RANS Simulations of a Simplified Tractor/Trailer Geometry

Roy, Christopher J.; Payne, Jeffrey L.; McWherter-Payne, Mary Anna; Salari, Kambiz

doi:10.1007/978-3-540-44419-0_21

Cited by 23 publications

(17 citation statements)

References 5 publications

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“…Re As can be seen, the C D obtained is in good agreement with the results reported in the literature, and the values reported without the strut correction will be used as the base case drag moving forward. In addition to the quantitative results shown in Table 2, the wake structure for the base GTS model has been visualized and compared to the results presented by Roy et al 14 Figures 9 and 10 have been generated to match the available data and are in good agreement with the RANS results reported in the literature, but as expected, the experimental Particle Image Velocimetry (PIV) results for the wake structure differ. Despite these differences, the wake stabilizing effect of the ground 11,12 allows for the accurate prediction of integrated forces.…”

Section: Contributionmentioning

confidence: 61%

Computational Design of Drag Diminishing Active Flow Control Systems for Heavy Vehicles

Manosalvas

Economon

Othmer

et al. 2016

8th AIAA Flow Control Conference

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The presented work describes the development of a computationally inexpensive method that has been used for the design and study of Active Flow Control (AFC) systems aimed towards drag reduction for heavy vehicles. The AFC devices chosen are Coanda jets, which have been positioned in all four trailing edges of the fully three-dimensional Ground Transportation System (GTS) model. The jet actuation reduces the wake size and increases the base pressure, reducing the pressure differential on the vehicle and ultimately causing a significant drag reduction. The integrated forces on the vehicle have been computed using Reynolds-Averaged Navier-Stokes (RANS) and the jet configuration changes were quantified using SU2, an open-source Computational Fluid Dynamics (CFD) suite. This study ultimately shows an optimized AFC configuration which reduces drag by 15% and the power used by 12% on a 6.5% scale version of the GTS model. of maintaining flow attached and of increasing the wake pressure 6 relative to cases without jets. These experiments have demonstrated not only drag reduction, resulting in a net power savings of more than 15%, but also an improvement in vehicle stability and safety through the use of flow injection to compensate for the effect of side forces. 4 For the effective design of AFC systems, it is necessary to understand the effects that each part of the system has on the flow features. Due to the complexity of the problem and appearance of separation, Large Eddy Simulation (LES) is believed to be the most reliable method when it comes to the simulation of these flows. Unfortunately, this approach increases the computational requirements significantly, making it prohibitively expensive for use in a design process, given current computational resources. In order to be able to use Computational Fluid Dynamics (CFD) for the design of AFC systems, it is necessary to use a combination of tools that will allow us to simulate the flow around heavy vehicles with an acceptable level of accuracy. In previous studies, 7, 8 the combination of Jameson-Schmidt-Turkel (JST) 9 numerical scheme and the Shear-Stress-Transport (SST) 10 turbulence model have been demonstrated as a good compromise between accuracy and computational cost for design purposes.This article builds upon the previous two-dimensional studies and leverages the knowledge of the flow physics that was gained in order to study the three-dimensional Ground Transportation System (GTS) model. The use of a three-dimensional ground vehicle introduces the stabilizing influence of the ground, 11, 12 which combined with the low frequency flow oscillations found by Khalighi et al. 13 using Unsteady Reynolds-Averaged Navier-Stokes (URANS), justifies the use of Reynolds-Averaged Navier-Stokes (RANS) for the computation of integrated forces in a similar manner as the work presented by Roy et al. 14 and Salari et al.. 15 Furthermore, the lowest drag condition for the two-dimensional GTS model, outfitted with Coanda Jets in the back, is achieved when the wake is confin...

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Section: Contributionmentioning

confidence: 61%

Computational Design of Drag Diminishing Active Flow Control Systems for Heavy Vehicles

Manosalvas

Economon

Othmer

et al. 2016

8th AIAA Flow Control Conference

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“…The information presented in this paper has been extracted from published works and where available accepted correlation parameters and summary data plots are presented. The evaluation of complete vehicles made use of references 6,8,9,11,13,15,16,18,20,21,22,23,24,26,27,29,33,36,37,44,45,48,49,50,51,52,56,64,70,73,74,77,80,81,82,83,84,85,86,87,91,93,94,95,96,97,100 …”

Section: Introductionmentioning

confidence: 99%

A Review of Reynolds Number Effects on the Aerodynamics of Commercial Ground Vehicles

Wood¹

2012

SAE Int. J. Commer. Veh.

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A review of Reynolds number scaling and high Reynolds number simulation for commercial ground vehicles is presented. The supporting data was extracted from published wind tunnel tests, coastdown tests and computational studies for complete vehicles and vehicle components. For complex vehicles the data suggest that the use of forced transition for higher Reynolds number simulation is a viable test technique however various component specific schemes may be required to replicate the aerodynamics of a full-scale vehicle. The Reynolds number sensitivity of; boundary layer transition control, surface curvature effects, bluff base flows, wheel/tire aerodynamics, and complete vehicle aerodynamics is presented. The data show that the width-based transcritical Reynolds number for commercial vehicles is between 1 and 3 million with the averaged values for edge radius, wheels and full vehicle data sets between 1.6 and 1.8 million. A minimum width-based transcritical Reynolds number value of 3 million is recommended for simulating the aerodynamics of full-scale commercial vehicles operating at a width-based Reynolds number greater than 3 million. If the dominant aerodynamic and fluid dynamic features of the vehicle are well established the minimum width-based transcritical Reynolds number value that may be used is 2 million. To ensure an accurate simulation of full-scale vehicles operating below the 3 million transcritical value future studies should be performed at the vehicle full-scale Reynolds number.

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“…The ability of CFD methods, such as those based on popular Reynolds averaged Navier-Stokes (RANS) turbulence models, to predict drag and surface pressure coefficients accurately for flow past complex vehicle geometries (Roy 2006;Roy and Ghuge 2009) has also led to increasing popularity of CFD-based optimization methods in vehicle design. Interesting recent examples include the design of high-speed trains (Krajnović 2009), and container truck wind deflectors (Gong et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective aerodynamic shape optimization of small livestock trailers

Gilkeson

Торопов

Thompson

et al. 2012

Engineering Optimization

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This article presents a formal optimization study of the design of small livestock trailers, within which the majority of animals are transported to market in the UK. The benefits of employing a headboard fairing to reduce aerodynamic drag without compromising the ventilation of the animals' microclimate are investigated using a multi-stage process involving computational fluid dynamics (CFD), optimal Latin hypercube (OLH) design of experiments (DoE) and moving least squares (MLS) metamodels. Fairings are parameterized in terms of three design variables and CFD solutions are obtained at 50 permutations of design variables. Both global and local search methods are employed to locate the global minimum from metamodels of the objective functions and a Pareto front is generated. The importance of carefully selecting an objective function is demonstrated and optimal fairing designs, offering drag reductions in excess of 5% without compromising animal ventilation, are presented.

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RANS Simulations of a Simplified Tractor/Trailer Geometry

Cited by 23 publications

References 5 publications

Computational Design of Drag Diminishing Active Flow Control Systems for Heavy Vehicles

Computational Design of Drag Diminishing Active Flow Control Systems for Heavy Vehicles

A Review of Reynolds Number Effects on the Aerodynamics of Commercial Ground Vehicles

Multi-objective aerodynamic shape optimization of small livestock trailers

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