ADAPTATION OF THE DYNAMIC MODE DECOMPOSITION METHODOLOGY TO HIGH REYNOLDS NUMBER FLOW OVER A GROUND VEHICLE : CHALLENGES AND MITIGATION

The Dynamic Mode Decomposition (DMD) has shown the ability to extract coherent structures and dominant modes from high dimensional, sequential flow field datasets by decomposing it into spatial patterns and associated time dynamics. This low-rank dataset can then be applied to a linear regression model to predict the future state of the flow. Additionally, the DMD with control (DMDc) algorithm enables the input of control signals to the system, a very promising avenue for developing active aero devices for ground and aerial vehicles. However, existing literature primarily consists of its applications to low Reynolds number flows past simple, and mostly two-dimensional geometries. Given that most flows of engineering interest involve three-dimensional turbulent flows having high Reynolds number, this paper explores and presents DMD analyses of the flow around an idealized ground vehicle (Ahmed body) at a Reynolds number of 2.7 million. The high dimensional dataset for this paper was generated using numerical simulations, i.e. Computational Fluid Dynamics (CFD). Based on the success of the hybrid turbulence modeling methodology, called the Improved Delayed Detached Eddy Simulation or IDDES, in predicting external flows past generic road vehicles, all simulations used in this paper were carried out using the IDDES approach. We observed that for such a complex and high Reynolds number flow, application of the DMD algorithms, as can be found in existing literature, failed to meet the desired objective of obtaining reliable reduced-order-model predictions of the flow fields. This necessitates enhancements to the existing algorithm to be explored, and thus, a modified DMD algorithm applicable to high Reynolds number, separation-dominated flows was developed and presented in this paper. The veracity of the modified DMD-based data-dimensionality reduction approach was tested by comparing the mean values, the root-mean-squared (RMS) values, and power spectral density (PSD) of force and moment coefficients that were obtained from the reconstruction of the surface-pressure field using the DMD-based reduced order model to true CFD simulation data.

Citation: A. Misar, S. Nichols, V. Korivi, N. Tison and M. Uddin, “Adaptation of the Dynamic Mode Decomposition Methodology to High Reynolds Number Flow over a Ground Vehicle : Challenges and Mitigation,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.