Inverse Solution of a Heat Conduction Problem Using Evolutionary Data Segregation Techniques
Engineering problems are typically solved by direct solution. For the direct solution of engineering problems the boundary conditions and physical properties of the domain are given, and the dependent variable is calculated throughout the domain. In contrast to this, for inverse engineering problems the dependent variable is known at select locations in the domain, and the material properties and/or the boundary conditions need to be determined. This paper will present a novel technique for the inverse solution of a heat transfer engineering design problem in which the temperature profile and materials are known, but the placement of these materials and the heat flux on the boundaries are unknown. This technique uses evolutionary optimization in the form of the Adaptive Modeling by Evolving Blocks Algorithm (AMoEBA) to determine the material configurations. The material configurations, geometry, and properties are defined by evolving binary trees. The evolved domains are solved directly and then compared with the known temperature profile. Fitness of the new designs is determined by the least squared error between the proposed and the known profile. When this fitness reaches a defined level, the material placement scheme of the real system is found, and boundary conditions matching the problem definition are identified.