Browse Topic: Wind tunnel tests
The present work demonstrates a transient Fluid-Structure-Interaction (FSI) based numerical methodology for estimation of aerodynamic-induced flutter of the rear bumper of a Sports Utility Vehicle (SUV). Finite Volume Method (FVM) based High-fidelity transient full vehicle aerodynamic simulations were conducted for the estimation of the transient aerodynamic load. Subsequently, by mapping this transient aero load onto the surface of the rear bumper, Finite Element Method (FEM) based dynamic structural simulations were performed to predict its response. The results obtained through simulations were then compared against experimental wind tunnel test data of a prototype car with modified bumper for the specific test-case. The pressure and the time series data of rear bumper deflection were captured at multiple probe locations from wind tunnel experiments at 140 and 200 kmph. The distribution of pressure on the rear surfaces of the car was well captured by the aerodynamic simulation at
The present work demonstrates a Fluid-Structure Interaction (FSI) based methodology that couples a Finite Volume Method (FVM) and Finite Element Method (FEM) based tools to estimate air guide deformation, thereby predicting accurate aerothermal performance. The method starts with a digital assembly step where the assembly shape and the induced stress due to assembly is predicted. A full vehicle Aerodynamic simulation is performed to extract the surface pressure on the air guide which is then used to estimate the extent of deformation of the air guides. Based on the extent a subsequent Aerodynamic simulation may be carried out to predict thermal efficiency. Comparison against pressure data and deflection data extracted from the wind tunnel experiments of vehicles has shown reasonable match demonstrating the accuracy and usefulness of the method.
Reducing drag forces and minimizing the rear wake region are the main goals of evaluating exterior aerodynamic performance in automobiles. Various literature and experiments shows that the overall fuel computations of the road vehicle improves significantly with the reduction in aerodynamic drag force. In the road vehicle major components of the drag is due the imbalance in pressure between front and rear of the vehicle. At high vehicle speed, aerodynamic drag is responsible for approximately 30 to 40% of the energy consumption of the vehicle. In the recent year, cost of high-performance computing (HPC) has reduced significantly, which helped computational fluid dynamics (CFD) is an affordable tool to the automotive industry for evaluating aerodynamic performance of the vehicle during developing phase. The vehicles aerodynamic performance is greatly impacted by the dynamic environmental conditions it encounters in the real world. Such environmental conditions are difficult to replicate
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing
Wind Tunnels are complex and cost-intensive test facilities. Thus, increasing the test efficiency is an important aspect. At the same time, active aerodynamic elements gain importance for the efficiency of modern cars. For homologation, such active aero-components pose an extra level of test complexity as their control strategies, the relevant drive cycles and their aerodynamics in different positions must be considered for homologation-relevant data. Often, active components have to be manually adjusted between test runs, which is a time-consuming process because the vehicle is not integrated into the test automation. Even if so, designing a test sequence stepping through the individual settings for each component of a vehicle is a tedious task in the test session. Thus, a sophisticated integration of the wind tunnel control system with a test management system, supporting the full homologation process is one aspect of a solution. The other is the integration of the vehicle’s active
Sound source identification based on beamforming is widely used today as a spatial sound field visualization technology in wind tunnel experiments for vehicle development. However, the conventional beamforming technique has its inherent limitation, such as bad spatial resolution at the low frequency range, and limited system dynamic range. To improve the performance, three deconvolution methods CLEAN, CLEAN-SC and DAMAS were investigated and applied to identify wind noise sources on a production car in this paper. After analysis of vehicle exterior wind noise sources distribution, correlation analysis between identified exterior noise sources and interior noise were conducted to study their energy contribution to vehicle interior. The results show that the algorithm CLEAN-SC based on spatial source coherence shows the best capability to remove the sidelobes for the uncorrelated wind noise sources, while CLEAN and DAMAS, which are based on point spread functions have definite
Experimental studies of wind tunnel blockage for road vehicles have usually been conducted in model wind tunnels. Models have been made in a range of scales and tested in a working section of fixed size. More recently CFD studies of blockage have been undertaken, which allow a fixed vehicle size and the blockage is varied by changing the cross section of the flow domain. This has some inherent advantages. A very recent database of CFD derived drag and lift coefficients for different road vehicle shapes and simple bodies tested in a closed wall tunnel with a wide range of blockage ratios has become available and provides some additional insight into the blockage phenomenon. In this paper a process is developed to derive the parameters influencing wind tunnel blockage corrections from CFD data. These are shown to be reasonably effective for correcting the measured drag and lift coefficients at blockage ratios up to 10%.
Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this
The current Range Rover is the fifth generation of this luxury SUV. With a drag coefficient of 0.30 at launch, it was the most aerodynamically efficient luxury SUV in the world. This aerodynamic efficiency was achieved by applying the latest science. Rear wake control was realised with a large roof spoiler, rear pillar and bodyside shaping, along with an under-floor designed to reduce losses over a wide range of vehicle configurations. This enabled manipulation of the wake structure to reduce drag spread, optimising emissions measured under the WLTP regulations. Along with its low drag coefficient, in an industry first, it was developed explicitly to achieve reduced rear surface contamination with reductions achieved of 70% on the rear screen and 60% over the tailgate when compared against the outgoing product. This supports both perceptions of luxury along with sensor system performance, demonstrating that vehicles can be developed concurrently for low drag and reduced rear soiling
The increased importance of aerodynamics to help with overall vehicle efficiency necessitates a desire to improve the accuracy of the measuring methods. To help with that goal, this paper will provide a method for correcting belt-whip and wheel ventilation drag on single and 3-belt wind tunnels. This is primarily done through a method of analyzing rolling-road only speed sweeps but also physically implementing a barrier. When understanding the aerodynamic forces applied to a vehicle in a wind tunnel, the goal is to isolate only those forces that it would see in the real-world. This primarily means removing the weight of the vehicle from the vertical force and the rolling resistance of the tires and bearings from the longitudinal force. This is traditionally done by subtracting the no-wind forces from the wind at testing velocity forces. The first issue with the traditional method is that a boundary layer builds up on the belt(s), which can then influence a force onto the vehicle’s
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