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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Scalable Multiphysics Block Preconditioning for Low Mach Number Compressible Resistive MHD with Application to Magnetic Confinement Fusion

This study investigates multiphysics block preconditioners that are critical in devising scalable Newton–Krylov iterative solvers for longer time-scale fully implicit fluid plasma models. The specific model of interest is the visco-resistive, low Mach number, compressible magnetohydrodynamics (MHD) model. This model describes the dynamics of conducting fluids in the presence of electromagnetic fields and can be used to study aspects of astrophysical phenomena, important science and technology applications, and basic plasma physics. The specific application of interest that motivates this study is the macroscopic simulation of longer time-scale stability and disruptions of magnetic confinement fusion devices, specifically the ITER Tokamak. The computational solution of the governing balance equations for mass, momentum, heat transfer, and magnetic induction for resistive MHD systems can be extremely challenging. These difficulties arise from both the strong nonlinear, nonsymmetric coupling of fluid and electromagnetic phenomena as well as the significant range of time and length scales that the interactions of these physical mechanisms produce. To handle the range of time and spatial scales of interest, a fully implicit unstructured variational multiscale finite element formulation is employed. For the scalable solution of the Newton linearized systems, fully coupled block preconditioners are designed to leverage algebraic multigrid subsolves. In conclusion, results are presented for the strong and weak scaling of the method as well as the robustness of these techniques for a large range of Lundquist numbers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Isopod (inverse Optimization And Design): Moose-based Application For Performing Inverse Optimization On Multiphysics Modeling And Simulation

ISOPOD (InverSe OPtimizatiOn and Design) will be a MOOSE (Multiphysics Object Oriented Simulation Environment)-based framework which will iteratively estimate the parameters by minimizing the difference between simulated and experimental observables. While inverse optimization has long been used for parameter estimation, the unique feature of ISOPOD is rapid solution of new inverse problems, leading to accelerated Research, Development and Demonstration (RD&D) for complex parameter estimation problems. This will be achieved by extending the software structure of MOOSE to inverse optimization, as well as building on the unique automatic differentiation feature of MOOSE.

Munday, LynnB↗
DOE OSTI2017

Clustering of Observed Diurnal Cycles of Precipitation over the United States for Evaluation of a WRF Multiphysics Regional Climate Ensemble

The diurnal cycle of precipitation during the summer season over the contiguous United States is examined in eight distinct regions. These were identified using cluster analysis applied to the diurnal cycle characteristics at 2141 rainfall gauges over the 10-yr period 1991–2000. Application of the clustering technique provides a physically meaningful way of identifying regions for comparison of model results with observations. The diurnal cycle for each region is specified in terms of 1) total precipitation, 2) frequency of precipitation occurrence, and 3) intensity of precipitation per occurrence on an hourly basis averaged over the 10-yr period. The amplitude and phase of each element of the diurnal cycle was obtained from harmonic analysis and has been compared with the results of a 24-member multiphysics ensemble of simulations produced by the Weather Research and Forecast (WRF) Model on a region-by-region basis. Three cumulus schemes, two radiation schemes, two microphysics schemes, and two planetary boundary layer schemes were included in the ensemble. Simulations of total precipitation showed reasonable agreement with observations in regions where the diurnal cycle is directly influenced by solar radiation, (e.g., the U.S. Southeast), but they were less successful in regions where other factors influence the diurnal cycle (e.g., the central United States). The diurnal cycle of precipitation frequency and intensity showed substantial biases in the simulations of all eight regions, namely, overestimation of occurrences and underestimation of intensities. Simulations were sensitive to the cumulus and radiation schemes but were largely insensitive to either microphysics or planetary boundary layer schemes.

Mooney, P. A.↗
DOE OSTI2023

Diagnosing Near-Surface Model Errors with Candidate Physics Parameterization Schemes for the Multiphysics Rapid Refresh Forecast System (RRFS) Ensemble during Winter over the Northeastern United States and Southern Great Plains

