate 6-MV photon beam of a Varian2100C linear accelerator that is being used for stereotactic radiosurgery (SRS) treatment with small radiation fields. Geometric models of 3 in-house designed radiosurgical divergent cones, with the diameters of their projections at the isocenter being 10, 20, and 30 mm, were simulated. The accuracy of the MC simulation technique was examined by reproducing several different simulated dosimetric parameters of the primary beams with the experimental data. The dose distributions are first checked for single beams for each cone, then standard multiple field (SMF) techniques are applied. A sample set of DICOM files from computed tomography (CT) scan imaging of a patient's head was converted to the Geant4 geometry format to implement MC-based engine for a clinical test. To validate the accuracy of the MC-based calculations for SMF arrangements, the isodose lines from MC simulation in water phantom were compared with the measured isodose lines using EBT3 Gafchromic film in Solid Water phantoms. Agreements between measured and simulated depth dose values and beam profiles for SRS cones were generally within 2%/2 mm. For output factors, the largest discrepancy was observed for 10 mm SRS cone, which was 1.7%. For SMF techniques, in SRS cones, the MC simulation and EBT3 Gafchromic film dosimetry were in acceptable agreement (5%/5 mm). Excellent agreement between the results of the MC-based and measured dose values for both single and SMF techniques in SRS cones indicates the ability of the Geant4 toolkit to be applied as the platform for treatment planning of advanced radiotherapy techniques.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Energy Frontier Research Centers: Center for the Computational Design of Functional Layered Materials (CCDM) August 1, 2014 - July 31, 2018; Center for Complex Materials from First Principles (CCM) August 1, 2018 - July 31, 2021 (Final Report)

The mission of the DOE Energy Frontier Research Centers CCDM (2014-2018) and CCM (2018-2021) was to theoretically develop, computationally apply, and experimentally validate electronic structure methods for all materials, with a focus on the complex materials, especially layered and two-dimensional materials, strongly-correlated materials, and liquid water. This was achieved by over 200 published journal articles authored by about 17 senior investigators from physics and chemistry and from theory, computation, and experiment, plus their collaborators. In particular, the Centers confirmed the predictive power of the SCAN (strongly constrained and appropriately normed) density functional, which was constructed to satisfy 17 known exact constraints and several appropriate norms. Without being fitted to real bonded systems, and at a modest computational cost, SCAN correctly predicted covalent, ionic, metallic, hydrogen, and van der Waals bonds in many challenging materials. SCAN gave an improved description of defects in semiconductors, surface properties of metals, seven phases of ice, liquid water, liquid and supercooled silicon, subtle structural distortions in ferroelectrics, formation energies and structural predictions for solids, and critical pressures for structural phase transitions. Perhaps most remarkably, SCAN correctly described some strongly-correlated materials that were previously believed to be beyond the reach of density-functional approximations. SCAN is the only density functional that correctly predicts the band gap closing under chemical doping of the cuprate high-temperature superconducting materials. SCAN also predicts a landscape of competing stripe and magnetic phases in the cuprates. For some materials with some codes, SCAN has convergence problems that are greatly reduced by the CCM-developed r 2 SCAN, without loss of accuracy or rigor. SCAN and r 2 SCAN still make some self-interaction error, which is greatly reduced by the CCDM/ CCM-developed local orbital scaling correction (LOSC). These Centers further proved that the fundamental energy gaps of a solid from an orbital energy difference and from total energy differences are the same for a large class of generalized Kohn-Sham (GKS) functionals, including SCAN and standard hybrid functionals, and that symmetry breaking arises when a dynamic density fluctuation drops to zero frequency. The Centers identified new mechanisms for catalysis in layered materials with ions intercalated between the layers, investigated charge density waves both in model systems and in real layered materials, studied changes of band gap with the number of layers, and explored topological ultrathin films, bent nanoribbons, and defects.

