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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.
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.
Author: Su Chiang Shu Faya
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
Author: Kenneth Joseph Dayman
Publisher:
ISBN:
Category :
Languages : en
Pages : 250
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Book Description
The Multi-Isotope Process Monitor is being developed at Pacific Northwest National Laboratory as a method to verify the process conditions within a nuclear fuel reprocessing facility using the gamma spectra of various process streams. The technique uses multivariate analysis techniques such as principal component analysis and partial least squares regression applied to gamma spectra collected of a process stream in order to classify the contents as belonging to a normal versus off-normal chemistry process. This approach to process monitoring is designed to function automatically, nondestructively, and in near real-time. To extend the Multi-Isotope Process Monitor, an analysis method to characterize spent nuclear fuel based on the reactor of origin, either pressurized or boiling water reactor, and burnup of the fuel using nuclide concentrations as input data has been developed. While the Multi-Isotope Process Monitor uses gamma spectra as input data, nuclide activities were used in this work as an initial step before Nuclide composition information was generated using ORIGEN-ARP for different fuel assembly types, initial 235U enrichments, burnup values, and cooling times. This data was used to train, tune, and test several multivariate analysis algorithms in order to compare their performance and identify the technique most suited for the analysis. To perform the classification based on reactor type, four methods were considered: k-nearest neighbors, linear and quadratic discriminant analysis, and support vector machines. Each method was optimized, and its performance on a validation set was used to determine the best method for classifying the fuel reactor class. Partial least squares was used to make burnup predictions. Three models were generated and tested: one trained on all the data, one trained for just pressurized water reactors, and one trained for boiling water reactors. Quadratic discriminant analysis was chosen as the best classifier of reactor class because of its simplicity and its potential to be extended to classify spent nuclear fuel's fuel assembly type, i.e, more specific classes, using nuclide concentrations as input data. In the case of predicting the burnup of spent fuel using partial least squares, it was determined that making reactor-specific partial least squares models, one trained for pressurized water reactors and one trained for boiling water reactors, performed better than a single, general model that was trained for all light water reactors. Thus, the the classifier, regression algorithm, and all the necessary intermediate data processing steps were combined into a single analysis method and implemented as a Matlab function called "burnup." This function was used to test the analysis routine on an additional set of data generated in ORIGEN-ARP. This dataset included samples with parameters that were not represented in the development data in order to ascertain the analysis method's ability to analyze data for which it has not been explicitly trained. The algorithm was able to achieve perfect binary classification of the reactor as being a pressurized or boiling water reactor on the dataset and made burnup predictions with an average error of 0.0297%.
Author: H.R. Madala
Publisher: CRC Press
ISBN: 1351090399
Category : Computers
Languages : en
Pages : 353
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Book Description
Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behavior; comprehensive coverage of all types of algorithms useful for this subject; and applications of various modeling activities (e.g., environmental systems, noise immunity, economic systems, clusterization, and neural networks). It presents recent studies on clusterization and recognition problems, and it includes listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs. Inductive Learning Algorithms for Complex Systems Modeling will be a valuable reference for graduate students, research workers, and scientists in applied mathematics, statistics, computer science, and systems science disciplines. The book will also benefit engineers and scientists from applied fields such as environmental studies, oceanographic modeling, weather forecasting, air and water pollution studies, economics, hydrology, agriculture, fisheries, and time series evaluations.
Author: Arrielle Christine Opotowsky
Publisher:
ISBN:
Category :
Languages : en
Pages : 179
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Book Description
Nuclear forensics is a nuclear security capability that is broadly defined as material attribution in the event of a nuclear incident. Improvement and research is needed for technical components of this process. One such area is the provenance of non-detonated special nuclear material; studied here is spent nuclear fuel (SNF), which is applicable in a scenario involving the unlawful use of commercial byproducts from nuclear power reactors. The experimental process involves measuring known forensics signatures to ascertain the reactor parameters that produced the material, assisting in locating its source. This work proposes the use of statistical methods to determine these quantities instead of empirical relationships. The purpose of this work is to probe the feasibility of this method with a focus on field-deployable detection. Thus, two experiments are conducted, the first informing the second by providing a baseline of performance. Both experiments use simulated nuclide measurements as observations and reactor operation parameters as the prediction goals. First, machine learning algorithms are employed with full-knowledge training data, i.e., nuclide vectors from simulations that mimic lab-based mass spectrometry. The error in the mass measurements is artificially increased to probe the prediction performance with respect to information reduction. Second, this machine learning workflow is performed on training data analogous to a field-deployed gamma detector that can only measure radionuclides. The detector configuration is varied so that the information reduction now represents decreasing detector energy resolution. The results are evaluated using the error of the reactor parameter predictions. The reactor parameters of interest are the reactor type and three quantities that can attribute SNF: burnup, initial U235 enrichment, and time since irradiation. The algorithms used to predict these quantities are k-nearest neighbors, decision trees, and maximum log-likelihood calculations. The first experiment predicts all of these quantities well using the three algorithms, except for k-nearest neighbors predicting time since irradiation. For the second experiment, most of the detector configurations predict burnup well, none of them predict enrichment well, and the time since irradiation results perform on or near the baseline. This approach is an exploratory study; the results are promising and warrant further study.
