Results for “Fluid Network” — Page 38

DOE OSTI2015

Modeling the Interaction Between Hydraulic and Natural Fractures Using Dual-Lattice Discrete Element Method

The interaction between hydraulic fractures (HF) and natural fractures (NF) will lead to complex fracture networks due to the branching and merging of natural and hydraulic fractures in unconventional reservoirs. In this paper, a newly developed hydraulic fracturing simulator based on discrete element method is used to predict the generation of complex fracture network in the presence of pre-existing natural fractures. By coupling geomechanics and reservoir flow within a dual lattice system, this simulator can effectively capture the poro-elastic effects and fluid leakoff into the formation. When HFs are intercepting single or multiple NFs, complex mechanisms such as direct crossing, arresting, dilating and branching can be simulated. Based on the model, the effects of injected fluid rate and viscosity, the orientation and permeability of NFs and stress anisotropy on the HF-NF interaction process are investigated. Combined impacts from multiple parameters are also examined in the paper. The numerical results show that large values of stress anisotropy, intercepting angle, injection rate and viscosity will impede the opening of NFs.

02 PETROLEUM↗

DOE OSTI2017

Modeling the influence of nozzle-generated turbulence on diesel sprays

The physical mechanisms governing spray breakup in direct injection engines, such as aerodynamic induced instabilities and nozzle-generated cavitation and turbulence, are not well understood due to the experimental and computational limitations in resolving these processes. Recent x-ray and visible extinction measurements have been con-ducted with a targeted interest in the spray formation region in order to characterize the distribution of droplet sizes throughout the spray. Detailed analysis of these measurements shows promise of yielding insight into likely mechanisms governing atomization, which can inform the improvement of spray models for engine computational fluid dynamic (CFD) codes. In order to investigate potential atomization mechanisms, we employ a joint experimental and computational approach to characterize the structure of the spray formation region using the Engine Combustion Network Spray D injector. X-ray tomography, radiography and ultra-small angle x-ray scattering measurements conducted at the Advanced Photon Source at Argonne National Laboratory quantify the injector geometry, liquid fuel mass and Sauter mean diameter (SMD) distributions under non-vaporizing conditions. Diffused back-illumination imaging measurements, conducted at the Georgia Institute of Technology, characterize the asymmetry of the spray structure. The selected range of injection pressures (50 – 150 MPa) and ambient densities (1.2 – 22.8 kg/m3) allow for the influence of aerodynamic forces on the spray to be studied in a controlled and systematic manner, while isolating the atomization process from the effects of vaporization. In comparison to high ambient density conditions, the spray is observed to be more asymmetric at low ambient density conditions. Although several mechanisms may cause asymmetries in the nozzle exit flow conditions and ultimately the spray distribution, irregularities in the internal nozzle geometry were identified, suggesting an increased sensitivity of the spray structure to internal nozzle surface finish imperfections at such conditions. The presence of these asymmetries may influence the ability to interpret line-of-sight measurements and their derived SMD values and trends from a single viewing angle of the spray. With this consideration in mind, the measured local sensitivities to ambient density suggest that for ambient densities less than 2.4 kg/m3, aerodynamic effects are likely suppressed, allowing the influence of turbulent-induced breakup to be isolated. In concert with the experimental measurements, we utilize three-dimensional, CFD Lagrangian-Eulerian spray simulations in CONVERGE to evaluate the details of the predicted spray structure. In particular, we compare measured and predicted sensitivities of the SMD distribution to changes in injection and ambient conditions from three different atomization models, namely Kelvin Helmholtz (KH), KH Aerodynamics Cavitation Turbulence (KH-ACT), and the newly developed KH-Faeth hybrid model. While none of the existing hybrid spray models were able to replicate the experimentally observed sensitivities, it was found that the scales characterizing the KH-Faeth model show promise of capturing the experimentally observed trends if the effects of secondary droplet breakup are neglected. These results inform recommendations for future experiments and computational studies that can guide the development of an improved spray breakup model.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗

