Every plant aimed for energy production on large scale mandatorily needs to include systems able to ensure a proper release of the residual heat into the external environment; in the case of nuclear power plants, cooling towers are the most popular answer to this compelling task. The development of a dedicated numerical model can be a powerful tool for the simulation of the tower performance, but for quantifying the amount of heat released into the atmosphere, experimentally measured data are needed, in order to properly characterize the external environment in terms of outlet air temperature, outlet water temperature and outlet air relative humidity. Material properties, correlations and boundary conditions are among the many model parameters which can influence the model’s responses; variations of the computed quantities of interest (or “model responses”) induced by variations in the values of the model parameters can be expressed in terms of the sensitivities (i.e., functional derivatives) of the model responses with respect to the model parameters. The methodology applied in this paper in order to compute these sensitivities is the general adjoint analysis methodology (ASAM) for nonlinear systems; with this procedure, the exact values of the sensitivities of the model responses to all of the 47 model parameters are calculated exactly and efficiently by means of a single adjoint computation. The sensitivities can then be used within the “predictive modeling for coupled multi-physics systems” (PM_CMPS) methodology, aimed at yielding best-estimate predicted nominal values and uncertainties for model parameters and responses.

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