Various assessments of RANS and Hybrid RANS-LES turbulence models have been conducted for automotive applications. However, their applicability for high performance vehicles which exhibit much more complex flow phenomena is not well studied yet. In this work, the predictive capabilities of RANS and DDES models are investigated through a comparative study on a high performance configuration of the DrivAer Fastback model at a low ground clearance in an open road computational domain. The results show much agreement in the general pressure distribution, except in areas of highly unsteady flow. Visualisation of the flow field depicts that the DDES simulation is able to capture a wider range of turbulent scales with a higher fidelity. Lastly, variation in the magnitude, distribution and decay of pressure losses in the wake are observed between both simulations. The presented results are used to illustrate the capabilities and limitations of these turbulence models for other academic or industrial users to make an informed decision on the turbulence model suited for their objectives

Blackburn, Kim

Brighton, James

Rijns, Steven

Teschner, Tom-Robin

Cranfield CERES

UKACM 2023 Conference, April 19-21, 2023, The University of Warwick, Coventry, UKCOMPARATIVE ANALYSIS OF RANS AND DDES METHODS FORAERODYNAMIC PERFORMANCE PREDICTIONS FOR HIGHPERFORMANCE VEHICLES AT LOW GROUND CLEARANCESS. Rijns1∗, T.-R. Teschner2, K. Blackburn3, J. Brighton41 Advanced Vehicle Engineering Centre, Cranfield University, Cranfield MK43 0AL, UK.steven.rijns@cranfield.ac.uk2 Centre for Computational Engineering Sciences, Cranfield University, Cranfield MK43 0AL, UK.tom.teschner@cranfield.ac.uk3 Advanced Vehicle Engineering Centre, Cranfield University, Cranfield MK43 0AL, UK.k.blackburn@cranfield.ac.uk, j.l.brighton@cranfield.ac.ukAbstract. Various assessments of RANS and Hybrid RANS-LES turbulence models have been con-ducted for automotive applications. However, their applicability for high performance vehicles whichexhibit much more complex flow phenomena is not well studied yet. In this work, the predictive capa-bilities of RANS and DDES models are investigated through a comparative study on a high performanceconfiguration of the DrivAer Fastback model at a low ground clearance in an open road computationaldomain. The results show much agreement in the general pressure distribution, except in areas of highlyunsteady flow. Visualisation of the flow field depicts that the DDES simulation is able to capture a widerrange of turbulent scales with a higher fidelity. Lastly, variation in the magnitude, distribution and decayof pressure losses in the wake are observed between both simulations. The presented results are usedto illustrate the capabilities and limitations of these turbulence models for other academic or industrialusers to make an informed decision on the turbulence model suited for their objectives.Key words: Hybrid RANS-LES; DDES; RANS; DrivAer; High-Performance1 IntroductionThe performance of automotive and motorsport vehicles is critically determined by their aerodynamicdesigns. The analysis and design process for more advanced and efficient aerodynamic concepts requiresthe use of Computational Fluid Dynamics (CFD) to complement the traditional wind tunnel experi-ments. Turbulence model selection is an important aspect in CFD which requires a trade-off to be madebetween accuracy and computational costs for specific design objectives. Reynolds-Averaged Navier-Stokes (RANS) turbulence models are well known for their low computational costs, but have limitedcapabilities to capture unsteady details in turbulent flows. In contrast, Scale-Resolving Simulations(SRS) like the Large Eddy Simulation (LES) are able to resolve complex turbulent flow structures, butrequire significant computational resources. Hybrid RANS-LES models like the Delayed Detached EddySimulation (DDES) are able to switch between a RANS model is the boundary layer to a LES model inregions of large flow separation and thereby improve overall computational efficiency. For ground vehi-cles, most prior research on the capabilities of these turbulence models has been devoted to automotivepassenger vehicles. This paper provides a comparative study on the predictive capabilities of RANS andDDES simulations dedicated to high performance vehicles, which exhibit significantly more complexflow phenomena.1S. Rijns, T.