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Advances in Predictive Modeling of Nuclear Energy Systems

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (20 February 2017) | Viewed by 21863

Special Issue Editor

Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29201, USA
Interests: all areas of nuclear engineering; sensitivity and uncertainty analysis of large-scale systems; predictive modeling by combining experimental and computational information to reduce uncertainties in predicted results
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue presents advances in predictive modeling and its applications to nuclear energy. Predictive modeling includes the following elements: (i) computation of model response sensitivities to model parameters; (ii) quantification of the uncertainties in model responses caused by uncertainties in the model parameters and by insufficient knowledge of the processes being modeled; (iii) developing “validation metrics” for quantifying the degree to which a model represents reality (“model validation”); (iv) integration (“assimilation”) of computational and experimental information for improving the model’s parameters (“model calibration”) and producing optimally predicted results with reduced predicted uncertainties. The applications of predictive modeling to nuclear energy includes sensitivity analysis and uncertainty quantification in reactor physics and thermal-hydraulics, reducing the uncertainties in reactor design margins, reprocessing, and waste disposal, non-proliferation and safeguards.

Prof. Dan Gabriel Cacuci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • predictive modeling
  • sensitivity analysis
  • uncertainty quantification and reduction
  • data assimilation
  • model calibration
  • model validation
  • nuclear energy system

Published Papers (5 papers)

