Browse Topic: Computational fluid dynamics (CFD)
The front wing is a critical component of a Formula 1 car, directly influencing aerodynamic efficiency and overall performance. This study focuses on optimizing the computational simulation process for a Formula 1 front wing, using the Imperial Front Wing (IFW) model as a benchmark. Computational Fluid Dynamics (CFD) simulations were for this study, with a particular emphasis on evaluating ground effect and aerodynamic drag characteristics. A higher ground height configuration of the IFW is evaluated in this study. The results, including aerodynamic coefficients and fluid flow visualizations, were compared with findings from previous literature to assess their accuracy and consistency. The study demonstrated strong alignment with theoretical expectations, validating the simulation approach. Additionally, this research lays the groundwork for further refinements in mesh optimization and simulation methodologies, contributing to more efficient aerodynamic analysis in high-performance
Combining simulation with probabilistic ML enables engineers to chart the full design landscape, quantify uncertainty and uncover viable options that intuition and brute force alone would miss. Components and systems are routinely designed and validated virtually through tools like CFD and FEA before any physical prototype is built. The benefits are obvious: faster iteration, reduced cost and better products. But simulation is not cheap. Each run can take hours, consume costly GPU/CPU resources and require highly skilled engineers who are already in short supply. Licenses and compute costs can easily reach tens of thousands of dollars per seat, and most teams can complete only a few runs per day.
Virtual reality (VR), Augmented Reality (AR) and Mixed reality (MR) are advanced engineering techniques that coalesces physical and digital world to showcase better perceiving. There are various complex physics which may not be feasible to visualize using conventional post processing methods. Various industrial experts are already exploring implementation of VR for product development. Traditional computational power is improving day-by-day with new additional features to reduce the discrepancy between test and CFD. There has been an increase in demand to replace actual tests with accurate simulation approaches. Post processing and data analysis are key to understand complex physics and resolving critical failure modes. Analysts spend a considerable amount of time analyzing results and provide directions, design changes and recommendations. There is a scope to utilize advanced features of VR, AR and MR in CFD post process to find out the root cause of any failures occurred with
In the electrical machines, detrimental effects resulted often due to the overheating, such as insulation material degradation, demagnetization of the magnet and increased Joule losses which result in decreased lifetime, and reduced efficiency of the motor. Hence, by effective cooling methods, it is vital to optimize the reliability and performance of the electric motors and to reduce the maintenance and operating costs. This study brings the analysis capability of CFD for the air-cooling of an Electric-Motor (E-Motor) powering on Deere Equipment's. With the aggressive focus on electrification in agriculture domain and based on industry needs of tackling rising global warming, there is an increasing need of CFD modeling to perform virtual simulations of the E-Motors to determine the viability of the designs and their performance capabilities. The thermal predictions are extremely vital as they have tremendous impact on the design, spacing and sizes of these motors.
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