08 HYDROGEN↗
DOE OSTI2025

Toward engineering lattice structures with the material point method (MPM)

This study examines the potential of two variants of the material point method—the generalized interpolation material point (GIMP) and dual domain material point (DDMP) methods—in developing a robust computational framework for engineering lattice structures under different loading conditions. The study begins with assessing the ability of the two methods in predicting elastic buckling phenomena using column geometries with and without initial geometric imperfections. The results indicate that both methods effectively capture buckling phenomena when initial geometric imperfections are introduced. After this verification step, we create several models of tetrahedral lattice structures with varying strut diameter and orientation and subject them to quasi-static loading. We then validate the numerical results using laboratory test results. The results show that, while both methods accurately predict load–displacement curves in the pre-buckling regime, their predictive capabilities diminish in the post-buckling regime. Through visual comparison between the numerical and experimental deformed shapes, it appears that the discrepancies between model and experimental results are attributed to initial geometric imperfections in the lattices that occurred during 3D printing. We then establish a second set of lattice models where different types of initial geometric imperfections are considered. The results from these models show that imperfections have a negligible influence in the pre-buckling regime but affect the behavior considerably in the post-buckling regime. As a final step in this work, we subject the lattice models to impact loading and employ hypothetical soft and stiff materials. These results show that the lattice stiffness, which depends on material stiffness, strut diameter, and orientation, significantly influences the ability of a lattice structure to resist impact. In particular, we find that a stiffer lattice (i.e., one made with a stiff material and thicker struts) is capable of absorbing more energy than a softer one during impact. Although material nonlinearities, inelasticity, and detailed contact formulations are not considered in this study, the findings obtained herein lay the groundwork for engineering lattice structures under extreme loading conditions through a simulation-driven framework based on particle-based methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Jet Noise Reduction

The Acoustics Branch is responsible for reducing noise levels for jet and fan components on aircraft engines. To do this, data must be measured and calibrated accurately to ensure validity of test results. This noise reduction is accomplished by modifications to hardware such as jet nozzles, and by the use of other experimental hardware such as fluidic chevrons, elliptic cores, and fluidic shields. To insure validity of data calibration, a variety of software is used. This software adjusts the sound amplitude and frequency to be consistent with data taken on another day. Both the software and the hardware help make noise reduction possible. work properly. These software programs were designed to make corrections for atmosphere, shear, attenuation, electronic, and background noise. All data can be converted to a one-foot lossless condition, using the proper software corrections, making a reading independent of weather and distance. Also, data can be transformed from model scale to full scale for noise predictions of a real flight. Other programs included calculations of Over All Sound Pressure Level (OASPL), Effective Perceived Noise Level (EPNL). OASPL is the integration of sound with respect to frequency, and EPNL is weighted for a human s response to different sound frequencies and integrated with respect to time. With the proper software correction, data taken in the NATR are useful in determining ways to reduce noise. display any difference between two or more data files. Using this program and graphs of the data, the actual and predicted data can be compared. This software was tested on data collected at the Aero Acoustic Propulsion Laboratory (AAPL) using a variety of window types and overlaps. Similarly, short scripts were written to test each individual program in the software suite for verification. Each graph displays both the original points and the adjusted points connected with lines. During this summer, data points were taken during a live experiment at the AAPL to measure Nozzle Acoustic Test Rig (NATR) background noise levels. Six condenser microphones were placed in strategic locations around the dome and the inlet tunnel to measure different noise sources. From the control room the jet was monitored with the help of video cameras and other sensors. The data points were recorded, reduced, and plotted, and will be used to plan future modifications to the NATR. The primary goal to create data reduction test programs and provide verification was completed. As a result of the internship, I learned C/C++, UNIX/LINUX, Excel, and acoustic data processing methods. I also recorded data at the AAPL, then processed and plotted it. These data would be useful to compare against existing data. In addition, I adjusted software to work on the Mac OSX platform. And I used the available training resources.