DOE OSTI2012

Correlation of Oil-Water and Air-Water Contact Angles of Diverse Silanized Surfaces and Relationship to Fluid Interfacial Tensions

The use of air-water, {Theta}{sub wa}, or air-liquid contact angles is customary in surface science, while oil-water contact angles {Theta}{sub ow}, are of paramount importance in subsurface multiphase flow phenomena including petroleum reocovery, nonaqueous phase liquid fate and transport, and geological carbon sequestration. In this paper we determine both the air-water and oil-water contact angles of silica surfaces modified with a diverse selection of silanes, using hexadecane as the oil. The silanes included alkylsilanes, alkylarylsilanes, and silanes with alkyl or aryl groups that are functionalized with heteroatoms such as N, O, and S. These silanes yielded surfaces with wettabilities from water-wet to oil wet, including specific silanized surfaces functionalized with heteroatoms that yield intermediate wet surfaces. The oil-water contact angles for clean and silanized surfaces, excluding one partially fluorinated surface, correlate linearly with air-water contact angles with a slope of 1.41 (R = 0.981, n = 13). These data were used to examine a previously untested theoretical treatment relating air-water and oil-water contact angles in terms of fluid interfacial energies. Plotting the cosines of these contact angles against one another, we obtain a linear relationship in excellent agreement with the theoretical treatment; the data fit cos {Theta}{sub ow} = 0.667 cos {Theta}{sub ow} + 0.384 (R = 0.981, n = 13), intercepting cos {Theta}{sub ow} = -1 at -0.284. The theoretical slope, based on the fluid interfacial tensions {Theta}{sub wa}, {Theta}{sub ow}, and {Theta}{sub oa}, is 0.67. We also demonstrate how silanes can be used to alter the wettability of the interior of a pore network micromodel device constructed in silicon/silica with a glass cover plate. Such micromodels are used to study multiphase flow phenomena. The contact angle of the resulting interior was determined in situ. An intermediate wet micromodel gave a contact angle in excellent agreement with that obtained on an open planar silica surface using the same silane.

02 PETROLEUM↗

DOE OSTI2021

Fracture Sustainability in Enhanced Geothermal Systems: Experimental and Modeling Constraints

Enhanced geothermal systems (EGS) offer the potential for a much larger energy source than conventional hydrothermal systems. Hot, low-permeability rocks are prevalent at depth around the world, but the challenge of extracting thermal energy depends on the ability to create and sustain open fracture networks. Laboratory experiments were conducted using a suite of selected rock cores (granite, metasediment, rhyolite ash-flow tuff, and silicified rhyolitic tuff) at relevant pressures (uniaxial loading up to 20.7 MPa and fluid pressures up to 10.3 MPa) and temperatures (150–250 °C) to evaluate the potential impacts of circulating fluids through fractured rock by monitoring changes in fracture aperture, mineralogy, permeability, and fluid chemistry. Because a fluid in disequilibrium with the rocks (deionized water) was used for these experiments, there was net dissolution of the rock sample: this increased with increasing temperature and experiment duration. Thermal-hydrological-mechanical-chemical (THMC) modeling simulations were performed for the rhyolite ash-flow tuff experiment to test the ability to predict the observed changes. These simulations were performed in two steps: a thermal-hydrological-mechanical (THM) simulation to evaluate the effects of compression of the fracture, and a thermal-hydrological-chemical (THC) simulation to evaluate the effects of hydrothermal reactions on the fracture mineralogy, porosity, and permeability. Furthermore, these experiments and simulations point out how differences in rock mineralogy, fluid chemistry, and geomechanical properties influence how long asperity-propped fracture apertures may be sustained. Such core-scale experiments and simulations can be used to predict EGS reservoir behavior on the field scale.

