A Meta-Model for Aerodynamic Properties of a Reversible Profile in Cascade With Variable Stagger and Solidity

Abstract

In this paper, a systematic CFD work is carried out with the aim to inspect the influence of different cascade parameters on the aerodynamic performance of a reversible fan blade profile. From the obtained results, we derive a meta-model for the aerodynamic properties of this profile. Through RANS simulations of different arrangements in cascades, the aerodynamic performance of airfoils are analyzed as Reynolds number, solidity, pitch angle and angle of attack are varied. The definition of a trial matrix allows the reduction of the minimum number of simulations required. The computed CFD values of lift and drag coefficients, stall margin and the zero-lift angle strongly depend on cascade configuration and differ significantly from standard panel method software predictions. In this work, X-Foil has been used as a benchmark. Particularly, the high influence of pitch angle and solidity is here highlighted, while a less marked dependence from the Reynolds number has been found. Meta-models for lift and drag coefficients have been later derived, and an analysis of variance has improved the models by reducing the number of significant factors. The application of the meta-models to a quasi-3D in-house software for fan performance prediction is also shown. The effectiveness of the derived meta-models is proven through a spanwise comparison of a reversible fan with the X-Foil based and meta-model based versions of the software and 3D fields from a standard CFD simulation. The meta-model improves the software prediction capability, leading to a very low global overestimation of the specific work of the fan.

On Surrogate-Based Optimization of Truly Reversible Blade Profiles for Axial Fans

“…Stall margin provides a measure of the stable operating range of the profile. When optimized geometries data are used in a quasi 3D axisymmetric code, for design or performance prediction of an axial fan, as in the work of Angelini et al [24] or Drela et al [25], stall margin is also a measure of solution reliability. The α objective function has the additional purpose to prevent the tendency of optimized elements to move their maximum aerodynamic efficiency in proximity of AoA (max(C L )).…”

“…Furthermore, elitism is ensured by non-dominated sorting of both parents and offspring and inclusion of non-dominated fronts in the new set of elements. NSGA-II has demonstrated its ability to find a much better spread of solutions and better convergence near the true Pareto-optimal front when compared with the Pareto achieved with different evolutionary algorithms [24].…”

“…The bi-level framework (or in-fill criterion), also known as the indirect fitness replacement (IFR) method [24,33], advocates the use of the fitness function during the EA process, adding new sample points to the current data set, while one or more components of the MOEA (typically the variation operators) are assessed in the SM. The IFR method is clearly a two-stage approach, as illustrated in Figure 5.…”