Nowadays the use of numerical simulations for predicting the performance of turbomachinery is widely accepted and used, also to predict very complex phenomena such as particle/droplet erosion or deposition that occur in several turbomachinery. For instance, some induced draft fans for industrial applications elaborate particle laden flows resulting in a large erosion of the blades and then performance loss. The possibility of predicting such a process would improve the sizing and maintenance scheduling processes. However, particle erosion simulations are usually limited to a fraction of seconds because of computational costs, and from these simulations it is hard to extract some reliable predictions. A main reason is that the relative flow field varies depending on the operating conditions referred to the machine and to the installation environment, therefore, a single numerical experiment cannot include all real-life situation. In some previous publications we developed an algorithm to perform long time predictions, also considering the variations in blade geometry with time. Here, we present a method to obtain, given a fan blade section, its relative velocity triangle and the particle concentration, the time specific erosion damage pattern per unit of span. The resulting pattern is given by an artificial neural network (ANN) trained on a dense dataset of numerical simulations, based on the modelling developed and used by the authors in the last decade.

The synopsis of the method is basically the following. Create a large database of erosion patterns for a specific airfoil, considering as aerodynamic features, the relative angle of attack, the relative flow velocity and Reynolds number, and as particle flow features, the diameter, concentration, hardness and density. Each pattern is obtained simulating the particle erosion on a blade strip, in the condition defined by a specific set of the features defined above. Once the dataset is complete, an ANN is trained to solve the regression problem and estimate the specific erosion damage pattern for any set of features in the dataset bounds.


Integration of Computational Fluid-Particle Dynamics Techniques for the Instantaneous Estimation of Particle Erosion Damage on Axial Fan Blade Sections

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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