Search NASA⌕ Search

SEARCH · Search NASA

Results for “multiphysics tool”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

ANS MultiApps tutorial

The Multiphysics Object-Oriented Simulation Environment (MOOSE) [1] is an open-source, parallel finite element framework which provides the foundation formany advanced modeling and simulation tools developed under the Department of Energy’s (DOE’s) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program [2] for the analysis of advanced reactors. The MOOSE framework provides the common foundational capability on which many NEAMS codes for reactor analysis are built. The MOOSE framework also includes several systems to assemble unique workflows and coupling amongMOOSE-based applications. In particular, the MultiApp and Transfer systems are widely used to assemble different MOOSE-based or MOOSE- wrapped physics applications together to perform loosely or tightly coupled multiphysics simulations. The National Reactor Innovation Center’s (NRIC’s) Virtual Test Bed (VTB) [3] hosts publicly available nuclear reactor multiphysics simulation examples which leverage MOOSE’s MultiApp system to meet the modeling needs of different reactor types. The flexibility and robustness of coupling provided by MOOSE permits rapid development of coupled physics models for a wide range of reactor types and events.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems in partial differential equations (PDEs). However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes, particularly involving turbulence, is yet to be fully established. Here, this study introduces NeuroSEM, a hybrid framework integrating PINNs with the highfidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages the strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into the Nektar++ solver. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where the data is available. We applied NeuroSEM to the Rayleigh-B´enard convection system, including cases with missing thermal boundary conditions and noisy datasets. Finally, we applied the proposed NeuroSEM framework to real particle image velocimetry (PIV) data to capture flow patterns characterized by horseshoe vortical structures. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework’s plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

42 ENGINEERING↗
DOE OSTI2025

Development and Validation of MALAMUTE model for Electric Field Assisted Sintering of Structural Materials

Fusion power plant designs feature extreme material performance requirements for structural material candidates. In addition to conventional alloys, more advanced composites and oxide dispersion strengthened (ODS) alloys are being explored, however, achieving the desired microstructures to maximize performance using traditional manufacturing methods can be challenging. The advanced manufacturing (AM) electric field-assisted sintering (EFAS) technique offers improved control over the final microstructure through higher heating and cooling rates and moderate pressures. Modeling and simulation tools show promise in elucidating the process-structure-property-performance (PSPP) correlation for AM-produced parts, including the EFAS process. An inherently multiscale process, the EFAS technique aligns well with the multiscale modeling capability of the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE)[cite]. We present here an electro-thermo-mechanical approach to modeling the EFAS process using the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) code. Prediction of the field and gradient distributions across the EFAS tooling is required to accurately describe the conditions for the lower-scale microstructural evolution models. In this work we present the MALAMUTE model developed to predict the electrical potential, temperature, and mechanical stress distribution across the EFAS graphite tooling and part at the larger engineering-scale. Validation of the MALAMUTE engineering-scale model is completed using data from experimental densification and pre-densified runs of iron powder via EFAS at 1000oC. These runs were conducted using a Thermal Technology DCS-5 EFAS system. Data collected during the experiment runs include the direct current (DC) supplied to the graphite tooling, the temperature of the graphite tooling as measured with a pyrometer, and the force applied to the top of the graphite tooling stack, and the data were recorded every 10 seconds. Our validation approach used the current and force data from the EFAS run as boundary condition inputs to the MAMALUTE simulation; the temperature data were used to evaluate the MALAMUTE EFAS model prediction. Results of the MALAMUTE simulations are employed to connect the external pyrometer temperature measurement to the temperature profile across the part undergoing consolidation. We investigate the impact of material property variation and mesh deformation on the temperature profile as predicted by MALAMUTE. We conclude by highlighting projects where the MALAMUTE EFAS modeling and simulation capabilities will be used to assist experimental design.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

MOOSE Reactor Module: An Open-Source Capability for Meshing Nuclear Reactor Geometries

