Numerical computation of internal & external flows: fundamentals of numerical discretization
Numerical computation of internal & external flows: fundamentals of numerical discretization
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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The aim of the paper is to discuss the possibility of conjunction of GA with the FV Fluent package in application to the optimisation of the Reactive Injection Moulding (RIM) and the Resin Transfer Moulding (RTM) technological process. The first part of the paper is devoted to the description of the fundamental flow, heat, mass and rheokinetics relations and possibility of numerical simulation of a such class of problems in view of optimisation of parameters governing the process progress. Then, the optimisation objective is formulated that takes into account both the total curing time and the residual strength of the RIM and RTM process as well as the set of control parameters treated as a measure of the correctness (effectiveness) of the technological process and called as the degree of cure. Among variety of parameters characterizng the process temperatures of heat sources (electric heaters placed around the mould) have been chosen as design variables (in the discretised form). The 2-D planar problem have been solved to illustrate the effectiveness of the proposed method. It should be emphasized also that in fact two different optimization problems have been formulated and solved, separately for the RIM and RTM processes.