CFD Application in Automotive Industry

Kobayashi, Toshio; Tsubokura, Makoto

doi:10.1007/978-3-540-70805-6_22

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Such techniques have benefited the aerospace, automotive and biomedical industries. The challenges are numerical and physical, and several reviews have been written on specific industrial applications or areas of physics [29][30][31][32]. However, despite significant advances in the field,there is still a need to improve efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Drikakis

Sofos

2023

Fluids

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The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions.

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Section: Introductionmentioning

confidence: 99%

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Drikakis

Sofos

2023

Fluids

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“…With the advancement in computational power, the Computational Fluid Dynamics (CFD) approach has been integrated during the design stage in automotive industries. 12 CFD offers more spatial freedom in investigating the flow structures of a vehicle over the experimental approach. Flow over a truck has been investigated numerically with Reynolds-averaged Navier-Stokes (RANS) [13][14][15][16] or with a higher spatial/ temporal resolution technique, such as Detached-Eddy Simulation (DES) or Large-Eddy Simulation (LES).…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic of a refrigerated truck and improvement to reduce its aerodynamic drag

Levin

Chen

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Despite the fact that improving heavy vehicle fuel efficiency has received considerable attention, few investigations have been done on reducing the aerodynamic drag of a refrigerated truck. This study has two objectives; first, the aerodynamic of the refrigerated truck (REF-BSL) is investigated, and second, passive add-on designs to reduce the aerodynamic drag of the refrigerated truck are proposed. Flow structure around the truck at Reynolds number (Re) equals 0.2 × 106 is numerically investigated with a steady-state Reynolds-averaged Navier-Stokes (RANS) simulation with the Shear Stress Transport (SST) k- ω turbulence model. For a refrigerated truck, a high-pressure region at the trailer’s front surface is concentrated at the pocket area between the refrigerator and the tractor. The presence of the refrigerator alters the shear layer at the trailer’s roof as it propagates downstream, causing the saddle point location at the near-wake to shift. The refrigerator installation increases the total drag by 9%, relative to a truck without the refrigerator. Three add-on designs (REF-A, REF-B, and REF-C) positioned at the pocket area are examined to reduce the aerodynamic drag of REF-BSL. The working principle of the add-ons is to re-direct the incoming fluid toward the sides of the truck, preventing the formation of the high-pressure region at the pocket area, thus lowering the total drag of the refrigerated truck. The REF-B design is found to be the most efficient in terms of reducing drag by 16.5%.

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“…The industrial penetration of Reynolds Averaged Navier-Stokes (RANS) has gone far beyond its mainstay of aerospace engineering. Today it is being heavily used in automative design [1], building design [2], ventilation system design [3], marine propeller design [4] and more recently in the understanding of oral transmissions [5,6]. In using a turbulence model, RANS avoids the need to resolve pertinent length-and time-scales of turbulence; significantly slashing the cost of generating a flowfield.…”

Section: Introductionmentioning

confidence: 99%

Polynomial Ridge Flowfield Estimation

Scillitoe,

Seshadri,

Wong

et al. 2021

Preprint

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Computational fluid dynamics plays a key role in the design process across many industries. Recently, there has been increasing interest in data-driven methods, in order to exploit the large volume of data generated by such computations. This paper introduces the idea of using spatially correlated polynomial ridge functions for rapid flowfield estimation. Dimension reducing ridge functions are obtained for numerous points within training flowfields. The functions can then be used to predict flow variables for new, previously unseen, flowfields. Their dimension reducing nature alleviates the problems associated with visualising high dimensional datasets, enabling improved understanding of design spaces and potentially providing valuable physical insights. The proposed framework is computationally efficient; consisting of either readily parallelisable tasks, or linear algebra operations. To further reduce the computational cost, ridge functions need only be computed at only a small number of subsampled locations. The flow physics encoded within covariance matrices obtained from the training flowfields can then be used to predict flow quantities, conditional upon those predicted by the ridge functions at the sampled points. To demonstrate the efficacy of the framework, the incompressible flow around an ensemble of aerofoils is used as a test case. On unseen aerofoils the ridge functions' predictive accuracy is found to be reasonably competitive with a state-of-the-art convolutional neural network (CNN). The local ridge functions can also be reused to obtain surrogate models for integral quantities such a loss coefficient, which is advantageous in situations where long-term storage of the CFD data is problematic. Finally, use of the ridge framework with varying boundary conditions is demonstrated on a three dimensional transonic wing flow.

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CFD Application in Automotive Industry

Cited by 9 publications

References 14 publications

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Aerodynamic of a refrigerated truck and improvement to reduce its aerodynamic drag

Polynomial Ridge Flowfield Estimation

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