Optimisation and thermal control of a multi-layered structure for space electronic devices and thermal shielding of re-entry vehicles

Abstract

All electronic devices, due to Joule effect, present heat dissipation, when they are electrically fed. The heat overstocking produces efficiency and performances reduction. On account of this the thermal control is mandatory. On small electronic equipments, the difficulty or impossibility of using a cooling fluid for the free or forced convection heat dissipation imposes the presence of cooling systems based on another kind of functioning principle such as the conduction. In this paper the thermal control, via pyroelectric materials, is presented. Furthermore, an optimisation of geometric, thermal and mechanical parameters, influencing the thermal dissipation, is studied and presented. Pyroelectric materials are able to convert heat into electrical charge spontaneously and, due to this capability, such materials could represent a suitable choice to increase the heat dissipation. The obtained electric charge or voltage could be used to charge a battery or to feed other equipments. In particular, a sequence of different materials such as Kovar®, molybdenum or copper–tungsten, used in a multi-layer pyroelectric wafer, together with their thicknesses, are design features to be optimised in order to have the optimal thermal dissipation. The optimisation process is performed by a hybrid approach where a genetic algorithm (GA) is used coupled with a local search procedure, in order to provide an appropriate balance between exploration and exploitation of the search space, which helps in the search for the optimal or quasi-optimal solution. Since the design variables used in the optimisation procedure are defined in different domains, discrete (e.g. the number of layers in the pyroelectric wafer) and continuous (e.g. the layers thickness) domains, the genetic representation for the solution should take it into account. The chromosome used in the genetic algorithm will mix both integer and real values, what will also be reflected in the genetic operators used in the optimisation process. Finally, numerical analyses and results complete the work.