Kenny, Patrick↗
NASA NTRS2017

Shape Effect Analysis of Aluminum Projectile Impact on Whipple Shields

The informed design with respect to hypervelocity collisions involving micrometeoroid and orbital debris (MMOD) is influential to the success of space missions. For an orbit comparable to that of the International Space Station, velocities for MMOD can range from 1 to 15 km/s, with an average velocity around 10 km/cu s. The high energy released during collisions at these speeds can result in damage to a spacecraft, or worst-case, loss of the spacecraft, thus outlining the importance of methods to predict the likelihood and extent of damage due to an impact. Through experimental testing and numerical simulations, substantial work has been conducted to better understand the effects of hypervelocity impacts (HVI) on spacecraft systems and shields; however, much of the work has been focused on spherical impacting particles. To improve environment models for the analysis of MMOD, a large-scale satellite break-up test was performed at the Arnold Engineering and Development Complex to better understand the varied impactor geometries that could be generated from a large impact. As a part of the post-experiment analysis, an undertaking to characterize the irregular fragments generated is currently being performed by the University of Florida under the management of NASA's Orbital Debris Program Office at Johnson Space Center (JSC). DebriSat was a representative, modern LEO satellite that was catastrophically broken up in a HVI test. The test chamber was lined with a soft-catch system of foam panels that captured the fragments after impact. Initial predictions put the number of fragments larger than 2mm generated from the HVI at roughly 85,000. The number of fragments thus far extracted from the foam panels has exceeded 100,000, with that number continuously increasing. The shapes of the fragments vary dependent upon the material. Carbon-fiber reinforced polymer pieces, for instance, are abundantly found as thin, flat slivers. The characterization of these fragments with respect to their mass, size, and material composition needs to be summarized in a form that can be used in MMOD analysis. The mechanism that brings these fragment traits into MMOD analysis is through ballistic limit equations (BLE) that have been developed largely for a few types of materials1. As a BLE provides the failure threshold for a shield or spacecraft component based on parameters such as the projectile impact velocity and size, and the target's materials, thickness, and configuration, it is used to design protective shields for spacecraft such as Whipple shields (WS) to an acceptable risk level. The majority of experiments and simulations to test shields and validate BLEs have, heretofore, largely used spheres as the impactor, not properly reflecting the irregular shapes of MMOD. This shortfall has motivated a numerical impact analysis study of HVI involving non-spherical geometries to identify key parameters that environment models should provide.

Carrasquilla, Maria J.↗
DOE OSTI2017

Discrete and Continuum Plane-Hopper Flow Simulations with Experimental Validation Using X-Ray Imaging, Arch Profilometry and Wall Pressure Measurements

The flow of spherical glass beads, approximately cubic wood crumbles®, and wood microchips are investigated using a combination of discrete element modeling (DEM), finite element analysis (FEA), and physical experiments using a custom plane-flow hopper in which the flow is characterized using x-ray imaging, profilometry to measure arching geometry and pressure measurements at the wall. The purpose of this study is to investigate fundamental flow behavior, and for this purpose the simulations and physical tests are specially designed. During the physical tests and simulations, the hopper geometry begins as a V shape with the two opposing side walls touching at the bottom of the V. Basic tests are performed by first filling the hopper with material, and then rotating the opposing side walls inward about their common intersection axis to cause an approximately uniform stress condition throughout most of the hopper. The side walls then slide upward to effectively increase the size of the opening at the bottom of the hopper until the arch of material in the hopper breaks, and all of the material falls out of the hopper. More advanced tests are also performed in which the initial rotation of the walls is slightly reversed to decrease the pressure in the hopper, corresponding to “preshear” and “shear” conditions in standard shear tests. Shear tests of the materials are also performed using a Schulze automated ring shear tester with a standard size M shear cell, and the shear tests of the glass beads and wood crumbles® are also simulated using DEM to calibrate the DEM with the FEA. Reasonable agreement between predicted and measured flow behavior is obtained indicating that this type of physical tests and modeling shows promise for characterizing and predicting the flow behavior of compressible and anisotropic materials.

