Use of computational fluid dynamics to implement an aerodynamic inverse design method based on exact Riemann solution and moving wall boundary

Abstract

An aerodynamic inverse design method, which is used in compressors, is introduced in this paper. This inverse design method is based on the exact solution of the local Riemann problem on moving blade surfaces. Furthermore, an improved relaxation factor is added to control the induced velocity for reasonable deformation sizes in different cases. The viscous flow analysis and the inverse design are integrated in the computational fluid dynamics software ANSYS Fluent using the dynamic mesh user-defined function, which reduces the time spent on remeshing the computational domain. A compressor cascade is taken to illustrate the effect of this inverse design method. Using the compressor model, the case of a different loading pattern design under a high subsonic flow regime and the case of a loading smooth design under a transonic flow regime have proved the capability of this inverse design method to realize the prescribed pressure loading distribution.

Intelligent Bayesian regularization networks for bio-convective nanofluid flow model involving gyro-tactic organisms with viscous dissipation, stratification and heat immersion

“…The reliability, stability, and convergence of the proposed ANN-BR algorithm were further certified by a mean squares errors based fitness metric, histogram illustrations, and regression analysis for each variant of the BCSNF model. In the future, new variants of artificial intelligence based integrated intelligent networks (Ahmadi, Sadeghzadeh, et al, 2019;Alotaibi et al, 2020;Duan et al, 2020;Park et al, 2020;Sabir et al, 2020) will be developed by the interested researcher for solving stiff/nonlinear/singular systems representing the fluid mechanics models (Ahmad et al, 2021;Ahmadi, Mohseni-Gharyehsafa, et al, 2019;Siddiqa et al, 2018;, circuit theory dynamics (Mehmood, Zameer, Aslam, et al, 2020, Mehmood, Zameer, Ling, et al, 2020, mathematical biological systems (Umar et al, 2020;Wang et al, 2020), information security models (Liu et al, 2020)), and astro/plasma/nuclear/atomic physics models (Bukhari et al, 2020;Ilyas et al, 2021;Jadoon et al, 2021;Zameer et al, 2020).…”

Optimization of the target pressure distribution and inverse design of an axial turbine blade

“…Other studies use the pressure difference method with the same concept of moving walls as well. [27][28][29] An overview of the blade design methods is illustrated in Figure 1. Choosing the inverse design method depends on the inputs to the problem.…”

A viscous blade body force model for computational fluid dynamics–based throughflow analysis of axial compressors

“…During the past couple of decades, the throughflow calculation has become one of the most important components for the multistage axial compressor design, and it still remains crucial roles in the aerodynamic design and optimization of axial compressors, 2 even though the advancement of computational power has made possible the fully three-dimensional calculation in the compressor design phase. 3,4 Traditional throughflow methods solve radial equilibrium equations on the meridional plane, such as the streamline curvature method developed by Smith 5 and the matrix throughflow method developed by Marsh. 6 In principle, they are different numerical approaches for the throughflow calculation as the streamline curvature method directly solves radial equilibrium equations, while the matrix throughflow method uses a stream function; however, as Davis 7 concluded that there is little to choose between them.…”