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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Autonomous Cryogenics Loading Operations Simulation Software: Knowledgebase Autonomous Test Engineer

Working on the ACLO (Autonomous Cryogenics Loading Operations) project I have had the opportunity to add functionality to the physics simulation software known as KATE (Knowledgebase Autonomous Test Engineer), create a new application allowing WYSIWYG (what-you-see-is-what-you-get) creation of KATE schematic files and begin a preliminary design and implementation of a new subsystem that will provide vision services on the IHM (Integrated Health Management) bus. The functionality I added to KATE over the past few months includes a dynamic visual representation of the fluid height in a pipe based on number of gallons of fluid in the pipe and implementing the IHM bus connection within KATE. I also fixed a broken feature in the system called the Browser Display, implemented many bug fixes and made changes to the GUI (Graphical User Interface).

Wehner, Walter S., Jr.↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2013

Experimental Challenges and Successes in Measuring Aerosol Concentrations at Prototypic Spray Conditions Encountered at the Hanford Waste Treatment and Immobilization Plant - 13327

To date, majority of the work done on measuring aerosol releases from failure of process piping was done using simple Newtonian fluids and small engineered-nozzles that do not accurately represent the fluids and breaches postulated during accident analysis at the Hanford Waste Treatment and Immobilization Plant (WTP). In addition, the majority of the work conducted in this area relies on in-spray measurements that neglect the effect of splatter and do not yield any information regarding aerosol generation rates from this additional mechanism. In order to estimate aerosol generation rates as well as reduce the uncertainties in estimating the aerosol release fractions over a broad range of breaches, fluid properties and operating conditions encountered at the WTP, the Pacific Northwest National Laboratory (PNNL) has designed, commissioned, and tested two experimental test stands. The first test stand, referred to as the large-scale test stand, was designed specifically to measure aerosol concentrations and release fractions under prototypic conditions of flow and pressure for a range of breaches postulated in the hazard analysis for 0.076 m (3-inch) process pipes. However, the size of the large-scale test stand, anticipated fluid loss during a breach, experimental risks, and costs associated with hazardous chemical simulant testing limited the large-scale test stand utility to water and a few non-hazardous physical simulants that did not fully span the particle size and rheological properties of the fluids encountered at the WTP. Overcoming these limitations and extending the range of simulants used, required designing and building a smaller test stand, which was installed and operated in a fume hood. This paper presents some of the features of both test stands, the experimental challenges encountered, and successes in measuring aerosol concentration in both test stands over a range of test conditions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Advanced Reactor Cyber Analysis and Development Environment (ARCADE) for System-Level Design Analysis

Cybersecurity is a persistent concern to the safety and security of Nuclear Power Plants (NPPs), but has lacked data-driven, evidence-based research. Rigorous cybersecurity analysis is critical for the licensing of advanced reactors using a performance-based approach. One tool that enables cybersecurity analysis is modeling and simulation. The nuclear industry makes extensive use of modeling and simulation throughout the decision process but lacks a method to incorporate cybersecurity analysis with existing models. To meet this need, the Advanced Reactor Cyber Analysis and Development Environment (ARCADE) was developed. ARCADE is a suite of publicly available tools that can be used to develop emulations of industrial control system devices and networks and integrate those emulations with physics simulators. This integration of cyber emulations and physics models enables rigorous cyber-physical analysis of cyber-attacks on NPP systems. This report provides an overview of key considerations for using ARCADE with existing physics models and demonstrates ARCADE’s capabilities for cybersecurity analysis. Using a model of the Small Modular Advanced High Temperature Reactor (SmAHTR), ARCADE was able to determine the sensitivity of the primary heat exchangers (PHX) to coordinated cyber-attacks. The analysis determined that while the PHX’s failures cause disruption to the reactor, they did not cause any safety limits to be exceeded because of the plant design, including passive safety features. Further development of ARCADE will enable rigorous, repeatable, and automated cyber-physical analysis of advanced reactor control systems. These efforts will also help reduce regulatory uncertainty by presenting similar types of cybersecurity analyses in a common format, driving standard approaches and reporting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Simulating star clusters with the AMUSE software framework. I. Dependence of cluster lifetimes on model assumptions and cluster dissolution modes