Abstract During the winter of 2020/21 an ensemble of FV3-LAM forecasts was produced over the contiguous United States for the Winter Weather Experiment using five physics suites. These forecasts are evaluated with the goal of optimizing physics parameterizations within the future operational Rapid Refresh Forecast System (RRFS) in the Unified Forecast System (UFS) realm and for selecting suitable physics suites for a multiphysics RRFS ensemble. The five physics suites have different combinations of land surface models (LSMs), planetary boundary layer (PBL) parameterizations, and surface layer schemes, chosen from those used in current and possible future operational systems and likely to be supported in the operational UFS. Full-season evaluation reveals a persistent near-surface cold bias in the U.S. Northeast from one suite and a nighttime warm bias in the southern Great Plains in another suite, while other suites have smaller biases. A representative case is chosen to diagnose the cause for each of these biases using sensitivity simulations with different physics combinations or modified parameters and verified with additional mesonet observations. The cold bias in the Northeast is attributed to aspects of the Noah-MP LSM over snow cover, where Noah-MP simulates lower soil water content, and thus lower thermal conductivity than other LSMs, leading to less upward ground heat flux during nighttime and consequently lower surface temperature. The nighttime warm bias found in the southern Great Plains is attributed to overestimation of vertical mixing in the K -profile-based eddy-diffusivity mass-flux (K-EDMF) PBL scheme and insufficient land–atmospheric coupling from the GFS surface layer scheme over short vegetation. A few key parameters driving these systematic biases are identified.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

AMReX: Block-structured adaptive mesh refinement for multiphysics applications

Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.

Zhang, Weiqun↗
DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗
DOE OSTI2022

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

Geochemistry & Geophysics↗
DOE OSTI2022

The Virtual Test Bed (VTB) repository: a library of multiphysics reference reactor models using NEAMS tools

With the next generation of nuclear reactors under development, modeling and simulation (MS) tools are being developed by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in order to support their design, licensing, and future operation. Mirroring the physical test beds currently under construction (i.e., EBR-II and ZPPR), the Virtual Test Bed (VTB) was launched by the National Reactor Innovation Center (NRIC) in collaboration with NEAMS to support the advanced reactor community. This collaborative effort, which involves multiple teams at both Idaho National Laboratory and Argonne National Laboratory aims to use NEAMS tools to model a wide range of reactor designs. Those models are automatically tested to ensure their continued functionality as the tools are further developed. Examples are extensively documented, each acting as a tutorial for applying the relevant NEAMS tools to that reactor design. Currently, five advanced reactor types (with a total of eight specific design variants) are simulated by a variety of different models. These models range from steady-state, core multiphysics simulations to integrated plant analysis during loss-of flow transients. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Multiphysics Simulations of Thermal Shock Testing of Nanofibrous High Power Targets

Increase of primary beam power for neutrino beam-lines leads to a reduced lifespan for production targets. New concepts for robust targets are emerging from the field of High Power Targetry (HPT); one idea being investigated by the HPT R&D Group at Fermilab is an electrospun nanofiber target. As part of their evaluation, samples with different densities were sent to the HiRadMat facility at CERN for thermal shock tests. The samples with the higher density, irradiated under a high intensity beam pulse, exhibit major damage at the impact site whereas those with the lower density show no apparent damage. The exact cause of this failure was unclear at the time. In this paper, we present the results of multiphysics simulations of the thermal shock experienced by the nanofiber targets that suggest the failure originates from the reduced permeability of the high density sample to air flow. The air present in the porous target expands due to heating from the beam, but is unable to flow freely in the high density sample, resulting in a larger back pressure that blows apart the nanofiber mat. We close with a discussion on how to further validate this hypothesis.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Multiphysics Simulation of the Thermal Response of a Nanofibrous Target in a High-Intensity Beam

Nanofibrous structures are of high interest to the fields of engineering and materials science, and investigation of their properties as well as discovery of novel applications for them both constitute lively areas of research. A very promising application of nanofiber mats lies in the field of accelerator technology: beam targets made from nanofiber mats offer a solution to the problem of advancing the "intensity frontier"–-the limit on the beam intensities that can be realized in fixed target experiments and neutrino production facilities. However, testing has shown that the survivability of these nanofiber targets depends strongly on their manufacturing parameters, such as the packing density of fibers. In this work, we will use multiphysics simulations to perform a thermal study on how nanofiber targets react to high intensity beams, so that the dependency of the targets’ lifetime on their construction parameters can be better understood.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

