Recurrence computational fluid dynamics for efficient predictions of long-term particle deposition on a cylinder

Particle deposition on an object from a turbulent flow is of considerable interest in many applications. Numerical predictions using conventional computational fluid dynamics (CFD) are challenging due to that a large number of individual deposition events must be observed over a long time for the deposition statistics to converge. Here, we investigate the potential for using recurrence CFD (rCFD) to efficiently and accurately predict particle deposition on a cylinder for Reynolds numbers (Re) in the interval 20≤Re≤10 000. We quantify the front- and back-side deposition efficiencies independently, analyze the locations and timings of deposition events, and benchmark the computational performance. We find that rCFD predicts deposition efficiencies with similar accuracy as the corresponding CFD simulations, but at a fraction of the computational cost. The most significant deposition occurs on the front side of the cylinder and is very well described for all Reynolds numbers investigated. For Re=10 000, we observe a dependence on the rCFD database length in the prediction of the much less effective back-side deposition, as the database only contains a limited subset of the more rare flow behaviors responsible for this deposition. These results can be used to accelerate particle deposition studies by several orders of magnitude, which would bring significant benefits for computationally challenging applications, such as sensor soiling in the car industry, icing on aircraft, and ash build-up in boilers.