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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.

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.

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.

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.

There is a distinct difference between plug-in hybrid electric vehicles in the market today. One key distinction that can be made is to classify a plug-in hybrid electric vehicle (PHEV) according to its operational behavior in charge depleting (CD) mode. Some PHEVs are capable of using the electric-only propulsion system to achieve all-electric operation for all driving conditions in CD mode, including full power performance. In contrast, some PHEVs, henceforth termed “blended PHEVs”, cannot satisfy the power requirements of all driving conditions with the electric-only propulsion system and occasionally utilize blended CD operation whereby it is necessary to blend the use of the internal combustion (IC) engine with the use of the electric motor(s) to help power the vehicle.

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.

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.

This paper covers work performed for the California Air Resources Board and US Environmental Protection Agency by Southwest Research Institute. Emission measurements were made on four in-use off-road two-stroke motorcycles and all-terrain vehicles utilizing oxygenated and non-oxygenated fuels. Emission data was produced to augment ARB and EPA's off-road emission inventory. It was intended that this program provide ARB and EPA with emission test results they require for atmospheric modeling. The paper describes the equipment and engines tested, test procedures, emissions sampling methodologies, and emissions analytical techniques. Fuels used in the study are described, along with the emissions characterization results. The fuel effects on exhaust emissions and operation due to ethanol content and fuel components is compared.

The study was aimed at assessing in-use emissions of a USEPA 2010 emissions-compliant heavy-duty diesel vehicle powered by a model year (MY) 2011 engine using West Virginia University's Transportable Emissions Measurement System (TEMS). The TEMS houses full-scale CVS dilution tunnel and laboratory-grade emissions measurement systems, which are compliant with the Code of Federal Regulation (CFR), Title 40, Part 1065 [1] emissions measurement specifications. One of the specific objectives of the study, and the key topic of this paper, is the quantification of greenhouse gas (GHG) emissions (CO2, N2O and CH4) along with ammonia (NH3) and regulated emissions during real-world operation of a long-haul heavy-duty vehicle, equipped with a diesel particulate filter (DPF) and urea based selective catalytic reduction (SCR) aftertreatment system for PM and NOx reduction, respectively.

The impact of biodiesel and new generation biofuels on emissions from heavy-duty diesel engines was investigated using a California Air Resources Board (CARB) certified diesel fuel as a base fuel. This study was performed on two heavy-duty diesel engines, a 2006 engine and a diesel particle filter (DPF) equipped 2007 engine, on an engine dynamometer over four different test cycles. Emissions from soy-based and animal-based biodiesel, renewable diesel fuel, and gas-to-liquid (GTL) diesel fuel were evaluated at blend levels ranging from 5 to 100%. Consistent with previous studies, particulate matter (PM), hydrocarbons (HC), and carbon monoxide (CO) emissions generally showed increasing reductions with increasing biodiesel and renewable/GTL diesel fuel blend levels for the non-DPF equipped engine. The levels of these reductions were generally comparable to those found in previous studies performed using more typical Federal diesel fuels.