AdditiveFOAM: A Continuum Multiphysics Code for Additive Manufacturing

AdditiveFOAM is a computational framework that simulates transport phenomena in Additive Manufacturing (AM) processes. It is built on OpenFOAM (Weller et al., 1998), the leading free, open-source software package for computational fluid dynamics (CFD). OpenFOAM offers an extensible platform for solving complex multiphysics problems using state-of-the-art finite volume methods. AdditiveFOAM leverages these capabilities to develop specialized tools aimed at addressing challenges in AM processing. Metal additive manufacturing, also known as metal 3D printing, is an advanced manufacturing technique that creates physical parts from a three-dimensional (3D) digital model by melting metal powder or wire feedstock. A significant area of research in metal AM focuses on process planning to mitigate anomalous features during printing that are deleterious to part performance (e.g., porosity and cracking), as well as controlling localized microstructure and material properties. Given the high costs and substantial time requirements associated with experimental methods for qualifying new materials and processes, there is a compelling incentive for researchers to utilize advanced computational simulations. In this context, AdditiveFOAM offers a simulation framework to better understand undesirable features in printing, thereby enhancing process planning and reducing the reliance on labor-intensive experimental campaigns.

Coleman, John [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2014

Scalable Adaptive Multilevel Solvers for Multiphysics Problems

In this project, we carried out many studies on adaptive and parallel multilevel methods for numerical modeling for various applications, including Magnetohydrodynamics (MHD) and complex fluids. We have made significant efforts and advances in adaptive multilevel methods of the multiphysics problems: multigrid methods, adaptive finite element methods, and applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Multiphysics Model of Palladium Hydride Isotope Exchange Accounting for Higher Dimensionality

This report summarizes computational model development and simulations results for a series of isotope exchange dynamics experiments including long and thin isothermal beds similar to the Foltz and Melius beds and a larger non-isothermal experiment on the NENG7 test bed. The multiphysics 2D axi-symmetric model simulates the temperature and pressure dependent exchange reaction kinetics, pressure and isotope dependent stoichiometry, heat generation from the reaction, reacting gas flow through porous media, and non-uniformities in the bed permeability. The new model is now able to replicate the curved reaction front and asymmetry of the exit gas mass fractions over time. The improved understanding of the exchange process and its dependence on the non-uniform bed properties and temperatures in these larger systems is critical to the future design of such systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Fully-Implicit Orthogonal Reconstructed Discontinuous Petrov-Galerkin Method for Multiphysics Problems

This document is focussed on the development of reconstructed discontinuous Galerkin (RDG P n P m ) method, based on orthogonal basis functions. Orthogonality of basis functions is essential for enabling robust and efficient fully-implicit Newton-Krylov based time discretization. The method is designed for generic partial differential equations, including transient hyperbolic, parabolic and/or elliptic operators, which are attributed to many multiphysics problems. Our driving application of interest is additive manufacturing, which requires simulations of fluid-solid-gas systems, with compressibility effects and phase change (melting/ solidification). Thus, we demonstrate the method capabilities for solving nonlinear heat conduction equation coupled with multi-material Navier-Stokes based compressible fluid (in low Mach number limit) systems. We focus our attention to the method accuracy (in both time and space), as well as solvability of linear algebra involved in linear steps of the Newton-based solver. We demonstrate the accuracy and efficacy of the method, emphasizing the advantages from the orthogonality of basis functions, which makes better conditioning of underlying (approximate) Jacobian matrices, and rapid convergence of Krylov methods. iii

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Multiscale Multiphysics Developments for Accident Tolerant Fuel Concepts

U 3 Si 2 and iron-chromium-aluminum (Fe-Cr-Al) alloys are two of many proposed accident-tolerant fuel concepts for the fuel and cladding, respectively. The behavior of these materials under normal operating and accident reactor conditions is not well known. As part of the Department of Energy’s Accident Tolerant Fuel High Impact Problem program significant work has been conducted to investigate the U 3 Si 2 and FeCrAl behavior under reactor conditions. This report presents the multiscale and multiphysics effort completed in fiscal year 2015. The report is split into four major categories including Density Functional Theory Developments, Molecular Dynamics Developments, Mesoscale Developments, and Engineering Scale Developments. The work shown here is a compilation of a collaborative effort between Idaho National Laboratory, Los Alamos National Laboratory, Argonne National Laboratory and Anatech Corp.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

