Nozzle design optimization for supersonic wind tunnel by using surrogate-assisted evolutionary algorithms

Masanobu Matsunaga, Chihiro Fujio, Hideaki Ogawa, Yoshitaka Higa, Taro Handa

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


For high-precision measurement in supersonic wind tunnel experiments, it is of crucial importance to produce a uniform flow in the measurement section downstream of the nozzle. This paper proposes and verifies a new design methodology for supersonic wind tunnel nozzles that can generate highly uniform airstream at a design Mach number at the nozzle exit. Shape design optimization has been conducted by employing surrogate-assisted evolutionary algorithms coupled with computational fluid dynamics. This approach has yielded higher flow uniformity than that of the nozzle designed by using the method of characteristics in the inviscid regime. By applying boundary layer correction to the nozzle contour obtained from the inviscid optimization, nearly uniform core flow of Mach 2.5 has been achieved at the nozzle exit in the presence of viscosity. In the viscous optimization, nozzle shape optimization has been performed by incorporating viscous simulations without using boundary layer correction to evaluate its efficacy in comparison with the former approach. It has been found that the deviation from the design Mach number and the flow deflection from the horizontal direction cannot be minimized simultaneously. This has been attributed to the constraint associated with the nozzle length. It has also been found that the result of the former approach combining the inviscid optimization and boundary layer correction can be regarded as one of the results of the viscous optimization.

Original languageEnglish
Article number107879
JournalAerospace Science and Technology
Publication statusPublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering


Dive into the research topics of 'Nozzle design optimization for supersonic wind tunnel by using surrogate-assisted evolutionary algorithms'. Together they form a unique fingerprint.

Cite this