Inverse thermal design technique for predetermined temperature distribution using reduced order model of 3D heat conduction simulation and artificial bee colony algorithm

Abstract

To design a heater that controls a predetermined temperature distribution, the distributions of the heat generation densities for the heater should be designed. Generally, designers adjust the heat generation densities and calculate the temperature distribution by 3D thermal fluid simulations repeatedly. However, this process leads to a long design period because it incurs high simulation cost to achieve the optimum result. To shorten the design period, we have proposed a more efficient thermal design technique that combines the superposition calculation of the reduced order model (ROM), which is constructed from the results of the 3D thermal simulations, and the optimizing algorithm. The ROM decreased the calculation time to estimate the temperature distribution in the heater. Furthermore, we applied the Artificial Bee Colony (ABC) algorithm, which is a type of swarm intelligence, to estimate the optimum combination of the heat generation density distributions of the heater. Our proposed technique has the advantage of being able to calculate the total trial number of the 3D thermal simulations, which was the same as the number of divisions of heater areas. This enabled the designers to estimate the time to achieve the optimum thermal design in advance. Therefore, the proposed technique could be applicable to the thermal design for real products. In this paper, we used this simulation process to design the temperature control unit for a capillary electrophoresis DNA sequencer as an example. The optimizing calculation was completed within only 500 s by using the ROM. The results of the optimization demonstrate that the designed heater controls the three designated and uniform temperature distributions. The measurement results of the temperature distributions of the prototype heater agreed well with the target temperature distributions.