We perform a series of simulations of evolving star clusters using the Astrophysical Multipurpose Software Environment (AMUSE), a new community-based multi-physics simulation package, and compare our results to existing work. These simulations model a star cluster beginning with a King model distribution and a selection of power-law initial mass functions and contain a tidal cutoff. They are evolved using collisional stellar dynamics and include mass loss due to stellar evolution. After studying and understanding that the differences between AMUSE results and results from previous studies are understood, we explored the variation in cluster lifetimes due to the random realization noise introduced by transforming a King model to specific initial conditions. This random realization noise can affect the lifetime of a simulated star cluster by up to 30%. Two modes of star cluster dissolution were identified: a mass evolution curve that contains a runaway cluster dissolution with a sudden loss of mass, and a dissolution mode that does not contain this feature. We refer to these dissolution modes as 'dynamical' and 'relaxation' dominated, respectively. For Salpeter-like initial mass functions, we determined the boundary between these two modes in terms of the dynamical and relaxation timescales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Goated: goal-oriented tensor decompositions in python

SAND2026-20464O Goated performs goal-oriented tensor decompositions in Python, enabling efficient compression of multi-dimensional simulation data. It extends common tensor decomposition methods by incorporating domain-specific knowledge, such as conservation laws in physics, through a penalty term in the optimization process. This approach improves data compression and modeling accuracy across various applications, including physics simulations, by using specialized algorithms and structure-aware subroutines to accelerate solver performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2024

A Simple Non-Planckian Radiation Source for ICF and HED Simulations (Rev.1)

The purpose of this paper is to present a simple way to build frequency dependent spectral (FDS) sources for use in inertial confinement fusion and high energy density physics simulations. This process takes an arbitrary temperature history and converts it into a radiation source with both Planckian and non-Planckian components, the latter of which is to describe high energy photon energies emitted by gold hohlraums. This method is then used to generate spectral energy sources for use in inertial confinement fusion (ICF) and high energy density (HED) simulations which are compared to integrated laser simulations along with experimental measurements.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

A hybridizable discontinuous Galerkin method for modeling fluid–structure interaction

This study presents a novel application of the hybridizable discontinuous Galerkin (HDG) finite element method to the multi-physics simulation of coupled fluid–structure interaction (FSI) problems. Recent applications of the HDG method have primarily been for single-physics problems including both solids and fluids, which are necessary building blocks for FSI modeling. Utilizing these established models, HDG formulations for linear elastostatics, a nonlinear elastodynamic model, and arbitrary Lagrangian–Eulerian Navier–Stokes are derived. The elasticity formulations are written in a Lagrangian reference frame, with the nonlinear formulation restricted to hyperelastic materials. With these individual solid and fluid formulations, the remaining challenge in FSI modeling is coupling together their disparate mathematics on the fluid–solid interface. This coupling is presented, along with the resultant HDG FSI formulation. Verification of the component models, through the method of manufactured solutions, is performed and each model is shown to converge at the expected rate. The individual components, along with the complete FSI model, are then compared to the benchmark problems proposed by Turek and Hron [1]. The solutions from the HDG formulation presented in this work trend towards the benchmark as the spatial polynomial order and the temporal order of integration are increased.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Accurate and rapid acoustic damage characterization in complex structures using sparse sensor networks and deep learning models

Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Simulant Development for LAWPS Testing

This report describes simulant development work that was conducted to support the technology maturation of the LAWPS facility. Desired simulant physical properties (density, viscosity, solids concentration, solid particle size), sodium concentrations, and general anion identifications were provided by WRPS. The simulant recipes, particularly a “nominal” 5.6M Na simulant, are intended to be tested at several scales, ranging from bench-scale (500 mL) to full-scale. Each simulant formulation was selected to be chemically representative of the waste streams anticipated to be fed to the LAWPS system, and used the current version of the LAWPS waste specification as a formulation basis. After simulant development iterations, four simulants of varying sodium concentration (5.6M, 6.0M, 4.0M, and 8.0M) were prepared and characterized. The formulation basis, development testing, and final simulant recipes and characterization data for these four simulants are presented in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA)

