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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.

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.

To evaluate the performance of Body-in-white design different attributes needs to be evaluated at various design levels. The current paper focus on evaluation and improvement of Body in white structure in detailed design stage of product development by identifying common performance contributors with multiple model inputs and design validation plans to achieve global performance of the structure. This paper explains the methodology to evaluate the results of Initial Analysis and design iterations for multiple Design verification plans individually and also combined. Sensitivity study is carried out by Multi model DOE (Design of experiments) optimization method to identify the global performance effecting contributors for each design validation plan. The methodology could generate a design which improve stiffness on local joinery sections and also global structural stiffness parameters in both static and dynamic condition by keeping the overall mass in acceptable range.

The idea of employing an infinite element to solve acoustic problems in an unbounded domain has demonstrated significant promise. Starting from first principles, the detailed element formulation for a mapped wave-envelope infinite element is presented. This, in conjunction with an efficient search algorithm to map receiver grid locations to the pertinent infinite element on the boundary, is used to enhance an established finite-element based vibro-acoustic solver for frequency response in order to solve large scale industrial problems. The solver is then subjected to a thorough validation and verification study using problems whose solutions are established either through classical texts or alternative approaches to demonstrate the accuracy, robustness and efficiency of the current solution.

It is typical in aircraft engine design to explore new configurations in a constant effort to achieve greater efficiency with respect to various considerations. An integral component of this process requires a complete and robust simulation of rotor dynamics. Tuning the design with results of rotor dynamics simulations can be made possible with a tool that has adequate modeling techniques to capture the physics associated with engine behavior under various operating conditions accurately.

A manufacturer of automotive equipment set out to implement a process to include sound and vibration quality targets for powertrain and road noise. CAE models have been successfully used in the early phase of the vehicle development process, but the use of these models to assess the customer’s subjective sound and vibration experience is often missing. The goal here was to use a CAE model driven by sound and vibration quality targets for early identification of problem areas based on jurors’ preference. These quality targets were cascaded via Source-Path-Contribution (SPC), and optimizations were performed to meet the targets using the CAE model.

For the use in flexible fuel vehicles able to operate with mixtures of alcohol fuels and gasoline, an intelligent alcohol sensor has been developed. Based on the measurement of the dielectric constant, this sensor overcomes the problems with optical measuring principles; these problems are due to sensitivity to different contents of aromatics. To increase the accuracy, a microprocessor evaluates the input signals (dielectric constant and other parameters). Thus, a compensation of misdetection due to impurities can also be achieved. The output characteristic of the sensor can be chosen freely; the output voltage can correspond to the alcohol content as well as to the required correction factor for the injection time.

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.

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.

This paper provides data concerning the enrichment of automotive carburetors with variation of inlet air pressure and temperature. These changes occur with weather and the seasons, with altitude, and because of underhood heating. The early opening of the conventional carburetor enrichment value at altitude can add greatly to the “ normal” carburetor enrichment. Means for compensating the mixture ratio for these changes in inlet air conditions are known, but will almost certainly add to the complexity and cost of the engine induction system. The cost of improved devices must be compromised with the possible reduction in exhaust emissions and improvement in fuel economy.

A process to achieve vehicle system level NVH objectives using CAE simulation tools is discussed. Issues of modeling methodology, already covered adequately in the literature, are less emphasized so that the paper can focus on the application of a process that encompasses objective setting, design synthesis, and performance achievement using simulation predictions. A reference simulation model establishes correlation levels and modeling methods that are applied to future predictions. The new model, called a “Digital Mule”, is an early new product “design intent” simulation used to arrive at subsystem goals to meet the vehicle level NVH objectives. Subsystem goals are established at discrete noise paths where structure borne noise enters the body subsystem. The process also includes setting limits on the excitation sources, such as suspension and powertrain.

It is often necessary to measure three-axis displacement of a deforming or moving part in static or dynamic impact tests. A point moving in the three-dimensional space can be monitored and measured using three string-pots or other distance measuring devices with a methodology developed here. A numerical algorithm along with required equations are shown and discussed. The algorithm was applied as an example to static seat pull test and compared to results from film analysis. The application with string pots is useful especially when the point of concern gets hidden or blocked by other parts disabling the photogrammetry technology.

At the present time, combustion process effects on vehicle interior noise can be evaluated only when vehicle and engine are physically available. This Paper deals with a new method for the prediction of combustion process induced vehicle interior noise. The method can be applied already in early combustion system development and allows a time and cost efficient calibration optimization of engine and vehicle. After establishing appropriate transfer weighting functions (engine) and structure transfer functions (vehicle), audible vehicle interior noise is generated based on appropriate cylinder pressure analysis. Combustion process effects on interior noise can be judged subjectively as well as objectively. Thus, combustion process development at the thermodynamic test bench is effectively supported to achieve an optimal compromise with respect to fuel consumption, exhaust emission and interior noise quality.

Cohesive modeling is one of the unique methods which has been used to model adhesive bonding in computer aided engineering (CAE) industry. There exist numerous conventional methodologies which involve the usage of hexa and penta elements by assigning cohesive material properties. These methods inherently are error-prone in terms of modeling errors and result in increased modeling and computation times. A conventional method of cohesive zone modeling (CZM) has a drawback of higher computation and modeling time. Due to this problem, sometimes engineers tend to avoid simulations and rely only on some sort of approximation of crack from previous designs. This approximation can lead to either product failure or overdesign of the product.