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Machine Learning applications are developed to disrupt product design methodology across all industries. Every design engineer would like to optimize his design at the concept stage only considering a few critical and essential load cases. The major challenge for the design engineer has not much simulation expertise required to prepare the CAE model, apply material properties, load case, solve and post-process to understand the CAE performance. Even, when the engineer has CAE expertise, it will take a considerable amount of time to prepare the CAE model, solve and post-process it.

Today, vehicle architectures are changing continuously due to the need for increasing vehicle electrification. Electric motors have helped sustain this requirement. Traditional internal combustion engines are being replaced or coupled with traction motors or in-wheel motor systems in full-electric or hybrid-electric vehicles. With the use of electric motor in a vehicle, the number of parts can be reduced. This leads to reduced packaging size and complexity. Also, CO2 emissions are reduced, and overall efficiency is increased. But the task of designing an electric motor which is assembled in a vehicle could be quite complex. The design of an electric motor can affect the durability, and noise and vibration characteristic of the vehicle structure to which it is connected. The design of the vehicle structure to which the motor attaches should be able to sustain the magnetic torque generated by the motor.

Tire pressure monitoring system (TPMS) is becoming ubiquitous in modern day vehicles with advanced safety and driver assist systems and plays a key role in predictive maintenance. One of the key challenges to realize an efficient TPMS system is to ensure good antenna coupling between the reader antenna in the cabin or on the roof of the vehicle and the antennas in the tires. Understanding the different external factors that affect the antenna coupling is vital to realize an efficient design. Computer aided simulations on antenna coupling is a cost-effective method to reduce the chances of failure before a TPMS is deployed in an actual vehicle. In this work, a computational approach is presented to optimize the antenna coupling and hence the link budget between the reader antennas and the TPMS antennas at 915 MHz. This is achieved by employing machine learning based optimization using commercially available tools, Altair’s HyperStudy and Altair’s Feko.

NVH (Noise Vibration & Harshness) is one of the main focus areas during the development of products such as passenger cars or trucks. Physical test methods have traditionally been used to assess NVH, but the necessity for reducing cost and creating a robust solution early in the design process has driven the increased usage of simulation tools. Development of well-defined methods and tools for NVH analysis allows today’s OEMs to have a virtual engineering based development cycle from concept to test. However, a subset of NVH problems including squeak and rattle (S&R) have not been generally focused upon. In a vehicle, S&R is a recurring problem for interior plastic parts such as an instrument panel or door trim. Since 2012, Altair has been developing S&R Director (SnRD), which is a solution that identifies and combats S&R issues by embedding the Evaluation-Line (E-Line) methodology [1] [2].

In this work, the multi-disciplinary problem arising from fluid sloshing within a partially filled tanker truck undergoing lateral acceleration is investigated through the use of multiphysics coupling between a computational fluid dynamics (CFD) solver and a multi-body dynamics (MBD) solver. This application represents a challenging test case for simulation technology within the design of commercial vehicles and is intended to demonstrate a novel approach in the field of computer aided engineering. Computer aided engineering is playing a more predominant role in the design process for commercial and passenger vehicles. Better understanding of the real time loading and responses on a vehicle during intended or unintended use can result in improved design and reduced cost over traditional designs that relied heavily on assumed loads.

Weight is a major factor during the development of Automotive Powertrains due to stringent fuel economy requirements. Light weighting constitutes a challenge to the engineering community when trying to deliver quieter powertrains. For this reason, the NVH (Noise Vibration Harshness) CAE engineers are adopting advanced vibro-acoustic simulation methods combined with topology optimization methods to drive the design of the under hood components for Noise Vibration and Harshness. Vibro-acoustic computational methods can be complex and require significant computation effort. Computation of Equivalent Radiated Power (referred to as ERP) is a simplified method to assess maximum dynamic radiation of components for specific excitations in frequency response analysis which in turn affects radiated sound. Topology Optimization is a mathematical technique used to find the best material distribution for structural systems in order to deliver a specific objective under clearly defined constraints.