Aim:

This project aims to simulate and design mechanisms that would allow for adaptive wing design in order to provide optimized  aircraft performance.Experimentation would be carried out on different methods for generating adaptive wings such as aerofoil morphing.

Introduction:

Progress in materials and manufacturing technologies has enabled the development of morphing wings capable of dynamically adjusting various airfoil parameters like twist, camber, and control surface deflection along the span. A novel twist effectiveness metric is introduced to assess different control mechanisms, accompanied by a computational algorithm for its calculation. This study contrasts the efficiency of continuous (morphing-wing) control surfaces with discrete (standard-wing) ones, examining induced and total drag across a spectrum of wing planform shapes and control mechanisms. Furthermore, comparisons between parabolic-shaped conformal trailing-edge flaps and articulated trailing-edge flaps are provided. Findings demonstrate that adopting continuous control surfaces yields a drag reduction of less than 5% across most scenarios compared to discrete control surfaces.

Methodology 

Computational Fluid Dynamics (CFD) Simulation Setup:

Utilize a suitable turbulence model such as Reynolds-averaged Navier-Stokes (RANS) equations with turbulence closure models like the k-ɛ model or the SST model.

Set up the CFD simulation to analyze the aerodynamic performance of the wing under various flight conditions, accounting for turbulence effects.

Objective Definition and Constraints:

Define the objective function as maximizing the lift-to-drag ratio, which is crucial for overall aircraft efficiency.

Establish constraints based on design requirements, such as maximum allowable deformation of the wing structure.

Employment of ANSYS Adjoint Solver Method:

Utilize the adjoint solver method within the ANSYS platform to optimize the wing shape efficiently.

Calculate sensitivity gradients of the objective function with respect to the design variables using the adjoint solver.

Optimization Process:

Conduct iterative optimization loops to adjust wing parameters, aiming to maximize the lift-to-drag ratio while satisfying defined constraints.

Post-Processing and Validation:

Apply post-processing techniques to analyze results, including flow visualization, lift and drag distributions, and comparison with experimental data.

Validate the optimized wing design to ensure improved aerodynamic performance across different flight conditions using Xfoil.

Results:

Conclusion:

Successfully generated simulation results for the performance of various wing topologies.

References:

https://sci-hub.se/10.1016/j.paerosci.2018.06.002

https://www.researchgate.net/publication/344069362_Finite_Element_Analysis_of_an_Aircraft_Morphing_Wing

https://www.sciencedirect.com/science/article/pii/S1000936120301771

https://sci-hub.se/10.1017/aer.2016.113

https://www.researchgate.net/publication/338398610_Aerodynamic_Efficiency_Analysis_of_Morphing_Wings_Relative_to_Non-Morphing_Wings