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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Uncertainty Quantification of Monolithic Tungsten Target Block Fatigue

The current STS spallation target design consists of 21 water cooled, tantalum clad, monolithic tungsten blocks. The target wheel assembly rotates such that each target block receives a direct proton pulse every 1.4 seconds (21 blocks / 15 Hz pulses). The quasi-steady heating induced by the repetitive beam pulses creates a significant mean stress distribution in the target. On top of this, the rapid energy deposition from a proton pulse induces dynamic stress waves that are of the same order of magnitude as the mean stresses. Over the design goal of 5,000 operational hours per year for a decade, each target must withstand over 130 million repetitions of these dynamic stress events. The fatigue life prediction of the target blocks is a complex sequence of simulations that depend upon a set of input parameters. For the tungsten block, the end goal is the fatigue factor-of-safety (FOS). This study documents a new structural analysis routine using software available to U.S. national laboratories (e.g. CUBIT, Sierra Multiphysics, Dakota) that enables more rapid target fatigue analysis. The increased throughput is amenable to parameter sensitivity and uncertainty quantification (UQ) studies. A set of input parameters (ultimate strength, fatigue strength, density, elastic modulus, Poisson’s ratio, thermal conductivity, coefficient of thermal expansion, specific heat, flow rate) with expected ranges are documented based on literature data, recent experimental testing, and engineering judgement. UQ bounds the resulting fatigue FOS at [0.5, 3.4] using epistemic interval analysis. These bounds are expected to be wider due to model effects (discretization) and beam effects (focus, offset) that were not included. Parameter sensitivity underscores the importance of ultimate strength and fatigue strength of irradiated tungsten on the fatigue calculations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Sierra/SolidMechanics for Target Pulse Analysis

Sierra is a Multiphysics engineering simulation software suite developed and maintained by Sandia National Laboratories. Created for nuclear weapon needs, it is available to national laboratories with a foreign national restriction. The STS Target Systems analysis team is interested in using Sierra to perform explicit dynamics simulations of the tantalum clad tungsten target blocks as a response to sudden temperature changes induced by proton beam pulses. The capability already exists with Abaqus|Explicit, however, there are time and cost limitations due to licensing. The following document serves as a “quick-start” guide to the successful setup, simulation, and post-processing of a target model in Sierra on the OZ3 computing cluster at the Spallation Neutron Source.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Sensitivity Analysis of Irradiated Fueled Experiments using the MOOSE Framework [Slides]

Modeling and simulation (M&S) methods are able to predict uncertainties in experimental parameters (e.g., power and fission density) during irradiation. A shortfall exists in predicting how sensitive some of the parameters will behave during the experimental process. Sensitivity and Uncertainty Quantification (SUQ) is critical in support of qualification and licensing reactor fuels. The application of a method to quantify the uncertainty in these experiments is critical to the prediction of their performance. In this work, we propose the use of a polynomial chaos expansion (PCE) method to quantify the sensitive parameters in these simulations and, in an extension, their experimental surrogates. We propose to perform M&S using PCE uncertainty quantification on a previously irradiated fueled experiment in order to provide a validation case for Griffin and expand its use as a verification and validation (V&V) tool for experiments with a neutronics component. Griffin is an advanced, deterministic neutronics analysis code built using the MOOSE (multiphysics object-oriented simulation environment) framework which can provide state-of-the-art neutronic analysis on M&S of experiments. We will use the stochastic tools module (STM) in MOOSE to provide PCE uncertainty quantification on the proposed experimental setup. Idaho National Laboratory (INL) does not yet have an in-house developed code with V&V approval for experiments performed on-site; this work would provide a necessary addition of support for experiments performed at INL. The Nuclear Regulatory Commission (NRC) has explicitly requested uncertainties in calculated values such as fuel power and burnup, and the development of this capability would benefit the relationship between INL and the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

SIERRA Code Coupling Module: Arpeggio User Manual (V.5.20)

The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling. More importantly Arpeggio orchestrates the execution of applications that participate in the coupling. This document describes the various components of Arpeggio and their operability. The intent of the document is to provide a fast path for analysts interested in coupled applications via simple examples of its usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

SIERRA Code Coupling Module: Arpeggio User Manual - Version 5.18

The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling. More importantly Arpeggio orchestrates the execution of applications that participate in the coupling. This document describes the various components of Arpeggio and their operability. The intent of the document is to provide a fast path for analysts interested in coupled applications via simple examples of its usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Design, Modeling, and Testing of a Novel Inductor for Electric Vehicles: Iron Nitride Soft Magnetic Composites

