Computational Analyses of the Effects of Wind Tunnel Ground Simulation

Article Computational Analyses of the Eﬀects of Wind Tunnel Ground Simulation and Blockage Ratio on the Aerodynamic Prediction of Flow over a Passenger Vehicle

Chen Fu 1,2 , Mesbah Uddin 1,* and Chunhui Zhang 1,3

1 NC Motorsports and Automotive Research Center, UNC Charlotte, Charlotte, NC 28223, USA; [email protected] (C.F.); [email protected] (C.Z.) 2 Chip Ganassi Racing Team, 8500 Westmoreland Dr NW, Concord, NC 28027, USA 3 AXISCADES Engineering Technologies Ltd., 3008 W Willow Knolls Dr, Peoria, IL 61614, USA * Correspondence: [email protected]

Received: 18 May 2020; Accepted: 5 June 2020; Published: 11 June 2020

Abstract: With the fast-paced growth of computational horsepower and its affordability,computational fluid dynamics (CFD) has been rapidly evolving as a popular and effective tool for aerodynamic design and analysis in the automotive industry. In the real world, a road vehicle is subject to varying wind and operating conditions that affect its aerodynamic characteristics, and are difficult to reproduce in a traditional wind tunnel. CFD has the potential of becoming a cost-effective way of achieving this, through the application of different boundary conditions. Additionally, one can view wind tunnel testing, be it a fixed-floor or rolling road tunnel, as a physical simulation of actual on-road driving. The use of on-road track testing, and static-floor, and rolling-road wind tunnel measurements are common practices in industry. Subsequently, we investigated the influences of these test conditions and the related boundary conditions on the predictions of the aerodynamic characteristics of the flow field around a vehicle using CFD. A detailed full-scale model of Hyundai Veloster with two vehicle configurations, one with the original and the other with an improved spoiler, were tested using a commercial CFD code STAR-CCM+ from Siemens. Both vehicle configurations were simulated using four different test conditions, providing overall eight different sets of simulation settings. The CFD methodology was validated with experimental data from the Hyundai Aero-acoustic Wind Tunnel (HAWT), by accurately reproducing the test section with static floor boundary conditions. In order to investigate the effect of the blockage ratio on the aerodynamic predictions, the vehicle models were then tested with moving ground plus rotating wheel boundary conditions, using a total of four virtual wind tunnel configurations, with tunnel solid blockage ratios ranging from 1.25%, which corresponds to the actual HAWT, to 0.04%, which presents an open air driving condition.

Keywords: computational ﬂuid dynamics (CFD); rans; wind tunnel; blockage ratio; ground simulation

1. Introduction As one of the three major source of road vehicle energy losses, besides the power train losses and vehicle mass [1], aerodynamic drag reduction remains the one of the major focuses of interest for both race and road vehicle industries over the last few decades. To minimize exhaust emissions, and improve the fuel eﬃciency and handling characteristics, automotive manufacture focuses on the aerodynamic drag reduction starting from the early stages of vehicle design. Wind tunnel testing, road testing, and CFD simulations are the three common tools used by the industry to evaluate the aerodynamic performance of a vehicle. However, during the early design and optimization stages,

Vehicles 2020, 2, 318–341; doi:10.3390/vehicles2020018 www.mdpi.com/journal/vehicles Vehicles 2020, 2 319 road testing cannot be applied, since a complete car has not been manufactured. Thus, manufacturers can only rely on wind tunnel testing and CFD simulation to improve the design. Automotive wind tunnels are designed to simulate the on-road vehicle performance with a physical vehicle model placed in bounded test sections. Wind tunnel test sections are categorized as closed wall, open jet, slotted wall, and adaptive wall; however, none of these can accurately reproduce the actual on-road aerodynamic performance of the vehicle [2]. Subsequently, correction factors must be applied to wind tunnel test results, to account for the restrictions in the testing conditions, such as the effects of wind tunnel solid blockage and other boundary corrections. Wind tunnel test section interference effects on ground vehicle aerodynamic testing have been investigated for decades. Both experimental and numerical studies have sought to quantify and correct the interference effects on the tested vehicle model caused by differences in the test section configuration and size. Since closed wall and open jet configurations are the two most commonly used test section configurations, a vast majority of the research works concentrated primarily on these two designs. Studies on the wind tunnel interferences were first reported by Glauert [3] when he introduced a blockage correction to measurements on aircraft wings and bodies carried out in a closed wall test section. Cooper [4] and his co-workers pioneered the automotive related studies with his complete view on “closed-test-section wind tunnel blockage correction for road vehicles”. Later, Mercker and Wiedemann [5] suggested four other interference effects evident in open jet tunnels, all of which are related to the changes of static pressure along the streamwise direction in the test section. Research works on the open jet and closed wall wind tunnels were carried over by Mercker’s team and others into the new century as well [5,6]. Wickern [7] had his first summary on the application of automotive wind tunnel corrections, followed by Wickern and Schwartekopp [8], who investigated the nozzle gradient effects in open jet wind tunnels. Hoffman et al. [9,10] studied the test section configuration effects on the aerodynamic drag and lift predictions as well, with different vehicle shapes using the Ford/Sverdrop Drivability Test Facility. Based on experiments carried out using various vehicle models, Mercker and his co-workers were able to quantify the effects of the static pressure gradients in wind tunnels [11]. They proposed two-measurement correction methods [12], which are currently widely used in the industry for aerodynamics prediction corrections for both experiments [13] and CFD simulations [14,15]. Even though CFD capabilities have greatly been improved with the rapid growth of computational power, showing much better correlation with wind tunnel measurements, the latter is still considered the favored reference, due to the real physical models and real air-flow conditions [16]. CFD simulations, however, can provide a much deeper insight into the flow field interacting with the vehicle [17,18], and can be a very effective tool during the design optimization process [19]. Researchers have demonstrated the capabilities of CFD in delivering valuable aerodynamic predictions, and even replicating the wind tunnel tests with certain mesh resolutions, boundary conditions, and physical modeling. By taking the Jaguar Land Rover program as an example, Gaylard [20] emphasized that a good combination of simulation and wind tunnel testing can provide more competitive aerodynamic performance than relying fully on either methodology. Similarly, Ueno et al. [21] reported a good agreement between the CFD simulation results and wind tunnel measurements with an error margin of 5% for their ± open-wheel race car study. Most recently, the Hyundai Motor Company used a quarter-scale Genesis model to investigate the reproduction of wind tunnel tests with CFD simulations [22]. Two major challenges of replicating wind tunnel tests in CFD are, firstly, correctly simulating the different types of ground configurations in the test section and secondly, the implementation of the upstream boundary conditions. Many numerical studies have been carried out on these topics as well. Wickern et al. [23] from AUDI AG studied the boundary layer suction effects on vehicle drag, and illustrated the importance of a suction or boundary layer control system. Buscariolo and Mariani [24] investigated the influence of three types of ground, viz. the static floor, elevated plate, and moving belt, using a small pickup truck model in 2D simulations. Hennig et al. [25] used CFD to Vehicles 2020, 2 320 evaluate measurement interference effects of various Rolling Road Systems (RRS), which include single belt, 3-belt, 5-belt, and T-belt systems. Most recently, Meederira et al. [26] presented CFD analyses characterizing the wake flow structures past an isolated wheel and its interaction with a 5-belt moving ground plane (MVP). Simulating the rolling-road system requires accurate modeling of the wheel rotation as well. In fact, the drag results coming from the configuration with only moving ground and no wheel rotation are higher, when compared to the static floor and fixed-wheel conditions; with both moving ground and wheel rotation active, the drag prediction will be lower [27]. The effects of the moving ground plane (MVP) and rotating wheel (RW) systems have been studied both experimentally and computationally during the last two decades. Research in this area covered isolated wheels, to cite but a few [26,28], and vehicle models with both simplified wheels [29] and detailed wheels [27,30]. More recently, several CFD works [27,31,32] have focused on investigating the MVG-RW influence on vehicle aerodynamic predictions using PowerFLOW, a Lattice Boltzmann based CFD approach. In these studies, different wheel rotation methods were applied, which include rotating boundary conditions, moving reference frames (MRF), and sliding mesh. Although a significant number of works have been published concerning CFD and wind tunnel correlations [33–40], very few of these studies investigated the comparison among wind tunnel tests with both static floor, rolling-road, and track testing from a numerical investigation point of view. A numerical comparison between different wind tunnel test sections, viz. open jet, slotted wall, and adaptive wall and a blockage free tunnel, was presented by Connor et al. [41]. The blockage free domain was designed to simulate conditions equivalent to the road testing. All the simulations were run with the rolling-road system. Moreover, the focuses on the interference effect of open jet test sections suggested that having different vehicle models or mounting the same model at different longitudinal locations caused pressure gradient and wake changes. Not many researchers investigated the influence of the test section volume on the prediction results, since the open jet test section itself is less sensitive to the blockage effects compared to the closed wall section. In the present study, a detailed full-scale model of Hyundai Veloster was tested using CFD under both wind tunnel and interference free conditions. The simulations were validated using the experimental results from the Hyundai Aero-acoustic Wind Tunnel (HAWT) in Namyang, South Korea. Both static floor with fixed wheels and moving ground with rotating wheels were modeled in both domains to evaluate ground simulation effects. To capture the open jet test section interference effects on the vehicle model, the virtual test section was scaled up by 225% and 400% of the original CAD (Computer-Aided Design) model. The static pressure distribution for each scale was studied in the streamwise direction. Changes of the vehicle drag coefficient caused by different pressure gradients were indicated and explained. In addition, a modified rear spoiler was designed during the study and its performance is assessed against the baseline vehicle model using wind tunnels with MVP and no-MVP. As a matter of fact, the discrepancy observed between the CFD and HAWT wind tunnel predicted performance gain is the primary reason of undertaking the studies presented in this paper.

