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Predictive Modeling of a Paradigm Mechanical Cooling Tower: I. Adjoint Sensitivity Model
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Energies 2016, 9(9), 747; doi:10.3390/en9090747

Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

Center for Nuclear Science and Energy, Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
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Academic Editor: Erich Schneider
Received: 23 June 2016 / Revised: 23 August 2016 / Accepted: 5 September 2016 / Published: 16 September 2016
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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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) 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. View Full-Text
Keywords: adjoint sensitivity analysis; data assimilation; model calibration; best-estimate predictions; reduced predicted uncertainties adjoint sensitivity analysis; data assimilation; model calibration; best-estimate predictions; reduced predicted uncertainties
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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Fang, R.; Cacuci, D.G.; Badea, M. Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties. Energies 2016, 9, 747.

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