title: ÐÐ¿ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸Ð¸, Ð½Ð°ÑÐµÐ»ÐµÐ½Ð½Ð¾Ð¹ Ð½Ð° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ñ Ð°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ð¾Ð±ÑÐµÐºÐ°Ð½Ð¸Ñ Ð¿Ð»Ð¾ÑÐºÐ¾Ð³Ð¾ ÐºÑÑÐ»Ð¾Ð²Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐ¸Ð»Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Experience in the application of neural networks to construct a regression function aimed at predicting the flow characteristics of an airfoil
Creators: name: ÐÐ¾ÐºÐ¾ÐºÐ¾Ð², ÐÐ¼Ð¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐºÑÑÐ»Ð¾Ð²Ð¾Ð¹ Ð¿ÑÐ¾ÑÐ¸Ð»Ñ
Ð´Ð¾Ð·Ð²ÑÐºÐ¾Ð²Ð¾Ðµ Ð¾Ð±ÑÐµÐºÐ°Ð½Ð¸Ðµ
Ð¼Ð½Ð¾Ð³Ð¾Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÐ°ÑÑÐµÑÑ
XFOIL
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
airfoil
subsonic flow
multi-parameter calculations
machine learning
fully connected neural network

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description: The given work is devoted to research of the possibilities of application of various machine learning (ML) methods, including artificial neural network, in the specifics of predicting the characteristics of the airfoil in subsonic flow. The objectives that have been accomplished during the research: 1. Review of literature sources covering the issues of practical application of ML methods for predicting the characteristics of aerodynamic flows, the use of methods for optimizing the shape of airfoils, the use of neural networks for the construction of regression functions. 2. Research and description of the physical formulation of the problem - subsonic flow of an airfoil at variation of determining parameters. Description of the realized numerical methods and peculiarities of using the XFOIL package. Carrying out mass parametric calculations to obtain an extensive tabular data set for subsequent application of ML methods. 3. Research and description of the methodology of applying ML approaches to construct regression functions to predict drag and lift coefficients and torque for airfoils based on input data. 4. Training of selected ML models on available data sets with variation of hyperparameters and quality assessment, construction of regression function for prediction of airfoil flow characteristics. As a result of this work, meaningful experience has been gained in database generation, through multi-parameter calculations of airfoils using XFOIL software, as well as application with comparison of different ML approaches, including artificial neural network. The scripts written and the results of the present work can be used in future investigations to extend the generalization ability of ML models to more complex types of flow when considering the flow of airfoils of different geometries.