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Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

When travelling in an open-jet wind tunnel, the path of an acoustic wave is affected by the flow causing a shift of source positions in acoustical maps of phased arrays outside the flow. The well-known approach of Amiet attempts to correct for this effect by computing travel times between microphones and map points based on the assumption that the boundary layer of the flow, the so-called shear-layer, is infinitely thin and refracts the acoustical ray in a conceptually analogy to optics. However, in reality, the turbulent nature of both the not-so thin shear-layer and the acoustic emission process itself causes an additional smearing of sources in acoustic maps, which in turn causes deconvolution methods based on these maps - the most prominent example being CLEAN-SC - to produce certain ring effects, so-called halos, around sources.

It is important to accurately predict the aerodynamic properties for designing applications which involves fluid flows, particularly in the aerospace industry. Traditionally, this is done through complex numerical simulations, which are computationally expensive, resource-intensive and time-consuming, making them less than ideal for iterative design processes and rapid prototyping. Machine learning, powered by vast datasets and advanced algorithms, offers an innovative approach to predict airfoil characteristics with remarkable accuracy, speed, and cost-effectiveness. Machine learning techniques have been applied to fluid dynamics and have shown promising results. In this study, machine learning model called the back-propagation neural network (BPNN) is used to predict key aerodynamic coefficients of lift and drag for airfoils.

Unsteady pressure measurements are crucial for understanding dynamic pressure distribution changes in fluid flow fields and over object surfaces, revealing insights into complex flow phenomena induced by shock waves, vortices, boundary layer interactions, and flow separation. While ground-based wind tunnel tests have conventionally provided these insights, this paper presents an on-board system for real-time unsteady pressure data acquisition. The system's main challenge is accurately resolving high-frequency static and dynamic pressure variations over very high base pressure values. To meet this challenge, the paper highlights the importance of sigma-delta Analog-to-Digital Converters (ADCs) due to their high resolution, oversampling techniques, noise filtering capabilities, and wide dynamic range. These ADCs seamlessly integrate with digital systems, ensuring reliable real-time pressure data acquisition during launch and flight operations.

Modern advances in the technical developments of Advanced Driver Assistance Systems (ADAS) have elevated autonomous vehicle (AV) operations to a new height. Vehicles equipped with sensor based ADAS have been positively contributing to safer roads. As the automotive industry strives for SAE Level 5 full driving autonomy, challenges inevitably arise to ensure ADAS performance and reliability in all driving scenarios, especially in adverse weather conditions, during which ADAS sensors such as optical cameras and LiDARs suffer performance degradation, leading to inaccuracy and inability to provide crucial environmental information for object detection. Currently, the difficulty to simulate realistic and dynamic adverse weather scenarios experienced by vehicles in a controlled environment becomes one of the challenges that hinders further ADAS development.

In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated.

Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire. A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at an 80 km/h constant speed.

Rotor-only ducted low-pressure axial fans play a crucial role in automotive thermal management of the tightly packed under-hood region. Most current scientific work concerning low-pressure axial fans investigate the aerodynamic performance of these fans while operating with uniform inlet flow conditions. This is rarely the case in real-world applications. This work aims to investigate the aerodynamic performance of low-pressure axial fans operating with upstream blockages. First, a validation study is performed in the absence of any upstream blockage. Numerical results are compared against publicly available experimental data. Steady-state, Reynolds-Averaged Navier Stokes (RANS) analysis is performed on a single-blade passage. The validation study also evaluates the choice of turbulence model and suggests the use of the k- ε turbulence model with wall functions for the best comparison against experimental data.

In the field of vehicle aerodynamic simulation, Reynold Averaged Navier-Stokes (RANS) model is widely used due to its high efficiency. However, it has some limitations in capturing complex flow features and simulating large separated flows. In order to improve the computational accuracy within a suitable cost, the Field Inversion and Machine Learning (FIML) method, based on a data-driven approach, has received increasing attention in recent years. In this paper, the optimal coefficients of the Generalized k-ω (GEKO) model are firstly obtained by the discrete adjoint method of FIML, utilizing the results of wind tunnel experiments. Then, the mapping relationship between the flow field characteristics and the optimal coefficients is established by a neural network to augment the turbulence model.

Compression ignition engine-based transportation is nowadays looking for cleaner combustion solutions. Among them, ducted fuel injection (DFI) is emerging as a cutting-edge technology due to its potential to drastically curtail engine-out soot emissions. Although the DFI capability to abate soot formation has been demonstrated both in constant-volume and optical engine conditions, its optimization and understanding is still needed for its exploitation on series production engines. For this purpose, computational fluid dynamics (CFD) coupled with low-cost turbulence models, like RANS, can be a powerful tool, especially in the industrial context. However, it is often challenging to obtain reliable RANS-based CFD simulations, especially due to the high dependence of the various state-of-the-art turbulence models on the case study.

Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer.

A new methodology is discussed for the development and implementation of a wind-averaged drag analysis technique for the development of aerodynamic-driven surfaces for use in the automotive industry. Current methods of vehicle design focus on reducing the straight-line coefficient of drag using wind tunnel testing and computational fluid dynamics and quote this value as the vehicle's aerodynamic performance. It is suggested to transition passenger vehicle aerodynamic design to the methodology employed for Class-A vehicles and design with a focus on reducing the wind-averaged drag value. Based on the methods used in J1252, the wind averaged drag calculation method utilizes the average wind speed a vehicle will experience in the continental United States and assumes an equal probability of the wind coming from all directions relative to the vehicle. Wind-averaged drag will optimize vehicle design for real-world improvements of vehicle efficiency.