09 BIOMASS FUELS↗
DOE OSTI2022

Optimization of WAAM Process to Produce AUSC Components with Increased Service Life

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Countermeasures for Mitigation of Sensorimotor Decrements Following Head-Down Bed Rest

BACKGROUND Astronauts experience postflight disturbances in postural and locomotor control due to sensorimotor adaptations during spaceflight. These alterations may have adverse consequences if a rapid egress is required after landing. Although exercise is partially effective for mitigating cardiovascular and muscular deconditioning, additional countermeasures are needed to further preserve sensorimotor function for exploration missions. We have identified proprioceptive training and electrical muscle stimulation (EMS) as promising in-flight countermeasures. Since prolonged head down bed rest (HDBR) is a spaceflight analog for body unloading and causes postural and locomotor control decrements that parallel those observed after spaceflight, it can be used to accelerate the development of these countermeasures. METHODS This study will determine the effects of proprioceptive training and EMS on functional task performance and sensorimotor function following 60 days of 6° HDBR. Subjects will be randomly assigned to one of four groups: 1) an EMS arm, 2) a proprioceptive training arm, 3) an exercise plus proprioceptive training arm, and 4) a control arm. The EMS countermeasure will include daily bilateral stimulation of the quadriceps femoris muscle (30 minutes per session). Proprioceptive training will be performed three days per week (20 minutes per session) consisting of body-loaded postural tasks in the horizontal position on an air bearing sled. Exercise training will mimic current protocols used on the International Space Station, but treadmill aerobic exercise will be replaced with additional cycling aerobic exercise. Primary outcome measures will include pre and post HDBR functional tests that are representative of high priority exploration mission tasks and require high demand for dynamic control of postural stability. Additional measures will be used to identify the key physiological factors contributing to countermeasure benefits. Given the constrained samples size, a Bayesian modelling approach will be used to quantify the probability that there is an effect of a given magnitude. COUNTERMEASURE UPDATES Proprioceptive countermeasure design enhancements and human in the loop pilot testing continued through the Crew Health Countermeasures (CHC) Systems Capability Leadership Team (SCLT). The primary goals of this work were to enhance the visual feedback system’s capabilities and develop a proprioceptive training program for 60 days of HDBR. Six healthy non-astronaut volunteers participated in four pilot training sessions to systematically examine how each training variable (e.g., axial load, foot placement, and software profile) affects the overall proprioceptive challenge. The resulting training program will maintain an appropriate challenge during 60 days of HDBR by progressively decreasing the subject’s base of support, increasing tilt board target distances, and increasing axial loads using both subjective verbal feedback and objective performance data. RELEVANCE The deliverable from this project will be proof-of-concept sensorimotor countermeasure designs for functional task performance with full assessment of efficacy in a spaceflight analog. If the countermeasures are effective, they will be translated for validation with the suite of operationally implemented in-flight countermeasures.

T R Macaulay↗
DOE OSTI2024

Core-Shell Oxidative Aromatization Catalysts for Single Step Liquefaction of Distributed Shale Gas (Final Technical Report)

