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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.

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.

With increasing efforts towards rapid electrification of powertrains, NVH engineers face new set of challenges. Elimination of the IC engines drastically reduces powertrain borne noise levels but unmasks other existing noises like wind, road, ancillary devices, and squeak & rattle. In addition, the new tonal sounds from electro-mechanical drive systems makes the noise more annoying even though it is lesser quantitatively. In summary, the electrification of powertrains has shifted powertrain NVH development from overall level to sound quality with different targets requiring several electro-mechanical solutions with innovative simulation, testing, and optimization approaches. The purpose of the paper is to present an approach to detect, quantify, and optimize the structure-borne radiated noise of an electric motor due to electromagnetic forces or maxwell pressure exerted by magnetic effects in electric 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.

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.

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.

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.

The recent growth of available computational resources has enabled the automotive industry to utilize unsteady Computational Fluid Dynamics (CFD) for their product development on a regular basis. Over the past years, it has been confirmed that unsteady CFD can accurately simulate the transient flow field around complex geometries. Concerning the aerodynamic properties of road vehicles, the detailed analysis of the transient flow field can help to improve the driving stability. Until now, however, there haven’t been many investigations that successfully identified a specific transient phenomenon from a simulated flow field corresponding to driving stability. This is because the unsteady flow field around a vehicle consists of various time and length scales and is therefore too complex to be analyzed with the same strategies as for steady state results.

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.

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.

Cameras and image processing hardware are rapidly evolving technologies, which enable real-time applications for passenger cars, ground robots, and aerial vehicles. Visual odometry (VO) algorithms estimate vehicle position and orientation changes from the moving camera images. For ground vehicles, such as cars, indoor robots, and planetary rovers, VO can augment movement estimation from rotary wheel encoders. Feature-based VO relies on detecting feature points, such as corners or edges, in image frames as the vehicle moves. These points are tracked over frames and, as a group, estimate motion. Not all detected points are tracked since not all are found in the next frame. Even tracked features may not be correct since a feature point may map to an incorrect nearby feature point. This can depend on the driving scenario, which can include driving at high speed or in the rain or snow.

Global surrogate modeling aims to build surrogate model with high accuracy in the whole design domain. A major challenge to achieve this objective is how to reduce the number of function evaluations to the original computer simulation model. To date, the most widely used approach for global surrogate modeling is the adaptive surrogate modeling method. It starts with an initial surrogate model, which is then refined adaptively using the mean square error (MSE) or maximizing the minimum distance criteria. It is observed that current methods may not be able to effectively construct a global surrogate model when the underlying black box function is highly nonlinear in only certain regions. A new surrogate modeling method which can allocate more training points in regions with high nonlinearity is needed to overcome this challenge. This article proposes an efficient global surrogate modeling method based on a multi-layer sampling scheme.

In 2016 the United States Automotive Materials Partnership (USAMP) approached several software vendors with the desire to establish the current state-of-the-art of explicit finite element software for predicting the crash behavior of composite laminates as it relates to application in the automotive industry. The nonlinear explicit solver, RADIOSS, was included in the investigation. Coupon and generic component level test data were supplied to help with the development of material models. The innovation of the approach taken with RADIOSS was to use a numerical Design of Experiments (DOE) to simultaneously fit the various modes of material damage and failure for the composite material. Final correlation was to a series of sled tests completed on a composite bumper and crush cans.

Correct kinematics and compliance modeling of a MacPherson strut suspension requires including the physics of strut rod bending. Various approaches to modeling this bending are available, but these require extensive testing or iteration to achieve reasonable results. This paper presents a new method of modeling strut bending that relies only on easily measured physical characteristics, and yet maintains a high degree of accuracy.

The authors are responsible for the development of a structural 3D shell based bead-to-bead model with sidewalls and belt that separately models all functional layers of a modern tire [4]. In this model, the inflation pressure is modeled as a uniform stress acting normal to the shell’s inner face. The pressure can vary depending on the application: prescribed by the MBS-tool to align to a constant pressure specified for a vehicle or scenario, but it can also be modified dynamically to simulate e.g. a sudden pressure loss in a tire [1]. For many applications, this description of the inflation pressure as a time dependent quantity is sufficient. However, there are applications where it is needed to describe the inflation gas using a dynamic gas equation (Euler or Navier-Stokes). One such example is when the tire model is used in NVH (Noise-Vibration-Harshness) applications where the frequency range extends the 200 Hz range.

Simulation based design and control optimization is widely used to assist the development of highly complex modern downsized turbocharged gasoline direct injection (GDI) engines. In such engines, knock phenomenon is a major constraint that limits performance and fuel economy enhancements. Thus, an accurate knock prediction model is critically important for virtual engine development process. In this paper, an enhanced ignition delay model is proposed for spark ignition (S)I combustion model based on previously developed empirical two-stage ignition delay model using fuel blends [1]. The ignition delay model provides a capability of predicting ignition delay of the end-gas zone for different fuel blends without additional calibration when fuel blending ratio changes. To adapt the ignition delay model to the SI combustion environment, the model is modified to have the sensitivity to the dilution effect by residual gas.

To reliably implement driver-assist features and ultimately self-driving cars, autonomous driving systems will likely rely on a variety of sensor types including GPS, RADAR, LASER range finders, and cameras. Cameras are an essential sensory component because they lend themselves to the task of identifying object types that a self-driving vehicle is likely to encounter such as pedestrians, cyclists, animals, other cars, or objects on the road. In this paper, we present a feature-based visual odometry algorithm based on a stereo-camera to perform localization relative to the surrounding environment for purposes of navigation and hazard avoidance. Using a stereo-camera enhances the accuracy with respect to monocular visual odometry. The algorithm relies on tracking a local map consisting of sparse 3D map points. By tracking this map across frames, the algorithm makes use of the full history of detected features which reduces the drift in the estimated motion trajectory.

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.

The problem of predicting the quality of weld is critical to manufacturing. A great deal of data is collected under multiple conditions to predict the quality. The data generated at Daimler Chrysler has been used to develop a model based on grammatical evolution. Grammatical Evolution Technique is based on Genetic Algorithms and generates rules from the data which fit the data. This paper describes the development of a software tool that enables the user to choose input variables such as the metal types of top and bottom layers and their thickness, intensity and speed of laser beam, to generate a three dimensional map showing weld quality. A 3D weld quality surface can be generated in response to any of the two input variables picked from the set of defining input parameters. This tool will enable the user to pick the right set of input conditions to get an optimal weld quality. The tool is developed in Matlab with Graphical User Interface for the ease of operation.

NVH targets for future vehicles are often defined by utilizing a competitive benchmarking vehicle in conjunction with an existing production and/or reference vehicle. Mode management of full vehicle modes is one of the most effective and significant NVH strategies to achieve such targets. NVH dynamic characteristics of a full vehicle can be assessed and quantified through experimental modal testing for determination of global body mode resonance frequency, damping property, and mode shape. Major body modes identified from full vehicle modal testing are primarily dominated by the vehicle's body-in-white structure. Therefore, an estimate of BIW modes from full vehicle modes becomes essential, when only full vehicle modes from experimental modal testing exist. Establishing BIW targets for future vehicles confines the fundamental NVH behavior of the full vehicle.