This paper attempts to develop a Dynamic Mode Decomposition (DMD)-based Reduced Order Model (ROMs) that can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle’s trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time dynamics. This ROM can then also be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7×106. The high-dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using the Menter’s Shear Stress Transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in the literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper, which includes: (a) a requirement of higher sampling rate to obtain a higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high-Reynolds-number, separation-dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and they were found to offer significant improvement.
<div class="section abstract"><div class="htmlview paragraph">Autonomous takeoff and landing maneuvers of an unmanned aerial vehicle (UAV) from/on a moving ground vehicle (GV) have been an area of active research for the past several years. For military missions requiring repeated flight operations of the UAV, precise landing ability is important for autonomous docking into a recharging station, since such stations are often mounted on a ground vehicle. The development of precise and efficient control algorithms for this autonomous maneuvering has two key challenges; one is related to flight aerodynamics and the other is related to a precise detection of the landing zone. The aerodynamic challenges include understanding the complex interaction of the flows over the UAV and GV, potential ground effects at the proximity of the landing surface, and the impact of the variations in the surrounding wind flow and ambient conditions. While a large body of work in this area can be found on the control aspect of the UAV landing and takeoff maneuvers, research on the aerodynamic aspects of such maneuvers is non-existent. This paper presents an in-depth computational fluid dynamics (CFD) based aerodynamic characterization of the transient flow fields associated with the landing of a hobby-model quadcopter (the UAV) on an idealized road vehicle (the GV), the 35-degree slant angle Ahmed body. Transient improved delayed detached eddy simulations (IDDES) are carried out using the commercial CFD code STAR-CCM+. Our study indicates that the pressure field is the first flow property that gets impacted by the proximity of the UAV to the GV.</div></div>
<div class="section abstract"><div class="htmlview paragraph">In spite of growing popularity of scale resolved transient simulations, like the Detached Eddy Simulation (DES), among the mainstream automotive OEMs for the aerodynamic optimization of the production vehicles, Reynolds Averaged Navier-Stokes (RANS) simulations is still the most widely used Computational Fluid Dynamics (CFD) approach in motorsports. This is partially due to the usage-limitations imposed by the sanctioning bodies like, the FIA and NASCAR, restricting not only the hours of wind tunnel operation but also limiting the amount of CFD compute resource. This, coupled with speed requirements for aerodynamic development prevent the widespread use of scale-resolved modeling, such as Large Eddy Simulation (LES) or Detached Eddy Simulation (DES) methodologies that require an order of magnitude more computational resources. However, a number of investigations on the efficacy of turbulence modeling approaches using the Ahmed body and DrivAer showed that the hybrid turbulence modeling increases the accuracy of the numerical predictions of force coefficients and general flow field. However, such studies involving a NASCAR Cup racecar geometry is yet to be seen. This paper, thus, presents an investigation of the effectiveness of the Shear Stress Transport (SST) k-ω based Improved Delayed Detached Eddy Simulation (IDDES) model for the prediction of the flow-fields around a Gen-6 NASCAR racecar. The IDDES simulations were validated against moving-ground, open-jet wind tunnel (Windshear) data using two ride-heights and two crosswind angles. The primary objectives of this study are, firstly to develop a framework for SST k-ω based IDDES simulation, and secondly, compare and contrast flow field predictions by the RANS and IDDES approaches. Additionally, a spectral analysis of all force and moment coefficients is presented to aid in the analyses presented in this study.</div></div>
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