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Languages : en
Pages : 9
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Book Description
In this project, we will address the challenges associated with constructing high fidelity multiscale models of nuclear fuel performance. We (*) propose a novel approach for coupling mesoscale and macroscale models, (*) devise efficient numerical methods for simulating the coupled system, and (*) devise and analyze effective numerical approaches for error and uncertainty quantification for the coupled multiscale system. As an integral part of the project, we will carry out analysis of the effects of upscaling and downscaling, investigate efficient methods for stochastic sensitivity analysis of the individual macroscale and mesoscale models, and carry out a posteriori error analysis for computed results. We will pursue development and implementation of solutions in software used at Idaho National Laboratories on models of interest to the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 9
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Book Description
In this project, we will address the challenges associated with constructing high fidelity multiscale models of nuclear fuel performance. We (*) propose a novel approach for coupling mesoscale and macroscale models, (*) devise efficient numerical methods for simulating the coupled system, and (*) devise and analyze effective numerical approaches for error and uncertainty quantification for the coupled multiscale system. As an integral part of the project, we will carry out analysis of the effects of upscaling and downscaling, investigate efficient methods for stochastic sensitivity analysis of the individual macroscale and mesoscale models, and carry out a posteriori error analysis for computed results. We will pursue development and implementation of solutions in software used at Idaho National Laboratories on models of interest to the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program.
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Languages : en
Pages : 52
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Book Description
Understanding and improving microstructural mechanical stability in metals and alloys is central to the development of high strength and high ductility materials for cladding and cores structures in advanced fast reactors. Design and enhancement of radiation-induced damage tolerant alloys are facilitated by better understanding the connection of various unit processes to collective responses in a multiscale model chain, including: dislocation nucleation, absorption and desorption at interfaces; vacancy production, radiation-induced segregation of Cr and Ni at defect clusters (point defect sinks) in BCC Fe-Cr ferritic/martensitic steels; investigation of interaction of interstitials and vacancies with impurities (V, Nb, Ta, Mo, W, Al, Si, P, S); time evolution of swelling (cluster growth) phenomena of irradiated materials; and energetics and kinetics of dislocation bypass of defects formed by interstitial clustering and formation of prismatic loops, informing statistical models of continuum character with regard to processes of dislocation glide, vacancy agglomeration and swelling, climb and cross slip.
Author: Yan Wang
Publisher: Woodhead Publishing
ISBN: 008102942X
Category : Technology & Engineering
Languages : en
Pages : 606
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Book Description
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales. Synthesizes available UQ methods for materials modeling Provides practical tools and examples for problem solving in modeling material behavior across various length scales Demonstrates UQ in density functional theory, molecular dynamics, kinetic Monte Carlo, phase field, finite element method, multiscale modeling, and to support decision making in materials design Covers quantum, atomistic, mesoscale, and engineering structure-level modeling and simulation
Author: Chunyu Liu
Publisher: Springer Spektrum
ISBN: 9783658434212
Category : Science
Languages : en
Pages : 0
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Book Description
In this book, the author provides a deep study into multiscale and multiphysics modeling of nuclear facilities, underscoring the critical role of uncertainty quantification and sensitivity analysis to ensure the confidence in the numerical results and to identify the system characteristics. Through an in-depth study of the liquid metal cooling system from the TALL-3D loop to the SMDFR core, the research highlights the natural circulation instability, strong coupling effects, perturbation tolerance, and system stability. The culmination of the research is the formulation of an optimized uncertainty-based control scheme, demonstrating its versatility beyond the nuclear domain to other energy sectors. This groundbreaking work not only advances the comprehension and utilization of coupling schemes and uncertainty methodologies in nuclear system modeling but also adeptly bridges the theoretical constructs with tangible application, positioning itself as an indispensable resource for design, safety analysis, and advanced numerical modeling in the broader energy sector.