The proposed paper will cover ongoing effort named HESTIA (Human Exploration Spacecraft Testbed for Integration and Advancement), led at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) to promote a cross-subsystem approach to developing Mars-enabling technologies with the ultimate goal of integrated system optimization. HESTIA also aims to develop the infrastructure required to rapidly test these highly integrated systems at a low cost. The initial focus is on the common fluids architecture required to enable human exploration of mars, specifically between life support and in-situ resource utilization (ISRU) subsystems. An overview of the advancements in both integrated technologies, in infrastructure, in simulation, and in modeling capabilities will be presented, as well as the results and findings of integrated testing,. Due to the enormous mass gear-ratio required for human exploration beyond low-earth orbit, (for every 1 kg of payload landed on Mars, 226 kg will be required on Earth), minimization of surface hardware and commodities is paramount. Hardware requirements can be minimized by reduction of equipment performing similar functions though for different subsystems. If hardware could be developed which meets the requirements of both life support and ISRU it could result in the reduction of primary hardware and/or reduction in spares. Minimization of commodities to the surface of mars can be achieved through the creation of higher efficiency systems producing little to no undesired waste, such as a closed-loop life support subsystem. Where complete efficiency is impossible or impractical, makeup commodities could be manufactured via ISRU. Although, utilization of ISRU products (oxygen and water) for crew consumption holds great promise of reducing demands on life support hardware, there exist concerns as to the purity and transportation of commodities. To date, ISRU has been focused on production rates and purities for propulsion needs. The meshing of requirements between all potential users, producers, and cleaners of oxygen and water is crucial to guiding the development of technologies which will be used to perform these functions. Various new capabilities are being developed as part of HESTIA, which will enable the integrated testing of these technologies. This includes the upgrading of a 20' diameter habitat chamber to eventually support long duration (90+ day) human-in-the-loop testing of advanced life support systems. Additionally, a 20' diameter vacuum chamber is being modified to create Mars atmospheric pressures and compositions. This chamber, designated the Mars Environment Chamber (MEC), will eventually be upgraded to include a dusty environment and thermal shroud to simulate conditions on the surface of Mars. In view that individual technologies will be in geographically diverse locations across NASA facilities and elsewhere in the world, schedule and funding constraints will likely limit the frequency of physical integration. When this is the case, absent subsystems can be either digitally or physically simulated. Using the Integrated Power Avionics and Software (iPAS) environment, HESTIA is able to bring together data from various subsystems in simulated surroundings, insert faults, errors, time delays, etc., and feed data into computer models or physical systems capable of reproducing the output of the absent subsystems for the consumption of a local subsystems. Although imperfect, this capability provides opportunities to test subsystem integration and interactions at a fraction of the cost. When a subsystem technology is too immature for integrated testing, models can be produced using the General-Use Nodal Network Solver (GUNNS) capability to simulate the overall system performance. In doing so, even technologies not yet on the drawing board can be integrated and overall system performance estimated. Through the integrated development of technologies, as well as of the infrastructure to rapidly and at a low cost, model, simulate, and test subsystem technologies early in their development, HESTIA is pioneering a new way of developing the future of human space exploration.

Banker, Brian F.↗
DOE OSTI2016

Model Calibration vs. Physics-guided Coverage Mapping: Case Study - Pressure Drop Calculations

This work deals with a fundamental question in model validation: given a body of available experiments and an envisaged domain of nuclear reactor operating conditions (referred to as the reactor application), how can one develop a quantitative approach that measures the portion of the prior uncertainties of the reactor application that is covered by the available experiments? Coverage here means that the uncertainties of the reactor application are originating from and behaving in exactly the same way as those observed at the experimental conditions. This approach is valuable as it provides a scientifically defendable criterion by which experimentally measured biases can be credibly extrapolated (i.e., mapped) to biases for the reactor applications. This work focuses on the application of a new methodology, referred to as physics-guided coverage mapping (PCM), to the estimation of hydraulic pressure losses in reactor pipes. PCM provides a generic, calibration-free approach to the mapping of experimental biases by relying on high fidelity physics simulation and information theoretic measure how much of the application domain is covered by the experiments. The idea is to employ the physics models to establish a joint probability distribution that relates the application and the available body of experiments. An information-theoretic measure, referred to as the mutual information, is used to calculate a quantitative metric that measures the inter-dependence between the application and the experiment. A virtual experiment is employed to demonstrate the application of PCM, wherein the pressure drop is calculated for both the experiment and the application using two different flow conditions, i.e., laminar and turbulent, and the experimental values are simulated. All parameters introducing uncertainties, such as geometry, friction factor formulas and their associated parameters are also incorporated. The results obtained with PCM are compared to a conventional calibration-based approach. This work show that the proposed approach can circumvent a number of challenges that face calibration-based methods, such as nonlinearity, ill-posedness and error compensation in high dimensional models with many parameters. This work focus primarily in this summary on parameter uncertainties and how they can be treated using PCM. In a separate summary, discuss performance of PCM under the presence of modeling uncertainties. This simple study has shown that calibration techniques for very simple situations cannot correctly scale the measurements from the experimental to the application domain. It is our view that is occurs primarily due the phenomenon of error compensation where on artificially changes the value of selected parameters to account for all sources of uncertainties. The situation gets more complicated when one calibrates multiple parameters where now the problem could potentially have an infinite number of ways by which the errors can be compensated. (authors)