The difficulties of testing a bluff automotive body of sufficient scale to match the on-road vehicle Reynolds number in a closed wall wind tunnel has led to many approaches being taken to adjust the resulting data for the inherent interference effects. But it has been impractical if not impossible to experimentally analyze the effects that are occurring on and around the vehicle when these blockage interferences are taking place. The present study is an extension of earlier work by the author and similarly to that study uses the CFD (computational fluid dynamics) analysis of several bodies of differing configurations to examine the interference phenomena in solid wall wind tunnels and the effects that they have on the pressures, forces and force increments experienced by the vehicle model. This is accomplished by executing a series of CFD configurations with varying sized cross sections from 0.2% to 16% blockage enabling an approximation of free air conditions as a reference.

In the authors’ previous work, a database was generated documenting the effects of variable blockage ratios on the drag and lift of simplified and generic automotive bodies in solid wall wind tunnels. This database displays significant differences in the responses of different vehicle architectures to changes in wind tunnel blockage. What was not examined in this previous work was the effect of wind tunnel blockage on the incremental values of geometry changes to these generic models. This is critical knowledge related to the aerodynamic development process of automotive vehicles in wind tunnels. To complement that work, the present paper examines the effects of changes in solid wall blockage on the incremental force values of geometry changes on the simplified sedan geometry known as the Pilot Fastback, the Pilot Squareback and the Ford GTU pickup.

This paper presents calculations of external car aerodynamics by using the Partial-Averaged Navier-Stokes (PANS) variable resolution model in conjunction with the Finite Volume (FV) immersed-boundary method. The work presented here is the continuation of the study reported in Basara et al. [1, 2]. In that work, it was shown that the same accuracy of predicted aerodynamic forces could be achieved for both types of computational meshes, the standard body-fitted mesh and the immersed boundary (IB) Cartesian mesh, by using the Reynolds-Averaged Navier-Stokes (RANS) k-ζ-f model as well as by using the Partially-Averaged Navier-Stokes (PANS) method. Based on the accuracy achieved, Basara et al. [2] concluded that further work could focus on evaluating the turbulence modelling on the immersed boundary meshes only.

Correct simulations of rotating wheels are essential for accurate aerodynamic investigations of passenger vehicles. Therefore, modern automotive wind tunnels are equipped with five-belt moving ground systems with wheel drive units (WDUs) connected to the underfloor balance. The pressure distribution on the exposed areas of the WDU belts results in undesired lift forces being measured which must be considered to obtain accurate lift values for the vehicle. This work investigates the parasitic WDU lift for various configurations of a crossover SUV using numerical simulations that have been correlated to wind tunnel data. Several parameters were considered in the investigation, such as WDU size, WDU placement, tyre variants and vehicle configurations. The results show that the parasitic lift is more sensitive to the width than the length of the WDU. However, the belt length is also important to consider, especially if the wheel cannot be placed centred.

This paper contributes to the Committee on Commonized Aerodynamics Automotive Testing Standards (CAATS) initiative, established by the late Gary Elfstrom. It is collaboratively compiled by automotive wind tunnel users and operators within the Subsonic Aerodynamic Testing Association (SATA). Its specific focus lies in automotive wind tunnel test techniques, encompassing both those relevant to passenger car and race car development. It is part of the comprehensive CAATS series, which addresses not only test techniques but also wind tunnel calibration, uncertainty analysis, and wind tunnel correction methods. The core objective of this paper is to furnish comprehensive guidelines for wind tunnel testing and associated techniques. It begins by elucidating the initial wind tunnel setup and vehicle arrangement within it.

This paper presents the application of statistical process control (SPC) methods to Windshear, a 180-mph motorsports and automotive wind tunnel equipped with a wide-belt rolling road system. The SPC approach captures the complete variability of the facility and offers useful process performance metrics that are based on a sound statistical framework. Traditional control charts are explored, emphasizing the uniqueness of variability experienced in wind tunnels which includes significant, unexplained short-term and long-term variation compared to typical manufacturing processes. This unique variation is elegantly captured by the three-way control chart, which is applied to estimate the complete process reproducibility with different levels of repeatability of vehicle drag coefficient. The sensitivity of three-way control charts is explored including the evaluation of an alternate group assignment within the same dataset.

The China Automotive Technology and Research Center (CATARC) has completed two new wind tunnels at its test centre in Tianjin, China: an aerodynamic/aeroacoustic wind tunnel (AAWT), and a climatic wind tunnel (CWT). The AAWT incorporates design features to provide both a very low fan power requirement and a very low background noise putting it amongst the quietest in the automotive world. These features are also combined with high flow quality, a full boundary layer control system with a 5-belt rolling road, an automated traversing system, and a complete acoustic measurement system including a 3-sided microphone array. The CWT, located in the same building as the AAWT, has a flexible nozzle to deliver 250 km/h with an 8.25 m2 nozzle, and 130 km/h with a 13.2 m2 nozzle. The temperature range of the CWT is -40 °C to +60 °C with a controlled humidity range of 5% to 95%. Additional integrated systems include a variable angle solar simulator array, and a rain and snow spray system.

This research addresses the pressing need for reducing vehicle aerodynamic resistance, with a specific focus on mitigating wheel and tire resistance, which constitutes approximately 25% of the overall vehicle drag. While the prevailing method for reducing resistance in mass production development involves wheel opening reduction, it inadvertently increases wheel weight and has adverse effects on brake cooling performance. To overcome these challenges, novel complementary resistance reduction methods that can be employed in conjunction with an appropriate degree of wheel opening reduction are imperative. In this study, we introduce symmetrical wheels with a fan-like shape as a solution. The fan configuration influences the surrounding flow by either drawing it in or pushing it out, depending on the direction of rotation. Application of these fan-type wheels to a vehicle's wheels results in the redirection of flow inwards or outwards during high-speed driving due to wheel rotation.