-R. Teschner, K. Blackburn and J. Brighton2 Problem descriptionTurbulence model selection is application and even case dependent, it should not only consider the trade-off in accuracy and computational costs but also the flow physics. In automotive applications, RANSmodels have shown adequate accuracy in drag predictions but an inability to produce detailed flow struc-tures [1]. Hybrid RANS-LES models have shown promising results both in terms of the mean pressuredistribution and velocity field, and also in capturing unsteady flow structures with a high fidelity [2].Yet, the applicability of these turbulence models on high performance vehicles is not well studied. Highperformance vehicles exhibit much more complex flow phenomena through the use of downforce gener-ating devices, especially at low ground clearances [3], which need to be accurately captured to asses theaerodynamic performance.Therefore, a comparative study on the predictive capabilities between RANS and DDES models is con-ducted on the DrivAer hp-F model [4]; a high performance configuration of the DrivAer Fastback model(Figure 1a). The numerical simulations are performed on a half car model at a ride height of 15.015 mmin a rectangular computational domain with a blockage ratio of ≈ 1.3%, designed to resemble open roadconditions (Figure 1b). The case is considered at a velocity of U∞ = 40 m/s using an air density of ρ= 1.1678 kg/m3 and a dynamic viscosity of µ = 1.8377e−5 kg m−1s−1. Two unstructured poly-hexcoremeshes with a vehicle surface mesh size and base mesh size of around 0.45% and 7.25% of the vehicle’slength (L) are created in ANSYS Fluent Meshing for the RANS and DDES simulations. The DDESmesh uses a low y+ treatment of y+ ≈ 0.8 with 15 inflation layers whereas a higher y+ treatment of y+ ≈168 with 4 inflation layers is used for the RANS mesh. Both meshes uses refinement zones, but the near-field element size is reduced from 6.5% to 2% of the base mesh size for the DDES mesh to allow a moregradual transition with the low y+ treatment (Figure 1c). Both simulations are performed with the k-ωSST turbulence model using the standard settings in ANSYS Fluent. The RANS simulation is performedfor 1000 iterations with force coefficients averaged over the last 500 iterations. The DDES simulationuses a fixed time-step of 3.125e−4 s with 10 inner iterations and is initialised using a RANS solution.The DDES simulation is performed for 2 s of flow time, equivalent to 49.6 convective time units (CTUs),including 24.8 CTUs to wash out the RANS initial condition, and the remaining 24.8 CTUs to averagethe flow field and collect unsteady statistics. These settings result in computational times of about 1 hourand 79 hours for the RANS and DDES simulations respectively.(a) (b) (c)Figure 1: (a) DrivAer hp-F model with dimensions, adapted from [4], (b) computational domain withdimensions and boundary conditions, and (c) close-up of the mesh strategy with element sizes expressedas a percentage of the base mesh size2S. Rijns, T.-R. Teschner, K. Blackburn and J. Brighton3 Numerical resultsThe RANS simulation predicts nearly 10% more downforce, but only over 3% more drag compared tothe DDES simulation, resulting in 6% higher aerodynamic efficiency. Even though the RANS simulationshows slightly earlier pressure recovery in the diffuser, it also displays a lower pressure on the floor anddiffuser inlet, resulting in a nearly 6% lower average pressure coefficient on the underbody (Figure 2a).Furthermore, the RANS simulation depicts less pressure build up on the windscreen, caused by thelarger recirculation zone in front of the windscreen which acts as an air deflector that redirects airflowfrom the hood more smoothly to the windscreen (Figure 2b). More variation is observed on the spoilerwhere the RANS simulation shows a centralised high pressure region whereas the DDES simulationdepicts a high pressure region around the periphery (Figure 2b). The diffuser, windscreen, slant andspoiler are also areas with high surface pressure fluctuations (Figure 2c) caused by large separation andunsteady flow behaviour. These effects are typically less accurately captured by RANS models, causingthe dissimilarities in surface pressure to the DDES simulations in these areas.