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Ensuring durability is one of the key requirements while developing cooling modules for various powertrains. Typically, road surface induced loads are the main driving force behind mechanical failures. While developing the components, road load accelerations are utilized in CAE simulations to predict the high-stress regions and estimate the fatigue life of the components mounted on the body. In certain scenarios where components are mounted to the body and attached to the engine with hoses, the components can experience additional loads associated with engine vibration. This attachment scheme requires a different analysis methodology to determine fatigue life. In the proposed paper, we look at the effect of engine motion (EM) on the fatigue life of internal transmission oil cooler (ITOC) which is mounted on the body through radiator and is simultaneously connected to the engine using a steel pipe. We propose a new CAE methodology taking into account the engine motion displacements.

In this work, we outline a process for traffic light detection in the context of autonomous vehicles and driver assistance technology features. For our approach, we leverage the automatic annotations from virtually generated data of road scenes. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network, without pre-training, for classification of traffic light signals (green, amber, red). After training on virtual data, we tested the network on real world data collected from a forward facing camera on a vehicle. Our new region proposal technique uses color space conversion and contour extraction to identify candidate regions to feed to the deep neural network classifier. Depending on time of day, we convert our RGB images in order to more accurately extract the appropriate regions of interest and filter them based on color, shape and size. These candidate regions are fed to a deep neural network.

An engine cooling system in an automotive vehicle comprises of heat exchangers such as a radiator, charge air cooler and oil coolers along with engine cooling fan. Typical automotive engine-cooling fan assembly includes an electric motor mounted on a shroud that encloses the radiator core. One of main drivers of fan shroud design is Noise, Vibration, and Harshness (NVH) requirements without compromising the main function of airflow for cooling requirements. In addition, there is also a minimum stiffness requirement of fan shroud which is often overlooked in arriving at optimal design of it. Low Speed Damageability (LSD) assessment of an automotive vehicle is about minimizing the cost of repair of vehicle damages in low speed crashes. In low speed accidents, these fan motors are subjected to sudden decelerations which cause fan motors to swing forward thereby damaging the radiator core. So designing fan shroud for low speed damageability is of importance today.