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Languages : en
Pages : 11
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The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy's resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this framework, the project team has focused on optimizing resource allocation for improving numerical models through further code development and experimentation. Related to further code development, we have developed a code prioritization index (CPI) for coupled numerical models. CPI is implemented to effectively improve the predictive capability of the coupled model by increasing the sophistication of constituent codes. In relation to designing new experiments, we investigated the information gained by the addition of each new experiment used for calibration and bias correction of a simulation model. Additionally, the variability of 'information gain' through the design domain has been investigated in order to identify the experiment settings where maximum information gain occurs and thus guide the experimenters in the selection of the experiment settings. This idea was extended to evaluate the information gain from each experiment can be improved by intelligently selecting the experiments, leading to the development of the Batch Sequential Design (BSD) technique. Additionally, we evaluated the importance of sufficiently exploring the domain of applicability in experiment-based validation of high-consequence modeling and simulation by developing a new metric to quantify coverage. This metric has also been incorporated into the design of new experiments. Finally, we have proposed a data-aware calibration approach for the calibration of numerical models. This new method considers the complexity of a numerical model (the number of parameters to be calibrated, parameter uncertainty, and form of the model) and seeks to identify the number of experiments necessary to calibrate the model based on the level of sophistication of the physics. The final component in the project team's work to improve model calibration and validation methods is the incorporation of robustness to non-probabilistic uncertainty in the input parameters. This is an improvement to model validation and uncerta ...
Author: Maria Avramova
Publisher: Woodhead Publishing Series in
ISBN: 9780128149546
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
Verification, Validation and Uncertainty Quantification in Multi-Physics Modeling of Nuclear Reactors is a key reference for those tasked with ensuring the credibility and reliability of engineering models and simulations for the nuclear industry and nuclear energy research. Sections discuss simulation challenges and revise key definitions, concepts and terminology. Chapters cover solution verification, the frontier discipline of multi-physics coupling verification, model validation and its applications to single and multi-scale models, and uncertainty quantification. This essential guide will greatly assist engineers, scientists, regulators and students in applying rigorous verification, validation and uncertainty quantification methodologies to the M&S tools used in the industry. The book contains a strong focus on the verification and validation procedures required for the emerging multi-physics M&S tools that have great potential for use in the licensing of new reactors, as well as for power uprating and life extensions of operating reactors. Uniquely--and crucially for nuclear engineers--demonstrates the application of verification, validation and uncertainty methodologies to the modeling and simulation (M&S) of nuclear reactors Equips the reader to develop a rigorously defensible validation process irrespective of the particular M&S tool used Brings the audience up-to-speed on validation methods for traditional M&S tools Extends the discussion to the emerging area of validation of multi-physics and multi-scale nuclear reactor simulations
Author: Yifeng Che
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Accurately predicting the behavior of nuclear fuel performance is essential for the safe and economic operation of nuclear reactors. Computer codes of different fidelities have been developed over past decades to simulate the behavior of nuclear fuels, such as the multi-dimensional, parallel, finite element-based code BISON, and the NRC-auditing code FRAPCON. Multiple areas of research remain to be addressed in fuel performance while physics-based approaches often reach their limits. Studies to be presented in this thesis therefore revolve around applying data-driven methods to address these issues. First, discrepancies always exist between code predictions and real-world responses, thus uncertainties must be quantified for the code predictions for benefit of decision making, operation safety and design optimization. Systematic validation and verification are performed for BISON first, followed by a holistic sensitivity analysis (SA) framework built upon a complete set of uncertain input parameters. The number of uncertain input parameters can be effectively reduced based on the obtained qualitative importance ranking, benefiting the subsequent uncertainty quantification (UQ). To enhance the predictability, a novel Bayesian inference framework is introduced to efficiently calibrate the expensive high fidelity tools, possibly without resorting to approximate surrogate methods. The calibrated prediction aligns better with experimental observations, and is subject to significantly reduced uncertainty. Second, while full-core monitoring of fuel behaviors can provide the most realistic assessment of safety margins, its computational cost for use in design and operation optimization is prohibitive. Machine learning (ML) methods were used to construct fast-running full-core surrogates, which achieves a runtime acceleration of more than 10,000 (1,000) times compared to FRAPCON for the standard (high burnup) PWR cores, allowing for direct coupling of full-core fuel response into core design optimization in the future. Then for purpose of full-core PCI monitoring which requires BISON as the high-fidelity simulation tool, a physics-informed multi-fidelity ML framework is introduced to significantly reduce the number of necessary code runs. Finally, deep learning models are trained to predict the spatiotemporal distribution of the cladding hoop stress. The proposed data-driven methods for the selected applications enlightens the nuclear community on practical pathways to realize meaningful improvements in fuel performance assessment.