Author: Curtis Smith
Publisher: Academic Press
ISBN: 0323998186
Category : Science
Languages : en
Pages : 388
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Book Description
Risk-informed Methods and Applications in Nuclear and Energy Engineering: Modelling, Experimentation, and Validation presents a comprehensive view of the latest technical approaches and experimental capabilities in nuclear energy engineering. Based on Idaho National Laboratory’s popular summer school series, this book compiles a collection of entries on the cutting-edge research and knowledge presented by proponents and developers of current and future nuclear systems, focusing on the connection between modelling and experimental approaches. Included in this book are key topics such as probabilistic concepts for risk analysis, the survey of legacy reliability and risk analysis tools, and newly developed tools supporting dynamic probabilistic risk-assessment. This book is an insightful and inspiring compilation of work from top nuclear experts from INL. Industry professionals, researchers and academics working in nuclear engineering, safety, operations and training will gain a board picture of the current state-of-practice and be able to apply that to their own risk-assessment studies. Based on Idaho National Laboratory’s summer school series, this book is a collection of entries from proponents and developers of current and future nuclear systems Provides an up-to-date view of current technical approaches and experimental capabilities in nuclear energy engineering, covering modeling and validation, and focusing on risk-informed methods and applications Equips the reader with an understanding of various case studies and experimental validations to enable them to carry out a risk-assessment study
Author: U.S. Nuclear Regulatory Commission
Publisher:
ISBN:
Category : Nuclear energy
Languages : en
Pages : 48
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Book Description
Author: Jun Wang
Publisher: Frontiers Media SA
ISBN: 2889712990
Category : Technology & Engineering
Languages : en
Pages : 155
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Author: Chengmin Liu
Publisher: Springer Nature
ISBN: 9811987807
Category : Science
Languages : en
Pages : 1238
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Book Description
“This is the second in a series of three volumes of proceedings of the 23rd Pacific Basin Nuclear Conference (PBNC 2022) which was held by Chinese Nuclear Society. As one in the most important and influential conference series of nuclear science and technology, the 23rd PBNC was held in Beijing and Chengdu, China in 2022 with the theme “Nuclear Innovation for Zero-carbon Future”. For taking solid steps toward the goals of achieving peak carbon emissions and carbon neutrality, future-oriented nuclear energy should be developed in an innovative way for meeting global energy demands and coordinating the deployment mechanism. It brought together outstanding nuclear scientists and technical experts, senior industry executives, senior government officials and international energy organization leaders from all across the world. The proceedings highlight the latest scientific, technological and industrial advances in Nuclear Safety and Security, Operations and Maintenance, New Builds, Waste Management, Spent Fuel, Decommissioning, Supply Capability and Quality Management, Fuel Cycles, Digital Reactor and New Technology, Innovative Reactors and New Applications, Irradiation Effects, Public Acceptance and Education, Economics, Medical and Biological Applications, and also the student program that intends to raise students’ awareness in fully engaging in this career and keep them updated on the current situation and future trends. These proceedings are not only a good summary of the frontiers in nuclear science and technology, but also a useful guideline for the researchers, engineers and graduate students.
Author: Xingjie Peng
Publisher: Frontiers Media SA
ISBN: 2832545378
Category : Technology & Engineering
Languages : en
Pages : 105
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Book Description
A nuclear reactor operates in an environment where complex multi-physics and multi-scale phenomena exist, and it requires consideration of coupling among neutronics, thermal hydraulics, fuel performance, chemical dynamics, and coupling between the reactor core and first circuit. Safe, reliable, and economical operation can be achieved by leveraging high-fidelity numerical simulation, and proper considerations for coupling among different physics and required to provide powerful numerical simulation tools. In the past simplistic models for some of the physics phenomena are used, with the recent development of advanced numerical methods, software design, and high-performance computing power, the appeal of multi-physics and multi-scale modeling and simulation has been broadened.
