A deep learning approach for the transonic flow field predictions around airfoils

Date

2022-01-01

Author

Duru, Cihat
Alemdar, Hande
Baran, Özgür Uğraş

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

399
views

0
downloads

Learning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quantitatively. The flow features associated with the transonic effects and the angle of attack variation, such as the shock waves and the flow separation, are well predicted. Furthermore, predicted flowfield data are used to compute the aerodynamic coefficients. The findings indicate that the presented model may allow avoiding time-consuming computational fluid dynamics (CFD) simulations, especially in the design optimization studies with a slight loss of accuracy.

Subject Keywords

Deep learning, Transonic flow, Airfoil aerodynamics, Shock waves

URI

https://hdl.handle.net/11511/95296

Journal

Computers & Fluids

DOI

https://doi.org/10.1016/j.compfluid.2022.105312

Collections

Department of Mechanical Engineering, Article

Suggestions

OpenMETU
Core

A deep learning methodology for the flow field prediction around airfoils Duru, Cihat; Baran, Özgür Uğraş; Alemdar, Hande; Department of Mechanical Engineering (2021-9-07) This study aims to predict flow fields around airfoils using a deep learning methodology based on an encoder-decoder convolutional neural network. Neural network training and evaluation are performed from a set of computational fluid dynamics (CFD) solutions of the 2-D flow field around a group of known airfoils at a wide range of angles of attack. Reynolds averaged Navier-Stokes (RANS)-based CFD simulations are performed at a selected Mach number on the transonic regime on high-quality structured computati...
A framework for a personalized e-learning system Özpolat, Ebru; Akar, Gözde; Department of Electrical and Electronics Engineering (2009) This thesis focuses on three of the main components of an e-learning system: Infrastructure model, data integration and personalization. For the infrastructure model, our aim is to get best use of heterogeneously structured, geographically distributed data resources. Therefore, a detailed analysis of the available infrastructure models is carried out and an open source reference implementation based on grid technology is implemented. Furthermore, a simple data integration mechanism is proposed for the sugge...
A topological system for codification of river basins in Turkey Ulucan, İbrahim; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2021-6-23) Recently, owing to incredible development in Geographic Information Systems (GIS) and software fields, besides effectively using the network and topology instruments, great advances have been made in the field of hydrologic coding through robust interaction among multidisciplinary sciences. If all hydrological properties can be combined in a network that has its own sui generis situation and topological properties, meaningful and holistic analyses can be formed more easily. Additionally, GIS topology is an ...
A Similarity Based Oversampling Method for Multi-Label Imbalanced Text Data Karaman, İsmail Hakkı; Köksal, Gülser; Erişkin, Levent; Department of Industrial Engineering (2022-9-1) In the real world, while the amount of data increases, it is not easy to find labeled data for Machine Learning projects, because of the compelling cost and effort requirements for labeling data. Also, most Machine Learning projects, especially multi-label classification problems, struggle with the data imbalance problem. In these problems, some classes, even, do not have enough data to train a classifier. In this study, an over sampling method for multi-label text classification problems is developed and s...
A method for supporting data collection in user research studies at domestic environments Öztoprak, Aydın; Erbuğ, Çiğdem; Department of Industrial Design (2011) This thesis analyzes data collection tools and methods in domestic environments for smart product development processes. The aim of the study is to create a method for supporting data collection studies in user research of smart products at domestic environments. The study examines the utilization of information and communication technologies in ethnographic data collection methods at domestic environments with a qualitative approach. Two case studies are conducted to understand and analyze the effects of c...

Citation Formats

C. Duru, H. Alemdar, and Ö. U. Baran, “A deep learning approach for the transonic flow field predictions around airfoils,” Computers & Fluids, vol. 236, pp. 1–32, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/95296.