TüB?TAK funded a new research project for AeroMDO Lab

We are excited to announce our new research project funded by?the TüB?TAK 1001?Program on “Development of Environmentally Friendly Aircraft with High Aspect Ratio Wings via Multi-fidelity Surrogate Modeling Based Aeroelastic Optimization” with grant number 222M019 started on April 1st, 2023. This 36-month-long project will be directed by Prof.Dr.? Melike Nikbay ?in? ITU AeroMDO Lab in collaboration with Prof. Dr. Zahit MECITOGLU .

?The effects of climate change are becoming more evident in our lives. One of the challenging goals in civil aviation transportation today is the development of high-aspect-ratio wings that are both economically and environmentally efficient. The global aviation industry is responsible for approximately 2.1% of all man-made carbon dioxide (CO2) emissions. With this awareness, as a part of the global effort to reduce CO2 emissions in recent years, it is of great importance to examine wings with a high aspect ratio and to reduce CO2 emissions by limiting induced drag with an increasing aspect ratio. The aspect ratio of aircraft such as Boeing 787 and Airbus A350, which are widely used in today's civil aviation transportation is approximately 11 and this ratio is still far below the optimum value. Thus, the design and development of high aspect ratio wings have become very crucial in the new generation of civil aviation transportation.

On the other hand, the flexibility that comes with increasing aspect ratio causes large deformations in the structure. Extremely flexible structures with high deformations become more susceptible to aeroelastic instabilities in flight with sufficient and high levels of perturbation. This situation revealed the necessity of examining the aeroelastic and aerodynamic effects of high aspect ratio wings with folding wing mechanisms, and the suppression of flutter out of the flight envelope to design more reliable structures.

Within the scope of this project, the flutter problem that causes damage to aircraft structures is prevented by using a multi-fidelity deep neural network and co-Kriging models and optimisation algorithms for high aspect ratio wing configurations with a folding wingtip mechanism. A more reliable flight will be possible for different flight conditions by increasing the flutter speed of the structure as compared to the initial design. In order to increase the flutter speed, it is aimed to perform (1) design changes in wing geometry, (2) placement/geometry of the folding wingtip system, and (3) changes in material properties. It is desirable that the optimization process be computationally efficient and at the same time reliable.?

We have 2 PhD student positions (36 months) and 2 Postdoctoral researcher positions (12 months) within this Project. The researchers will contribute one of the following tracks.

Track -1 will be in Aeroelasticity, Computational Aerodynamics, Structural Dynamics, and Fluid-Structure Interaction.

Track- 2 will be in Machine Learning, Multifidelity Modeling, Surrogate Modelling, and Multidisciplinary Design Optimization.

Please send your resume and relevant documents to?[email protected]?if you are interested in these positions.?

We are looking forward to sharing the technical outcomes of this project with our?#MDOcolleagues in the near future.

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