Background and Motivation

Flow separation is a common phenomenon that occurs over airfoils and affects their performance. Understanding flow separation is crucial in the design and optimization of airfoils, as it affects the lift and drag characteristics. Various numerical methods such as computational fluid dynamics (CFD) and experimental techniques have been used to predict flow separation over airfoils. However, these methods can be computationally expensive, and accurate predictions can require large amounts of data. Machine learning has been used to predict fluid flow behavior, and it has shown promising results in reducing computational costs and providing accurate predictions. In this study, we propose to investigate the potential of machine learning in predicting flow separation over the NACA 0012 airfoil.

‍

Objectives

The main objectives of this research are:

1. To develop a machine learning model for predicting flow separation over the NACA 0012 airfoil.
2. To compare the accuracy of the machine learning model predictions with experimental data and CFD simulations.
3. To investigate the effect of training data type (experimental data or CFD simulations) on the accuracy of the machine learning model.
4. To identify the important input variables that affect the flow separation over the NACA 0012 airfoil.

‍

Methodology

The proposed research will be conducted in the following stages:

1. Literature review: A comprehensive literature review will be carried out to identify the existing methods for predicting flow separation over airfoils and the use of machine learning in fluid mechanics.
2. Data collection and preprocessing: Experimental data or CFD simulations will be used as the training data to develop the machine learning model. The data will be preprocessed to ensure that it is in a suitable format for training the machine learning algorithm.
3. Machine learning model development: Various machine learning algorithms such as artificial neural networks, decision trees, and support vector machines will be implemented to develop the machine learning model for predicting flow separation over the NACA 0012 airfoil. The performance of each algorithm will be evaluated to select the best-performing one.
4. Model evaluation: The developed machine learning model will be evaluated by comparing the predicted results with experimental data and CFD simulations.
5. Sensitivity analysis: A sensitivity analysis will be carried out to identify the important input variables that affect flow separation over the NACA 0012 airfoil.

‍

Expected outcomes

The expected outcomes of this research are:

1. The development of a machine learning model for predicting flow separation over the NACA 0012 airfoil.
2. Comparison of the accuracy of the machine learning model predictions with experimental data and CFD simulations.
3. Investigation of the effect of training data type (experimental data or CFD simulations) on the accuracy of the machine learning model.
4. Identification of the important input variables that affect flow separation over the NACA 0012 airfoil.

‍

Significance and relevance

The proposed research will contribute to the development of efficient and accurate methods for predicting flow separation over airfoils. The use of machine learning can provide a cost-effective and reliable approach to predicting flow separation, which can lead to improved performance and efficiency of airfoils. The findings of this study can be beneficial in various fields such as aerospace engineering, wind turbine design, and fluid mechanics.

‍