The U.S. Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has developed numerous physics solvers utilizing the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) framework for multiphysics reactor analysis. These solvers require input finite element meshes representing the discretized spatial domain. Typically, reactor analysts turn to licensed tools for the creation of reactor geometry meshes. Recently, open-source functionality has been added to the MOOSE framework to mesh common reactor geometries and improve MOOSE-based nuclear reactor application user workflows. The new functionality is primarily contained in the new Reactor module of MOOSE and includes support for hexagonal pins, assemblies, and cores, extended Cartesian geometry support, options for modeling static and rotating control drums within a hexagonal assembly, core periphery triangulation, and automatic tagging of pin, assembly, plane, and depletion regions for easier post processing of physics results. A set of reactor geometry mesh builder objects further streamlines the construction of hexagonal and Cartesian cores and allows mapping of materials to regions during mesh generation. The meshes produced with the MOOSE Reactor module may be used directly within MOOSE-based applications or exported as Exodus II files for use in other finite element solvers. The tools have been demonstrated and verified using a variety of NEAMS physics solvers on a range of reactor applications, including a sodium-cooled fast reactor core analysis using Griffin, a fast reactor assembly thermal deformation analysis using MOOSE Tensor Mechanics, and a heat pipe–cooled microreactor coupled analysis using Griffin, Bison, and Sockeye. MOOSE’s Reactor module provides significant advantages compared to the use of external meshing tools when analyzing Cartesian and hexagonal reactor lattices using MOOSE-based applications: immediate accessibility (open-source) to the end user, low barrier to entry for new users, speed of mesh generation, volume preservation of meshed fuel pins, and simplification of analysis workflow when used in conjunction with MOOSE-based applications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Microreactors could play a crucial role in decarbonizing our energy portfolio. However, their development and implementation come with specific challenges, particularly regarding cost. Due to their compact size and the harsh operational environment, collecting real-time data on reactor operation can be challenging. Many probe designs are unable to withstand extreme conditions (e.g., temperature, radiation) in the reactor. In this context, using convolutional neural networks (CNNs) can pave the way for developing a nonintrusive approach that relies solely on ex-core sensors. A well-trained physics-informed CNN can reconstruct the distribution of a given physical quantity over a domain using only a few sensors, allowing us to reconstruct the desired field distribution even in a limited space or complex geometries where a large array of sensors is impractical. In this work, we present the initial steps toward developing a real-time tool for monitoring the thermal behavior of nuclear reactor pressure vessels. Based on an experimental setup, a computational model using the Multiphysics Object-Oriented Simulation Environment (moose) framework was built, where the Ray Tracing and Heat Conduction modules were used to evaluate the temperature distribution over a convex metal surface heated through radiative heat transfer. This metal surface represents a section of a heated nuclear reactor vessel wall. The model also accounts for solid mechanics physics through the moose Solid Mechanics module. In situ experimental data, acquired from a Texas A&M facility, were used to validate the computational model. Part of the data generated by the moose model was used to train the convolutional neural network to reconstruct the vessel wall's outer surface temperature. The CNN generalization was then compared against the experimental and computational data.

Aldeia Machado, Luiz Carlos↗
DOE OSTI2022

MOOSE framework enhancements for meshing reactor geometries

MOOSE is an open-source, parallel finite element framework designed to permit rapid development of robust multi-physics modeling capabilities. Under the DOE-NEAMS program, numerous solvers have been developed utilizing the open-source MOOSE framework for multiphysics reactor analysis. These solvers require input finite element meshes representing the discretized geometry. Typically, reactor analysts turn to licensed external tools for creation of reactor geometry meshes. Recently, enhancements have been added to the MOOSE framework to mesh common reactor geometries and improve MOOSE-based application user workflows. Support for hexagonal pins, assemblies, and cores has been added, and Cartesian support has been extended. Options for modeling static and rotating control drums within a hexagonal assembly are now available. Pin, assembly, and plane regions can be identified through automatically applied tags on the mesh called 'reporting IDs' for easier post- processing of physics results. An external open-source triangle routine has been leveraged within MOOSE to mesh core periphery zones. A set of reactor geometry builder routines further streamline the construction of hexagonal and Cartesian cores and include the ability to assign materials to regions during mesh generation. The new meshing routines are available through the MOOSE framework in the open-source 'Reactor' module, and the resulting directly within MOOSE-based applications or exported as Exodus II files for use in other finite element solvers. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

