Optimization of endwall contouring in axial compressor S-shaped ducts

Abstract

This paper presents a numerical investigation of the potential aerodynamic benefits of using endwall contouring in a fairly aggressive duct with six struts based on the platform for endwall design optimization. The platform is constructed by integrating adaptive genetic algorithm (AGA), design of experiments (DOE), response surface methodology (RSM) based on the artificial neural network (ANN), and a 3D Navier-Stokes solver. The visual analysis method based on DOE is used to define the design space and analyze the impact of the design parameters on the target function (response). Optimization of the axisymmetric and the non-axisymmetric endwall contouring in an S-shaped duct is performed and evaluated to minimize the total pressure loss. The optimal ducts are found to reduce the hub corner separation and suppress the migration of the low momentum fluid. The non-axisymmetric endwall contouring is shown to remove the separation completely and reduce the net duct loss by 32.7%.

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

“…With the assumption of uniform flow without swirl at all circumferential direction of the S-shaped duct inlet, a 3-D S-shaped duct can be represented well by the approach of 2-D axisymmetric. This will reduce complexity as well as computational time for convergence of the fluid domain [2,15,19,38,40,41]. Modeling and meshing of the 2-D axisymmetric S-shaped duct are accomplished by using 'GAMBIT' software.…”

“…Equation (18) calculates the new velocity V i+1 the individual particle by using the previous velocity, best location ('pBest') and obtained global best location ('gBest') into the entire population as well and at the same time, individual particle's location ( X i ) in the hyperspace is updated by the Eq. (19). In the Eq.…”

“…The computational fluid dynamics (CFD) can be implemented as a design tool for performance evaluation with a combination of various optimization techniques [16]. Many researchers have reported that applying the response surface methodology (RSM) altogether with the design of experiments (DOE) may be a decisive and sturdy method for CFD-based optimization techniques [17][18][19][20]. Wallin and Eriksson [17] applied RSM together with DOE to the baseline S-shaped duct containing eight struts for minimizing the total pressure loss and achieved a 24% reduction of the total pressure loss.…”

“…Wallin and Eriksson [17] applied RSM together with DOE to the baseline S-shaped duct containing eight struts for minimizing the total pressure loss and achieved a 24% reduction of the total pressure loss. Reduction of the total pressure loss by suppressing the peak deceleration in the strut-hub corner region could be achieved through an optimized non-axisymmetric end wall profile that is developed using the RSM approach [18,19]. Lu et al [20] exercised the combination of RSM and genetic algorithm (GA) to optimize the S-shaped duct and found a reduction of 36.9% in total pressure loss with higher uniformity at the outlet.…”

Design optimization of a compressor transition S-shaped duct using a teaching–learning-based optimization algorithm

“…Due to the scarcity of design guidelines and performance correlations of the annular S-shaped duct, to obtain a better design, CFD can be implemented as a design approach for performance evaluation with a combination of various optimization techniques [19]. Many researchers have reported that applying the response surface methodology (RSM) altogether with the design of experiments (DOE) may be a decisive and sturdy method for CFD-based optimization techniques [20][21][22][23]. In this field, the first work came from Wallin et al [20].…”