The objective of this project was to design and demonstrate a core-shell structured multifunctional catalyst to convert the light (dry) components of shale gas into liquid aromatic compounds (primarily benzene and toluene) in a single step. Operated in a modular oxidative aromatization system (OAS) under a cyclic redox scheme, the novel catalyst and process can significantly improve the value and transportability of distributed shale gas. Since the project started, each quarter addressed a different set of tasks related to the completion of the milestone detailed in the project award. The yearly summaries of these tasks are summarized below: Q1-Q4: • Conducted project planning and literature search. • Investigated a number of SHC redox catalysts using thermogravimetric analysis and fixed-bed reactor experiments. • Initiated process modeling towards generating two process models for the methane DHA base case and OAS process. • Developed DHA catalysts capable of producing >500 g/kg-cat-hr aromatics at 80% or greater aromatics selectivity at 700°C. Q5-Q8: • Developed alternative approaches with sequential bed configurations to enhance the aromatic yields based on OCM+DHA • Improved the zeolite synthesis efficiency by using the microwave-assisted technique and investigated the synthesis conditions on the zeolite yield, crystalline structure and morphology • Constructed a set of Aspen Plus process models with significant energy savings for OAS as compared to the base case non-oxidative DHA. • Adapted conventional hydrothermal method to be applicable to the microwave synthesizer unit for more efficient catalyst synthesis. • Studied the structure of the OCM catalyst and the dispersion of the carbonate in the redox reactions and in methane flow with Raman Spectroscopy. Q9-Q12: • Scaled up the catalyst synthesis with the microwave synthesis method. Based on its performance, procedural characterizations and catalytic performance testing were further conducted for the new microwave synthesized catalysts with the newly-developed product analysis procedure. • Developed the reaction system setup for the C2-DHA or OCM+DHA reaction product and achieved a better product collection-analysis method for the aromatic products with an improved carbon balance. The product from the OCM reaction exhibited complicated effects on the DHA catalyst. • Conducted additional OCM catalyst characterization using Near Ambient Pressure X-ray Photoelectron Spectroscopy and in situ Raman characterization • Validated the significant energy savings for OAS as compared to the base case non-oxidative DHA. Successfully set up the simulation model for the OCM+DHA+SHC reaction system based on the updated experimental results from NCSU. Q13-End of project: • Synthesized new zeolite catalysts by the microwave method, conducted characterizations (XRD, SEM, and TEM) and catalytic behavior testing. • Explored the “wet” C 2 H 6 and C 2 H 4 DHA reactions with using steam co-feed. A subsequent reduction as the regeneration step can regenerate the DHA catalyst and recover 99% activity of the fresh performance. • Achieved a 15.3% single-pass aromatic yield from methane by rationally combining the OCM and DHA at different temperatures. • Conducted a 105-hour stability test with an improved regeneration procedure, with an average aromatic yield of 13.8%. • Developed new catalyst and achieved a record-high 23.2% yield.

03 NATURAL GAS↗
DOE OSTI2019

Development and refinement of an in situ ‘molecular microscope’ utilizing ultrahigh resolution mass spectrometry

The ability to elucidate complex metabolic behavior of living organisms in situ and with cellular resolution is a bioanalytical grand challenge that is yet to be realized. Mass spectrometry imaging (MSI) using laser ablation electrospray ionization mass spectrometry (LAESI) has recently been demonstrated, and holds promise for direct spatially resolved molecular profiling in a variety of living systems of DOE relevance (e.g., plant leaf, stem, and root tissue). The LAESI MSI approach has revealed cell-to-cell heterogeneity of metabolites within living plant tissue, in a multiplexed fashion and without the need for labeling. Cellular resolution by in situ, ambient pressure MSI methods are far from routine, however, and have inadequate spatial resolution for many biological questions of interest. We propose to develop next-generation LAESI approaches, using advanced fiber optics and transmission-based geometries, in efforts to attain dynamic bioanalysis of living systems at better than 20 µm lateral resolution. In parallel with LAESI innovations, attendant increases in sensitivity and dynamic range of the mass spectrometer system will also be necessary for detection of the limited amount of material being ionized. To respond to this need, a new, high magnetic field (21 Tesla) Fourier Transform Ion Cyclotron Resonance (FTICR) MS at the EMSL/PNNL will be coupled with the newly developed LAESI source. This mass spectrometer, currently being assembled, will be one of only two such systems in the world, and the only one in a DOE laboratory or user facility. Fundamentally, all key figures of merits of FTICR MS performance improve with increased magnetic field strength, providing the ability to achieve unparalleled molecular specificity (near unequivocal molecular constituent identification) at the highest possible mass resolving power and sensitivity. The work proposed here will greatly increase the utility of this FTICR MS instrument, providing unique capabilities to support BER-relevant science. The research plan is comprised of three tasks, two of which are directed by the academic leads (University of Missouri and George Washington University), and one of which will be performed in conjunction with the BER user facility at EMSL/PNNL. In task 1, advancements of the LAESI technology will be achieved, with oversight and leadership of Professor Akos Vertes of George Washington University. In task 2, incorporation of this ion source with EMSL’s new 21T FTICR MS system will be undertaken, supplemented by global omics measurements and calibrated against more conventional MSI platforms also available at EMSL (Drs. Pasa-Tolic, Anderton, and Koppenaal team). In the final task, the resultant innovations and advancements will be tested and validated by addressing important BER relevant science questions on model systems of interest under the direction of Professor Gary Stacey of the University of Missouri. This research follows on key recommendations of the recent BER “Research in Sustainable Bioenergy” report that, for example, stressed that “The design of sustainable biofuel systems requires knowledge about key plant-microbe-environment interactions that provide a range of ecosystem services.” This task will focus on validation of the instrumentation developed through this proposal, advertising this capability to the community through demonstration and, finally, discovering new biology relevant to the BER mission. This final objective will make use of Setaria viridis, a model C4 grass species, as well as switchgrass and sorghum, two promising biofuel crops. Specifically, we will demonstrate applicability to plant-microbe rhizosphere interactions focusing on the biological nitrogen fixation by monitoring metabolic changes in real-time with nutrient, growth condition, and other perturbations.

