03.4 - Applied Aerodynamics
AN OPTIMIZATION OF ANTI-ICING CHAMBER BASED ON POD AND KRIGING
J. Niu¹, W. Sang¹, A. Qiu¹, Q. Guo¹, D. Li¹; ¹School of Aeronautics, Northwestern Polytechnical University, China
In recent years, UAVs play an important role in many fields. However, the icing imposes restrictions on the application of the UAVs. UAVs have a smaller load and provide less energy for the use of anti/de-icing. Comparing with the conventional transport aircraft, there are some differences between the UAVs and the transport aircraft: the speed of the UAVs is lower, the scale of the UAVs is smaller and the energy resource used for the anti-icing is limited. However, the temperature of the engine exhaust is about 600 ??Therefore, the engine exhaust has a potential as a hot air resource used for the anti-icing system.rnThe present paper aims to develop and optimize the configuration of the hot air anti-icing chamber used for the UAVs. The objective is to optimize the heat distribution in such a way to minimize power requirements, while meeting the anti-icing heat load requirements and ensuring the wing not overheated. The anti-icing heat load requirement and the anti-icing area of the wing are computed firstly. Based on this, the basic anti-icing chamber configuration is developed. In the present paper, the location of the piccolo tube, the angle between the jet holes are set as the design variables. The design variables are the ratio s/d and the angles ?1 and ?2. The s represents the distance between the jet hole and the leading edge and d means the diameter of the jet hole. The numerical simulations are conducted with the optimized latin hypercubic sampling (OLHS). The wall temperature of each simulation is extracted. With the proper orthogonal decomposition (POD) and the Kriging interpolation, the surrogate model is established. Finally, the genetic algorithm is used to search the optimal configuration. The paper may be helpful for the anti-icing system design of the UAVs and the optimization method can greatly reducing the computational cost.rn