New technology for electric vehicles (EVs) must meet the requirements of higher energy usage, lower costs, and more sustainable source materials. One promising material for EV power system components is iron nitride (IN) soft magnetic composites (SMCs) because of their competitive magnetic properties and high abundance of the source materials. As part of an ongoing program at Sandia National Laboratories, this project focused on using computer modeling to optimize the prototyping process for an iron nitride SMC toroidal inductor to reach a target inductance of 600 μH. Four inductors with different combinations of wiring (26 AWG and 20 AWG) and vol% loading of iron nitride (65 vol% and 50 vol%) were fabricated at Cal Poly and characterized using an LCR meter. These inductors were also modeled using COMSOL Multiphysics™ with the Magnetic Fields module. The inductance data from the experiment and the model show that the 65 vol% IN prototypes and models agree with about 8% difference, while the 50 vol% IN samples show about a 9% difference between the prototype and the model. These results suggest that the model can predict inductance with both accuracy and precision with low confidence for the given sample size of four. An additional parameter of AC resistance is studied but the AC resistance results from the inductors and from the model generally do not agree closely, suggesting that the current model used in the project does not fully capture the mechanisms behind AC resistance of the inductor. With the focus of the project on inductance, the percent difference results of less than 9% across the four inductors that were tested increases confidence in the model’s predictive capabilities for inductance only. Using the inductance results from both the model and experiment, the final suggested inductor design is a 65 vol% core with 150 windings of 20 AWG wire that is 8 cm across and 1.5 cm tall to reach the inductance goal of 600 μH based on analysis using the optimized COMSOLTM model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Continued performance improvement and integration of MOOSE's thermal-hydraulics capabilities (M3 Milestone Report)

This work introduces performance, robustness and workflow improvements to Multiphysics Object-Oriented Simulation Environment (MOOSE)-based thermal-hydraulics solvers. It presents work related to the acceleration of segregated fluid dynamics algorithms, which show approximately a factor of 10 speedup compared to the preceding implementation. Additionally, we discuss approaches to use advanced, Schurr complement-based, field split preconditioners for monolithic solution algorithms relying on the finite volume method. The presence of the Rhie-Chow interpolation makes the utilization of this preconditioner challenging, but the results indicate that for a moderately large problem a factor of 3.4 speedup can be achieved in conjunction with a factor of 3.5 reduction in memory usage. Furthermore, we introduce several pseudo-time stepping approaches to MOOSE for the robust convergence to steady-state solutions when steady-state solves don't converge due to the initial guesses being too far from the solution in Newton's method. Every MOOSE-based application has access this algorithm and can benefit from its use. Moreover, several new avenues have been presented for importing meshes from commercial software which make meshing easier. Lastly, the Component system within the Thermal-Hydraulics Module (THM) of MOOSE is abstracted by separating geometry- and physics-related properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
DOE OSTI2024

Material Discovery and Design Principles of Perovskite Oxides for Reversible Solid Oxide Cells (R-SOC)

Reversible solid oxide cells (R-SOCs) are highly efficient devices for energy conversion and storage, capable of operating for both hydrogen utilization and production. In fuel cell mode, an R-SOC consumes hydrogen or natural gas to generate electricity, while in electrolysis mode, it produces hydrogen from steam. The discover of new materials with rapid oxygen surface exchange kinetics and enduring stability is crucial for the economically viable commercialization of R-SOCs. To facilitate this pursuit, we conducted extensive Density Functional Theory (DFT) calculations and developed Machine Learning (ML) models to predict critical catalytic properties essential for R-SOCs, such as oxygen surface exchange/diffusivity, and area-specific resistance (ASR). BaCoxFeyZrzO3-d(BFCZ)(x+y+z=1) emerged as a promising family of electrode materials with high activity and stability, validated through systematic experimental study. Moreover, a robust numerical multiphysics model was developed to optimize materials and microstructure parameters, providing the ability to predict the performance of functional R-SOCs.

Liu, Jian↗
DOE OSTI2024

FLEXO: A Portably Performant Code for Pulsed Power Target Physics

FLEXO (Flux-Limited Extended-MHD Ohm's Law) is a production-line multiphysics code developed at Sandia to enable more predictive modeling of target physics on pulsed-power devices. FLEXO uses an extended magnetohydrodynamics (XMHD) model which includes a generalized Ohm's law (GOL), an electron inertia term, and Hall physics. This report describes the code's numerical methods, its computational performance, and test problems of interest.

