The noise emitted by axial fans plays an integral role in product design. When conventional design procedures are applied, the aeroacoustic properties are controlled via an extensive trial-and-error process. This involves building physical prototypes and performing acoustic measurements. In general, this procedure makes it difficult for a designer to gain an understanding of the functional relationship between the noise and geometrical parameters of the fan. Hence, it is difficult for a human designer to control the aeroacoustic properties of the fan. To reduce the complexity of this process, we propose an inverse design methodology driven by a genetic algorithm. It aims to find the fan geometry for a set of given objectives. These include, most notably, the sound pressure frequency spectrum, aerodynamic efficiency, and pressure head. Individual bands of the sound pressure frequency spectrum may be controlled implicitly as a function of certain geometric parameters of the fan. In keeping with inverse design theory, we represent the design of axial fans as a multi-objective multiparameter optimization problem. The individual geometric components of the fan (e.g., rotor blades, winglets, guide vanes, shroud, and diffusor) are represented by free-form surfaces. In particular, each blade of the fan is individually parameterized. Hence, the resulting fan is composed of geometrically different blades. This approach is useful when studying noise reduction. For the analysis of the flow field and associated objectives, we utilize a standard Reynolds averaged Navier–Stokes (RANS) solver. However, for the evaluation of the generated noise, a meshless lattice-Boltzmann solver is employed. The method is demonstrated for a small axial fan, for which tonal noise is reduced.

A set of aeroacoustic optimization strategies for axial fans is presented. Their efficiency is demonstrated for small axial fans. Thereby, the generated noise could be reduced significantly while retaining or even improving the aerodynamic performance. In particular, we discuss the following two optimization strategies in detail: Firstly, we consider the design of winglets using a parametric model for genetic optimization. The resulting winglet geometry helps to control the tip vortex over a large range of operating points, thereby reducing the generated noise. In addition, the power consumption of the fan could be reduced. Various choices of geometrical parameter sets for optimization are evaluated. Secondly, we discuss the reduction of fan noise via contour optimized turbulators. For axial fans it is desirable to reduce sound emission across a broad operating range, not just for the design point. However, operation in off-design points may be accompanied by flow separation phenomena, which contribute predominantly to noise generation and reduce the aerodynamic performance of the fan. Turbulators can help to minimize these adverse effects. The advantages of various contoured turbulator geometries are discussed for off-design operating points. The optimization of the above mentioned strategies was driven by aeroacoustic measurements via physical tests as well as numerical analysis based on the Lattice-Boltzmann method. The merits of either method are discussed with respect to the two optimization strategies.

The noise emitted by axial fans plays an integral role in product design. When conventional design procedures are applied, aeroacoustic properties are controlled via an extensive trial-and-error process. This involves building physical prototypes and performing acoustic measurements. In general, this procedure makes it difficult for a designer to gain an understanding of the functional relationship between noise and geometrical parameters of the fan. Hence, it is difficult for a human designer to control the aeroacoustic properties of the fan. To reduce the complexity of this process, we propose an inverse design methodology driven by a genetic algorithm. It aims to find the fan geometry for a set of given objectives. These include, most notably, the sound pressure frequency spectrum, aerodynamic efficiency, pressure head and flow rate. Individual bands of the sound pressure frequency spectrum may be controlled implicitly as a function of certain geometric parameters of the fan. In keeping with inverse design theory, we represent the design of axial fans as a multi-objective, multi-parameter optimization problem. The individual geometric components of the fan (e.g., rotor blades, winglets, guide vanes, shroud and diffusor) are represented by free-form surfaces. In particular, each blade of the fan is parameterized individually. Hence, the resulting fan is composed of geometrically different blades. This approach is useful when studying noise reduction. For the analysis of the flow field and associated objectives, we utilize a standard RANS solver. However, for the evaluation of the generated noise, a meshless Lattice-Boltzmann solver is employed. The method is demonstrated for a small axial fan, for which tonal noise is reduced.