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Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties
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Energies 2016, 9(12), 1028; doi:10.3390/en9121028

Predictive Modeling of a Buoyancy-Operated Cooling Tower under Unsaturated Conditions: Adjoint Sensitivity Model and Optimal Best-Estimate Results with Reduced Predicted Uncertainties

1
Department of Astronautic, Electric and Energy Engineering (DIAEE), “Sapienza”—University of Rome, 00185 Roma, Italy
2
Center for Nuclear Science and Energy, Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Hiroshi Sekimoto
Received: 18 October 2016 / Revised: 28 November 2016 / Accepted: 28 November 2016 / Published: 8 December 2016
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)

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 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. View Full-Text
Keywords: cooling tower; adjoint sensitivity analysis; adjoint cooling tower model solution verification; data assimilation; model calibration; best-estimate predictions; reduced predicted uncertainties cooling tower; adjoint sensitivity analysis; adjoint cooling tower model solution verification; data assimilation; model calibration; best-estimate predictions; reduced predicted uncertainties
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Di Rocco, F.; Cacuci, D.G. 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, 1028.

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