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

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.

MULTI-PRED: A Software Module for Predictive Modeling of Coupled Multi-Physics Systems

“…The PM-CMPS methodology has been successfully applied to reducing the predicted uncertainties in model parameters and responses in several forward and inverse problems. [13][14][15][16][17][18] The MULTI-PRED software module provides three illustrative problems: (1) a simple neutron diffusion problem, 19 (2) an inverse problem in particle transport, 18 and (3) a predictive modeling of the F-Area cooling towers at the Savannah River National Laboratory. 16,17 Sections II and III describe the quantities required as inputs to MULTI-PRED, along with the optimally predicted best-estimate values for the model responses and model parameters, with reduced predicted uncertainties, which are obtained as the outputs of MULTI-PRED.…”

Thermal Performance for Wet Cooling Tower with Different Layout Patterns of Fillings under Typical Crosswind Conditions

“…They can be classified by means of contacting mode into dry [3,4] and wet types. Moreover, they can also be classified by the driving force of air stream as mechanical-draft [5,6] and natural-draft types. As one of the main types, the natural-draft wet cooling towers play an important role to cool the circulating water from the condenser in thermal power plants or some inland nuclear power plants, and the cooling towers can directly affect the total power generation capacity [7].…”