MASTODON: An Open-Source Software for Seismic Analysis and Risk Assessment of Critical Infrastructure

Seismic analysis and risk assessment of safety-critical infrastructure like hospitals, nuclear power plants, dams, and facilities handling radioactive materials involve computationally intensive numerical models and coupled multiphysics scenarios. They are also performed in a strict regulatory environment that requires high software quality assurance standards, and in the case of safety-related nuclear facilities, a conformance to the American Society of Mechanical Engineers Nuclear Quality Assurance (NQA-1) standard. This paper introduces the open-source finite-element software, MASTODON (Multi-hazard Analysis of Stochastic Time-Domain Phenomena), which implements state-of-the-art seismic analysis and risk assessment tools in a quality-controlled environment. MASTODON is built on MOOSE (Multi-physics Object-Oriented Simulation Environment), which is a highly parallelizable, NQA-1 conforming, coupled multiphysics, finite-element framework developed at Idaho National Laboratory. MASTODON is capable of fault rupture and source-to-site wave propagation using the domain reduction method, nonlinear site response, and soil-structure interaction analysis, implicit and explicit time integration, automated stochastic simulations, and seismic probabilistic risk assessment. When coupled with other MOOSE applications, MASTODON can also solve strongly and weakly coupled multiphysics problems. This paper presents a summary of the capabilities of MASTODON and some demonstrative examples.

42 ENGINEERING↗
DOE OSTI2024

Lattice Design and Advanced Modeling to Guide the Design of High-Performance Lightweight Structural Materials

Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Verification and Validation Approach for COMSOL Multiphysics to Support High Flux Isotope Reactor (HFIR)

Over the last several decades, many reactors have successfully been converted from high enriched uranium (HEU) to low enriched uranium (LEU) fuels in United States in support of its global non-proliferation objectives. Of the reactors slated for conversion, five high-performance research reactors (HPRRs) remain. The high-flux isotope reactor (HFIR) at Oak Ridge National Laboratory (ORNL) is one of the five. Conversion of HFIR requires the qualification of a new fuel. To aid in the conversion process as well as improve safety margins, COMSOL Multiphysics was chosen to support and eventually supplement the steady-state heat-transfer code (SSHTC). In order to use COMSOL as a supplementary tool, i.e. one used for safety basis calculations, the code needs to undergo a verification and validation process. Verification and validation are important processes for the use of any software; however, physics-based solvers have a unique challenge, in that the validation is much more rigorous and its domain is only applicable to the general scope of the problem. This document details the verification and validation of COMOSL for analysis of HFIR. This report is intended for two different users: (1) current and future users of COMSOL for HFIR and (2) for individuals interested in assessing the scope of the validation of COMSOL for HFIR. This report is divided into six sections. The first of which details the process of verification and validation. Section one provides an overview of key concepts from ASME V&V 10, 20, and 40 and how these concepts are used to provide a validation of any multiphysics software. Section two covers the approach needed in order to validate multiphysics software for a given HFIR analysis. Parts three through five split up the key concepts highlighted in section two. The final section provides concluding remarks which demonstrate the scope and limitations of the validation study and how these may be expanded and improved.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Particle Impact Simulation and Ignition Prediction

