NSU CTRG 2021-2022
Model Order Reduction for Aircraft Wing Shape Optimization (CTRG-21/SEPS/15)
- Funded by: North South University
- Principle Investigator: Dr. Mohammad Monir Uddin (monir.uddin@northsouth.edu)
- Duaration: 1 years (Started from April 2022)
Project Overview
Over the last few decades, enormous researches have been done on WSO which has mainly emphasized the physical model development rather than the generation of the data model. However, the data model is crucial for numerical analysis since it reflects all the characteristics of the physical model and easy to handle for optimization. Due to the presence of redundant data, the smooth computation can never be performed on the unoptimized mathematical model. Therefore, ROM techniques can be applicable to turn the unoptimized data model optimized by reducing the useless dimension from the data model. As a result, the physical model generated from the optimized data model becomes smooth to analyze and gives an optimal geometric shape easily. Dealing with WSO using ROM techniques is a new concept that is excepted to complete flawlessly in this project. The possible significant outcomes of this research are as follows:
- The physical model of the aircraft wings will be constructed and data of the shape deformation due to aerodynamic drag will be extracted to generate a mathematical models.
- By linearization procedure, the linear state-space formation will be generated based on the input-output relations what will mainly define the properties of the physical model.
- ROM of the generated mathematical models will be created in order to truncate the redundant design parameters for boosting up the entire simulation procedures.
- Hopefully, an optimized aircraft wing structure will be found from this project, which will be beneficial to use practically in the future.
- The project will help to acquire knowledge on WSO in terms of Control theory, Scientific computing, and Mathematical Algorithms.
- Several scientific papers will be published in renowned journals highlighting the outcomes of this project.
Team
Project Outcome
In this project we proposed to develop algorithms for the frequency limited model reduction of large-scale sparse descriptor systems. The proposed research work has been successfully done theoretically and some numerical experiments also have been carried out. The results have been shown in the attached papers. Some numerical experiments are being carried out at a automation lab of Shanghai University, China. The project was supposed to be ended by September 2020. Due to pandemic problem it was delayed by three months. In future work we will generalize the ideas obtained here for limited time interval case.
Sumulation & Results