The Proper Orthogonal and Dynamic Mode Decomposition of Wake Behind a Fastback DrivAer Model

Ahani, Hamed; Nielsen, Jonas B.; Uddin, Mesbah

doi:10.4271/2022-01-0888

Cited by 5 publications

(9 citation statements)

References 34 publications

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“…The reduction of the aerodynamic drag force remains a core objective of vehicle aerodynamic development and is motivated by the desire to reduce fuel consumption [1,2]. Researchers have attempted to achieve aerodynamic drag reduction through different types of passive flow control devices such as the front bypass ducts [3], rear bypass ducts [4], and various deflector designs [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Before a control signal can be applied to the moving vehicle, predictions of the future state of aerodynamic forces and moments are required. A Reduced Order Model (ROM) can be used to make future state predictions of the aerodynamic flow field [1]. For adaptive systems, the future state predictions can then be coupled with a control input to obtain the desired performance characteristics [18].…”

Section: Introductionmentioning

confidence: 99%

“…Fluid flows around road and race vehicles are highly turbulent and consist of many dynamic coherent structures that are characterized by a wide range of length and time scales. The evolution and convection of these structures produce macroscopic spatio-temporal patterns [1,[19][20][21]. Hybrid turbulence modeling simulation approaches, such as the Improved Delayed Detached Eddy Simulation (IDDES), have shown greater success in elucidating these finer vortical structures embedded in the flow field.…”

Section: Introductionmentioning

confidence: 99%

“…Decomposing the fluid flow into its constituent components can be very helpful in this regard. Researchers in this field have largely resorted to methods of modal decomposition to analyze the flow field [1,24,25].…”

Section: Introductionmentioning

confidence: 99%

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Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

et al. 2023

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show abstract

Section: Introductionmentioning

confidence: 99%

Application of the DMD Approach to High Reynolds Number Flow Over an Idealized Ground Vehicle

Misar¹,

Tison²,

Korivi³

et al. 2023

Preprint

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This paper attempts to develop a Dynamic Mode Decomposition (DMD) based Reduced Order Model (ROMs) which 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 then also can 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 2.7E6. The high dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using Menter's shear stress transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in 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 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 found to offer significant improvement.

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On the Dynamic Mode Decomposition of the Flow Over an Idealized Ground Vehicle at High Reynolds Number

Misar

Nichols

Uddin

2023

AIAA AVIATION 2023 Forum

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The Proper Orthogonal and Dynamic Mode Decomposition of Wake Behind a Fastback DrivAer Model

Cited by 5 publications

References 34 publications

Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

Application of the DMD Approach to High Reynolds Number Flow Over an Idealized Ground Vehicle

On the Dynamic Mode Decomposition of the Flow Over an Idealized Ground Vehicle at High Reynolds Number

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