Application of the PODFS method to inlet turbulence generated using the digital filter technique

•The digital filter method is used to generate synthetic turbulent inflow data.•The PODFS method is used to compress this data for three test cases.•The PODFS compressed data produces similar results to the original data.•The PODFS method is 2.5 to 8.5 times faster than other methods.•The code used to generate the PODFS/digital filter data is shared and explained. In the past the digital filter technique has been used to successfully generate inflow turbulence for a number of academic and industrially relevant reacting and non-reacting flows. Weaknesses of the method include the requirement that the filter be computed over a structured mesh and can require extremely long computation times in cases where the turbulent length scales or filter widths are large as compared to the mesh spacing. Once computed, the inflow data may be saved and reused, however tens-of-thousands of time steps worth of data must be saved, copied and re-loaded into memory for each new computation and temporal interpolation must be used if the time step is adjusted. The newly developed PODFS (proper orthogonal decomposition Fourier series) method uses proper orthogonal decomposition (POD) to compress this large data set into a handful of modes that are optimally chosen to represent the high energy turbulent structures while a Fourier series representation of the temporal components of the POD modes means that the data becomes continuous, periodic and flexible in terms of time step. The PODFS is first applied to a set of inflow data generated using the digital filter method and used to simulate a turbulent planar jet with the results compared against a DNS simulation. A practical application of the method is then demonstrated where it is used to generate inlet turbulence from a time averaged URANS profile for a swirl stabilised combustor case. In this case, two PODFS models are linearly combined, one that represents acoustic forcing from downstream and one that represents turbulent fluctuations. This highlights a feature of the method which is its ability to represent different flow phenomena using the linear addition of two or more PODFS models. A subsequent LES calculation shows that the method results in the correct penetration of the airflow jets whilst neglecting the inlet turbulence results in the incorrect jet penetration depth.