15 GEOTHERMAL ENERGY↗

NASA NTRS1997

Distributed-Memory Computing With the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA)

The Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA), a Navier-Stokes solver, has been modified for use in a parallel, distributed-memory environment using the Message-Passing Interface (MPI) standard. A standard domain decomposition strategy is used in which the computational domain is divided into subdomains with each subdomain assigned to a processor. Performance is examined on dedicated parallel machines and a network of desktop workstations. The effect of domain decomposition and frequency of boundary updates on performance and convergence is also examined for several realistic configurations and conditions typical of large-scale computational fluid dynamic analysis.

Riley, Christopher J.↗

DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.

40 EE - Geothermal Technologies Office (EE-4G)↗

DOE OSTI2019

Development of Transient Coupled Chemical-Thermal-Fluids Simulation for Unsealed Aluminum-Clad Spent Nuclear Fuel Storage Canister

This study examines a preliminary three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model of the unsealed canisters containing aluminum-clad spent nuclear fuel from INL’s ATR in the irradiated fuel storage facility. This 3D CFD model considers fully resolved fuel plates in an unsealed canister with small mass exchange with surrounding air as time evolves in the simulation and contains a network of chemical reactions to consider due to several of the side reactions occurring in the presence of water vapor, nitrogen, oxygen and carbon dioxide rather than in helium backfilled DOE standard canisters. Prior to the CFD modeling, the full set of chemical reactions under these conditions was analyzed, reduced, and refit to a more manageable number of equations for the computational efficiency of the 3D CFD simulation. To deal with the long time-scale of interest for tracking the possible buildup of trace species such as nitric acid, NOx, and hydrogen, a segregated time stepping algorithm is used, wherein the velocity field is only updated periodically, and assume to be in a pseudo steady state. This study first examines the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields, hydrogen gas and nitric acid concentrations within unsealed canisters in conditions relevant to the INL INTEC CPP-603 facility over a long period of time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

Development of Transient Coupled Chemical-Thermal-Fluids Simulation for Unsealed Aluminum-Clad Spent Nuclear Fuel Storage Canister - 19273

This study examines a preliminary three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model of the unsealed canisters containing aluminum-clad spent nuclear fuel from INL's ATR in the irradiated fuel storage facility. This 3D CFD model considers fully resolved fuel plates in an unsealed canister with small mass exchange with surrounding air as time evolves in the simulation and contains a network of chemical reactions to consider due to several of the side reactions occurring in the presence of water vapor, nitrogen, oxygen and carbon dioxide rather than in helium backfilled DOE standard canisters. Prior to the CFD modeling, the full set of chemical reactions under these conditions was analyzed, reduced, and refit to a more manageable number of equations for the computational efficiency of the 3D CFD simulation. To deal with the long time-scale of interest for tracking the possible buildup of trace species such as nitric acid, NOx, and hydrogen, a segregated time stepping algorithm is used, wherein the velocity field is only updated periodically, and assumed to be in a pseudo steady state. This study first examines the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields, hydrogen gas and nitric acid concentrations within unsealed canisters in conditions relevant to the INL INTEC CPP-603 facility over a long period of time. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

LINAC Electrical Cabinets - Structural Analysis

Along LINAC s downstream path is a floor containing various cabinets above the LINAC. Of the dozens of cabinets are 4 specific cabinet types: PFN (pulse-forming network) Module, PFN Power Supply, Water Skid, and Marx. Each of these cabinets may contain some of the various contents that include electronics, wiring, pipe lines, circuits, conduits. In addition, high voltage and flowing fluids are also present within the cabinets. Occasionally, Lab Technicians perform maintenance and inspections of the cabinets and need to access every part, including the top section that is subject to the weight of the technician standing on top. To ensure the cabinet is structurally safe, a CAD model will be developed for each cabinet and a Finite Element Analysis (FEA) will be performed on each cabinet to confirm it is safe to stand on.

