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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.

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.

Two of the most commonly exercised standards for electromagnetic compatibility (EMC) by automotive engineers are CISPR 12 and CISPR 25. While CISPR 12 is imposed as a regulation to ensure uninterrupted communication for off-board receivers, CISPR 25 is often applied to ensure the quality of services of on-board receivers. Performing these tests becomes challenging until the vehicle is prototyped which may prolong the production time in case of failure or need for modification. However, conducting these tests in a simulation environment can offer more time and cost-efficient ways of analyzing the electromagnetic environment of automotive vehicles. In this paper, a computational approach is proposed in order to predict electromagnetic disturbance from on-board electronics/electrical systems using 3D computational electromagnetic (CEM) tool; Altair Feko.

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.

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.

In this era of technologies, Remote Keyless Entry (RKE) system has become an integral part of motor vehicles. Over the years, a lot of functionalities have been added to RKE systems. To achieve functional communication between key-fob antennas and vehicular receiving antennas, it is necessary to analyze the impact of a human body as well as the receiving antenna placements on the vehicle’s body. Taking these variations into account during the antenna development phase becomes expensive and tedious since achieving an efficient design would require several iterations, testing, and modification, in the design. Hence, Computational Electromagnetic (CEM) techniques become a feasible solution to explore such scenarios and adopt necessary modifications as needed. This paper introduces a methodological process of designing RKE antennas using 3D CEM Simulation tool; namely Altair Feko.

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.

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.

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].

A low profile high directivity antenna is designed to operate at 5.9 GHz for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to ensure connectivity in different propagation channels. Patch antennas are still an ongoing topic of interest due to their advantages: low profile, low cost, and ease of fabrication. One disadvantage of the patch antenna is low directivity which results in low range performance. In this paper, we introduce an efficient and novel way to improve the directivity of patch antenna using topology optimization and design of experiments (DoE). Numerical simulations are done using Method of Moments (MoM) technique in the commercially available tool, FEKO. We use global response surface method (GRSM) for double objectives topology optimization. Numerical results show a promising use of topology optimization and DoE techniques for the systematic design of high directivity of low profile single element patch antennas.

The human thermal comfort, which has been a subject of extensive research, is a principal objective of the automotive climate control system. Applying the results of research studies to the practical problems require quantitative information of the thermal environment in the passenger compartment of a vehicle. The exposure to solar radiation is known to alter the thermal environment in the passenger compartment. A photovoltaic-cell based sensor is commonly used in the automotive climate control system to measure the solar radiation exposure of the passenger compartment of a vehicle. The erroneous information from a sensor however can cause thermal discomfort to the occupants. The erroneous measurement can be due to physical or environmental parameters. Shading of a solar sensor due to the opaque vehicle body elements is one such environmental parameter that is known to give incorrect measurement.

The Chevrolet Volt is an electric vehicle with extended-range that is capable of operation on battery power alone, and on engine power after depletion of the battery charge. First generation Chevrolet Volts were driven over half a billion miles in North America from October 2013 through September 2014, 74% of which were all-electric [1, 12]. For 2016, GM has developed the second-generation of the Volt vehicle and “Voltec” propulsion system. By significantly re-engineering the traction power inverter module (TPIM) for the second-generation Chevrolet Volt extended-range electric vehicle (EREV), we were able to meet all performance targets while maintaining extremely high reliability and environmental robustness. The power switch was re-designed to achieve efficiency targets and meet thermal challenges. A novel cooling approach enables high power density while maintaining a very high overall conversion efficiency.

The development of the WorldSID 50th percentile male dummy was initiated in 1997 by the International Organization for Standardization (ISO/SC12/TC22/WG5) with the objective of developing a more biofidelic side impact dummy and supporting the adoption of a harmonized dummy into regulations. More than 45 organizations from all around the world have contributed to this effort including governmental agencies, research institutes, car manufacturers and dummy manufacturers. The first production version of the WorldSID 50th male dummy was released in March 2004 and demonstrated an improved biofidelity over existing side impact dummies. Full-scale vehicle tests covering a wide range of side impact test procedures were performed worldwide with the WorldSID dummy. However, the vehicle safety performance could not be assessed due to lack of injury risk curves for this dummy. The development of these curves was initiated in 2004 within the framework of ISO/SC12/TC22/WG6 (Injury criteria).

Automotive engines are regularly utilized in the material handling market where LPG is often the primary fuel used. When compared to gasoline, the use of gaseous fuels (LPG and CNG) as well as alcohol based fuels, often result in significant increases in valve seat insert (VSI) and valve face wear. This phenomenon is widely recognized and the engine manufacturer is tasked to identify and incorporate appropriate valvetrain material and design features that can meet the ever increasing life expectations of the end-user. Alternate materials are often developed based on laboratory testing – testing that may not represent real world usage. The ultimate goal of the product engineer is to utilize accelerated lab test procedures that can be correlated to field life and field failure mechanisms, and then select appropriate materials/design features that meet the targeted life requirements.

The auto industry has had decades of experience with designing safe vehicles. The introduction of highly integrated features brings new challenges that require innovative adaptations of existing safety methodologies and perhaps even some completely new concepts. In this paper, we describe some of the new challenges that will be faced by all OEMs and suppliers. We also describe a set of generic top-level potential hazards that can be used as a starting point for the Preliminary Hazard Analysis (PHA) of a vehicle software-intensive motion control system. Based on our experience with the safety analysis of a system of this kind, we describe some general categories of hazard causes that are considered for software-intensive systems and can be used systematically in developing the PHA.

By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

We start our study with a survey of existing variable valve actuation (VVA) devices. We then describe our work, taken place over a time period from 2001 to 2007, on several VVA concepts. All of our projects described include pre-design modeling and simulation. Also, for each one of the proposed designs, a bench-top motorized test fixture was built and ran for proof of concept. Our projects represent a mixture of exploratory research and production-related development work. They can be classified in four broad categories: discrete-step systems; mechanical continuously-variable systems; active stationary-hydraulic lash adjusters; cam-driven hydraulic-lost-motion mechanism. These devices differ in their complexity and versatility but offer a spectrum of design solutions applicable to a range of products. Specific attributes of these different approaches are analyzed and discussed, and some test results are presented.

The Advanced Engineering (AE) group within General Motors Powertrain (GMPT) develops next generation engines and transmissions for automotive and marine products. As a research organization, AE needs to prototype design ideas quickly and inexpensively. To this end, AE has embraced model-based development techniques and is currently investigating the benefits of software in-the-loop (SIL) testing. The underlying obstacle faced in developing a practical SIL system lays in the ability to integrate a plant model with sufficient fidelity together with target application software. ChiasTek worked with AE utilizing their CosiMate tool chain to eliminate these barriers and delivered a flexible SIL system simulation solution.