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Control of vehicle powertrain thermal management systems is becoming more challenging as the number of components is growing, and as a result, advanced control methods are being investigated. Model predictive control (MPC) is particularly interesting in this application because it provides a suitable framework to manage actuator and temperature constraints, and can potentially leverage preview information if available in the future. In previous SAE publications (2015-01-0336 and 2016-01-0215), a robust MPC control formulation was proposed, and both simulation and powertrain thermal lab test results were provided. In this work, we discuss the controller deployment in a vehicle; where controller validation is done through road driving and on a wind tunnel chassis dynamometer. This paper discusses challenges of linear MPC implementation related to nonlinearities in this over-actuated thermal system.

The airflow that enters the front grille of a ground vehicle for the purpose of component cooling has a significant effect on aerodynamic drag. This drag component is commonly referred to as cooling drag, which denotes the difference in drag measured between open grille and closed grille conditions. When the front grille is closed, the airflow that would have entered the front grille is redirected around the body. This airflow is commonly referred to as cooling interference airflow. Consequently, cooling interference airflow can lead to differences in vehicle component drag; this component of cooling drag is known as cooling interference drag. One mechanism that has been commonly utilized to directly influence the cooling drag, by reducing the engine airflow, is active grille shutters (AGS). For certain driving conditions, the AGS system can restrict airflow from passing through the heat exchangers, which significantly reduces cooling drag.

Cooling drag is a metric that measures the influence of air flow travelling through the open grille of a ground vehicle on overall vehicle drag, both internally (engine air flow) and externally (interference air flow). With the interference effects considered, a vehicles cooling drag can be influenced by various air flow fields around the vehicle, not just the air flow directly entering or leaving the engine bay. For this reason, computational fluid dynamics (CFD) simulations are particularly difficult. With insights gained from a previously conducted set of experimental studies, a CFD validation effort was undergone to understand which air flow field characteristics contribute to CFD/test discrepancies. A Lattice-Boltzmann Large Eddy Simulation (LES) method was used to validate several test points. Comparison using integral force values, surface pressures, and cooling pack air mass flows was presented.

Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARs and RADARs. This information is processed by the on-board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this could result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge.

With the rapid development of artificial intelligence, the autonomous vehicles (AV) have attracted considerable attention in the automotive industry. However, different factors negatively impact the adoption of the AVs, delaying their successful commercialization. Accretion of atmospheric icing, especially wet snow, on AV sensors causes blockage on their lenses, making them prone to lose their sight, in turn, increasing potential chances of accidents. In this study, two different designs are proposed in order to prevent snow accretion on the lenses of AVs via air flow across the lens surface. In both designs, lenses made of plain glass and superhydrophobic coated glass surfaces are tested. While some researchers have shown promise of water repellency on superhydrophobic surfaces, more snow accretion is observed on the superhydrophobic surfaces, when compared to the plain glass lenses.

A new methodology to predict vehicle interior wind noise using CFD results has been developed. The CFD simulation replaces wind tunnel testing for providing flow field information around vehicle greenhouse. A loadcase model based on the CFD results is used to excite an SEA vehicle model. This new approach has been demonstrated on a production vehicle with success for the frequency range of 250-10K Hz. The CAE prediction of interior wind noise agrees within 0.2 sones from wind tunnel testing. The model has been used to evaluate wind noise performance with different door glass design parameters. A glass thickness change from 3.8 mm to 4.8 mm results in 1.1 sones improvement, which agrees well to 1.4 sones improvement from testing. Laminated glass with about 3 times higher damping results in 2.5 sones improvement. This methodology using CFD results can be used in the early stage of product development to impact designs.

A computational design study, performed in conjunction with experiments, to reduce the howl noise caused by the roof rack crossbars of a production automobile is presented. This goals were to obtain insight into the flow phenomenon causing the noise, and to do a design iteration study that would lead to a small number of cross-section recommendations for crossbars that would be tested in the wind tunnel. The flow condition for this study is 0 yaw at 30 mph inlet speed, which experimentally gives the strongest roof rack howl for the vehicle considered for this study. The numerical results have been obtained using the commercial CFD/CAA software PowerFLOW. The simulation kernel of this software is based on the numerical scheme known as the Lattice Boltzmann Method (LBM), combined with a two-equation RNG turbulence model.

