Three-Dimensional Design Optimization of Multistage Axial Flow Turbines Using a RSM Based Approach

Abstract

The problem of the automatic design optimization for multistage axial flow turbines is considered and a design strategy based on a 3D Navier-Stokes solver and a RSM (Response Surface Method) approach is described. A multi-objective optimization code based on non-dominated sorting genetic algorithm (NSGA-2) is used to drive the optimization process in order to maximize the specific power while keeping the massflow rate constrained. In the present work the meridional channel is kept unchanged while for each blade the spanwise distribution of the profile restaggering is considered together with the inclusion of compound lean. The performance from the multistage turbine for the optimization loop are obtained from surrogate models built through a set of artificial neural networks. The neural networks are trained and tested using large DoEs and are not updated during the optimization process. This aspect is considered important to guarantee that the optimization converges to an optimum. The use of the 3D flow solver with coarse meshes in order to validate large DoEs in short times is discussed in some details. The above strategy has been applied to a four stage axial turbine from the open literature.

Hydraulic design and optimization of a modular pump-turbine runner

“…Jang et al [16] applied this method to optimization of a single stage axial compressor and Kim et al [17] used this methodology on a centrifugal compressor. Li et al [18], Rubechini et al [19], and Cravero and Macelloni [20] optimized multistage turbines with a response surface methodology. Schleicher et al [21] have also successfully applied this method to optimizing a portable hydrokinetic turbine and continue to be active in developing small, hydropower turbomachines [22][23][24][25].…”

Strategies for the Numerical Modelling of Regenerative Pre-heating Systems for Recycled Glass Raw Material

“…A design optimization strategy based on the response surface approach has been developed. This approach has been used for several different applications and it is very effective when the numerical simulation of the system is computationally expensive of if many design variables are considered [24]. The goal of this optimization procedure is to obtain the pipe geometry (thickness and diameter) that maximizes the final temperature of the raw material and minimizes the mass flow rate of the hot gases.…”

Optimization of organic Rankine cycle power systems considering multistage axial turbine design

“…In order to improve the reliability of the validation, the results of TURAX were also compared to the Computational Fluid-Dynamic (CFD) data documented in Gerolymos and Hanisch [80] and Croce et al [81]. These two are representative of state-of-the-art modelling techniques for multistage turbines [80][81][82] and have been validated using other experimental turbomachinery data [83,84]. Table 5 lists the input data used for model validation.…”