2. Wind Tunnels and Boundary Conditions Two types of computational domains were modeled in the study, with both static and moving floor cases in each model. Firstly, the Hyundai Aero-acoustic Wind Tunnel (HAWT) at Namyang Technical Center was simulated by accurately reproducing its three-quarter semi-open jet test section, using information available in the literature. The purpose of reproducing HAWT is to validate the CFD methodology with the static floor test data from that tunnel. The blockage effect of the open jet test section was evaluated by scaling the wind tunnel model in width and height to 1.5 and 2.0 times its original dimensions, but keeping the same length, which resulted in 225% and 400% of the actual HAWT test section in volume, respectively. Secondly, for the road-testing simulations, a nearly interference-free domain (blockage ratio of 0.04%) was built to eliminate the blockage effect, where the CFD predictions are more comparable to the real vehicle performance. As mentioned Vehicles 2020, 2 321 Vehicles 2020, 2, FOR PEER REVIEW 4 spoilerbefore, geometries two spoiler were geometries tested in were all four tested domains, in all four and domains, the results and will the be results discussed will be in discussed the following in the sections.following sections.

2.1.2.1. Hyundai Hyundai Aero-Acoustic Aero-Acoustic Wind Wind Tunnel Tunnel (HAWT) (HAWT) Model Model TheThe Hyundai Hyundai Aero-acoustic Aero-acoustic Wind Wind Tunnel, HAWT forfor short,short,is is a a full-scale, full-scale, closed closed circuit, circuit, open open jet jetworking-section working-section wind wind tunnel tunnel designed designed for carryingfor carry outingaerodynamic, out aerodynamic, aero-acoustic, aero-acoustic, and engine and engine cooling coolingrelated related tests [42 tests]. HAWT [42]. HAWT has a test has sectiona test section 25.5 m 25.5 long, m 17.7long, m 17.7 wide, m wide, 9.65 m 9.65 high, m high, and a and nozzle a nozzle outlet 2 outletmeasuring measuring 28 m 28; this m2; corresponds this corresponds to a wind to a wind tunnel tunnel solid blockagesolid blockage ratio of ratio 1.25% of 1.25% for the for tested the tested vehicle vehiclemodel, model, 2014 Hyundai 2014 Hyundai Veloster. Veloster. Interested Interested readers are readers referred are to referred Kim et al.to [Kim42] for et moreal. [42] details for more of the detailsHAWT of design the HAWT methodologies, design methodologies, layout, instrumentations, layout, instrumentations, flow controls, flow and visuals,controls, which and visuals, includes whichphotographs includes of photographs the critical sectionsof the critical and top sections and side and viewstop and of side the testviews section. of the Atest CAD section. model A CAD of the modelHAWT of tunnel the HAWT was created tunnel using was thecreated information using the available information in [42], available and is shown in [42], in Figure and is1. shown This CAD in Figuremodel 1. includes This CAD a simplified model includes flow inlet, a simplified an outlet di flowffuser, inlet, and an a moving-belt outlet diffuser, system and under a moving-belt the vehicle. systemThe nozzle under contraction the vehicle. for The the nozzle inlet was contraction not included for the in inlet the model,was not because included it hasin the little model, impact because on the itflow has little in the impact test section on the [15 flow,43]. in the test section [15,43].

FigureFigure 1. 1. ComputationalComputational fluid ﬂuid dynamics dynamics (CFD) (CFD) representa representationtion of of the the Hyundai Hyundai Aero-Acoustic Aero-Acoustic Wind Wind TunnelTunnel (HAWT) (HAWT) test test section, section, drawn drawn using using the the geometry geometry details details given given in in Kim Kim et et al. al. [42]. [42].

InIn order order to to investigate investigate the the effects effects of of the the test test section section volume volume on on the the prediction prediction of of aerodynamic aerodynamic characteristicscharacteristics of of the the test test vehicle, vehicle, the the computat computationalional test test section section was was in inflatedflated in in horizontal horizontal (𝑌 (Y) )and and verticalvertical (𝑍 (Z) )directions directions to to reach reach expanded expanded test test section section vo volumes,lumes, which which are are 225% 225% and and 400% 400% of of the the actual actual testtest section section volume. volume. Figure Figure 2 repr2 representsesents top top and and side side views views of the of thescaled-up scaled-up test testsections. sections. These These two inflationstwo inflations of the of test the section test section in vertical in vertical and and spanwise spanwise directions directions resulted resulted in inthe the wind wind tunnel tunnel solid solid blockageblockage ratios ratios of of 0.56% 0.56% and and 0.30%, 0.30%, respectively, respectively, for for the the tested tested vehicle. vehicle.

