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Data analysis tool flowTorch
flowTorch - a Python library for analysis and reduced-order modeling of fluid flows
The flowTorch library [1, 2] enables researchers to access, analyze, and model fluid flow data from experiments or numerical simulations. Instead of a black-box end-to-end solution, flowTorch provides modular components allowing to assemble transparent, automated, and reproducible processing workflows with ease. Fluid flow data coming from experiments (PIV, iPSP, Schlieren) or simulations (TAU, Flexi, OpenFOAM) may be accessed via a common interface in a few lines of Python code. Internally, the data are organized as PyTorch tensors. Relying on PyTorch tensors as primary data structure enables fast array operations, parallel processing on CPU and GPU, and exploration of novel deep learning-based analysis and modeling approaches. The flowTorch documentation includes a rich collection of Jupyter notebooks  demonstrating how to apply the library components in a variety of different use cases, e.g., finding coherent flow structures with modal analysis or creating reduced-order models.
POD and DMS
POD und DMD Moden charakteristisch für die Stoß-Grenzschicht-Interaktionen. Die dominante Buffet-Frequenz liegt bei etwa 340Hz.
Comparative view of experimental surface pressure and DMD reconstruction.
The image shows the dominant pattern (mode) in the unsteady surface pressure distribution on the XRF1 model. Both proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) are available in flowTorch for the analysis of unsteady flows.
The DMD decomposes the flow into several characteristic spatial flow structures, so-called modes. Each mode is associated with a unique frequency. By superposition of a few dominant modes close to the buffet frequency, the main flow features can be recovered, as seen in the animation.