Blas, Efren↗

NASA NTRS2024

3D Scanning System to Assess Gravity-Dependent Body Shape Changes

The human body shows unique physiological and morphological changes when exposed to different gravity conditions, including muscle atrophy, fluid shift, spinal elongation, and body posture adjustments. Such changes need to be incorporated for human-system integration in the vehicle habitat, garment, and spacesuit designs, as inaccurate body measurements can result in suboptimal crew protection that can potentially decrease injury tolerance. However, the traditional linear measurements, such as stature, segment lengths or circumferences measured using a caliper or tape measure, often show limited consistency and are unable to capture the nonlinear characteristics of the human body. While 3D body scanning can provide significant advantages over linear measurements, the technologies have not been fully developed or customized for in-flight scanning. For ground laboratory use, several different scanner types are commercially available. However, in-flight scanning requires additional technical considerations, such as minimal scan time, simplified calibration, and sufficient capture volume. This work aims to develop a prototype 3-D body scanning system that is customized for in-flight use to scan crewmembers, with the configuration and performance optimized for detecting known gravity-dependent body shape and posture changes. A hardware system will be custom built using a network of commercial off-the-shelf 3D sensors. The sensor parameters and settings will be optimized to detect the targeted body shape changes, specifically using body manikins of which the shape and size are iteratively permuted to simulate the known changes by fluid shift and spinal elongation. The capture volume will be also matched for a range of body sizes from a 1st percentile female to 99th percentile male. Unintentional body motions or floating in microgravity will be also simulated and incorporated. A data acquisition software will be developed for efficient in-flight operations with minimal overhead and easy-to-use user interface. A simplified calibration procedure will be designed for robust scanning against frequent vibration or unexpected sensor position shifts. The system will be tested in the ground laboratory for accuracy and reliability against a reference scanning system. New anthropometry measurements will be also identified to sensitively capture the gravity dependent body shape changes, in addition to the traditional anthropometry measurements. A novel landmark-based technique will be tested for consistent anthropometry measurements across posture variations. If successfully developed and deployed, the new system is expected to provide previously unavailable body shape and size data from different gravitational environments, including 0-g, 1/6-g, and 1-g. Such data can improve suit fit, habitat design, exercise efficacy quantification and sizing of orthostatic intolerance garments.

K H Kim↗

NASA NTRS2024

3D Scanning System to Assess Gravity-Dependent Body Shape Changes

The human body shows unique physiological and morphological changes when exposed to different gravity conditions, including muscle atrophy, fluid shift, spinal elongation, and body posture adjustments. Such changes need to be incorporated for human-system integration in the vehicle habitat, garment, and spacesuit designs, as inaccurate body measurements can result in suboptimal crew protection that can potentially decrease injury tolerance. However, the traditional linear measurements, such as stature, segment lengths or circumferences measured using a caliper or tape measure, often show limited consistency and are unable to capture the nonlinear characteristics of the human body. While 3D body scanning can provide significant advantages over linear measurements, the technologies have not been fully developed or customized for in-flight scanning. For ground laboratory use, several different scanner types are commercially available. However, in-flight scanning requires additional technical considerations, such as minimal scan time, simplified calibration, and sufficient capture volume. This work aims to develop a prototype 3-D body scanning system that is customized for in-flight use to scan crewmembers, with the configuration and performance optimized for detecting known gravity-dependent body shape and posture changes. A hardware system will be custom built using a network of commercial off-the-shelf 3D sensors. The sensor parameters and settings will be optimized to detect the targeted body shape changes, specifically using body manikins of which the shape and size are iteratively permuted to simulate the known changes by fluid shift and spinal elongation. The capture volume will be also matched for a range of body sizes from a 1st percentile female to 99th percentile male. Unintentional body motions or floating in microgravity will be also simulated and incorporated. A data acquisition software will be developed for efficient in-flight operations with minimal overhead and easy-to-use user interface. A simplified calibration procedure will be designed for robust scanning against frequent vibration or unexpected sensor position shifts. The system will be tested in the ground laboratory for accuracy and reliability against a reference scanning system. New anthropometry measurements will be also identified to sensitively capture the gravity dependent body shape changes, in addition to the traditional anthropometry measurements. A novel landmark-based technique will be tested for consistent anthropometry measurements across posture variations. If successfully developed and deployed, the new system is expected to provide previously unavailable body shape and size data from different gravitational environments, including 0-g, 1/6-g, and 1-g. Such data can improve suit fit, habitat design, exercise efficacy quantification and sizing of orthostatic intolerance garments.