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Figure 2. Top and side views of the computational domain used for various blockage ratios, based on FigureFigure 2. 2. TopTop and and side side views views of of the the computational computational domain domain used used for for various various blockage blockage ratios, ratios, based based on on the HAWT baseline tunnel, as used by Kim et al. [42]. thethe HAWT HAWT baseline baseline tunnel, tunnel, as as used used by by Kim Kim et et al. al. [42]. [42]. 2.2.2.2. Blockage-Free Blockage-Free Domain Domain 2.2. Blockage-Free Domain TheThe blockage blockage free free domain, domain, which which is is a a rectangular rectangular box, box, has has a a negligible negligible blockage blockage ratio ratio of of 0.04%; 0.04%; The blockage free domain, which is a rectangular box, has a negligible blockage ratio of 0.04%; therefore,therefore, the the model model can can be be considered considered as as placed placed in in fr freeee air air simulating simulating road road testing testing conditions conditions for for the the therefore, the model can be considered as placed in free air simulating road testing conditions for the vehicle.vehicle. The The domain domain extends extends 12-car 12-car length length (50 (50 m) m) in in 𝑋X,, 21-car 21-car lengths lengths (90 (90 m) m) in in 𝑌Y,, and and 10.5-car 10.5-car lengths lengths vehicle. The domain extends 12-car length (50 m) in 𝑋, 21-car lengths (90 m) in 𝑌, and 10.5-car lengths (45(45 m) m) in in 𝑍Z,, as as shown shown in in Figure Figure 33.. The boundary conditions onon the ground planeplane of this free air tunnel (45 m) in 𝑍, as shown in Figure 3. The boundary conditions on the ground plane of this free air tunnel isis the the same same as as the the MVP version ofof thethe HAWTHAWT tunnel.tunnel. ThatThat isis an an area area on on the the floor floor around around the the vehicle vehicle is is the same as the MVP version of the HAWT tunnel. That is an area on the floor around the vehicle isdesignated designated as as a a moving moving ground, ground, which which has hasa a no-slipno-slip boundaryboundary condition with a tangential tangential velocity velocity is designated as a moving ground, which has a no-slip boundary condition with a tangential velocity equalequal to to the free-stream velocityvelocity assignedassigned to to it, it, while while the the rest rest of of the the ground ground plane plane is treated is treated as slip-wall as slip- equal to the free-stream velocity assigned to it, while the rest of the ground plane is treated as slip- wallfor computational for computational efficiency. efficiency. The The two two side side walls walls and and the roofthe roof of the of domainthe domain were were also also set toset as to slip as wall for computational efficiency. The two side walls and the roof of the domain were also set to as slipwalls, walls, to avoid to avoid any any possible possible effects effects from from the growththe growth of boundary of boundary layers layers over over these these boundaries. boundaries. slip walls, to avoid any possible effects from the growth of boundary layers over these boundaries.

Figure 3. Schematics of the blockage-free tunnel. FigureFigure 3.3. SchematicsSchematics ofof thethe blockage-free blockage-free tunnel. tunnel. 2.3. Spoiler Geometries 2.3. Spoiler Geometries One purpose of this study is to verify the performance increase of the redesigned spoiler in One purpose of this study is to verify the performance increase of the redesigned spoiler in terms termsOne of dragpurpose reduction, of this study compared is to verify to the the original perfor spoiler.mance increase In order of to the reduce redesigned the vehicle spoiler drag in andterms lift, of drag reduction, compared to the original spoiler. In order to reduce the vehicle drag and lift, the ofthe drag redesigned reduction, spoiler compared was extended to the original by 29 spoiler. mm in chordIn order length to reduce (Figure the4), vehicle and featured drag and a smoother lift, the redesigned spoiler was extended by 29 mm in chord length (Figure 4), and featured a smoother redesignedtrailing edge. spoiler The newwas spoilerextended also by features 29 mm a in smoother chord length transition (Figure from 4), the and roof, featured to positively a smoother impact trailing edge. The new spoiler also features a smoother transition from the roof, to positively impact trailingthe ﬂow edge. resistance The new at that spoiler point, also as features shown ina smoother Figure5. transition from the roof, to positively impact the flow resistance atat thatthat point,point, asas shownshown inin FigureFigure 5.5.

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Both spoilers were tested in the HAWT test section and free air environment, with both static and moving floor boundary conditions. The comparisons showed a consistent improvement for the drag with theVehicles redesigned 2020, 2, FOR PEER spoiler. REVIEW 6

Both spoilers were tested in the HAWT test section and free air environment, with both static and moving floor boundary conditions. The comparisons showed a consistent improvement for the Vehicles 2020, 2 323 drag with the redesigned spoiler.

Figure 4. Top views of the original (a) and improved (b) spoilers. Figure 4. Top views of the original (a) and improved (b) spoilers. Figure 4. Top views of the original (a) and improved (b) spoilers.

Figure 5. Side views of the original (a) and improved (b) spoiler.

3. Numerical Setup Figure 5.Figure Side 5. viewsSide views of the of the original original (a()a and) and improved improved (b) spoiler. (b) spoiler. 3.1. HAWT Model Both spoilers were tested in the HAWT test section and free air environment, with both static and 3. Numericalmoving SetupThe floor CFD boundary model was conditions. meshed using The comparisons the trimmer showedmesh; prism a consistent layer mesh improvement was activated for the to better drag withcapture the redesignedthe boundary spoiler. layers along the no-slip boundaries. As seen in Figure 6, higher mesh density was applied to the regions surrounding the vehicle and its wake, to capture the areas of high flow 3.1. HAWT Model3.gradients. Numerical The Setup underbody mesh was sufficiently refined to capture the ground effect. The shear layer The CFDdevelopment model was and meshed the possible using flow therecirculation trimmer at themesh nozzle; prism exit were layer also mesh accounted was for activated with a to better 3.1.relatively HAWT dense Model mesh. The total cell number for the HAWT model configuration with the full-scale capture the boundaryVelosterThe CFDwas layersabout model 70 was along million. meshed the using no-slip the trimmer bounda mesh;ries. prism As layerseen mesh in Figure was activated 6, higher to better mesh density was appliedcapture to the the regions boundary surroun layers alongding the the no-slip vehicle boundaries. and Asits seenwake, in Figure to capture6, higher meshthe areas density of high flow gradients. Thewas underbody applied to the regionsmesh was surrounding sufficiently the vehicle refined and its to wake, capture to capture the ground the areas effect. of high The flow shear layer gradients. The underbody mesh was sufficiently refined to capture the ground effect. The shear layer development and the possible flow recirculation at the nozzle exit were also accounted for with a development and the possible flow recirculation at the nozzle exit were also accounted for with a relatively denserelatively mesh. dense The mesh. total The cell total cellnumber number for for the HAWTHAWT model model configuration configuration with the full-scalewith the full-scale Veloster wasVeloster about was 70 about million. 70 million.

