Browse Topic: Design Engineering and Styling
Video analysis plays a major role in many forensic fields. Many articles, publications, and presentations have covered the importance and difficulty in properly establishing frame timing. In many cases, the analyst is given video files that do not contain native metadata. In other cases, the files contain video recordings of the surveillance playback monitor which eliminates all original metadata from the video recording. These “video of video” recordings prevent an analyst from determining frame timing using metadata from the original file. However, within many of these video files, timestamp information is visually imprinted onto each frame. Analyses that rely on timing of events captured in video may benefit from these imprinted timestamps, but for forensic purposes, it is important to establish the accuracy and reliability of these timestamps. The purpose of this research is to examine the accuracy of these timestamps and to establish if they can be used to determine the timing
The pre-validation process for door trim noise has gained increasing importance as noise standards have become more stringent with the transition to electric vehicles. Currently, the validation process employs squeak and rattle director simulations to evaluate noise based on relative displacement values. However, this approach is time-intensive. To address this limitation, we have improved process efficiency by developing a database of relative displacement values derived from the cross-sectional and structural characteristics of matching parts. This advancement enables noise pre-validation using only cross-sectional and structural information.
This paper reports on the development of a simulation model to predict engine blowby flow rates for a common rail DI diesel engine. The model is a transient, three-dimensional computational fluid dynamics (CFD) model. Managing blowby flow rates is beneficial for managing fuel economy and oil consumption. In doing so, an improved understanding of the blowby phenomenon is also possible. A mesh for the sub-micron level clearances (up to 0.5 microns) within the piston ring pack is created using a novel approach. Commercial CFD software is used to solve the pressure, velocity, and temperature distributions within the fluid domain. Ring motions within the piston grooves are predicted by a rigorous force balance. This model is the first of its kind for predicting engine blowby using a three-dimensional simulation model while solving the complete set of governing transport equations, without neglecting any terms in the equations. The predicted blowby flow rate has been validated with
The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements, arises the need for increased electric current supply to motors. Increased amperage through the stator causes higher losses resulting in elevated temperature across the motor components and its housing. In most of the cases, stator is mounted on the housing through interference fit to avoid any slippage during operation conditions. High temperature across the stator and housing causes significant thermal expansions of the components which is uneven in nature due to the differences in corresponding coefficient of thermal expansion (CTE) values. Housings are generally made of aluminium and tends to expand more having higher value of CTE than that of steel core of stator which may give rise to a failure mode related to stator slippage. To address this slippage if the amount of interference fit is increased, that’ll result in another failure mode
Structural topology optimization for vehicle structures under static loading is a well-established practice. Unfortunately, extending these methods to components subjected to dynamic loading is challenged by the absence of sensitivity coefficients: analytical expressions are unavailable and numerical approximations are computationally impractical. To alleviate this problem, researchers have proposed methods such as hybrid cellular automata (HCA) and equivalent static load (ESL). This work introduces a new approach based on equivalent static displacement (ESD). The proposed ESD method uses a set of prescribed nodal displacements, simulating the resultant reaction forces of a body subjected to dynamic loading, at different simulation time steps to establish the boundary conditions for each corresponding model—one model for each simulation time. A scalarized multi-objective function is defined considering all the models. A gradient-based optimizer is incorporated to find the optimal
Model-based developers are turning to DevOps principles and toolchains to increase engineering efficiency, improve model quality and to facilitate collaboration between large teams. Mature DevOps processes achieve these through automation. This paper demonstrates how integrating modern version control (Git) with collaborative development practices and automated quality enforcement can streamline workflows for large teams using Simulink. The focus is on enhancing model consistency, enabling team collaboration, and development speed.
The main purpose of the semi-active hydraulic damper (SAHD) is for optimizing vehicle control to improve safety, comfort, and dynamics without compromising the ride or handling characteristics. The SAHD is equipped with a fast-reacting electro-hydraulic valve to achieve the real time adjustment of damping force. The electro-hydraulic valve discussed in this paper is based on a valve concept called “Pilot Control Valve (PCV)”. One of the methods for desired force characteristics is achieved by tuning the hydraulic area of the PCV. This paper describes a novel development of PCV for practical semi-active suspension system. The geometrical feature of the PCV in the damper (valve face area) is a main contributor to the resistance offered by the damper. The hydraulic force acting on the PCV significantly impacts the overall performance of SAHD. To quantify the reaction force of the valve before and after optimization under different valve displacements and hydraulic pressures were simulated
This paper introduces an innovative digital solution for the categorization and analysis of fractures in Auto components, leveraging Artificial Intelligence and Machine Learning (AI/ML) technologies. The proposed system automates the fracture analysis process, enhancing speed, reliability, and accessibility for users with varying levels of expertise. The platform enables users to upload images of fractured parts, which are then processed by an AI/ML engine. The engine employs an image classification model to identify the type of fracture and a segmentation model to detect and analyze the direction of the fracture. The segmentation model accurately predicts cracks in the images, providing detailed insights into the direction and progression of the fractures. Additionally, the solution offers an intuitive interface for stakeholders to review past analyses and upload new images for examination. The AI/ML engine further examines the origin of the fracture, its progression pattern, and the
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