42 ENGINEERING↗
DOE OSTI2021

Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules

Advances in machine learning provide the ability to leverage data from expensive simulations of high-energy-density experiments to significantly cut down on computational time and costs associated with the search for optimal target designs. This study presents an application of cutting-edge Bayesian optimization methods to the one-dimensional (1D) design optimization of double shell graded layer targets for inertial confinement fusion experiments. This investigation attempts to reduce hydrodynamic instabilities while retaining high yields for future NIF experiments. Machine learning methods can use predictive physics simulations to identify graded layer designs from within the vast design space that demonstrate high predicted performance, including novel designs with high uncertainty in performance that may hold unexpected promise. By applying machine learning tools to the simulation design, we map the trade-off between 1D yield and instability, specifically isolating parameter ranges, which maintain high performance while showing significantly improved Rayleigh–Taylor stability over the point design. Furthermore, the groundwork laid in this study will be a useful design tool for future NIF experiments with graded layer targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

System-level risk assessment of carbon sequestration into a naturally fractured aquifer at Kevin Dome, Montana

As actual CO2 injection is unlikely to take place at Kevin Dome, Montana, the Big Sky Carbon Sequestration Partnership has turned to maximizing the value of existing data acquired at the site. We present the risk assessment work done using the National Risk Assessment Partnership (NRAP) to Kevin Dome, Montana. Geologic CO2 sequestration in saline aquifers poses certain risks including CO2/brine leakage through wells or non-sealing faults into ground water or land surface. These risks are difficult to quantify due to data availability and uncertainty. One solution is running large numbers of numerical simulations on the primary CO2 injection reservoir, shallow reservoirs/aquifers, faults, and wells to assess leakage risks and uncertainties. However, a full-physics simulation is usually too computationally expensive. NRAP integrated assessment model (NRAP-IAM) uses reduced order models (ROMs) developed from numerical reservoir simulations of a primary CO2 injection reservoir to address this issue. A powerful stochastic framework allows NRAP-IAM to explore complex interactions among many uncertain variables and evaluate the likely performance of potential sequestration sites. In this study, we investigate the sensitivity of a variety of uncertain parameters to CO2/brine leakage through (1) legacy wellbore and (2) fault pathways. We found major uncertain parameters to which the potential CO2 leakage through legacy wellbore is sensitive including values of fracture permeability, end-point CO2 relative permeability, capillary pressure, and permeability of confining rocks. CO2 and brine leakage through fault pathways is sensitive to fracture permeability, length of the faults, and fault displacement.

Nguyen, Minh↗
DOE OSTI2017

Sea ice thermohaline dynamics and biogeochemistry in the Arctic Ocean: Empirical and model results

Here, large changes in the sea ice regime of the Arctic Ocean have occurred over the last decades justifying the development of models to forecast sea ice physics and biogeochemistry. The main goal of this study is to evaluate the performance of the Los Alamos Sea Ice Model (CICE) to simulate physical and biogeochemical properties at time scales of a few weeks and to use the model to analyze ice algal bloom dynamics in different types of ice. Ocean and atmospheric forcing data and observations of the evolution of the sea ice properties collected from 18 April to 4 June 2015, during the Norwegian young sea ICE expedition, were used to test the CICE model. Our results show the following: (i) model performance is reasonable for sea ice thickness and bulk salinity; good for vertically resolved temperature, vertically averaged Chl a concentrations, and standing stocks; and poor for vertically resolved Chl a concentrations. (ii) Improving current knowledge about nutrient exchanges, ice algal recruitment, and motion is critical to improve sea ice biogeochemical modeling. (iii) Ice algae may bloom despite some degree of basal melting. (iv) Ice algal motility driven by gradients in limiting factors is a plausible mechanism to explain their vertical distribution. (v) Different ice algal bloom and net primary production (NPP) patterns were identified in the ice types studied, suggesting that ice algal maximal growth rates will increase, while sea ice vertically integrated NPP and biomass will decrease as a result of the predictable increase in the area covered by refrozen leads in the Arctic Ocean.