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Figure 6. Mesh on the center plane of the model. Figure 6. Mesh on the center plane of the model. 3.2. Blockage-Free Model 3.2. Blockage-Free Model The mesh around the vehicle for the free air condition was identical to the mesh used in the

HAWTThe model mesh case,around including the vehicle the wakefor the and free underbody air condition treatments. was identical To control to the themesh cell used number in the in thisHAWT big domainmodel case, within including the boundsFigure the wake 6. of Mesh existing and on underbody the computational center plane treatments. of power, the model. To ﬁve control levels the of cell volumetric number controlin this sourcesbig domain were within used to the restrain bounds the ﬁneof existing regions. computational The cell size decreases power, five from levels 384 mm of volumetric to 24 mm (from control the region3.2.sources Blockage-Free marked were used as Model “5” to torestrain the region the fine marked regions. as “1”), The ascell listed size indecreases Table1 and from shown 384 mm in Figureto 24 mm7. (from the regionThe mesh marked around as “5” the to vehicle the region for markedthe free asair “1”), condition as listed was in Tableidentical 1 and to shownthe mesh in Figureused in 7. the HAWT model case, includingTable the 1.wakeComputational and underbody grid levels treatments. vs. grid sizes.To control the cell number in this big domain within the Gridbounds Level of existing 1 computational 2 3power, five 4 levels 5 of volumetric control sources were used to restrain the fine regions. The cell size decreases from 384 mm to 24 mm (from Grid Size (mm) 24 48 96 192 384 the region marked as “5” to the region marked as “1”), as listed in Table 1 and shown in Figure 7.

Figure 7. Blockage-free domain mesh.

Table 1. Computational grid levels vs. grid sizes.

Grid Level 1 2 3 4 5 GridFigureFigure Size 7.(mm)7. Blockage-free 24 48 domain96 192 mesh. 384

3.3. Spoiler Mesh Table 1. Computational grid levels vs. grid sizes. As a more accurate predictionGrid ofLevel the spoiler 1 pe 2rformance 3 4 was 5critical to this study, the flow surrounding the spoiler neededGrid to beSize predicted (mm) with24 48a sufficient 96 192 level 384 of accuracy. As such, the region

3.3. Spoiler Mesh As a more accurate prediction of the spoiler performance was critical to this study, the flow surrounding the spoiler needed to be predicted with a sufficient level of accuracy. As such, the region

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3.3. Spoiler Mesh Vehicles 2020As, 2, a FOR more PEER accurate REVIEW prediction of the spoiler performance was critical to this study, the flow 8 surrounding the spoiler needed to be predicted with a sufficient level of accuracy. As such, the region surroundingsurrounding the thespoiler spoiler was was resolved, resolved, with with a a very very finefine meshmesh of of 6 6 mm mm to to minimize minimize the the numerical numerical error error accumulation;accumulation; our our preliminary preliminary studies studies showed showed that that going belowbelow this this mesh mesh size size does does not not affect affect the the resultsresults at all. at all.This This mesh mesh scheme scheme for for the the spoiler spoiler is is shown inin Figure Figure8. 8.

FigureFigure 8. Top 8. Top (top (top figure)ﬁgure) and and side side (bottom (bottom ﬁgure)figure) views views of of the the spoiler spoiler mesh. mesh.

3.4. Initial and Boundary Conditions 3.4. Initial and Boundary Conditions A velocity inlet boundary condition was applied to the domain upstream with a constant uniformedA velocity incoming inlet boundary flow of 31.293 condition m/s or 70 was mph applied and a turbulence to the intensitydomain ofupstream 0.5% to match with the a windconstant uniformedtunnel testincoming conditions. flow A of pressure 31.293 outlet m/s boundaryor 70 mph condition and a turbulence is used at the intensity domain outlet.of 0.5% The to moving match the windground tunnel e fftestect wasconditions. achieved A by pressure applying outlet a tangential boundary velocity, condition the same is as used the airat the speed, domain to the no-slipoutlet. The movingfloor ground patch under effect the was vehicle. achieved The tire by rotation applying in this a tangential case was simulated velocity, by the a constant same tireas the rotation air speed, rate. to the no-slip floor patch under the vehicle. The tire rotation in this case was simulated by a constant tire rotation3.5. Turbulence rate. Model The simulations were carried out using a commercial CFD code, STAR-CCM+ by Siemens, using 3.5. Turbulencea steady-state Model Reynolds-Averaged Navier-Stokes (RANS) solver. The two turbulence models tested during the study which include the Realizable k ε (RKE) [44] and Shear Stress Transport k ω − − (SST)The simulations [45] models. were The k carriedε turbulence out using model a commercial solves for the CFD turbulent code, STAR-CCM+ kinetic energy by (k) Siemens, and kinetic using a steady-state Reynolds-Averaged− Navier-Stokes (RANS) solver. The two turbulence models tested during the study which include the Realizable 𝑘−𝜀 (RKE) [44] and Shear Stress Transport 𝑘−𝜔 (SST) [45] models. The 𝑘−𝜀 turbulence model solves for the turbulent kinetic energy (𝑘) and kinetic energy dissipation rate (𝜀). The RKE model is an improvement over the standard 𝑘−𝜀 model, as it features a different formulation for the turbulence viscosity, and a new transport equation for the dissipation rate. Instead of a constant 𝐶, the RKE model uses a variable 𝐶 to make the model to be more compatible. The existing literature suggests that the SST model predicts both free-stream flows and boundary layers all the way down to the viscous sub-layer with good accuracy; however, it usually predicts larger turbulence levels when compared to the the 𝑘−𝜀 model.

Vehicles 2020, 2 326 energy dissipation rate (ε). The RKE model is an improvement over the standard k ε model, as it − features a different formulation for the turbulence viscosity, and a new transport equation for the dissipation rate. Instead of a constant Cµ, the RKE model uses a variable Cµ to make the model to be more compatible. The existing literature suggests that the SST model predicts both free-stream flows and boundary layers all the way down to the viscous sub-layer with good accuracy; however, it usually predicts larger turbulence levels when compared to the the k ε model. − Preliminary investigations with the models show that he RKE and SST models over-predicted the vehicle drag coefficient by 10 and 22 counts, respectively, compared to the test data from HAWT. This small three to five percent over-prediction of drag may come from two reasons. Firstly, our CFD replication of the HAWT may not be exact, and we might have missed some flow control of the HAWT. Secondly, the RANS simulations of the unsteady flow around a vehicle are far from perfect. In consideration of these two facts, the CFD results seems to be well within the acceptable level of accuracy, which adds a reasonable confidence in the of the CFD results presented in this paper. Since the RKE results better correlate with the wind tunnel data, with less than 3% of difference in drag prediction, it was chosen as the turbulence model for further investigations. The transport equations for turbulent kinetic energy k, and the dissipation rate ε, for the RKE model are given by " ! # ∂k ∂k ∂Ui ∂ νt ∂k + Uj = τij ε + ν + , (1) ∂t ∂xj ∂xj − ∂xj σk ∂xj 2 " # ∂k ∂k ε ∂Ui ε ∂ νt ∂ε + Uj = C1 τij C2 + ν + , (2) ∂t ∂xj k ∂xj − k +√νε ∂xj σε ∂xj k2 νt = C (3) µ ε 1 Cµ = (4) κU∗ A0 + As ε √ 1 1 √ ˜ ˜ p where A0 = 4.04, As = 6 cos φ, φ = 3 cos− 6W, W = SijSjiSki /S and S = SijSji, where Sij is the mean rate of train tensor.

3.6. Solver and Convergence The simulations were carried out with a segregated solver. A 2nd order discretization was used for the source and diffusion terms, and a 2nd order upwind scheme for the convection terms of the momentum equations. Each simulation took 4000 iterations to converge. The oscillations of drag and lift coefficient values of the vehicle were within 0.5% during the last 1000 iterations, and the reported mean drag coefficient CD and lift coefficient CL were calculated by averaging the values over last 500 iterations.

