# Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains
###### Abstract  
Aerodynamic noise increases with the sixth power of the running speed. As the speed increases, aerodynamic noise becomes predominant and begins to be the main noise source at a certain high speed. As a result, aerodynamic noise has to be focused on when designing new high-speed trains. In order to perform the aerodynamic noise optimization, the equivalent continuous sound pressure level (SPL) has been used in the present paper, which could take all of the far field observation probes into consideration. The Non-Linear Acoustics Solver (NLAS) approach has been utilized for acoustic calculation. With the use of Kriging surrogate model, a multi-objective optimization of the streamlined shape of high-speed trains has been performed, which takes the noise level in the far field and the drag of the whole train as the objectives. To efficiently construct the Kriging model, the cross validation approach has been adopted. Optimization results reveal that both the equivalent continuous sound pressure level and the drag of the whole train are reduced in a certain extent. a +
Footnote †: journal: Applied Sciences  
technical view from results of their tests using the actual railcar and the wind test tunnel [9]. [PERSON] proposed a new aerodynamic noise reduction method that involves covering the surface of objects with a particular porous material [10]. [PERSON] investigated on different designs of cab windows and cowcatchers and gave a reasonable advice on low noise design of the streamlined shape [6]. It can be seen that much effort has been put to the design of low-noise pantograph, which engineering experience is mostly dependent on for noise reduction. This kind of design is usually constrained by engineering experience and cannot obtain the most optimal shape. In addition, study on the noise reduction of the streamlined head of high-speed trains could be seldom referred to, since abundant flow phenomena exist on the surface of the streamline and the design approach which relies solely on engineering experience could not meet the accuracy requirement. Consequently, optimization design with corresponding algorithms is the only way to obtain the relationships between design variables and aerodynamic noise and gets the chance to obtain the optimal design. In recent years, optimization algorithms have been widely used in the streamlined shape design of high-speed trains [11; 12; 13; 14; 15; 16]. [PERSON] and [PERSON] dealt with the nose shape design of high-speed railways to minimize the maximum micro-pressure wave and suggested an optimal nose shape that is an improvement over current design in terms of micro-pressure wave [11]. [PERSON] used the Kriging model to find the optimum shape of a two-dimensional nose shape of a high-speed train traveling at 350 km/h considering both the induced aerodynamic drag and the generated aerodynamic noise [12]. [PERSON] used the VMF parameterization method on the train nose to reduce a micro-pressure wave and an aerodynamic drag of a high-speed train [13]. [PERSON] performed a constrained multi-objective optimization design on a simplified CRH380A high-speed train with three carriages to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car and got agreeable optimal results [14]. [PERSON] and [PERSON] presented a novel design technique for high-speed trains using a multi-objective optimization method to balance plural aerodynamic properties [15]. [PERSON] used genetic algorithms to optimize the nose shape of a high-speed train entering a tunnel in term of the compression wave generated at the entry of the train and the aerodynamic drag of the train [16]. It could be concluded that the optimization objectives are mostly focused on the aerodynamic loads or tunnel effects. Even aerodynamic noise is considered, it is only a two-dimensional study. Due to the massive cost of computational aero-acoustics (CAA), aerodynamic noise optimization always seems untouchable for researchers.