## 1 Introduction
[27] used DMD to analyze the shedding mode frequency at \(Re=345\) of a square-back [PERSON] body and developed a controller to reduce base pressure drag and re-symmetrize the wake. The Reynolds number, \(Re_{W}\), with the width of the geometry (\(W\)) as the characteristic length, was defined as \(Re_{W}\equiv UW/\
u\). Building on this, [PERSON] et al. [28] applied DMD to a simplified truck model at \(Re_{H}=20,000\), identifying toroidal vortex structures near the wake and large hairpin vortices shed from the longest part of the wake, which were associated with bistability triggers. [PERSON] et al. [29] conducted a DMD analysis on a simplified fastback generic car body, known as DrivAer [30], capturing the most dominant and energetic dynamic modes of the flow. They identified modes with \(St=0.08\) and \(0.13\) as the most energetic, with their shapes and frequencies aligning well with existing literature, despite differences in geometry and Reynolds number. In an experimental study, [PERSON] and [PERSON] [31] examined the flow around an elliptical Ahmed body using DMD. They found that the most energetic mode decays faster in the elliptical body compared to the standard Ahmed body. This effect shifts the high-drag critical angle from 30\({}^{\circ}\) to a lower-drag angle of 25\({}^{\circ}\). Recently, [PERSON] et al. [32] successfully applied DMD to develop a reduced-order model (ROM) that predicts force and moment coefficients for flow past an idealized ground vehicle at a Reynolds number of \(2.7\times 10^{6}\). Most research studies that used dynamic mode decomposition (DMD) to analyze flow dynamics in road vehicle aerodynamics have employed sampling frequencies (\(f_{s}\)) ranging from 10 Hz to 10 kHz, sampling periods (\(T_{w}\)) from \(10^{-2}\) to \(10^{3}\) s, and Reynolds numbers between \(10^{3}\) and \(10^{6}\). The choice of the sampling window length can significantly affect the trade-off between frequency and time resolution, making the selection of an appropriate windowing strategy challenging for non-stationary signals. In this study, where DMD is the selected decomposition technique, there is a very limited understanding of the relationship between sampling parameters and the consistency and uniqueness of DMD outputs, especially in applications such as ground vehicles that deal with complex geometry at high Reynolds numbers. Most DMD studies discussing DMD output uniqueness involve more periodic flows, such as jets or flow past 2D cylinders at low Reynolds numbers. For example, two years after the introduction of DMD by [PERSON], [PERSON] et al. [33], in their numerical study of the flow past a circular cylinder, stated that, unlike the discrete Fourier transform (DFT), DMD does not require a specific number of cycles for the target frequency to capture dynamic components. They also mentioned that individual modes with decaying or growing rates enable DMD to handle non-periodic flows as well. To the best of the authors' knowledge, one of the few research studies that has conducted a parametric investigation into the effect of data sampling on DMD performance is by [PERSON] et al. [34]. They identified four stages of DMD convergence with respect to cycle numbers: initialization, transition, stabilization, and divergence. They also discovered that convergence of the sampling frequency is mode-specific, suggesting that an oscillatory structure might be resolved by 15 frames. However, their case study, limited to flow past a square prism dominated by Karman vortex street, did not provide deep insights into the uniqueness of DMD performance in mode shapes and energy levels. Nevertheless, since flow past a vehicle involves a mix of wall-bounded, free shear, and wake flows at very high Reynolds numbers, the interactions between these flows can have a significant impact on the complexity level of DMD decomposition.