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Optimisation under uncertainty and constraints of a tube bundle heat exchanger

Optimization is a discipline of numerical mathematics which aims to improve the operation of a system or process as good as possible in some defined sense. Optimization algorithms work to minimize (or maximize) an objective function, typically calculated by the user simulation code, subject to constraints on design variables and responses. Parameters and settings of tube bank heat exchanger multi- objective optimization are described. The problem consists of finding the best locations of the tubes to increase heat exchange (i.e. the temperature increase of the fluid) while at the same time limit the pressure loss. The two corresponding numerical parameters to optimize are the average temperature difference and pressure drop between inflow and outflow. The set of coupled numerical tools to solve the multi-objective optimization consists of open source optimization software Dakota, open source CFD toolbox OpenFOAM and open source software for geometry creation Salome. Multi- objective Genetic Algorithm is used to obtain optimal designs. Results of the optimization process are presented on corresponding Pareto fronts. Different parameters of the Multi- objective Genetic Algorithm are examined and discussed. Uncertainty quantification or nondeterministic analysis is the process of characterizing input uncertainties, forward propagating these uncertainties through a computational model, and performing statistical or interval assessments on the resulting responses. Uncertainty quantification is related to sensitivity analysis in that the common goal is to gain an understanding of how variations in the parameters affect the response functions of the engineering design problem. For uncertainty quantification, some or all of the components of the parameter vector, are considered to be uncertain as specified by particular probability distributions. Effect of uncertain tube coordinates and uncertain tubes’ temperature is examined and presented in graphs and histograms. Three optimal points from are chosen and their robustness evaluated by means of the uncertainty quantification methods.

optimization under uncertainty ; constraints ; multi - objective optimization ; computational fluid dynamics

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