4. Results and Discussion Several cross-sections were created around the vehicle to analyze the ﬂow-ﬁled around it which are shown in Figure9. To facilitate the subsequent discussion these cross sections are labels with numbers 1–7, the list below shows the location and plane-type of each of these sections.

1. XZ-plane along the vehicle center line at Y = 0; 2. XZ-plane through the right-tire center at Y = 0.78 m; 3. XY-plane thru the spoiler at Z = 1.17 m; 4. XY-plane near the ground at Z = 0.05 m; 5. YZ-plane at the rear fascia at X = 3.4 m, wake cross-Section1; 6. YZ-plane at X = 3.6 m, wake cross-Section2; 7. XY-plane observing jet expansion at Z = 0.47 m. Vehicles 2020,, 22, FOR PEER REVIEW 10 327

Figure 9. Cross-sections layout. Figure 9. Cross-sections layout. 4.1. Wind Tunnel Boundary Interference Effects 4.1. Wind Tunnel Boundary Interference Effects The simulation results of the detailed full-scale model of Veloster was validated in the virtual HAWTThe test simulation section with results the of test the data detailed from full-scale HAWT. Table model2 shows of Veloster the CFD was and validated wind tunnel in the correlationvirtual results.HAWT test HAWT section data with are proprietarythe test data and from confidential. HAWT. Table As such,2 shows all resultsthe CFD presented and wind in thistunnel paper werecorrelation normalized results. byHAWT an arbitrary data are proprietary frontal area, and to co matchnfidential. the drag As such, coeffi allcients results seen presented in open in domainthis publishedpaper were resources. normalized by an arbitrary frontal area, to match the drag coefficients seen in open domain published resources.

Table 2. Tabulated values of drag coeﬃcient CD. Table 2. Tabulated values of drag coefficient 𝐶. CD Original Spoiler Improved Spoiler ∆CD 𝑪𝑫 Original Spoiler Improved Spoiler ∆𝑪𝑫 Wind-tunnelWind-tunnel 0.328 0.328 0.326 0.326 0.002 0.002 CFD 0.338 0.336 0.002 CFD 0.338 0.336 0.002

The discrepancies discrepancies between between the the CFD CFD prediction prediction and and wind wind tunnel tunnel result result are areconsistent consistent for both for both spoilers. If Ifthe the reported reported drag drag coefficient coefficient value value from from wind windtunnel tunnel is the measured is the measured data without data withoutany anyinterference interference correction, correction, the numerical the numerical model model over-predicted over-predicted the vehicle the vehicle drag dragby 3% by (10 3% counts); (10 counts); however, because of of the the lack lack of of details details about about the the HAWT HAWT test test proced procedure,ure, the theauthors authors cannot cannot guarantee guarantee if the reported data data had had been been corrected. corrected. Despite Despite that, that, an anattempt attempt has has been been made made to correct to correct the theCFD CFD predicted drag drag value value thus thus obtained obtained using using a simplifi a simplifieded wind wind tunnel tunnel interference interference correction correction procedure. procedure. An interestedinterested readerreader is is referred referred to to a recenta recent publication publication by Cooperby Cooper et al. et [ 46al.], [46], for details for details of why of boundary why correctionsboundary corrections are a standard are a practicestandard for practice the open-jet for the open-jet wind tunnel wind testing tunnel oftesting automobiles. of automobiles. The current current literature literature is is rich rich with with various various meth methodsods proposed proposed for forwind wind tunnel tunnel interference interference effect eff ect corrections. Some of these procedures are rather simple, and some are very sophisticated and require corrections. Some of these procedures are rather simple, and some are very sophisticated and require multi-point measurements. This paper attempts to apply wind tunnel interference corrections to the multi-point measurements. This paper attempts to apply wind tunnel interference corrections to the uncorrected CFD data, using one of these simple methods, as outlined below. uncorrected CFD data, using one of these simple methods, as outlined below. The correction methodology used in this paper is a twostep process. This process starts with first The correction methodology used in this paper is a twostep process. This process starts with applying the solid blockage correction to the uncorrected 𝐶 measurements to account for wind first applying the solid blockage correction to the uncorrected C measurements to account for wind tunnel test section solid blockage restriction. The next step is to applyD the buoyancy corrections to tunnelthis first test = order section corrected solid blockagedrag to account restriction. for the The higher-order next step wall is to interference apply the buoyancy induced variation corrections of to thispressure first =gradientorder corrected along the dragvehicle to accountcenterline. for the higher-order wall interference induced variation of pressurePer Katz gradient [2], one along of the the simplest vehicle centerline.solid blockage corrections can be formulated as: Per Katz [2], one of the simplest solid blockage corrections can be formulated as: 1 𝐶 =𝐶 , 11 𝐴 (5) C = C 1+ ∙ , (5) dB du 4 𝐶2 1 + 1 A 4 · C where 𝐶 is the uncorrected drag coefficient, 𝐶 is the corrected 𝐶 after blockage correction, 𝐴 whereis the modelCdu is thefront uncorrected area, and 𝐶 drag is the coe cross-sectionalfficient, CdB is thearea corrected of the openCD jetafter test blockage section. correction,The correctionA is the modelmethod front proposed area, andby MerckerC is the cross-sectionaland his co-workers area of[11,12] the open was jetthen test applied section. to Thethe correctionsolid-blockage method proposedcorrected drag by Mercker coefficient and𝐶 his. co-workersThis procedure, [11,12 as] wasoutlined then below, applied corrects to the solid-blockagemeasurement errors corrected drag coefficient CdB. This procedure, as outlined below, corrects measurement errors induced due to Vehicles 2020, 2 328 the wake distortion caused by the pressure gradient over the vehicle wake [12]. The corrected drag coefficient (Cdc), after considering of the wake distortion and horizontal buoyancy corrections, can be written as: h i CdB ∆CpD C = − ± (6) dc n

∆CpD = ∆CpW + ∆CDHB (7)

∆CpW = CPwc C (8) − Pmb where n = qc/qu is the dynamic pressure correction factor, ∆CDHB is the horizontal buoyancy correction term, qc denotes the corrected dynamic pressure of the wind tunnel stream, and qu is the measured dynamic pressure, CPmb and CPwc are the pressure coeﬃcients in the empty tunnel at the base of the vehicle model and at the location of wake closure, respectively. ! . dCp ∆C = G V S (9) DHB m f dx

dCp where Vm is the model volume and dx denotes the static pressure gradient. The Glauert factor [47] G is defined as: t G = 1 + 0.4 (10) L q 2S f where L is the model length, and the model thickness t is given by t = 2 π , where S f is model frontal area. An interested reader is referred to Mercker et al. [12] for further details. When the corrections detailed above are applied to the CFD calculations, the corrected CFD predicted Cd of the original spoiler case becomes 0.329, which shows only 0.5% discrepancy when compared to the wind tunnel data. Without further verification from the experiment, it is hard to claim that the CFD predicted the vehicle drag accurately; however, the fact that the CFD result is close to the corrected wind tunnel test data gives some confidence in the numerical settings for the CFD model.

