Multi-Objective RANS Aerodynamic Optimization of a Hypersonic Intake Ramp at Mach 5

Francesco De Vanna, Danilo Bof and Ernesto Benini
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Francesco De Vanna: Department of Industrial Engineering, University of Padova, Via Venezia 1, 35100 Padova, Italy
Danilo Bof: Department of Industrial Engineering, University of Padova, Via Venezia 1, 35100 Padova, Italy
Ernesto Benini: Department of Industrial Engineering, University of Padova, Via Venezia 1, 35100 Padova, Italy

Energies, 2022, vol. 15, issue 8, 1-27

Abstract: The work describes a systematic optimization strategy for designing hypersonic inlet intakes. A Reynolds-averaged Navier-Stokes database is mined using genetic algorithms to develop ideal designs for a priori defined targets. An intake geometry from the literature is adopted as a baseline. Thus, a steady-state numerical assessment is validated and the computational grid is tuned under nominal operating conditions. Following validation tasks, the model is used for multi-objective optimization. The latter aims at minimizing the drag coefficient while boosting the static and total pressure ratios, respectively. The Pareto optimal solutions are analyzed, emphasizing the flow patterns that result in the improvements. Although the approach is applied to a specific setup, the method is entirely general, offering a valuable flowchart for designing super/hypersonic inlets. Notably, because high-quality computational fluid dynamics strategies drive the innovation process, the latter accounts for the complex dynamics of such devices from the early design stages, including shock-wave/boundary-layer interactions and recirculating flow portions in the geometrical shaping.

Keywords: hypersonic flows; multi-objective opmization; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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