K H Kim↗

DOE OSTI2019

Development of Transient Coupled Chemical-Thermal-Fluids Simulation for Unsealed Aluminum-Clad Spent Nuclear Fuel Storage Canister

This study examines a preliminary three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model of the unsealed canisters containing aluminum-clad spent nuclear fuel from INL’s ATR in the irradiated fuel storage facility. This 3D CFD model considers fully resolved fuel plates in an unsealed canister with small mass exchange with surrounding air as time evolves in the simulation and contains a network of chemical reactions to consider due to several of the side reactions occurring in the presence of water vapor, nitrogen, oxygen and carbon dioxide rather than in helium backfilled DOE standard canisters. Prior to the CFD modeling, the full set of chemical reactions under these conditions was analyzed, reduced, and refit to a more manageable number of equations for the computational efficiency of the 3D CFD simulation. To deal with the long time-scale of interest for tracking the possible buildup of trace species such as nitric acid, NOx, and hydrogen, a segregated time stepping algorithm is used, wherein the velocity field is only updated periodically, and assume to be in a pseudo steady state. This study first examines the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields, hydrogen gas and nitric acid concentrations within unsealed canisters in conditions relevant to the INL INTEC CPP-603 facility over a long period of time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems. In: EASC '16 Proceedings of the Exascale Applications and Software Conference 2016, Article No. 5

The present work is targeted at performing a strong scaling study of the high-order spectral element fluid dynamics solver Nek5000. Prior studies such as [5] indicated a recommendable metric for strong scalability from a theoretical viewpoint, which we test here extensively on three parallel machines with different performance characteristics and interconnect networks, namely Mira (IBM Blue Gene/Q), Beskow (Cray XC40) and Titan (Cray XK7). The test cases considered for the simulations correspond to a turbulent flow in a straight pipe at four different friction Reynolds numbers Reτ = 180, 360, 550 and 1000. Considering the linear model for parallel communication we quantify the machine characteristics in order to better assess the scaling behaviors of the code. Subsequently sampling and profiling tools are used to measure the computation and communication times over a large range of compute cores. We also study the effect of the two coarse grid solvers XXT and AMG on the computational time. Super-linear scaling due to a reduction in cache misses is observed on each computer. The strong scaling limit is attained for roughly 5000-10,000 degrees of freedom per core on Mira, 30,000 - 50,0000 on Beskow, with only a small impact of the problem size for both machines, and ranges between 10,000 and 220,000 depending on the problem size on Titan. This work aims at being a reference for Nek5000 users and also serves as a basis for potential issues to address as the community heads towards exascale supercomputers.

Offermans, Nicolas↗

DOE OSTI2023

A Comparative Study of Deep Learning Models for Fracture and Pore Space Segmentation in Synthetic Fractured Digital Rocks

This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.

Wang, Hongsheng↗

NASA NTRS1988

SINDA - Systems Improved Numerical Differencing Analyzer

Capability to model fluid flow added. SINDA is software system developed to solve physical problems governed by diffusion-type equations mathematically modeled by lumped-parameter representations. Used as general thermal analyzer with resistor-and-capacitor-network representations. Adapted to wide range of problems represented by Fourier, Possion, Laplace, or other differential equations. Written in FORTRAN and Assembler.

Damico, Steve J.↗