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Languages : en
Pages : 27
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Book Description
Author:
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Languages : en
Pages :
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This report considers the problem of calibrating a numerical model to data from an experimental campaign (or series of experimental tests). The issue is that when an experimental campaign is proposed, only the input parameters associated with each experiment are known (i.e. outputs are not known because the experiments have yet to be conducted). Faced with such a situation, it would be beneficial from the standpoint of resource management to carefully consider the sequence in which the experiments are conducted. In this way, the resources available for experimental tests may be allocated in a way that best 'informs' the calibration of the numerical model. To address this concern, the authors propose decomposing the input design space of the experimental campaign into its principal components. Subsequently, the utility (to be explained) of each experimental test to the principal components of the input design space is used to formulate the sequence in which the experimental tests will be used for model calibration purposes. The results reported herein build on those presented and discussed in [1,2] wherein Verification & Validation and Uncertainty Quantification (VU) capabilities were applied to the nuclear fuel performance code LIFEIV. In addition to the raw results from the sequential calibration studies derived from the above, a description of the data within the context of the Predictive Maturity Index (PMI) will also be provided. The PMI [3,4] is a metric initiated and developed at Los Alamos National Laboratory to quantitatively describe the ability of a numerical model to make predictions in the absence of experimental data, where it is noted that 'predictions in the absence of experimental data' is not synonymous with extrapolation. This simply reflects the fact that resources do not exist such that each and every execution of the numerical model can be compared against experimental data. If such resources existed, the justification for numerical models would be reduced considerably. The authors note that the PMI is primarily intended to provide a high-level, quantitative description of year-to-year (or version-to-version) improvements in numerical models, where these descriptions can be used as a means of justifying funding requests to support further model development research. It is in this context that the present report should be considered: the availability of data from experimental tests should be viewed as a time-dependent variable, where experiments are added to the calibration suite as resources become available. For the present report, the experimental data is of course already available (permitting demonstration of the proposed methodology). Furthermore, the authors are not proposing this methodology as the answer to the question of how to allocate resources for experimental tests, and readers are directed to [5] and the references contained in Section 1 of [5] for additional information on the subject. However, the strength of this methodology is that it offers a means by which to select the sequence of experiments in a pre-arranged experimental campaign (a situation for which the methods discussed in [5] are less appropriate). The report is organized as follows. Section 2 describes the methodology employed to formulate the sequences of experiments for the calibrations performed for this study. Section 3 then presents the results associated with two sequences; supplementary results are provided in the Appendix. The report then concludes in Section 4 with a brief summary.
Author: Xiang Ma
Publisher:
ISBN:
Category :
Languages : en
Pages : 224
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Book Description
To accurately predict the performance of physical systems, it becomes essential for one to include the effects of input uncertainties into the model system and understand how they propagate and alter the final solution. The presence of uncertainties can be modeled in the system through reformulation of the governing equations as stochastic partial differential equations (SPDEs). The spectral stochastic finite element method (SSFEM) and stochastic collocation methods are the most popular simulation methods for SPDEs. However, both methods utilize global polynomials in the stochastic space. Thus when there are steep gradients or finite discontinuities in the stochastic space, these methods converge slowly or even fail to converge. In order to resolve the above mentioned issues, an adaptive sparse grid collocation (ASGC) strategy is developed using piecewise multi-linear hierarchical basis functions. Hierarchical surplus is used as an error indicator to automatically detect the discontinuity region in the stochastic space and adaptively refine the collocation points in this region. However, this method is limited to a moderate number of random variables. To address the solution of high-dimensional stochastic problems, a computational methodology is further introduced that utilizes the High Dimensional Model Representation (HDMR) technique in the stochastic space to represent the model output as a finite hierarchical correlated function expansion in terms of the stochastic inputs starting from lower-order to higher-order component functions. An adaptive version of HDMR is also developed to automatically detect the important dimensions and construct higherorder terms using only the important dimensions. The ASGC is integrated with HDMR to solve the resulting sub-problems. Uncertainty quantification for fluid transport in porous media in the presence of both stochastic permeability and multiple scales is addressed using the developed HDMR framework. In order to capture the small scale heterogeneity, a new mixed multiscale finite element method is developed within the framework of the heterogeneous multiscale method in the spatial domain. Several numerical examples are considered to examine the accuracy of the multiscale and stochastic frameworks developed. A summary of suggestions for future research in the area of stochastic multiscale modeling are given at the end of the thesis.