A correlation of aerodynamic wind tunnels was initiated between Chrysler, Ford and General Motors under the umbrella of the United States Council for Automotive Research (USCAR). The wind tunnels used in this correlation were the open jet tunnel at Chrysler's Aero Acoustic Wind Tunnel (AAWT), the open jet tunnel at the Jacobs Drivability Test Facility (DTF) that Ford uses, and the closed jet tunnel at General Motors Aerodynamics Laboratory (GMAL). Initially, existing non-competitive aerodynamic data was compared to determine the feasibility of facility correlation. Once feasibility was established, a series of standardized tests with six vehicles were conducted at the three wind tunnels. The size and body styles of the six vehicles were selected to cover the spectrum of production vehicles produced by the three companies. All vehicles were tested at EPA loading conditions. Despite the significant differences between the three facilities, the correlation results were very good.

Aerodynamic measurements in automotive wind tunnels are degraded by test section interference effects, which increase with increasing vehicle blockage ratio. The current popularity of large vehicles (i.e. trucks and sport utility vehicles) makes this a significant issue. This paper describes the results of an experimental investigation carried out in support of the Ford/Sverdrup Driveability Test Facility (DTF), which includes an aero-acoustic wind tunnel (Wind Tunnel No. 8). The objective was to quantify the aerodynamic interference associated with two candidate test section configurations for Wind Tunnel No. 8-semi-open jet and slotted wall. The experiments were carried out at 1/11-scale in Sverdrup laboratories. Four automobile shapes (MIRA models) and six Sport Utility Vehicle (SUV) shapes representing blockages from 7% to 25% were used to evaluate changes in measured aerodynamic coefficients for the two test section configurations.

Pressure-sensitive paint (PSP) technology is a technique used to experimentally determine surface pressures on models during wind tunnel tests. The key to this technique is a specially formulated pressure-sensitive paint that responds to, and can be correlated with the local air pressure. Wind tunnel models coated with pressure-sensitive paint are able to yield quantitative pressure data on an entire model surface in the form of light intensity values in recorded images. Quantitative results in terms of pressure coefficients (Cp) are obtained by correlating PSP data with conventional pressure tap data. Only a small number of surface taps are needed to be able to obtain quantitative pressure data with the PSP method. This technique is gaining acceptance so that future automotive wind tunnel tests can be done at reduced cost by eliminating most of the expensive pressure taps from wind tunnel models.

Velocity profiles for air flowing under a vehicle body are determined by analyzing videotapes of neutrally buoyant soap bubbles using motion analysis software and equipment. What had heretofore been primarily a qualitative flow visualization technique has been extended to provide quantitative data. The light sources, cameras, and bubble generator, mounted on the vehicle, are powered by the vehicle's electrical system, making it possible to compare underbody velocities measured in a wind tunnel with those over the road. Results are presented for a heavy-duty 4×4 pickup truck at speeds up to 25m/s (55 mph). The velocity profiles in the tunnel and on the road were quite similar.

A numerical methodology based on the finite element and boundary element methods is presented for computing the noise radiated from an elastically supported structure subject to turbulent boundary layer excitation. The new algorithm utilizes the fluctuating wall pressure in order to define the excitation on the structural-acoustic system. The developments target wind noise prediction for the sound radiated by the side glass window of an automobile. The glass-seal assembly is modeled as a flexible plate mounted on an elastic foundation with stiffness and damping characteristics. Numerical predictions are compared successfully to wind tunnel test data. Parametric analyses are performed in order to identify the characteristics of the seal that can lead to noise reduction.

