Browse Topic: Fuels and Energy Sources
Reducing drag forces and minimizing the rear wake region are the main goals of evaluating exterior aerodynamic performance in automobiles. Various literature and experiments shows that the overall fuel computations of the road vehicle improves significantly with the reduction in aerodynamic drag force. In the road vehicle major components of the drag is due the imbalance in pressure between front and rear of the vehicle. At high vehicle speed, aerodynamic drag is responsible for approximately 30 to 40% of the energy consumption of the vehicle. In the recent year, cost of high-performance computing (HPC) has reduced significantly, which helped computational fluid dynamics (CFD) is an affordable tool to the automotive industry for evaluating aerodynamic performance of the vehicle during developing phase. The vehicles aerodynamic performance is greatly impacted by the dynamic environmental conditions it encounters in the real world. Such environmental conditions are difficult to replicate
Aluminum alloy wheels have become the preferred choice over steel wheels due to their lightweight nature, enhanced aesthetics, and contribution to improved fuel efficiency. Traditionally, these wheels are manufactured using methods such as Gravity Die Casting (GDC) [1] or Low Pressure Die Casting (LPDC) [2]. As vehicle dynamics engineers continue to increase tire sizes to optimize handling performance, the corresponding increase in wheel rim size and weight poses a challenge for maintaining low unsprung mass, which is critical for ride quality. To address this, weight reduction has become a priority. Flow forming [3,4], an advanced wheel rim production technique, which offers a solution for reducing rim weight. This process employs high-pressure rollers to shape a metal disc into a wheel, specifically deforming the rim section while leaving the spoke and hub regions unaffected. By decreasing rim thickness, flow forming not only enhances strength and durability but also reduces overall
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost
The purpose of this research is to examine the fundamental principles of a circular economy (CE) in relation to the automotive industry in India, which plays a vital role in the country's economy. As a result, energy consumption and environmental impacts also pose significant challenges. CE provide a transformative approach through the life cycle of a vehicle, guiding the automotive industry toward a more sustainable transportation system. In order to decarbonize this industry, the global automotive commission recommends that recycled plastic content in vehicles be increased to 20-25% by 2030. This target necessitates the recovery of plastics from end-of-life vehicles, though these materials are rarely integrated into compounds today. The automotive industry's reliance on plastics has grown substantially due to their lightweight properties, which enhance fuel efficiency, reduce CO₂ emissions, and improve versatility and mechanical performance. polypropylene polymer and several other
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