Numerical simulation of flow over a racing motorbike using OpenFOAM®

Dubey, Pujan; Pramod, Malandkar Yash; Kumar, Aravind Satheesh; Kannan, B. T.

doi:10.1063/5.0025199

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Therefore the use of computational technology is used in this research. The use of computational technology in this research is the use of computational fluid dynamics software or abbreviated as CFD [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Exhaust Prototype Design of L12B Engine

2021

IJETER

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The CFD simulation test carried out on the prototype exhaust, produce an average fluid flow velocity value of 8.36 m/s, and the experimental test results using a flowbench engine on the prototype exhaust, produce an average fluid flow velocity value of 8.49 m/s, so the errorcomparison from themeasurement results on the prototype exhaust between the CFD simulation and experimental results is 1.53%. Fluid flow velocity is a measurement reference in this study because it directly affectsthe scavenging effect on the internal combustion engine, which of course will directly affect the level of power generated by the engine.

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Section: Introductionmentioning

confidence: 99%

Exhaust Prototype Design of L12B Engine

2021

IJETER

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Large Language and Text-to-3D Models for Engineering Design Optimization

Rios,

Menzel,

Sendhoff

2023

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

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Machine Learning for Road Vehicle Aerodynamics

Ananthan,

Ashton,

Chadwick

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">This paper discusses an emerging area of applying machine learning (ML) methods to augment traditional Computational Fluid Dynamics (CFD) simulations of road vehicle aerodynamics. ML methods have the potential to both reduce the computational effort to predict a new geometry or car condition and to explore a greater number of design parameters with the same computational budget. Similar to traditional CFD methods, there exists a broad range of approaches. In particular, the accuracy and computational efficiency of a CFD simulation vary greatly depending on the choice of turbulence model (DNS, LES, RANS) and the underlying spatial and temporal numerical discretizations. Similarly, the end-user must select the correct ML method depending on the use-case, the available input data, and the trade-off between accuracy and computational cost. In this paper, we showcase several case studies using various data-driven ML methods to highlight the promise of these approaches. Whilst these case studies are not comprehensive investigations of the underlying methods and do not include all possible ML approaches (i.e., physics-driven), they highlight the ability of these models to in general predict new designs in near real-time (i.e., less than 5 seconds), after typically less than 1 hour of training on a single GPU. There still exists a need for high quality training data from traditional CFD methods and high-fidelity CFD simulations to validate the ML predictions. Thus, ML approaches should be seen as tools to augment traditional CFD methods rather than to replace them. While this work focuses on preliminary studies, future work will look at more comprehensive real-world/industrial-size calculations for the more promising technologies identified here.</div></div>

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Numerical simulation of flow over a racing motorbike using OpenFOAM®

Cited by 4 publications

References 5 publications

Exhaust Prototype Design of L12B Engine

Exhaust Prototype Design of L12B Engine

Large Language and Text-to-3D Models for Engineering Design Optimization

Machine Learning for Road Vehicle Aerodynamics

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