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Research

8640 KiB  
Article
Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems
Energies 2017, 10(2), 173; https://doi.org/10.3390/en10020173 - 04 Feb 2017
Cited by 6 | Viewed by 5565
Abstract
The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR) unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical [...] Read more.
The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR) unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO) algorithm that is based on random perturbation (RP-PSO). The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA) as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions. Full article
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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2513 KiB  
Communication
Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies
Energies 2016, 9(12), 1039; https://doi.org/10.3390/en9121039 - 09 Dec 2016
Cited by 8 | Viewed by 4728
Abstract
The quantification of uncertainties of various calculation results, caused by the uncertainties associated with the input nuclear data, is a common task in nuclear reactor physics applications. Modern computation resources and improved knowledge on nuclear data allow nowadays to significantly advance the capabilities [...] Read more.
The quantification of uncertainties of various calculation results, caused by the uncertainties associated with the input nuclear data, is a common task in nuclear reactor physics applications. Modern computation resources and improved knowledge on nuclear data allow nowadays to significantly advance the capabilities for practical investigations. Stochastic sampling is the method which has received recently a high momentum for its use and exploration in the domain of reactor design and safety analysis. An application of a stochastic sampling based tool towards nuclear reactor dosimetry studies is considered in the given paper with certain exemplary test evaluations. The stochastic sampling not only allows the input nuclear data uncertainties propagation through the calculations, but also an associated correlation analysis performance with no additional computation costs and for any parameters of interest can be done. Thus, an example of assessment of the Pearson correlation coefficients for several models, used in practical validation studies, is shown here. As a next step, the analysis of the obtained information is proposed for discussion, with focus on the systems similarities assessment. The benefits of the employed method and tools with respect to practical reactor dosimetry studies are consequently outlined. Full article
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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7479 KiB  
Article
Predictive Modeling of a Buoyancy-Operated Cooling Tower under Unsaturated Conditions: Adjoint Sensitivity Model and Optimal Best-Estimate Results with Reduced Predicted Uncertainties
Energies 2016, 9(12), 1028; https://doi.org/10.3390/en9121028 - 08 Dec 2016
Cited by 26 | Viewed by 3302
Abstract
Nuclear and other large-scale energy-producing plants must include systems that guarantee the safe discharge of residual heat from the industrial process into the atmosphere. This function is usually performed by one or several cooling towers. The amount of heat released by a cooling [...] Read more.
Nuclear and other large-scale energy-producing plants must include systems that guarantee the safe discharge of residual heat from the industrial process into the atmosphere. This function is usually performed by one or several cooling towers. The amount of heat released by a cooling tower into the external environment can be quantified by using a numerical simulation model of the physical processes occurring in the respective tower, augmented by experimentally measured data that accounts for external conditions such as outlet air temperature, outlet water temperature, and outlet air relative humidity. The model’s responses of interest depend on many model parameters including correlations, boundary conditions, and material properties. Changes in these model parameters induce changes in the computed quantities of interest (called “model responses”), which are quantified by the sensitivities (i.e., functional derivatives) of the model responses with respect to the model parameters. These sensitivities are computed in this work by applying the general adjoint sensitivity analysis methodology (ASAM) for nonlinear systems. These sensitivities are subsequently used for: (i) Ranking the parameters in their importance to contributing to response uncertainties; (ii) Propagating the uncertainties (covariances) in these model parameters to quantify the uncertainties (covariances) in the model responses; (iii) Performing model validation and predictive modeling. The comprehensive predictive modeling methodology used in this work, which includes assimilation of experimental measurements and calibration of model parameters, is applied to the cooling tower model under unsaturated conditions. The predicted response uncertainties (standard deviations) thus obtained are smaller than both the computed and the measured standards deviations for the respective responses, even for responses where no experimental data were available. Full article
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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423 KiB  
Article
Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties
Energies 2016, 9(9), 747; https://doi.org/10.3390/en9090747 - 16 Sep 2016
Cited by 6 | Viewed by 3297
Abstract
This work uses the adjoint sensitivity model of the counter-flow cooling tower derived in the accompanying PART I to obtain the expressions and relative numerical rankings of the sensitivities, to all model parameters, of the following model responses: (i) outlet air temperature; (ii) [...] Read more.
This work uses the adjoint sensitivity model of the counter-flow cooling tower derived in the accompanying PART I to obtain the expressions and relative numerical rankings of the sensitivities, to all model parameters, of the following model responses: (i) outlet air temperature; (ii) outlet water temperature; (iii) outlet water mass flow rate; and (iv) air outlet relative humidity. These sensitivities are subsequently used within the “predictive modeling for coupled multi-physics systems” (PM_CMPS) methodology to obtain explicit formulas for the predicted optimal nominal values for the model responses and parameters, along with reduced predicted standard deviations for the predicted model parameters and responses. These explicit formulas embody the assimilation of experimental data and the “calibration” of the model’s parameters. The results presented in this work demonstrate that the PM_CMPS methodology reduces the predicted standard deviations to values that are smaller than either the computed or the experimentally measured ones, even for responses (e.g., the outlet water flow rate) for which no measurements are available. These improvements stem from the global characteristics of the PM_CMPS methodology, which combines all of the available information simultaneously in phase-space, as opposed to combining it sequentially, as in current data assimilation procedures. Full article
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
9688 KiB  
Article
Predictive Modeling of a Paradigm Mechanical Cooling Tower: I. Adjoint Sensitivity Model
Energies 2016, 9(9), 718; https://doi.org/10.3390/en9090718 - 08 Sep 2016
Cited by 7 | Viewed by 4324
Abstract
Cooling towers discharge waste heat from an industrial process into the atmosphere, and are essential for the functioning of large energy-producing plants, including nuclear reactors. Using a numerical simulation model of the cooling tower together with measurements of outlet air relative humidity, outlet [...] Read more.
Cooling towers discharge waste heat from an industrial process into the atmosphere, and are essential for the functioning of large energy-producing plants, including nuclear reactors. Using a numerical simulation model of the cooling tower together with measurements of outlet air relative humidity, outlet air and water temperatures enables the quantification of the rate of thermal energy dissipation removed from the respective process. The computed quantities depend on many model parameters including correlations, boundary conditions, material properties, etc. Changes in these model parameters will induce changes in the computed quantities of interest (called “model responses”). These changes are quantified by the functional derivatives (called “sensitivities”) of the model responses with respect to the model parameters. These sensitivities are computed in this work by applying the general Adjoint Sensitivity Analysis Methodology (ASAM) for nonlinear systems. These sensitivities are needed for: (i) ranking the parameters in their importance to contributing to response uncertainties; (ii) propagating the uncertainties (covariances) in these model parameters to quantify the uncertainties (covariances) in the model responses; (iii) performing predictive modeling, including assimilation of experimental measurements and calibration of model parameters to produce optimal predicted quantities (both model parameters and responses) with reduced predicted uncertainties. Full article
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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