Search Results

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.

Wind noise is becoming a higher priority in the automotive industry. Several past studies investigated whether Statistical Energy Analysis (SEA) can be utilized to predict wind noise. Because wind noise analysis requires both radiation and transmission modeling in a wide frequency band, turbulent-structure-acoustic-coupled-SEA is being used. Past research investigated coupled-SEA’s benefit, but the model is usually simplified to enable easier consideration on the input side. However, the vehicle is composed of multiple interior parts and possible interior countermeasure consideration is needed. To enable this, at first, a more detailed coupled-SEA model is built from the acoustic-SEA model which has a larger number of degrees of freedom for the interior side. Then, the model is modified to account for sound radiation effects induced by turbulent and acoustic pressure.

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.

Previous studies and recent practical aerodynamic evaluations have shown that aerodynamic drag of passenger vehicles with “ground simulation” with moving ground and rotating wheels may increase in some cases and decrease in other cases relative to the fixed ground and stationary wheel conditions. Accordingly, the effects of the ground simulation on the aerodynamic drag should be deeply understood for further drag reduction. Although the previous studies demonstrated what is changed by the ground simulation, the reason for the change has not been fully understood. In this article, the effects of wheels and wheel houses attachment and those by the ground simulation with ground movement and wheel rotation on the aerodynamic drag were investigated by quantification of the underfloor flow that plays a crucially important role on the formation of vortical structure around vehicles.

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.

Roadway departure mitigation systems for helping to avoid and/or mitigate roadway departure collisions have been introduced by several vehicle manufactures in recent years. To support the development and performance evaluation of the roadway departure mitigation systems, a set of commonly seen roadside surrogate objects need to be developed. These objects include grass, curbs, metal guardrail, concrete divider, and traffic barrel/cones. This paper describes how to determine the representative color of these roadside surrogates. 24,762 locations with Google street view images were selected for the color determination of roadside objects. To mitigate the effect of the brightness to the color determination, the images not in good weather, not in bright daylight and under shade were manually eliminated. Then, the RGB values of the roadside objects in the remaining images were extracted.

This study used finite element (FE) simulations to analyze the injury mechanisms of driver spine fracture during frontal crashes in the World Endurance Championship (WEC) series and possible countermeasures are suggested to help reduce spine fracture risk. This FE model incorporated the Total Human Model for Safety (THUMS) scaled to a driver, a model of the detailed racecar cockpit and a model of the seat/restraint systems. A frontal impact deceleration pulse was applied to the cockpit model. In the simulation, the driver chest moved forward under the shoulder belt and the pelvis was restrained by the crotch belt and the leg hump. The simulation predicted spine fracture at T11 and T12. It was found that a combination of axial compression force and bending moment at the spine caused the fractures. The axial compression force and bending moment were generated by the shoulder belt down force as the driver’s chest moved forward.

This paper deals with friction under wet condition in the disk brake system of automobiles. In our previous study, the variation of friction coefficient μ was observed under wet condition. And it was experimentally found that μ becomes high when wear debris contains little moisture. Based on the result, in this paper, we propose a hypothesis that agglomerates composed of the wet wear debris induce the μ variation as the agglomerates are jammed in the gaps between the friction surfaces of a brake pad and a disk rotor. For supporting the hypothesis, firstly, we measure the friction property of the wet wear debris, and confirm that the capillary force under the pendular state is a factor contributing to the μ variation. After that, we simulate the wear debris behavior with or without the capillary force using the particle-based simulation. We prepare the simulation model for the friction surfaces which contribute to the friction force through the wear debris.

The present paper describes an application of non-parametric shape optimization to disc brake squeal phenomena. A main problem is defined as complex eigenvalue problem in which the real part of the complex eigenvalue causing the brake squeal is chosen as an objective cost function. The Fre´chet derivative of the objective cost function with respect to the domain variation, named as the shape derivative of the objective cost function, is evaluated using the solution of the main problem and the adjoint problem. A selection criterion of the adoptive mode number in component mode synthesis (CMS), which is used in the main problem, is presented in order to reduce the computational error in complex eigenvalue pairs. A scheme to solve the shape optimization problem is presented using an iterative algorithm based on the H1 gradient method for reshaping. For an application of the optimization method, a numerical example of a practical disc brake model is presented.

It is important for vehicle concept planning to estimate fuel economy and the influence of vehicle vibration in advance. This can be accomplished using virtual engine specifications and a virtual vehicle frame. In this paper, I will show the power plant model with electric starter and battery that can predict fuel economy, combustion heat results and transient torque. The power plant is a 1.3L 4cyl designed for NA Spark Ignition. The power plant model was realized using an energy based model using VHDL-AMS. Here, VHDL-AMS is modeling language stored in IEC international standard (IEC61691-6) and can realize multi physics in 1D simulation. The modeling language supports electrical, magnetic, thermal, mechanical, fluidic and compressive fluidic domains. The model was created in house using VHDL-AMS and validated on ANSYS SIMPLORER. The simulated results of fuel energy consumption agreed with driving energy and amount of energy losses, e.g. cooling loss, exhaust loss.