An experimentally calibrated tool is needed to predict if a system is susceptible to failure by particle impact ignition (PI) based on use conditions, materials, and flow geometry. This tool will accelerate new components, evaluating existing hardware, and help disposition anomalies. Conduct particle impact testing with in-situ diagnostics and complementary simulations on subset of key engineering materials (IN718, M400, 316L, 6061, Ti64, Zr) to develop a proof-of-concept predictive tool for assessing the risk of PI for idealized geometries (spherical particles) in realistic environments. Assess particle/target interactions (coefficient of restitution, ignition, kindling) using instrumented particle impact rigs while systematically varying key parameters (materials, particle size, environment, target configuration). Determine key field variables (temperature, strain, stress) in particle impacts using Multiphysics finite element and hydrocode simulations validated through comparison with experimental measurements and observations. Synthesize experiments and simulations into constitutive models for PI that can be integrated with existing computational fluid dynamics (CFD) and Debris Transport Analysis (DTA) tools in future efforts

particle impact↗
NASA NTRS2024

Particle Impact Simulation and Ignition Prediction

An experimentally calibrated tool is needed to predict if a system is susceptible to failure by particle impact ignition (PI) based on use conditions, materials, and flow geometry. This tool will accelerate new components, evaluating existing hardware, and help disposition anomalies. - Conduct particle impact testing with in-situ diagnostics and complementary simulations on subset of key engineering materials (IN718, M400, 316L, 6061, Ti64, Zr) to develop a proof-of-concept predictive tool for assessing the risk of PI for idealized geometries (spherical particles) in realistic environments. - Assess particle/target interactions (coefficient of restitution, ignition, kindling) using instrumented particle impact rigs while systematically varying key parameters (materials, particle size, environment, target configuration). - Determine key field variables (temperature, strain, stress) in particle impacts using Multiphysics finite element and hydrocode simulations validated through comparison with experimental measurements and observations. - Synthesize experiments and simulations into constitutive models for PI that can be integrated with existing computational fluid dynamics (CFD) and Debris Transport Analysis (DTA) tools in future efforts.

Jonathan Tylka↗
DOE OSTI2024

Towards the Development of Molten Salt Reactor Multiphysics Modeling Capabilities: Depletion, Thermochemistry, Reactor Dynamics, and Species Tracking

This report documents the simulation and modeling work performed under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, utilizing the tools and codes developed under the program. The focus in this Fiscal Year (FY) is on the depletion, thermochemistry, reactor dynamics, and species transport in MSRs. The fission products travelling in the entire primary loop is unique in MSRs. There are many physical/chemical mechanisms that can significantly affect the fission products concentration in the fuel salt, e.g., diffusion into gas bubbles, diffusion in the core moderators, deposition on the surface of structures/pipes, etc. The fission product concentration in the fuel salt can potentially affect the neutronics, system dynamics, chemical potentials, etc., so accurately predicting the fission products distribution in the primary loop is important. Under the main objective, different activities are pursued to assess the NEAMS tools applicability for a variety of areas on species transport, including the depletion analyses using Griffin (fission product source), the redox potential assessment at different depletion points, the assessment of xenon transport effect on the neutronics and reactor dynamics, a survey of correlations for calculating the mass transfer coefficient of different fission products, etc.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Prediction of the Lorentz Force Detuning and pressure sensitivity for a Pillbox cavity

The Lorentz Force Detuning (LFD) and the pressure sensitivity are two critical concerns during the design of a Superconducting Radio Frequency (SRF) cavity resonator. The mechanical deformation of the bare Niobium cavity walls, due to the electromagnetic fields and fluctuation of the external pressure in the Helium bath, can dynamically and statically detune the frequency of the cavity and can cause beam phase errors. The frequency shift can be compensated by additional RF power, that is required to maintain the accelerating gradient, or by sophisticated tuning mechanisms and control-compensation algorithms. Passive stiffening is one of the simplest and most effective tools that can be used during the early design phase, capable of satisfying the Radio Frequency (RF) requisites. This approach requires several multiphysics simulations as well as a deep mechanical and RF knowledge of the phenomena involved. In this paper, is presented a new numerical model for a pillbox cavity that can predict the frequency shifts caused by the LFD and external pressure. This method allows to greatly reduce the computational effort, which is necessary to meet the RF requirements and to keep track of the frequency shifts without using the time consuming multiphysics simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Enhanced mechanical property evaluation using innovative data analytics capability

