Experimental and numerical methods for concept design and flow transition prediction on the example of the bionic high-speed train

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Abstract

In the times of significant development of high-speed train transportation and taking the primacy over the others, one of the major designing tasks is to predict accurately and fast the vehicle’s main performances, especially aerodynamics. Motivated by the bionic design of representative Japan’s train Shinkansen, the presented bionic design was based on the observations of the kingfisher from Serbian national heritage collection. The specimen beak shape has been measured, by laser scanning, and converted to a mesh. Afterward, the longitudinal cross-section of the beak was implemented to a bionic high-speed train design. As the critical, the conditions of the train’s forehead entering into the tunnel were selected, while the case scheme employed relative motion. The forehead contour distributions of the surface temperature, skin friction coefficient, and the pressure distributions, obtained with computational fluid dynamics, were used for the prediction of the transition zone extension. The anomalies in surface temperature behavior were additionally analyzed by pressure and density distribution inside the tunnel and over the forehead. Besides the gross time history of the pressure derivative of time is in correspondence with the referent, it was interpreted that the true biological form is not fully suitable for adoption for bionic design. The development of this design, employing contour simplification by the close parabolic function will continue in the future. This work suggests an economic and efficient approach to analyze the results of the Reynolds-Averaged Navier-Stokes equations adequate for the concept design stage.

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Linic, S., Lucanin, V., Zivkovic, S., Rakovic, M., & Puharic, M. (2021). Experimental and numerical methods for concept design and flow transition prediction on the example of the bionic high-speed train. In Lecture Notes in Networks and Systems (Vol. 153, pp. 65–82). Springer. https://doi.org/10.1007/978-3-030-58362-0_5

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