09 BIOMASS FUELS↗
DOE OSTI2023

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE (FY23 Version, Rev. 1)

This report summarizes the development of neutron imaging capabilities and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature-driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. Yttrium hydride is the leading candidate to serve as a solid neutron moderator in microreactor cores, owing to its high density of hydrogen atoms as well as its superior thermal stability compared to all other metal hydrides. The experimental results and technique developments reported herein support the U.S. Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, it addresses the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in high-temperature microreactor designs by means of high-spatial-resolution neutron imaging. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. In principle, neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metal hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on three in-situ heating measurements of YH x performed over the 2020 to 2022 LANSCE operation cycles. The aims of these experiments ranged from furnace commissioning, determining sample quality, i.e., hydrogen uniformity via neutron computed tomography, and studying the effects of applied temperature-gradients on YH x pellets. Analyses and results from these neutron imaging measurements are given along with outlooks and guidelines for optimal future hydrogen diffusion measurements. Our conclusions are as follows. Image analyses indicate that centimeter-sized yttrium hydride cylindrical pellets exhibit uniform, whole-body hydrogen desorption and absorption without clear temperature dependence as reflected in the image attenuation at the opposing ends of each sample. This suggests that despite the large magnitude in temperature gradients applied by the furnace heating elements, the sample equilibrates to an unknown intermediate temperature. The origin of this result is likely the combination of short sample length (∼1cm) and use of a TZM can for containment where the latter created a thermal short across the sample. Nevertheless, the results from the most recent measurements indicate that neither significant concentration gradients of hydrogen were formed in centimeter-sized samples through the entire temperature range (25 °C to 950 °C) nor any formed due to temperature gradients on the order of 50 °C/cm up to 700 °C/cm. Furthermore, images from the FY2021 and FY2022 measurements indicate that samples of YH x , fabricated from either the direct hydride or powder metallurgy methods, are highly uniform in their hydrogen concentration to within the measurements’ spatial resolutions. The following questions arise from these latest results: 1) What is the intermediate temperature of the pellets in the TZM cans? 2) How quickly does the temperature equilibrate within the sample? and, 3) Do the observed changes in image attenuation follow known pressure-composition-temperature relations of yttrium hydride?

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The LSST DESC data challenge 1: generation and analysis of synthetic images for next-generation surveys

Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r -band observations of 40 deg 2 to 10 yr LSST depth. In this paper, we present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N -body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star–galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2σ–6σ) in the estimated power spectra at small scales (ℓ > 1200), highlighting the impact of blending in studies at small angular scales in LSST.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Cryogenic Carbon Capture Development (Final Report)

