Equation-informed data-driven identification of flow budgets and dynamics

• URL: http://arxiv.org/abs/2411.09545v1
• Date: Thu, 14 Nov 2024 15:59:41 GMT
• Title: Equation-informed data-driven identification of flow budgets and dynamics
• Authors: Nataliya Sevryugina, Serena Costanzo, Steve de Bruyn Kops, Colm-cille Caulfield, Iraj Mortazavi, Taraneh Sayadi,
• Abstract summary: We propose a novel hybrid approach to flow clustering. It consists of characterising each sample point of the system with equation-based features. The algorithm is implemented in both the Eulerian and Lagrangian frameworks.
• Score: 0.0
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• Abstract: Computational Fluid Dynamics (CFD) is an indispensable method of fluid modelling in engineering applications, reducing the need for physical prototypes and testing for tasks such as design optimisation and performance analysis. Depending on the complexity of the system under consideration, models ranging from low to high fidelity can be used for prediction, allowing significant speed-up. However, the choice of model requires information about the actual dynamics of the flow regime. Correctly identifying the regions/clusters of flow that share the same dynamics has been a challenging research topic to date. In this study, we propose a novel hybrid approach to flow clustering. It consists of characterising each sample point of the system with equation-based features, i.e. features are budgets that represent the contribution of each term from the original governing equation to the local dynamics at each sample point. This was achieved by applying the Sparse Identification of Nonlinear Dynamical systems (SINDy) method pointwise to time evolution data. The method proceeds with equation-based clustering using the Girvan-Newman algorithm. This allows the detection of communities that share the same physical dynamics. The algorithm is implemented in both Eulerian and Lagrangian frameworks. In the Lagrangian, i.e. dynamic approach, the clustering is performed on the trajectory of each point, allowing the change of clusters to be represented also in time. The performance of the algorithm is first tested on a flow around a cylinder. The construction of the dynamic clusters in this test case clearly shows the evolution of the wake from the steady state solution through the transient to the oscillatory solution. Dynamic clustering was then successfully tested on turbulent flow data. Two distinct and well-defined clusters were identified and their temporal evolution was reconstructed.

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