42 ENGINEERING↗
DOE OSTI2024

SIERRA Code Coupling Module: Arpeggio User Manual (V. 5.22)

The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CFD modeling of high-flux plate-and-frame membrane modules for industrial carbon capture

In this work, we study the application of membrane-based separation systems for carbon capture, considering plate-and-frame membrane modules. The successful deployment of membrane CO2 capture system relies on high-performing membranes as well as effective membrane modules that can fully exploit the developed membranes. A plate-and-frame membrane module is especially attractive for CO2 capture from industrial flue gas due to its lower pressure drop compared to its counterparts such as spiral wound modules and hollow fiber modules. To design better plate-and-frame modules, we investigate their basic unit - a single membrane stack through a combination of computational modeling and experimental investigations. The modeling approach is based on Computational Fluid Dynamics (CFD) to represent a multiphysics problem, including the fluid flow and diffusion processes within a membrane module. We use experimental data collected under different operating conditions to validate the CFD model. Numerical results suggest a good agreement between experiments and model outputs for the CO2 recovery, CO2 mole fraction in the retentate and permeate, and stage-cut. The CFD model is able to predict accurately the flow behavior, providing valuable insights on the effects of fluid dynamics on mass transfer of CO2. We also carry out a sensitivity analysis to identify the effect of key parameters on the CO2 recovery and the CO2 purity of the outlet streams.

Dosso, Cheick↗
DOE OSTI2024

Description of the Gas Sampling and Circulation System for the LYNM PE1-A Experiment

A series of multiphysics experiments, referred to as Physics Experiment 1 (PE1) is underway at the U.S. Nevada National Security Site. The PE1 series includes detonations of three underground chemical explosions. As the name implies, there are a number of experiments investigating the signals generated by the explosion. The experiment series objectives are outlined in a report from Lawrence Livermore National Laboratory.1 One of the experiments is a gas migration experiment. Gas tracers were imbedded in the explosives and gas sampling boreholes were installed in the formation in the test bed. Connected to the monitoring points is a circulation system that moves gas to a central measurement location. This report does not describe the gas analysis or collection systems, but rather the circulation system that provides the gas for measurement. Here, we have combined four project documents into a single report that describes the design, build, installation, testing, and operation of the gas sampling network.

42 ENGINEERING↗
DOE OSTI2024

Improved Fuel Cycle Capability of Griffin for Fast Reactor Applications

Griffin is a MOOSE based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory under the Department of Energy Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation Program. This fiscal year, the fuel cycle capability has been significantly extended by improving the assembly shuffling option to allow flexible fuel reloading in the multi-cycle depletion calculation and incorporating decay between cycles. An equilibrium core calculation capability was also implemented to find an equilibrium core. Additionally, an enrichment search capability was added to determine the enrichment condition that allows a core to reach an equilibrium cycle with the end-of-cycle k-effective meeting a user-specified target value. The updated fuel management capability has been extensively tested using the three-dimensional ABTR problem with different batch schemes, exhibiting reasonable solutions in terms of manual shuffling, equilibrium cycle, and enrichment search calculations. The cross section generation workflow capability for fast reactors was further verified to produce microscopic cross sections as well as Griffin core inputs for an ring-heterogeneous configuration. Rigorous verification tests using the ABTR problem demonstrated that the RH core calculations, with cross sections and Griffin inputs generated from the cross section workflow of Griffin, produced accurate solution for fast reactor problems. In addition, an option to convert delay neutron parameter data generated from MC 2 -3 in the DLAYXS format into XML format was added to support transient calculations using MC 2 -3-generated data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

FY24 Mole Development Updates and SAM-Mole Coupling for MSR Species Transport Applications

In this report, a new coupling strategy linking Mole to SAM is demonstrated where SAM is used to drive the solution and Mole is used primarily as a library for mass transport coefficient models. Groundwork is laid to transition Mole into a harbor for various mass transfer coefficient models, which generally take the form of scalar, auxiliary Multiphysics Object-Oriented Simulation Environment (MOOSE) kernels. It is envisioned that these models will be agnostic to the spatial discretization scheme and can be used across the MOOSE ecosystem, providing a centralized library for certain mass transfer coefficients to MOOSE codes that must account for liquid-to-vapor and vapor-to-liquid mass transport phenomena for both high-volatility and low-volatility species.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Viscoelastic Modeling of Polymers in ALEGRA with the GAP Model

The Glassy Amorphous Polymer (GAP) model is a viscoelastic/plastic model developed at Los Alamos National Laboratory to accurately model a variety of polymers across a wide range of conditions and loading rates, including shock loading. In the present report we introduce and assess this model, newly implemented in the ALEGRA shock and multiphysics code, using a series of verification and application-related validation problems. We describe the mathematical and theoretical formulation of the model, as well as its implementation in ALEGRA, in detail. We provide verification results that assess the model implementation against published computational results, as well as validation results which we compare to existing experimental results when possible. These comparisons instill confidence in the implementation and indicate that the addition of the GAP model to the ALEGRA code provides users with a high-fidelity polymer modeling capability that is capable of recreating complex polymer phenomena.

36 MATERIALS SCIENCE↗