4.2. Static and Moving Ground Effects The static and moving ground planes in the wind tunnel were simulated in CFD using two different boundary conditions. For fixed ground and fixed wheels (FG&FW) simulations, the physics conditions for tires, wheels, and ground were set to be static walls. Denser prism layers were applied to the frictional ground to capture the boundary layer development, similar to [26]. For the moving ground and rotating wheels (MVG&RW) simulations, two common methods of wheel rotation are (1) moving reference frame (MRF) and (2) local rotation rate. To avoid numerical artifacts arising from the geometry complexity near the wheels, a local rotation rate boundary condition was applied to all rotating boundaries, including the tires, wheel frames, wheel hubs, and brake rotors. Since the four wheels of the Hyundai Veloster model have the same radius, they were all set to a rotation rate of 100.1 rad/s. A moving-ground boundary condition was also applied to the no-slip floor at the same speed of the incoming flow (70 mph or 31.393 m/s). A comparison between the drag coefficients obtained from the static and moving floor CFD simulations, with the vehicle model placed inside HAWT, can be seen in Table3. For both the original and improved spoiler cases, the delta CD between the static floor and moving ground simulations is 5 counts. In other words, the moving ground boundary condition simply translated the Veloster performance line vertically down by 5 counts. This trend agrees with the work done by Waschle [48] on the influence of rotating wheels on the vehicle’s aerodynamics characteristics obtained using a detailed Mercedes E class model, where a reduction of 12 counts of drag was reported when changing from static to moving ground. Waschle [48] stated that the change in drag due to the rotating wheels and moving ground is mainly caused by the change of underbody flow and rear tire wakes. Vehicles 2020, 2 329 Vehicles 2020, 2, FOR PEER REVIEW 12

from static to moving ground. Waschle [48] stated that the change in drag due to the rotating wheels Table 3. Drag coeﬃcient values obtained from the static and moving ground plane CFD simulations and moving ground is mainly caused by the change of underbody flow and rear tire wakes. with the vehicle model placed inside the HAWT. Table 3. Drag coefficient values obtained from the static and moving ground plane CFD simulations HAWT (CD) Original Spoiler Improved Spoiler with the vehicle model placed inside the HAWT. Static Floor 0.338 0.336 MovingHAWT Floor (𝑪𝑫) Original 0.333 Spoiler Improved Spoiler 0.331 ∆StaticCD Floor 0.3380.005 0.336 0.005 Moving Floor 0.333 0.331

∆𝐶 0.005 0.005 Figure 10 compares the streamwise mean velocity on a Z-plane at Z = 0.05 (plane 4) obtained from the staticFigure floor 10 compares and moving the streamwise ground CFD mean simulations velocity on witha Z-plane the vehicleat Z = 0.05 model (plane placed 4) obtained inside the HAWT.from The the velocity static floor contours and moving show ground a marked CFD di simufferencelations between with the the vehicle cases model in the placed wake regioninside the behind the tires.HAWT. For The the velocity static floor contours case, show both a front marked and differen rear tiresce between generated the cases much in strongerthe wake region wakes behind compared to thethe moving tires. For ground the static case, floor especially case, both for front the rearand rear tires. tires This generated is in line much with stronger the findings wakes of compared Waschle [48], and isto the the main moving reason ground why case, the especially static floor for CFD the rear simulations tires. This showis in line a higher with the drag findings for the of sameWaschle vehicle [48], and is the main reason why the static floor CFD simulations show a higher drag for the same configuration. The change in the front tire wakes can be identified more clearly on the side view vehicle configuration. The change in the front tire wakes can be identified more clearly on the side analysis, as shown in Figure 11. It is noticeable that the wake has a shorter streamwise extent for the view analysis, as shown in Figure 11. It is noticeable that the wake has a shorter streamwise extent movingfor groundthe moving case. ground case.

Figure 10. Streamwise mean velocity on a Z-plane at Z = 0.05 (plane 4) for static floor vs. moving Figure 10. Streamwise mean velocity on a Z-plane at Z = 0.05 (plane 4) for static floor vs. moving ground with vehicle placed inside HAWT. ground with vehicle placed inside HAWT. A similar phenomenon was observed too in the CFD simulations with the vehicle model placed in the blockage free domain, labelled as the free air environment, as presented in Figures 12 and 13. The rotating tires and moving ground significantly reduced the wake behind the tires (Figure 12), which indicates higher velocity flow under the vehicle. From the side view of the velocity magnitude scalar image in Figure 13, the increase of velocity is obvious with the expansion of high velocity contour area. The change of the velocity profile under the car also leads to a lift change, the MVG&RW system in the simulations caused a decrease in vehicle lift for both domains. Table4 presents the drag and lift coefficient delta between the static and moving ground boundary condition cases; the data presented correspond to the vehicle model with the original spoiler mounted. Although no reported lift Vehicles 2020, 2 330 coefficient values were available from the wind-tunnel test to validate the lift prediction, the reduction in lift for the free air case is close to what Waschle [48] has reported in his work. Vehicles 2020, 2, FOR PEER REVIEW 13

VehiclesFigure 2020Figure 11., 2, Velocity FOR11. Velocity PEER magnitude REVIEW magnitude for for the the static static ﬂoorfloor and moving moving belt belt cases cases with with the thevehicle vehicle in HAWT in HAWT 14 model;model; the Y-plane the Y-plane is taken is taken through through the the right-tire right-tire center center at Y == 0.780.78 (plane (plane 2). 2).

A similar phenomenon was observed too in the CFD simulations with the vehicle model placed in the blockage free domain, labelled as the free air environment, as presented in Figures 12 and 13. The rotating tires and moving ground significantly reduced the wake behind the tires (Figure 12), which indicates higher velocity flow under the vehicle. From the side view of the velocity magnitude scalar image in Figure 13, the increase of velocity is obvious with the expansion of high velocity contour area. The change of the velocity profile under the car also leads to a lift change, the MVG&RW system in the simulations caused a decrease in vehicle lift for both domains. Table 4 presents the drag and lift coefficient delta between the static and moving ground boundary condition cases; the data presented correspond to the vehicle model with the original spoiler mounted. Although no reported lift coefficient values were available from the wind-tunnel test to validate the lift prediction, the reduction in lift for the free air case is close to what Waschle [48] has reported in his work.

FigureFigure 12. 12.Streamwise Streamwise mean mean velocity velocity onon aa Z-planeZ-plane atat ZZ == 0.05 (plane 4) 4) for for static static floor ﬂoor vs. vs. moving moving groundground with with vehicle vehicle placed placed in in blockage blockage free free environment. environment.

Figure 13. Velocity magnitude for the static floor and moving belt cases with the vehicle in free air environment; the Y-plane is taken through the right-tire center at Y = 0.78 (plane 2).

Vehicles 2020, 2, FOR PEER REVIEW 14

4 Figure 12. Streamwise mean velocity on a Z-plane at Z = 0.05 (plane 4) for static floor vs. moving Vehicles 2020, 2 331 ground with vehicle placed in blockage free environment.

FigureFigure 13. Velocity 13. Velocity magnitude magnitude for for the the static static floor floor and movingmoving belt belt cases cases with with the vehiclethe vehicle in free in air free air environment;environment; the Y-plane the Y-plane is taken is taken through through the the right-tire center center at Yat= Y0.78 = 0.78 (plane (plane 2). 2). Table 4. Influence of moving ground and rotating wheels on drag and lift coefficients.

