Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Zachary Eckley, Senior
Lead Presenter's Name
Zachary Eckley
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Khem Poudel
Abstract
As the world increases the use of data mining and artificial intelligence to improve everyday life, machine learning algorithms and practices have become more widely studied and utilized. One such machine learning algorithm is a generative adversarial network (GAN) that uses a series of convolutions and neural layers to create new instances of data that resemble real instances of data very closely. This study applied a GAN to generate unique airfoil geometries based on a set of airfoil performance data. Typically, airfoil geometry is designed using Computational Fluid Dynamics (CFD) and optimization algorithms. By applying a GAN, new geometries can be created in a fraction of the time reducing the resources spent during the design and rendering process. The results of the study show promise for GANs as an alternative to traditional design methods, however the results are far from perfect. Additional methods exist that could further improve the model but they require additional data and higher computing power.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, SURF
The Utilization of Generative Adversarial Networks for the Production of Airfoil Geometries
As the world increases the use of data mining and artificial intelligence to improve everyday life, machine learning algorithms and practices have become more widely studied and utilized. One such machine learning algorithm is a generative adversarial network (GAN) that uses a series of convolutions and neural layers to create new instances of data that resemble real instances of data very closely. This study applied a GAN to generate unique airfoil geometries based on a set of airfoil performance data. Typically, airfoil geometry is designed using Computational Fluid Dynamics (CFD) and optimization algorithms. By applying a GAN, new geometries can be created in a fraction of the time reducing the resources spent during the design and rendering process. The results of the study show promise for GANs as an alternative to traditional design methods, however the results are far from perfect. Additional methods exist that could further improve the model but they require additional data and higher computing power.