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In an automotive power train system, the differential gear system plays a vital role of enabling the vehicle to transfer the engine torque to the wheels. The differential system consists of complex system of gears which are meshed with each other. Effective lubrication of the differential system ensures that the metal to metal contact between the gears is avoided. In addition, the lubricants also acts as a thermal medium to effectively dissipate the heat produced due to frictional resistances. For dipped lubrication system, the use of lubrication oil leads to a loss of transmission power, and the loss increases with increasing rotational speeds. Prediction and an understanding of the transmission loss inside the differential system is important as it provides a means to increase the power transmission efficiency. In addition, it provides insights to optimize the lubrication methods, gear profile, and gear housings.

Passenger comfort and safety are major drivers in a typical automotive design and optimization cycle. Addressing thermal comfort requirements and the thermal management of the passenger cabin within a car, which involves accurate prediction of the temperature of the cabin interior space and the various aggregates that are present in a cabin, has become an area of active research. Traditionally, these have been done using experiments or detailed three-dimensional Computational Fluid Dynamics (CFD) analysis, which are both expensive and time-consuming. To alleviate this, recent approaches have been to use one-dimensional system-level simulation techniques with a goal to shorten the design cycle time and reduce costs. This paper describes the use of Modelica language to develop a one-dimensional mathematical model using Modelica language for automotive cabin thermal assessment when the car is subjected to solar heat loading.

Increasing demands on engine power to meet increased load carrying capacity and adherence to emission norms have necessitated the need to improve thermal management system of the vehicle. The efficiency of the vehicle cooling system strongly depends on the fan and fan-shroud design and, designing an optimum fan and fan-shroud has been a challenge for the designer. Computational Fluid Dynamics (CFD) techniques are being increasingly used to perform virtual tests to predict and optimize the performance of fan and fan-shroud assembly. However, these CFD based optimization are mostly based on a single performance parameter. In addition, the sequential choice of input parameters in such optimization exercise leads to a large number of CFD simulations that are required to optimize the performance over the complete range of design and operating envelope. As a result, the optimization is carried out over a limited range of design and operating envelope only.

Liquid ring pumps are used in aircraft fuel systems in conjunction with main impeller pumps. These pumps are used for priming the pump system as well as to remove fuel vapor and air from the fuel. Prediction of cavitation in liquid ring pumps is important as cavitation degrades the performance of these pumps and leads to their failure. As test based assessment of cavitation risk in liquid ring pump is expensive and time consuming, recent approaches have been to assess and predict the risk of cavitation using Computational Fluid Dynamics (CFD) methods with the goal to quicken the design process and optimize the performance of these pumps. The present study deals with the development and assessment of a CFD methodology to simulate cavitation for a liquid fuel pump used in aircraft fuel systems. The study simulates the cavitation phenomena using a multi-phase flow model consisting of fuel vapor, air, and liquid fuel phases.

There has been an increased interest in low speed aerodynamics for Unmanned Aerial Vehicles (UAVs) and Micro Aerial Vehicles (MAVs). These vehicles which are increasingly being used for reconnaissance purposes operate in the Root Chord Reynolds number range of 104 to around 105 and thus, the flow regime encountered is in the low Reynolds number transitional flow range. Computational Fluid Dynamics (CFD) methods which employ eddy viscosity based RANS turbulence models that are formulated for high Reynolds number flow are not well suited for such low Reynolds number range as they cannot accurately model the formation of laminar separation bubble and subsequent onset of transition. In this paper, the transition k-ω SST model is assessed for aerodynamics prediction for the SD7003 airfoil for Reynolds number ranging from 104 to 9 × 104 and angle of attack ranging from 0° to 8°. The assessment is carried out against available experimental and Large Eddy Simulation results.

Front end cooling module which consists of condenser, intercooler, radiator and fan is an important part of vehicle design as it affects radiator thermal performance. The current study describes a CFD based method to predict air-to-boil (ATB) temperature of a sport utility vehicle radiator. The predicted ATB values were compared with experimental data obtained from outdoor cooling trials. The predicted and the experimental values agreed to within 3-5 °C for operating conditions ranging from low gear-low speed condition to high gear-high speed conditions. It is anticipated that such analyses will lead to reduction of design cycle time and prototyping costs.

Braking performance of a vehicle is affected by the temperature rise in the wheel end components. In this study, the brake cooling performance of drum brake assembly for a heavy truck has been simulated using a three-dimensional time-transient CFD model. The predicted brake drum temperatures were compared with the indoor dynamometer test results. The agreement between the predictions and the test data was within 8°C for the braking cycle analyzed. The validated model was used to asses the effect of air flow angle of attack on the brake drum and rim temperatures.