This report focuses on efforts to improve Multiphysics Object-Oriented Simulation Environ-ment (MOOSE) for mechanical property evaluation using data analytics. These efforts involveimprovements to the stochastic tools module (STM) for stochastic simulations of MOOSE mul-tiphysics model and the development of inverse optimization capabilities. The report gives anoverview of the STM and describes recent updates to its core capabilities and theory on itsreduced-order model (ROM) schemes. Examples are also provided showing the impact of theseupdates and exhibits the usefulness of ROMs. An overview of the gradient based inverse opti-mziation algorithms are given along with examples of their application to source identification.Inverse optimization will provide a new methodology in STM for fitting model parameters toexperimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Lattice Structured Lightweight Structural Materials

The development of lightweight structural materials is crucial for enhancing the performance and deployment feasibility of fission batteries. This study aims to produce lightweight structural materials whose strength-to-weight ratios exceed those of current widely used structural materials. To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structured models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The modeling results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison to the solid material; the increase in solid shell thickness and blend radius enhances the mechanical performance; and the effect of unit cell size on macro scale stress is minimal. Tensile testing was conducted on the solid and lattice-structured materials fabricated by laser powder bed fusion (LPBF) additive manufacturing. The experimental results agreed well with the model prediction. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for new material development.

lightweight material↗
DOE OSTI2022

COUPLED MULTIPHYSICS PRIMARY LOOP SIMULATIONS OF THE Mk1-FHR IN THE VIRTUAL TEST BED

The Virtual Test Bed (VTB) repository hosts a wide range of challenge problems to showcase modeling and simulation capabilities to support advanced reactor demonstration. A coupled multiphysics model of the Mk1 pebble-bed fluoride-salt-cooled high-temperature reactor (PB-FHR) is presented here. The analysis leverages NEAMS tools (Griffin), SAM, Pronghorn, and the MOOSE heat conduction module) for core neutronics, thermal hydraulics of the core and the primary loop, and multiscale fuel performance simulations. The analysis was entirely created by coupling standalone simulations of the reactor previously available on the VTB. All input files and documentation developed for this example are available on the VTB website: \url{mooseframework.inl.gov/virtual_test_bed/}}. This model was featured in the NRIC Tech Talk presented in December 2021.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Status of the NEAMS and ARC fast reactor tools integration to the NEAMS Workbench

The Workbench initiative was launched in FY-2017 within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to facilitate the transition from conventional tools to high-fidelity tools. The NEAMS Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes. The integration of the Argonne Reactor Computation (ARC) suite of codes into the NEAMS Workbench through the PyARC module was initiated in FY-2017. The ARC codes, which focus on fast reactor multiphysics analysis, contain both legacy codes like DIF3D and REBUS-3 that were developed with over 30 years of experience, and newer NEAMS additions like MC 2 -3, PERSENT, and PROTEUS. Recent work expended this integration to other tools to support the U.S. fast reactor community such as DASSH for sub-channel thermal-hydraulics, Griffin for high-fidelity deterministic transport calculations, and OpenMC for Monte Carlo simulations (with Shift integration planned for FY-2023). The integration of the “extended ARC” suite of codes into the NEAMS Workbench interface relies on the PyARC and PyGriffin modules to handles the pre- and post-processing of these codes input, and the runtime environment. The PyARC module together with the NEAMS Workbench interface are both released under Open Source Software licenses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

NEAMS Technical Area Support in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to this ubiquity, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work transcribed in this report was conducted in direct support of the simulation tools and has already been deployed. The capabilities outlined in this report include addition of Times and Positions systems, redesign of mechanical contact constraints to enable the augmented Lagrange algorithm, overhaul of the restart system, and incorporation of p-refinement in MOOSE.

97 MATHEMATICS AND COMPUTING↗