This project extends and leverages the work completed under funding from ARPA-E and other sources by exploring aspects of the Cryogenic Carbon Capture™ (CCC) process and issues discovered during those projects, but that were not within the scope or budget of previous projects or that became apparent during field tests. Our successful completion of the tasks outlined in this project has increased the reliability, efficiency, and scalability of the CCC process and we are better prepared for pilot-scale testing. The first phase of this project improved key areas of the process through iterative design and experiment, culminating with recommendations for improvements to the existing skid-scale system developed under non-federal funding. The second phase of this project implemented the improved unit operations into the Cryogenic Carbon Capture External Cooling Loop (CCC ECL™) skid and field tests the improved system at a coal-fired power plant. The specific areas of work in this project are: Budget Period 1: 1) Selection of the most energy-efficient drying system for ambient-pressure flue gas. Sustainable Energy Solutions, LLC (SES) has successfully demonstrated a dryer that removes bulk water using a low-temperature brine to remove about 99% of the water followed by a mol sieve 3A desiccant dryer to remove the remaining 1% water; 2) Selection and demonstration of efficient solution to fouling due to dissolved CO 2 . SES has tested several commercial heat exchangers that purport to be able to handle fouling issues similar to those experienced in the contact liquid chiller. Replicated tests using the CCC ECL™ skid and dimpled brazed plate heat exchangers demonstrate that quick throttling of refrigerant combined with a burst of clean gas mitigate the fouling caused by CO 2 solubility in contact liquid with only minor increases in parasitic load. SES models indicate that parasitic load would increase by approximately 0.0125 GJ/tonne, an order of magnitude less than what was determined to be the maximum allowable additional parasitic load caused by this system; 3) Selection and demonstration of efficient and scalable solid–liquid separation system. The screw press technology met or exceeded the 70% solids loading benchmark during several test runs exceeding 24 hours on the CCC ECL™ skid. The average solids loading during the entire 50-hour run was 67%. The energy demand for this system is less than 0.1 GJ e /tonne of CO 2 processed; 4) Optimize design and demonstration of patented desublimating heat exchangers. We designed and built a hybrid spray tower that was tested over many hundreds of hours and a 50-hour continuous test run was completed capturing over 0.5 tonne CO 2 /day at >90% capture; (5) Plan for safe dilution of oxygen-depleted, cleaned flue-gas stream. The AERSCREEN simulator developed by the EPA modeled the dispersion of oxygen-depleted flue gas from the CCC process at a commercial plant. The results indicate that this plan effectively mitigates the flue gas hazards; (6) Simulation of and experimental work related to multi-pollutant capture. SES programmed an upgrade to its solid/vapor/liquid equilibrium (SVLE) algorithm. This enables simultaneous modeling of multiple pollutants (i.e., CO 2 , SO x , NO x , Hg, HCl). Budget Period 2; (7) Modify the CCC ECL™ skid for field testing and complete shakedown testing. The plans for modifying the CCC ECL™ skid were finalized and put into place. Shakedown testing of the updated CCC ECL™ skid occurred at SES using closed-loop mode (i.e., using a synthetic flue gas consisting of only N 2 and CO 2 ) and at the Hunter Power Plant using flue gas from the coal-fired boiler. Shakedown testing took longer than expected due to delays in receiving equipment and unexpected equipment performance in the field; (8) Operate the CCC ECL™ skid in the field. Longer testing of the CCC ECL™ skid occurred at the Hunter Power Plant. Multiple tests exceeding 35 hours were completed, with a total of over 600 hours of CO 2 capture completed using flue gas at the Hunter Power Plant with an average CO 2 capture rate of over 91% during those hours. Extensive notes and documentation were acquired informing improved methods for controls, startup, operation, and shut down; (9) Determine figures of merit and quantify process improvement. SES defined figures of merit for unit operation performance and overall CCC ECL™ process performance. All figures of merit were achieved or surpassed, except for 500 continuous hours of demonstration at the Hunter Power Plant; (10) Complete an Environmental Health and Safety Risk assessment by a third party. Tri-State completed an Environmental Health and Safety Risk assessment; (11) Update techno-economic analysis with third-party validation. The techno-economic analysis has been completed. It confirms the potential large reduction in energy and cost of the CCC process compared with leading alternatives and that the estimates and estimation techniques are reliable.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

