Abstract
Turbomachinery design is an iterative process that can be time-consuming and expensive, especially when an extensive knowledge of the performance envelope is required. The approach described in the present paper can significantly cut the turnaround times down without jeopardizing the accuracy of the final result. A parameterization technique based on radial basis functions (RBF) is used and Reynolds Averaged Navier-Stokes (RANS) simulations are subsequently performed on a set of selected morphed meshes, the goal of which is to produce an aerodynamic database containing first-order, second-order and second-order cross derivatives of objectives with respect to parameters. New solutions, corresponding to any variations of the selected parameters, can thus be extrapolated thanks to the information included in the aforementioned database. In this way, a meta-model is built and can be easily explored by a genetic algorithm. This approach has been experimented on a new concept of engine cooling fan featuring low torque and high efficiency. A reference fan design has been adapted for the particular surrounding of the vehicle underhood, where the downstream flow is radially deviated from its axis by the engine. The optimization process has resulted in an efficiency improvement of three points for one of the obtained optima.
Highlights
A new concept engine cooling fan characterized by a conical hub was studied with a meta-model based multiobjective optimization tool assisted by a mesh morphing technique
Three global performance objectives and four geometric parameters were taken into consideration during the optimization process
Derivatives up to second order were calculated with ordinary least squares method
Summary
Fan systems are used in automotive engine cooling modules to increase air flow rate through heat exchangers. A conical hub has been integrated to the new geometry in order to produce a down-stream semi-radial flow. This particularity is more suitable for vehicle underhood conditions, where the flow is radially deviated by the engine. A local separation is often seen near the hub, which partially blocks the main flow and brings significant losses This is mainly due to the fact that the downstream flow creates a stagnation region, and an adverse pressure gradient. – Non-Dominated Sorting Genetic Algorithm 2 (NSGA–2) is coupled with the meta-model and explores the design space
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