58 GEOSCIENCES↗
DOE OSTI2020

Use of Micro-Gravity Sensors for External Fluid Level Monitoring in Waste and Nuclear Related Applications - 20315

There are a number of applications in nuclear energy and hazardous waste disposal that require monitoring of fluids under extreme environments, including high levels of temperature, pressure, toxicity and radioactivity. Many of these applications will benefit from a monitoring technique that is external and non-invasive. Currently the sensors used are invasive, must reside inside the pressurized vessels and must penetrate the vessel walls, which can create a weakness in the vessel. Additionally, instruments that are used inside such containers must be exceptionally hardened to the environment. Information Systems Laboratories (ISL) has developed an external mass (gravimetric) measuring technique for monitoring nuclear coolant in Small Modular Reactors (SMRs), which will also work for measuring fluid levels in waste tanks, that avoids the problems inherent in invasive sensors. It utilizes a COTS gravitational sensor of unprecedented accuracy, leveraged via proper sensor placement geometry, to detect fluid changes of small amplitude from an outside position, obviating the need to penetrate the vessel. The technique is called Gravisense{sup TM}. ISL has proven via simulation and experiment that this concept can be usefully applied to monitoring fluid levels in both nuclear reactors and large waste tanks. Numerical simulation algorithms were developed to calculate the gravity effect of small changes in water level, which were verified by experiments at the NIST Physical Simulator facility at the Oregon State University. The measured ultralow noise levels of the superconducting gravimeter type which utilizes a Niobium sphere suspended in a magnetic field to attain its phenomenal accuracy, demonstrated that fluid levels in SMRs can be measured at least to within 3 cm. Furthermore, the method can distinguish between a contained leak (from reactor to containment vessel) from an external leak (from reactor to outside of containment). Additionally, simulations of waste canisters that hold spent fuel rods show that the fluid level measuring accuracy can potentially do better than 1 cm accuracy by measuring from below the vessel, and judicious placement of sensors on top of large waste tanks can potentially achieve a very impressive 2 mm measurement accuracy. These encouraging results prove that the Gravisense{sup TM} technique for fluid determination can be very useful in nuclear energy generation, testing, and research, as well as in waste monitoring situations that are difficult to monitor via traditional sensing technology. We believe that the next step should be to test the technique on canisters of the type that are currently storing waste in various DOE locations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Multi-species Modeling of Molten-Salt Electrochemical Process

Spent nuclear fuel has been produced and stored in over 30 different countries which are operating commercial nuclear power plants. Molten-salt pyroprocessing has been developed for treating both oxide and metal spent nuclear fuels. This process separates long-lived actinide isotopes together as a group from fission products for burning the long-lived isotopes in fast reactors. Main processes of pyroprocessing such as oxide reduction, electrorefining, and electrowinning are based on a concept of electrolytic cell consuming energy to proceed electrochemical reactions. To scale-up the electrochemical processes, the design optimization of the key steps is required to reach high throughput that improves economic competitiveness and reduces an environmental impact. For the optimization, it is significant to compare different unit process designs and find optimum designs and operating conditions. However, this optimization process is very costly if we need to conduct experiments on every case with spent nuclear fuel. Instead, a validated simulation model can help to rapidly calculate and evaluate different case at significantly reduced costs. This paper presents recent development of multi-physics simulation of molten salt electrochemistry for partitioning spent nuclear fuel with limiting factors. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Implementation of a Red-Black SOR CMFD Solver in MPACT

As part of the Consortium for Advanced Simulation of Light Water Reactors (CASL), the Virtual Environment for Reactor Applications (VERA) is being developed to provide high-fidelity multi-physics simulations of nuclear reactor cores. The MPACT code being developed collaboratively by Oak Ridge National Laboratory and the University of Michigan includes the primary deterministic neutron transport solver available within VERA. MPACT employs the 2D/1D method to solve three-dimensional problems, using 2D method of characteristics (MOC) radial transport solvers with 1D NEM-SP3 axial transport solvers, which are coupled using axial and radial transverse leakages. A 3D coarse mesh finite difference (CMFD) solver is used to provide accelerated convergence that is more stable. The Portable, Extensible Tool kit for Scientific Computation (PETSc) generalized minimal residual (GMRES) iterative solver with a block Jacobi preconditioner has been used to solve the CMFD linear systems. This work focused on the addition of a red-black successive over-relaxation (RBSOR) solver to reduce overall runtime by reducing time spent solving the CMFD linear systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