Minimize Solvent Oxidation with NO X Pre-Scrubbing

A novel method to remove nitrogen dioxide (NO 2 ) from the flue gas of coal-fired power plants with CO 2 capture was further developed for commercial implementation. The technology leverages the equipment and chemistry in an existing (sulfur dioxide) SO 2 polishing scrubber upstream of the main CO 2 capture unit to remove the NO 2 , preventing degradation of the CO 2 capture solvent and formation of nitrosamines (environmental hazards). The research in this report focuses on further evaluation of the chemical additives and operating conditions associated with the NO 2 removal process to define conditions for commercial scale testing and deployment. Experimental work systematically evaluated a series of potential additives to minimize the oxidation of sulfite in a representative SO 2 pre-scrubber solution (sulfite, in turn, absorbs NO 2 ). The additive combinations and concentrations were varied alongside important process conditions such as temperature, oxygen concentration, and metals present in solution to mimic the conditions expected in a commercial system. Important results of the parametric experimental work include identifying a new, potent sulfite oxidation inhibitor, revealing the importance of combining inhibitors with metal chelating agents, validation of a low-cost additive process, and development of a new semi-empirical model to represent mechanisms associated with sulfite oxidation. In addition, the experimental work reveled the impact of operating at higher temperatures (representative of a field test unit), which will guide the selection and concertation of additives as well. Engineering analysis found that waste solutions from the pre-scrubber with NO 2 additives may potentially be integrated with existing processes on site (e.g., flue gas desulfurization unit). In addition, techno-economic analysis identified potential net savings as large as $1.30/tonne CO 2 captured and quantified the potential benefit of low cost additive options actively being pursued by the development team. Finally, the experimental results and engineering analysis supported the development of a detailed field testing plan and protocol to evaluate the technology at near-commercial scale. The field test preparation included development of procedures to introduce chemical additives to an existing SO 2 polishing unit and identification of representative flue gas conditions based on a review of existing plants. These activities will have direct bearing on operation and design of commercial units.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2003

Structural Reliability Using Probability Density Estimation Methods Within NESSUS

A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.

Chamis, Chrisos C.↗
DOE OSTI2022

A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Virtual Fit Assessment: Validation using Historical Spacesuit Fit Data

Virtual fit tests using 3D body scans have provided a cost-effective means to predictively assess spacesuit fit for the current and future astronaut population. However, fit is a complex issue influenced by physical interferences, subjective preference, and many other factors. Fit can substantially differ between the type of hardware, environmental conditions, and tasks being performed. Namely, fit changes across different contexts, such as 3D printed mockup evaluations, pressurized one-g suited test events, neutral buoyancy and other training events, and flight extravehicular activities (EVA). These challenges make it difficult to validate virtual fit frameworks to physical suit sizing. This study proposes a new method of validation using the wealth of historically archived suit fit data. Physically Assessed Fit (PAF) data was assessed from astronauts and test volunteers who wore the legacy Extravehicular Mobility Unit (EMU). The data was collected during the past decades at NASA and considered to be most reliable and dependable. Each participant was 3D scanned for body shape and spacesuit-critical dimensions were measured per NASA guidelines. The participants’ size preference for hard upper torso (HUT) assembly was retrieved from their pressurized suit fit check records. Fit assessments were also updated after one-g EVA training, neutral buoyancy training, and EVA flights. Virtually Assessed Fit (VAF) was done by overlaying the CAD model of a HUT with the 3D body scans of the PAF participants. Each body scan was iteratively adjusted for the position inside the HUT to minimize the suit-to-body contact interference while satisfying a set of prescribed requirements. Then the residual contact area, depth, and volume were quantified as VAF metrics. This process was repeated for the different HUT sizes, including medium, large, and extra-large. Statistical modeling is currently in progress and will be presented at the conference. A statistical classifier will be developed to predict a fit probability for different HUT sizes as a function of the corresponding person’s VAF metrics. The HUT size with the highest fit probability will constitute the most likely size selection for the person, and the prediction will be compared against the corresponding PAF data. The participant data will be randomly pre-grouped into either a model development or validation subset. While the model development subset will be used to build the probability model, the validation subset will be used to assess the model accuracy. Also, the locations and magnitudes of suit-body-contacts will be estimated from VAF. This information can identify the critical suit geometry and body shape features that influence suit fit. The variations found in PAF size selections by subjects with similar VAF metrics will allow for investigations into subjective preferences, for example, tight versus loose fit. Overall, this study is expected to provide a structured validation of a virtual suit fit framework, which has not been possible in the past. The outcome can also provide useful insights and potential limitations for interpreting virtual fit tests for future spacesuit designs and population accommodation.

Han Kim↗