COMSOL Multiphysics Model for HLW Canister Filling

The U.S. Department of Energy (DOE) is building a Tank Waste Treatment and Immobilization Plant (WTP) at the Hanford Site in Washington to remediate 55 million gallons of radioactive waste that is being temporarily stored in 177 underground tanks. Efforts are being made to increase the loading of Hanford tank wastes in glass while meeting melter lifetime expectancies and process, regulatory, and product quality requirements. Wastes containing high concentrations of Al 2 O 3 and Na 2 O can contribute to nepheline (generally NaAlSiO 4 ) crystallization, which can sharply reduce the chemical durability of high level waste (HLW) glass. Nepheline crystallization can occur during slow cooling of the glass within the stainless steel canister. The purpose of this work was to develop a model that can be used to predict temperatures of the glass in a WTP HLW canister during filling and cooling. The intent of the model is to support scoping work in the laboratory. It is not intended to provide precise predictions of temperature profiles, but rather to provide a simplified representation of glass cooling profiles within a full scale, WTP HLW canister under various glass pouring rates. These data will be used to support laboratory studies for an improved understanding of the mechanisms of nepheline crystallization. The model was created using COMSOL Multiphysics, a commercially available software. The model results were compared to available experimental data, TRR-PLT-080, and were found to yield sufficient results for the scoping nature of the study. The simulated temperatures were within 60 ºC for the centerline, 0.0762m (3 inch) from centerline, and 0.2286m (9 inch) from centerline thermocouples once the thermocouples were covered with glass. The temperature difference between the experimental and simulated values reduced to 40 ºC, 4 hours after the thermocouple was covered, and down to 20 ºC, 6 hours after the thermocouple was covered. This level of precision is considered acceptable for the scoping nature of the model and the subsequent laboratory glass studies Using the model, two additional glass pouring cycles were conducted. Representative thermocouple data were plotted to show the variations between the two cycles. This provides preliminary data that will be used in laboratory experiments to determine the potential for controlling nepheline crystallization in glass by varying the glass pouring conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Research and Development of Multiphysics Models in Support of the Conversion of the High Flux Isotope Reactor to Low Enriched Uranium Fuel

The findings presented in this report are results of a five year effort led by the RRD Division of the ORNL, which is focused on research and development toward the conversion of the High Flux Isotope Reactor (HFIR) fuel from high-enriched uranium (HEU) to low-enriched uranium (LEU). This report focuses on the tasks accomplished by the University of Tennessee Knoxville (UTK) team from the Department of Mechanical, Aerospace, and Biomedical Engineering (MABE) that provided expert support in multiphysics modeling of complex problems associated with the LEU conversion of the HFIR reactor. The COMSOL software was used as the main computational modeling tool, whereas Solidworks was also used in support of computer-aided-design (CAD) modeling of the proposed LEU fuel design. The UTK research has been governed by a statement of work (SOW), which was updated annually to clearly define the specific tasks reported herein. Ph.D. student Isaac T. Bodey has focused on heat transfer and fluid flow modeling issues and has been aided by his major professor Dr. Rao V. Arimilli. Ph.D. student Franklin G. Curtis has been focusing on modeling the fluid-structure interaction (FSI) phenomena caused by the mechanical forces acting on the fuel plates, which in turn affect the fluid flow in between the fuel plates, and ultimately the heat transfer, is also affected by the FSI changes. Franklin Curtis has been aided by his major professor Dr. Kivanc Ekici. M.Sc. student Adam R. Travis has focused two major areas of research: (1) on accurate CAD modeling of the proposed LEU plate design, and (2) reduction of the model complexity and dimensionality through interdimensional coupling of the fluid flow and heat transfer for the HFIR plate geometry. Adam Travis is also aided by his major professor, Dr. Kivanc Ekici. We must note that the UTK team, and particularly the graduate students, have been in very close collaboration with Dr. James D. Freels (ORNL technical monitor and mentor) and have benefited greatly from his leadership and expertise in COMSOL modeling of complex physical phenomena. Both UTK and ORNL teams have used COMSOL releases 3.4 through 5.0 inclusive (with particular emphasis on 3.5a, 4.3a, 4.3b, and 4.4) for most of the work described in this report, except where stated otherwise. Just as in the performance of the research, each of the respective sections has been originally authored by respective authors. Therefore, the reader will observe a contrast in writing style throughout this document.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Request for Information FEARCE: An extensible finite element multiphysics software suite for complex and reactive modeling

FEARCE is a modular multiphysics software package configured as a suite of fully-integrated tools that leverages custom templates and models alongside finite element analysis and computational fluid dynamics. FEARCE produces fast, high-fidelity solutions for any aspiring designer needing to model and predict turbulent, reactive, and otherwise complex fluid flows.

97 MATHEMATICS AND COMPUTING↗