Oleh Havaza, Vitalii Sukhov, Ruslan Nikitin

DOI Number: N/A

Conference number: IFASD-2024-235

Every year, the air transportation market increases the requirements for aircraft performance in order to obtain greater profits. Satisfaction of this requirement is the main purpose
for aircraft manufacturer. One of the ways to achieve this purpose is improving the design process by developing and implementing new approaches and tools for modelling different phenomena (in considered case – Aeroelastic phenomena) inherent to flight vehicles. The purpose of this report is to present a modern mathematical method that allows modelling static and dynamic Aeroelastic Phenomena by a symbolic operation, in contrast to the numerical methods that are widely used today. The theory of this method based on science analogies approach which allow to present interaction (considering 6 DOF) between aeroelastic forces in analytical formulation, using modern Computer Algebra System tools and as results exclude iteration calculations which inherent inversing and eigenvalue extraction of large dimension matrixes. Approach of this method based on reduced order modelling principle and main idea is presenting lifting surface (wing, blade, etc.) as a principal scheme which look as parallel, serial and star connection of three types of elements: aerodynamical, elastic and inertial. Each element describes by respective matrixes of parameters with maximum dimension of 6×6 for 6 DOFs (3 translations 3 rotations).
Analysis is performing by transformation scheme (connection nodes condensation) and finding equivalent matrix of “Aeroelasticity” using recurrent equations for each type of connection (analogical with electrical circuit). Described method was validate by modelling: Wing load distribution, Divergence, Effectiveness of control surface and Flutter.
Finally, gotten Math model describes dependences between design variables and aeroelastic characteristic in explicit symbolic form which allow performs wide fast parametric investigation at preliminary stage design. Additional this model will be useful for structure optimization process using analytical methods and for machine learning and will allow expand using artificial intelligence in aerospace structure design.

Read the full paper here

Email
Print
LinkedIn
The paper above was part of  proceedings of a CEAS event and as such the author has signed a publication agreement to have their paper published in the repository. In the case this paper is found somewhere else CEAS always links to the other source.  CEAS takes great care in making the correct content available to the reader. If any mistakes are found  in the listings please contact us directly at papers@aerospacerepository.org and we will correct the listing promptly.  CEAS cannot be held liable either for mistakes in editorial or technical aspects, nor for omissions, nor for the correctness of the content. In particular, CEAS does not guarantee completeness or correctness of information contained in external websites which can be accessed via links from CEAS’s websites. Despite accurate research on the content of such linked external websites, CEAS cannot be held liable for their content. Only the content providers of such external sites are liable for their content. Should you notice any mistake in technical or editorial aspects of the CEAS site, please do not hesitate to inform us.