(a) (b) (c)Figure 2: Mean pressure coefficient at the (a) underbody and (b) upper surface, and (c) Root Mean SquareError (RMSE) for the mean pressure coefficient of the DDES simulationOther concentrations of pressure fluctuation are seen in the wakes of the strakes and mirrors. The RANSsimulation succeeds to capture these main turbulent structures, but the DDES simulation provides muchmore detail and further propagation (Figure 3a). This provides more insights into the downstream devel-opment, as seen for the vortices formed at the side of the splitter which move towards the low pressureregion underneath the vehicle and exit out of the diffuser. Furthermore, the DDES simulation capturesmore turbulent structures like the one running over the centre line which is formed at the centre of thesplitter, and the one formed at the A-pillar which moves towards the slant. Moreover, the DDES is ableto resolve smaller structures in the separated flow on the slant and in the wake, which translates into alower total pressure in those regions (Figure 3b). Increased separation on the slant and diffuser in theDDES simulation creates a more squared primary near-wake region at x = 1.35 m compared to the RANSsimulation. Further downstream at x = 1.55 m, both simulations depict two circular concentrations ofhigh pressure losses which corresponds to the two counter-rotating vortices formed at the spoiler and ve-hicle base. The DDES simulation shows less decay of pressure losses and the distribution is concentratedhigher in the wake. Similarly, the DDES simulation shows more intense traces of the vortices and flowseparation of the diffuser in the lower region of the wake.3S. Rijns, T.-R. Teschner, K. Blackburn and J. Brighton(a) (b)Figure 3: (a) Visualisation of coherent turbulent structures (Q = 8∗104 s−2) of the averaged flow coloredby normalised streamwise velocity (Ux/U∞), and (b) total pressure contour plots on a plane through theslant (x = 1.15 m) and two planes in the near wake (x = 1.35 m & x = 1.55 m)4 ConclusionsA comparative study between RANS and DDES approaches intended to study their applicability for ex-ternal aerodynamics simulations of high performance vehicles is conducted on the DrivAer hp-F model.Both simulations show a similar pressure distribution, bar from areas with highly unsteady flow where theRANS simulation predicts less separation resulting in 10% and 3% more downforce and drag comparedto the DDES simulation. Dominant turbulent structures are captured by the RANS simulation, howeverthe DDES simulation resolves a wider range of turbulent scales with a higher fidelity. Furthermore, theDDES simulation depicts a larger near-wake with higher pressure losses that decay slower comparedto the RANS model. Considering the nearly 80 times faster computational time, the RANS simulationprovides adequate information for analysis of the mean flow field and force coefficients. However, indepth analysis of specific devices and turbulent structures would benefit of the additional detail providedby the DDES simulation, especially in regions of unsteady flow and large separation.REFERENCES[1] Heft, A. I., Indinger, T., Adams, N. A., Investigation of Unsteady Flow Structures in the Wake of aRealistic Generic Car Model. AIAA Applied Aerodynamics Conference, 29.,(2011).[2] Jakirlic, S., Kutej, L., Unterlechner, P., Topea, C., Critical Assessment of Some Popular Scale-Resolving Turbulence Models for Vehicle Aerodynamics. SAE International Journal of PassengerCars - Mechanical Systems, 10.,(2017).[3] Porcar, L., Toet, W., Gamez-Montero, P. J., Study of the Effect of Vertical Airfoil Endplates onDiffusers in Vehicle Aerodynamics. Designs, 5.,(2021).[4] Soares, R. F., Knowles, A., Olives, S. G., Garry, K. et al., On the Aerodynamics of an Enclosed-Wheel Racing Car: An Assessment and Proposal of Add-On Devices for a Fourth, High-Performance Configuration of the DrivAer Model. SAE Technical Paper, 2018-01-0725.,(2018).4Cranfield UniversityCERES https://dspace.lib.cranfield.ac.ukSchool of Aerospace, Transport and Manufacturing (SATM) Staff publications (SATM)2023-04-21Comparative analysis of RANS andDDES methods for aerodynamicperformance predictions for highperformance vehicles at low ground clearancesRijns, StevenRijns S, Teschner T-R, Blackburn K, Brighton J. (2023) Comparative analysis of RANS andDDES methods for aerodynamic performance predictions for high performance vehicles at lowground clearances. Presented at: UKACM 2023 Conference (UK Association for ComputationalMechanics), 19-21 April 2023, Warwick University, Coventry, UKhttps://dspace.lib.cranfield.ac.uk/handle/1826/19570Downloaded from Cranfield Library Services E-Repository

Comparative analysis of RANS and DDES methods for aerodynamic performance predictions for high performance vehicles at low ground clearances

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Comparative analysis of RANS and DDES methods for aerodynamic performance predictions for high performance vehicles at low ground clearances

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