HAWT CD CL Static Floor 0.338 0.157 Moving Floor 0.333 0.105 Delta (∆) 0.005 0.052

Free Air CD CL Static Floor 0.326 0.140 Moving Floor 0.320 0.098 Delta (∆) 0.006 0.042

4.3. Blockage Effects of the Semi Open Jet Test Section The frontal area of the virtual HAWT test section was expended by 225% and 400%, which resulted in reduced blockage ratios of 0.56% and 0.30%, respectively. The simulations were carried out with the moving ground and rotating wheel boundary conditions, and the results are presented in Table5. All three scales ran without the vehicle model in the tunnel first, to obtain the pressure gradient in the empty plenum chamber. The mean static pressure coefficients along the center line of nozzle outlet are shown in Figure 14. It is noticeable, in the zoomed-in figure, that the original HAWT test section has a longitudinal negative pressure gradient in the empty model before it reaches the collector, resulting in an over-predicted drag value, since the negative pressure gradient causes the air flow to accelerate when passing over the test vehicle model. By keeping the nozzle and collector at the same location, Vehicles 2020, 2, FOR PEER REVIEW 15

Table 4. Influence of moving ground and rotating wheels on drag and lift coefficients.

HAWT 𝑪𝑫 𝑪𝑳 Static Floor 0.338 0.157 Moving Floor 0.333 0.105 Delta (∆) 0.005 0.052

Free Air 𝑪𝑫 𝑪𝑳 Static Floor 0.326 0.140 Moving Floor 0.320 0.098 Delta (∆) 0.006 0.042

4.3. Blockage Effects of the Semi Open Jet Test Section The frontal area of the virtual HAWT test section was expended by 225% and 400%, which resulted in reduced blockage ratios of 0.56% and 0.30%, respectively. The simulations were carried out with the moving ground and rotating wheel boundary conditions, and the results are presented Vehicles 2020, 2 332 in Table 5. All three scales ran without the vehicle model in the tunnel first, to obtain the pressure gradient in the empty plenum chamber. The mean static pressure coefficients along the center line of nozzlebut inﬂating outlet are the shown open jetin Figure test section 14. It is in noticeable, horizontal in (Y) the and zoomed-in vertical (Z)figure, directions, that the theoriginal magnitude HAWT of testthat section negative has pressurea longitudinal gradient negative is reduced, pressure becoming gradient nearly in the zero empty when model the test before section it reaches was scaled the collector,up to 400% resulting of its originalin an over-predicted size. The pressure drag value, gradient since change the negative caused apressure drag reduction gradient for causes the larger the airtest flow sections. to accelerate when passing over the test vehicle model. By keeping the nozzle and collector at the same location, but inflating the open jet test section in horizontal (Y) and vertical (Z) directions, the magnitude of that negativeTable pressure 5. Eﬀect ofgradient blockage is ratio reduced, on drag becoming prediction. nearly zero when the test section was Testscaled Section up to Blockage 400% Ratioof its original 1.25% size. (HAWT) The pressure 0.56% gradient 0.30% change 0.04%caused a drag reduction for the larger test sections. CD 0.333 0.328 0.325 0.320

Figure 14. Empty test section static pressure coefficients in the longitudinal direction along the Figure 14. Empty test section static pressure coefficients in the longitudinal direction along the nozzle nozzle centerline. centerline. After putting the vehicle model into all three scales of the open jet test sections, (shown in Table 5. Effect of blockage ratio on drag prediction. Figure 15), the mean static pressure distributions on the center plane at different Z positions were measured. FigureTest Section15 shows Blockage the five Z-locationsRatio 1.25% of pressure(HAWT) probes. 0.56% At all0.30% these locations,0.04% the original

HAWT test section has the𝐶 lowest static pressure, while0.333 the pressure 0.328 in the0.325 plenum 0.320 chamber increases with the increase of the test section volume. At Z = 3.5 m and above, the favorable pressure gradient in theAfter 100% putting test section the vehicle becomes model obvious into all comparedthree scales to of other the open scales. jet Attest Z sections,= 6.5 m, (shown since it in is Figure close to 15),the the roof mean of the static 100% pressure domain, distributions the favorable on the pressure center gradientplane at different becomes Z stronger, positions while were themeasured. pressure distributions in 225% and 400% scales also start turning favorable, as they are approaching to the top boundary. Vehicles 2020, 2, FOR PEER REVIEW 16

Vehicles 2020, 2, FOR PEER REVIEW 16 Figure 15 shows the five Z-locations of pressure probes. At all these locations, the original HAWT Figuretest section 15 shows has the the lowest five Z-locations static pressure, of pressure while the probes. pressure At all in thesethe plenum locations, chamber the original increases HAWT with testthe increasesection has of the the test lowest section static vo lume.pressure, At Zwhile = 3.5 them and pressure above, in the the favorable plenum pressurechamber gradientincreases in with the the100% increase test section of the becomes test section obvious volume. compared At Z = 3.5to otherm and scales. above, At the Z =favorable 6.5 m, since pressure it is close gradient to the in roof the 100%of the test 100% section domain, becomes the obvious favorable compared pressure to othergradient scales. becomes At Z = 6.5 stronger, m, since whileit is close the to pressure the roof ofdistributions the 100% indomain, 225% and the 400% favorable scales also pressure start turning gradient favorable, becomes as theystronger, are approaching while the topressure the top distributionsboundary. in 225% and 400% scales also start turning favorable, as they are approaching to the top Vehicles 2020, 2 333 boundary.

2.88 m 9.13 m

2.88 m 9.13 m Figure 15. Static pressure probes layout. Figure 15. Static pressure probes layout. The phenomenon of smaller test sections having lower static pressure in the plenum chamber of open jet wind tunnel, as Figure 16 indicates, can also be observed in Figures 17 and 18, when the The phenomenon of smaller test sections having lower static pressure in the plenum chamber of vehicle model is placed inside the tunnel, through the jet expansion change corresponding to different open jetjet windwind tunnel, tunnel, as as Figure Figure 16 16indicates, indicates, can can also also be observed be observed in Figures in Figures 17 and 17 18 and, when 18, thewhen vehicle the test section volumes. Both figures present the test section blockage effect on the semi-open jet modelvehicle ismodel placed is placed inside theinside tunnel, the tunnel, through through the jet th expansione jet expansion change change corresponding corresponding to di tofferent different test expansion. From the side view of the center plane, the comparisons in Figure 17 show that the smaller sectiontest section volumes. volumes. Both figuresBoth figure presents present the test the section test blockagesection blocka effectge on effect the semi-open on the semi-open jet expansion. jet plenum chamber causes the jet to expand earlier. The velocity magnitude in the outer layer of the jet Fromexpansion. the side From view the of side the view center of plane,the center the plane, comparisons the comparisons in Figure in17 Figure show that17 show the smallerthat the plenumsmaller is higher compared to the larger scale test-section models, which corresponds to higher dynamic chamberplenum chamber causes the causes jet to the expand jet to earlier.expand The earlier. velocity The magnitudevelocity magnitude in the outer in the layer outer of the layer jet isof higherthe jet pressure. As the total pressure of the domain remains constant, the static pressure in that region is comparedis higher compared to the larger to the scale larger test-section scale test-secti models,on which models, corresponds which corresponds to higher to dynamic higher pressure.dynamic relatively low. Aspressure. the total As pressure the total of pressure the domain of the remains domain constant, remains the constant, static pressure the static in that pressure region in is relativelythat region low. is relatively low.