The importance of the automotive test facility has increased significantly due in large part to continuous pressure on manufactures to shorten product development cycles. Test facilities are no longer used only for regulatory testing, or development testing in which the effects of small design changes (A-to-B testing) are determined; automotive manufacturers are beginning to use these facilities for final design validation, which has traditionally required on road testing. A host of resources have gone into the design and construction of facilities with the capability to simulate nearly any environment of practical importance to the automotive industry. As a result, there are now a number of test facilities, and specifically wind tunnels, in which engineers can test most aspects of a vehicle's performance in real-world environments.

The Sverdrup Driveability Test Facility (DTF) represents a new type of partnership in automotive testing between a supplier (Sverdrup Technology) and an original equipment manufacturer (Ford Motor Company). The facility was designed and built by Sverdrup to Ford's specifications. It is also operated and maintained by Sverdrup, with Ford as its “anchor” client under a long-term lease-back arrangement. Test time that goes unused by Ford is made available to other customers. Wind Tunnel 8 (WT8) is one of the test facilities within the DTF, which includes two other climatic wind tunnels and several supporting test cells. This tunnel combines aerodynamic, acoustic, climatic, and powertrain capabilities within one facility. The airline was optimized during the design stage for the competing requirements of excellent flow quality, very low background noise, and climatic capability.

Pressure Sensitive Paint (PSP) has been used for several years by the aircraft industry in transonic wind tunnel testing where the oxygen concentrations are low and the luminescence of the paint is easily recorded. Extending PSP to slower speeds where the oxygen concentrations are closer to atmospheric conditions is much more challenging. For the past few years, work has been underway at both Wright Patterson Air Force Base and Ford Motor Company to advance PSP techniques for testing at slower speeds. The CRADA (Cooperative Research and Development Agreement) provided a way for comparisons to be made of the different PSP systems that were being investigated. This paper will report on PSP tests conducted as part of the CRADA.

Wind Tunnel 8 of the Driveability Test Facility (DTF), which achieved full operational status in 2001, is designed to provide full powertrain, aerodynamic, and aero-acoustic test capabilities for automotive product development. In order for it to be fully integrated into product testing, the Ford product engineering community needed to correlate the facility. The major objective of the correlation is quantitative aerodynamic correlation, which will be achieved when aerodynamic coefficients measured in Wind Tunnel 8 can be understood in the context of aerodynamic measurements obtained in other wind tunnels that Ford has used for product testing. The motivation for this study is the aerodynamic interference that is present in all wind tunnels. Aerodynamic interference is the deviation between the true result—which is difficult to determine—and the actual result obtained from the wind tunnel.

A wind noise model of a vehicle has been developed using Statistical Energy Analysis (SEA) with measured turbulent pressure data as the source input. Empirical formulas are used to scale the input data for changes in flow and design parameters. Wind tunnel tests have been conducted on a standard and modified vehicle to validate the SEA model and the input scaling. The results show good correlation with both the exterior turbulent pressure levels and the interior sound pressure levels across the audio frequency range.

An L18 Taguchi-style Design of Experiments (DOE) with eight factors was used to optimize exterior mirrors for wind noise and drag. Eighteen mirror properties were constructed and tested on a full size greenhouse buck at the Lockheed low-speed wind tunnel in Marietta, GA. Buck interior sound data and drag measurements were taken at 80 MPH wind speed (0° yaw angle). Key wind noise parameters were the fore/aft length of mirror housing and the plan view angle of the mirror housing's inboard surface. Key drag parameters were the fore/aft length of the mirror housing, the cross-section shape of the mirror pedestal, and the angle of the pedestal (relative to the wind).

The effects of substituting a 12 mm thick subcool on top condenser in place of a 16 mm subcool on bottom condenser are evaluated in a vehicle level AC pull down test. The A to B testing shows that a thinner condenser with subcool on top exhibits no degradation in AC performance while resulting in a lower total system refrigerant charge. The results are from vehicle level tests run in a climatically controlled vehicle level wind tunnel to simulate an AC pull down at 43°C ambient. In addition to cabin temperature and AC vent temperatures, comparison of compressor head pressures was also done. The conclusion of the study was that a standard 16 mm thick subcool on bottom IRD condenser can be replaced by a 12 mm thick subcool on top IRD condenser with no negative effects on performance.

Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry.