Machine Learning Approach to Predict Aerodynamic Performance of Underhood and Underbody Drag Enablers

Dube, Pradip; Hiravennavar, Sadashiv

doi:10.4271/2020-01-0684

Cited by 5 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dasari et al [143] constructed a random forest surrogate to support design space exploration and extracted design parameter importance for better understanding of the design space. Dube and Hiravennavar [144] compared a range of ML approaches, including kriging, decision trees, linear regression, random forests, and ANN for automotive drag predictions and concluded that ANN gave the best performance.…”

Section: Decision Tree and Random Forestmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

View full text Add to dashboard Cite

Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

show abstract

Section: Decision Tree and Random Forestmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This trained mathematical model, in turn, reliably predicts the coefficient of drag of a given silhouette. Similarly, Dube et al [36] employed data driven drag prediction for studying the aerodynamic performance of the underhood and underbody drag enablers by using linear regression, neural network, and random forest approaches to generate models for a fairly accurate prediction of the associated aerodynamic drag coefficients.…”

Section: A Aerodynamic Study Of Vehicle Platoonmentioning

confidence: 99%

Prediction of Drag Force on Vehicles in a Platoon Configuration Using Machine Learning

et al. 2020

View full text Add to dashboard Cite

Machine learning is used for extraction of valuable information from data thus helping in exploration of hidden patterns, leading to learning models that can be used for prediction. In the domain of autonomous vehicles machine learning techniques have been applied in several areas, vehicle platooning being one of them. Vehicle platooning is a vital feature of automated highways which provides the key benefits of fuel economy, road safety and environmental protection coupled with safe road transportation. However, high computational cost associated with the numerical simulation of vehicle aerodynamics makes the Computational Fluid Dynamics (CFD) study of vehicle platoon prohibitively expensive and complex. Machine learning, with its high predictive power, has emerged as a promising compliment to CFD studies of external aerodynamics. This paper presents estimation error based performance comparison of five different supervised learning algorithms: Support Vector Regression, Polynomial Regression, Linear Regression and two different models of Neural Networks for prediction of aerodynamic drag coefficient corresponding to each vehicle in a two, three and four vehicle platoon configurations based on the drag coefficients provided by experimental study at different inter-vehicle distances. Predicted drag coefficients are then juxtaposed with CFD data from numerical simulations to evaluate closeness to experimental drag coefficients. Results reveal that polynomial regression model best fits the aerodynamics with 0.0223 estimation error. To the best of our knowledge no machine learning based methods have been applied before for modeling aerodynamic drag on vehicle platoon.

show abstract

Prediction model of automobile wind drag coefficient based on moth optimization algorithm and ELM

Liu

Qin

et al. 2022

2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE)

View full text Add to dashboard Cite

Machine Learning Approach to Predict Aerodynamic Performance of Underhood and Underbody Drag Enablers

Cited by 5 publications

References 3 publications

Machine Learning in Aerodynamic Shape Optimization

Machine Learning in Aerodynamic Shape Optimization

Prediction of Drag Force on Vehicles in a Platoon Configuration Using Machine Learning

Prediction model of automobile wind drag coefficient based on moth optimization algorithm and ELM

Contact Info

Product

Resources

About