Figure 16. Cont.

Vehicles 2020, 2 334 Vehicles 2020, 2, FOR PEER REVIEW 17

Figure 16. Cont. Vehicles 2020, 2 335 Vehicles 2020, 2, FOR PEER REVIEW 18

Figure 16. StaticStatic pressure pressure distributions distributions on on center center plane plane at atdifferent different 𝑍 Z locations;locations; from from top top to tobottom bottom 𝑍 Z = 2.5= 2.5 m, m3.5, 3.5 m, m, 4.5 4.5 m, m,5.5 5.5 m, m,and and 6.5 6.5 m, m,respective respectively.ly. Please Please see see Figure Figure 15 for15 forthe the reference reference of these of these 𝑍- Z -locations with respect to vehicle. locations with respect to vehicle. As both the model and numerical representation of the HAWT domain are symmetric, additional features of this jet expansion can be observed using a XY-plane, covering only the right or left half of the simulation domain, which is presented in Figure 18. The velocity magnitude contour shows the original plenum chamber of HAWT forced the jet to expand much earlier than the other two larger-scale test-section models in the horizontal direction as well. The early expansion causes higher jet outer layer velocity at the nozzle outlet, as well as around the vehicle. With faster flow coming toward the test vehicle, the predicted drag coefficient of the model will be greater than that obtained from a larger test section with the same jet. To summarize, the volume change of the open jet test section has a significant effect on the drag predictions of the test vehicle. The differences in drag coefficient measurements come from the streamwise pressure gradient change in the plenum chamber and the jet expansion rate. The smallest test section had a negative pressure gradient when tested as an empty tunnel, while a nearly zero pressure gradient was found in the larger scales. The early jet expansion causing faster jet flow passing the vehicle is another important contribution to the higher drag coefficient value in the 100% HAWT test section.

Vehicles 2020, 2 336 Vehicles 2020, 2, FOR PEER REVIEW 19

Figure 17. Mean velocity magnitude on a Y-plane along the vehicle centerline. Figure 17. Mean velocity magnitude on a Y-plane along the vehicle centerline.

Vehicles 2020, 2 337 Vehicles 2020, 2, FOR PEER REVIEW 20

FigureFigure 18.18. Mean velocity magnitude atat nozzlenozzle outletoutlet (top(top view).view).

4.4. HAWTAs both and the Free-Air model Tunnel and Comparison numerical representation of the HAWT domain are symmetric, additionalFor a directfeatures comparison of this jet betweenexpansion the can aerodynamic be observed characteristics using a XY-plane, observed covering in a limitedonly the area right test or sectionleft half like of the the simulation HAWT test domain, section which and those is presented that might in haveFigure been 18. observedThe velocity in themagnitude free air domain,contour Figureshows 19the compares original plenum the velocity chamber magnitude of HAWT contours forced onthe the jet centerto expand plane, much when earlier the vehiclethan the model other istwo placed larger-scale in 100% test-section HAWT model models and in in the free horizontal air, respectively. direction For as both well. cases, The early the ﬂow expansion ﬁeld near causes the higher jet outer layer velocity at the nozzle outlet, as well as around the vehicle. With faster flow coming toward the test vehicle, the predicted drag coefficient of the model will be greater than that obtained from a larger test section with the same jet.

Vehicles 2020, 2 338 vehicle is similar except for the area above the roof (A), and the region above the hood-windshield conjunction (B). The deformation of the velocity contours in these regions for the HAWT simulations is because of the jet shear layer eﬀect in the test section, which changes the ﬂow velocity over the vehicle.

As expected,Vehicles 2020 the, aerodynamic2, FOR PEER REVIEW coe ﬃcients are overestimated in wind tunnel simulations compared22 to the free air domain.

A B

B A

Figure 19.FigureVelocity 19. Velocity magnitude magnitude for free for air free and air 100% and 100% HAWT HAWT model model a Y-plane a Y-plane along along the vehiclethe vehicle centerline. centerline. 5. Conclusions 5. Conclusions In this paper, the full-scale model of 2014 Hyundai Veloster was tested in four different In this paper, the full-scale model of 2014 Hyundai Veloster was tested in four different computational domains using a finite volume CFD code. The CFD simulations were validated computational domains using a finite volume CFD code. The CFD simulations were validated with with thethe wind windtunnel tunnel test data data from from the the Hyundai Hyundai Aero-acoustic Aero-acoustic Wind Tunnel Wind (HAWT) Tunnel (HAWT)located at the located at the HyundaiHyundai Namyang Namyang TechnicalTechnical Center in in South South Korea. Korea. The The correlation correlation results results are encouraging. are encouraging. AlthoughAlthough the CFD the simulation CFD simulation over-predicted over-predicted the drag the coedragffi coefficientcient by a by small a small amount amount in the in reproducedthe reproduced HAWT test section compared to the reported experimental data, the change in 𝐶 in HAWT test section compared to the reported experimental data, the change in CD in CFD from the improvedCFD spoiler from the to improved the original spoiler spoiler to the closelyoriginal matchesspoiler closely the windmatches tunnel the wind results. tunnel results. The blockage effects of the open jet wind tunnel test section were evaluated by different scales Theof blockagethe plenum eff chamber,ects of thewith open a same jet longitudinal wind tunnel extent. test A section negative were pressure evaluated gradient by was diff founderent in scales of the plenumthe original chamber, empty with HAWT a same test section longitudinal in the streamwise extent. A direction, negative as pressure well as an gradient earlier jet wasexpansion, found in the originalwhich empty contributed HAWT test to sectionthe higher in drag the streamwise predictions. direction,By increasing as the well volume as an earlierof the test jet section, expansion, the which contributednegative to thepressure higher gradient drag turned predictions. to a nearly By zero increasing pressure thegradient volume before of approaching the test section, the collector, the negative pressureand gradient the jet expansion turned to was a nearlydelayed. zero As a pressureresult, the gradientlarger test beforesections approachinghave less interference the collector, effect onand the the test models in open jet wind tunnels, and the drag predictions are closer to the free air condition. jet expansion was delayed. As a result, the larger test sections have less interference effect on the test models in open jet wind tunnels, and the drag predictions are closer to the free air condition. Vehicles 2020, 2 339

Two different ground plane boundary conditions, static floor with fixed wheels and moving ground with rotating wheels, were tested in both HAWT and free-air environments. The changes in vehicle drag and lift agree with the findings of Waschle [48]. These results also agree well with the works of Buscariolo et al. [24]. The reasons for the drag and lift reduction with moving floor and rotating wheels were explained by observing the change in tire wakes and underbody flow velocity from both top and side views; this study concluded that the reduction of the tire wake size is the primary attributer to drag reduction.

Author Contributions: Conceptualization, C.F., and M.U.; Investigation, C.F., M.U. and C.Z.; Methodology, C.F., M.U., and C.Z.; Supervision, M.U.; Validation, C.F., M.U. and C.Z.; Writing–original draft, C.F. and M.U.; Writing–review & editing, C.F., M.U., and C.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. However, Chen Fu and Chunhui Zhang were supported as Graduate Teaching Assistants by the UNC Charlotte Department of Mechanical Engineering and Engineer Science. Acknowledgments: The authors thank UNC Charlotte College of Engineering MOSAIC Computing and University Research Computing for their consistent support. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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