Browse Topic: Design processes
The design of thermal components (such as automotive heat exchangers) requires balancing multiple competing objectives—thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design workflows rely on manual Computer Aided Design (CAD) modeling and iterative simulations, which are both labor-intensive and time-consuming. Recent advances in Large Language Models (LLMs) present untapped potential for automating parametric CAD generation. However, current LLM-based approaches primarily handle simple, isolated geometric primitives rather than complex multi-component assemblies. This work introduces a progressive framework that leverages fine-tuned LLMs (Qwen2.5-3B-SFT) integrated with the CadQuery CAD kernel to automatically generate parametric geometries from natural language descriptions. As a foundational study, this work focuses on Step 1 of the framework: generating and optimizing isolated geometric primitives (cylinders, pipes, etc.) that
The automotive industry is undergoing a fundamental transformation in Electrical/Electronic (E/E) architecture, evolving from traditional distributed and domain-based designs toward zonal configurations. The rapid growth of software-defined functionality, cross-domain integration, and centralized computing has exposed inherent limitations of legacy architectures in scalability, wiring complexity, and system integration. Zonal E/E architecture addresses these challenges by consolidating computing and Input/Output (I/O) resources into high-performance controllers distributed across physical zones of a vehicle. This transformation, however, cannot occur instantaneously, as contemporary vehicle designs and E/E system solutions are the result of decades of incremental development based on distributed and domain-based paradigms. Moreover, key enabling technologies for zonal E/E architecture—such as high-performance Central Compute Platform (CCP) and zonal controllers, high-speed automotive
In recent years, computer-aided engineering (CAE) has become an essential practice in design and durability analysis of industrial components such as weldments. The current analytical trend for CAE-based fatigue life prediction of weldments includes procedures based on design guidelines, mesh-sensitive methods (e.g., local strain-life approach) and mesh insensitive methods (e.g., Volvo and Verity methods). As an inherent characteristic of weldments, the geometry of the weld is often simplified in failure analysis and important hotspots such as start/stop of the weld beads are not considered in the design process. However, such critical locations cannot be avoided in complex welded structures. Therefore, incorporating main geometrical details of the weld can improve the accuracy of critical regions identification and damage calculation using mesh-sensitive CAE-based methodologies. Herein, a framework for life prediction of welded components including the weld geometry is discussed and
Performing transportation and exploration tasks on rugged terrain requires both high load-bearing capacity and large suspension stroke. However, the corner module configurations applied to challenging terrain have rarely been explored. This article proposes an integrated framework that combines bionic principles with topology graph–based type synthesis. This framework leads to the creation of a reconfigurable wheel-legged mechanism capable of switching between wheeled locomotion and legged gait modes, which is then implemented as a corner module system. First, inspired by the skeletal–muscular system of the equine leg, a structure–function mapping relationship between the biological system and the mechanical system is established. Second, a multi-loop closed-chain mechanism with biomimetic morphology is represented in the form of graph theory. A configuration atlas of the wheel-legged hybrid mechanism is generated based on the contracted graph and open-loop kinematic chains, and
This study presents an integrated vehicle dynamics framework combining a 12-degree-of-freedom full vehicle model with advanced control strategies to enhance both ride comfort and handling stability. Unlike simplified models, it incorporates linear and nonlinear tire characteristics to simulate real-world dynamic behavior with higher accuracy. An active roll control system using rear suspension actuators is developed to mitigate excessive body roll and yaw instability during cornering and maneuvers. A co-simulation environment is established by coupling MATLAB/Simulink-based control algorithms with high-fidelity multibody dynamics modeled in ADAMS Car, enabling precise, real-time interaction between control logic and vehicle response. The model is calibrated and validated against data from an instrumented test vehicle, ensuring practical relevance. Simulation results show significant reductions in roll angle, yaw rate deviation, and lateral acceleration, highlighting the effectiveness
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT and ML technologies can enhance the efficiency of engine reliability testing. The paper includes the following case studies:
The design and improvement of electric motor and inverter systems is crucial for numerous industrial applications in electrical engineering. Accurately quantifying the amount of power lost during operation is a substantial challenge, despite the flexibility and widespread usage of these systems. Although it is typically used to assess the system’s efficiency, this does not adequately explain how or why power outages occur within these systems. This paper presents a new way to study power losses without focusing on efficiency. The goal is to explore and analyze the complex reasons behind power losses in both inverters and electric motors. The goal of this methodology is to systematically analyze the effect of the switching frequency on current ripple under varying operating conditions (i.e., different combinations of current and speed) and subsequently identify the optimum switching frequency for each case. In the end, the paper creates a complete model for understanding power losses
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis
The Mahindra XUV 3XO is a compact SUV, the first-generation of which was introduced in 2018. This paper explores some of the challenges entailed in developing the subsequent generation of this successful product, maintaining exterior design cues while at the same time improving its aerodynamic efficiency. A development approach is outlined that made use of both CFD simulation and Coastdown testing at MSPT (Mahindra SUV proving track). Drag coefficient improvement of 40 counts (1 count = 0.001 Cd) can be obtained for the best vehicle exterior configuration by paying particular attention to: AGS development to limit the drag due to cooling airflow into the engine compartment Front wheel deflector optimization Mid underbody cover development (beside the LH & RH side skirting) Wheel Rim optimization In this paper we have analyzed the impact of these design changes on the aerodynamic flow field, Pressure plots and consequently drag development over the vehicle length is highlighted. An
The integration of hydrogen (H2) as a fuel source in internal combustion engines (ICE) necessitates stringent design measures to mitigate leakage risks and ensure operational safety. This study focuses on the design optimization of vanity cover for hydrogen engines. Computational fluid dynamics (CFD) analysis is carried out to assess and control hydrogen leakage through fuel rail connections, injector interfaces and associated high pressure fuel system components. Detailed modelling of hydrogen flow behavior, diffusion characteristics of leaked hydrogen are simulated for worst case scenarios. Design iterations targeted improvement in ventilation pathways, strategic placement of vent holes, and internal flow management to minimize localized hydrogen buildup. The final design achieved hydrogen concentration, which was less than 4%. This paper validates the critical role of CFD driven design methodology in proactively identifying leakage risks and optimizing component geometries for
In pursuit of a distinct sporty interior sound character, the present study explores an innovative strategy for designing intake systems in passenger vehicles. While most existing literature primarily emphasizes exhaust system tuning for enhancing vehicle sound quality, the current work shifts the focus toward the intake system’s critical role in shaping the perceived acoustic signature within the vehicle cabin. In this research work, target cascading and settings were derived through a combination of benchmark and structured subjective evaluation study and aligning with literature review. Quantitative targets for intake orifice noise was defined to achieve the desired sporty character inside cabin. Intake orifice targets were engineered based on signature and sound quality parameter required at cabin. Systems were designed by using advanced NVH techniques, Specific identified acoustic orders were enhanced in the intake system to reinforce the required signature in acceleration as well
It all started when Owen Kent and Todd Roberts became roommates at the University of California Berkeley. Owen has muscular dystrophy and had recently acquired a robotic arm, which he noticed he was using to do range of motion. Todd had come to Berkeley to study mechanical engineering with a focus on biomechanics, and both were enrolled in Designing for the Human Body, a biomechanics course taught by Mechanical Engineering Professor Grace O’Connell.
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