Multipoint optimization of an axial turbine cascade using a hybrid algorithm

Abstract

This paper presents a multipoint optimization of the LS89 cascade. The objective of the optimization consists in minimizing the entropy losses generated inside the cascade over a predefined operating range. Two aerodynamic constraints are imposed in order to conserve the same performance as the original cascade. The first constraint is established on the outlet flow angle in order to achieve at least the same flow turning as the LS89. The second constraint limits the mass-flow passing through the cascade. The optimization is performed using a hybrid algorithm which combines a classical evolutionary algorithm with a gradient-based method. The hybridization between both methods is based on the Lamarckian approach which consists in incorporating the gradient method inside the loop of the evolutionary algorithm. In this methodology, the evolutionary method allows to globally explore the design space while the gradient-based method locally improves certain designs located in promising regions of the search space. First, the better performance of the hybrid method compared to the performance of an evolutionary algorithm is demonstrated on benchmark problems. Then, the methodology is applied on the LS89 application. The optimization allows to find a new profile which reduces the entropy losses over the entire operating range by at least 9.5 %. Finally, the comparison of the flows computed in the baseline and in the optimized cascades demonstrates that the reduction of the losses is due to a decrease of the entropy generated downstream the trailing edges and within the passages between the optimized blades.

Numerical optimisation of a micro-wave rotor turbine using a quasi-two-dimensional CFD model and a hybrid algorithm

“…Examining the data for Branin, Goldstein-Price, Hartman 3, and the Fig. 26 Comparison of the convergence rate of the hybrid GA and MATSuMoTo with other hybrid optimisation routines for a the five-dimensional Rastrigin function and b the two-dimensional Rosenbrock function [65,66] two-dimensional Rastrigin demonstrates a wide spread of between the 25th and 75th percentile for both GA and PSO with a number of outliers in particular seen for the GA applied to the Branin problem. Overall, the data show that PSO may converge faster than the standard elitist GA.…”

“…Finally, to see how the hybrid GA and MATSuMoTo fare against other recently introduced hybrid methods, two single trials are run on the Rastrigin function with five parameters and the two-dimensional Rosenbrock function. The comparison is made with another evolutionary algorithm, namely a classical differential evolution (DE) algorithm, hybridised with a steepest descent method developed at the von Karman Institute [65,66]. The convergence rate for both test problems is presented in Fig.…”

Method for mean-line design and performance prediction of one-stage axial turbines

“…In other words, rather than designing an expander for one single design point, the expander is designed considering a range of operating conditions. This multipoint optimization strategy has been adopted in several high-fidelity design methods, and shows great promise to rise the efficiency of the turbine at off-design conditions (Aissa et al 2019;Bonaiuti and Zangeneh 2009;Châtel, Verstraete, and Coussement 2020;M. Pini, G. Persico, and Dossena 2014;Sanchez Torreguitart, Verstraete, and Mueller 2018).…”

Proceedings of the 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022

“…In other words, rather than designing an expander for one single design point, the expander is designed considering a range of operating conditions. This multipoint optimization strategy has been adopted in several high-fidelity design methods, and shows great promise to rise the efficiency of the turbine at off-design conditions (Aissa et al 2019;Bonaiuti and Zangeneh 2009;Châtel, Verstraete, and Coussement 2020;M. Pini, G. Persico, and Dossena 2014;Sanchez Torreguitart, Verstraete, and Mueller 2018).…”