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.
Transition, radiation and propagation loss in polymer multimode waveguide bends
