Pertiwi, Fungky Dyan (2022) Optimasi Kinerja Dan Kestabilan Unmanned Aerial Vehicle Sebagai Pengaruh Konfigurasi Blended Winglet Dengan Menggunakan Metode Backpropagation Neural Network – Genetic Algorithm. Masters thesis, Institut Teknologi Sepuluh Nopember Surabaya.
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Abstract
Vortex pada pesawat unmanned aerial vehicle terjadi akibat pergerakan aliran udara pada bagian bawah sayap (tekanan tinggi) ke bagian atas sayap (tekanan rendah), aliran tersebut bergerak menuju ke wingtip pesawat sehingga membuat pusaran udara (vortex). Vortex menyebabkan drag pada pesawat yang biasa disebut induced drag. Efek induced drag adalah menurunkan gaya lift, sehingga kinerja pesawat menjadi berkurang. Namun, efek tersebut dapat dikurangi dengan menambah winglet di wingtip pesawat, salah satu jenis winglet adalah blended winglet. Untuk meningkatkan kinerja pesawat dengan blended winglet, diperlukan pemilihan konfigurasi yang tepat pada blended winglet tersebut dengan melakukan optimasi terhadap kinerja dan kestabilan pesawat unmanned aerial vehicle. Pada penelitian ini kinerja pesawat yang diamati adalah rasio koefesien lift-koefesien drag maksimum dan koefesien drag pada sudut serang 0o, sedangkan kestabilan pesawat yang diamati adalah kestabilan statis, short period, roll damping, dan dutch roll.
Penelitian ini dilakukan untuk memilih satu pengaturan diantara empat level dari tiga faktor blended winglet. Pemilihan ini dilakukan untuk mengoptimalkan kinerja dan kestabilan pesawat. Untuk mendapatkan nilai kinerja dan kestabilan pesawat tersebut dilakukan dengan menggunakan simulasi XFLR5. Simulasi dilakukan dengan memvariasikan tiga faktor blended winglet yaitu tinggi winglet, tip chord winglet, dan cant angle menggunakan metode full factorial 4x4x4. Hasil dari XFLR5 digunakan sebagai data input dan target pada simulasi backpropagation neural network. Simulasi backpropagation neural network digunakan untuk mendapatkan hubungan matematis faktor dengan respon serta nilai fitness function yang dibutuhkan dalam optimasi genetic algorithm. Selanjutnya, hasil optimasi genetic algorithm yaitu konfigurasi optimal blended winglet, digunakan untuk melihat fenomena aliran fluida di wingtip pesawat dengan simulasi Ansys Fluent.
Dari penelitian yang telah dilakukan didapatkan bahwa hubungan antara ketiga faktor blended winglet terhadap masing-masing respon diwakili oleh hasil pelatihan backpropagation neural network yang mempunyai 3 input, 2 hidden layer, dan 1 output. Hasil optimasi genetic algorithm menunjukkan bahwa konfigurasi blended winglet optimal adalah tinggi winglet 160 mm, tip chord winglet 33 mm, dan cant angle 60o. Penggunaan blended winglet menunjukkan peningkatan kinerja aerodinamis dari sayap. Semua kestabilan yang didapatkan dari XFLR5 menunjukkan bahwa pesawat dengan blended winglet optimal memiliki kestabilan yang baik, sedangkan pesawat baseline tidak memiliki kestabilan. Pesawat dengan blended winglet optimal mampu memecah vortex yang terjadi di wingtip, sedangkan pesawat baseline, vortex yang terjadi semakin besar seiring bertambahnya sudut serang.
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Vortex in unmanned aerial vehicle aircraft occurs due to airflow movement at the bottom of the wing (high pressure) to the top (low pressure); the flow moves towards the aircraft's wingtip, creating a vortex. The vortex causes drag on the plane, known as induced drag. The effect of induced drag is to reduce the lift force to reduce the aircraft's performance. However, winglets can reduce induced drag's product, one type of winglet is the blended winglet. A blended winglet can improve the performance of aircraft. It is necessary to choose the suitable configuration for the blended winglet by optimizing the performance and stability of the unmanned aerial vehicle. This research observes aircraft performance, namely the ratio of maximum lift-drag coefficient and drag coefficient at an angle of attack 0o. In contrast, the observed stability of the aircraft is static, short period, roll damping, and dutch roll.
This study was conducted one set among four levels of three blended winglet factors. This selection was made to optimize the performance and stability of the aircraft. To get the value of the performance and stability of the aircraft is done by using the XFLR5 simulation. The simulation was carried out by varying the three blended winglet factors, namely winglet height, winglet tip chord, and cant angle, using the full factorial 4x4x4 method. The results of XFLR5 are used as input and target data in backpropagation neural network simulations. Backpropagation neural network simulation is used to obtain a mathematical relationship between factors and responses, and the value of the fitness function requires the genetic algorithm. Furthermore, the results of the optimization of the genetic algorithm, namely the optimal configuration of blended winglets, are used to see the phenomenon of fluid flow in the wingtip of the aircraft with the Ansys Fluent simulation.
The research found the relationship between the three blended winglet factors on each response. These results represented backpropagation neural network training with 3 inputs, 2 hidden layers, and 1 output. The results of the genetic algorithm optimization show that the optimal blended winglet configuration is a winglet height of 160 mm, a winglet tip chord of 33 mm, and a cant angle of 60o. The use of blended winglets shows an increase in the aerodynamic performance of the wings. All the stability obtained from XFLR5 shows that the aircraft with the optimally blended winglet has good stability, while the baseline aircraft has no stability. An aircraft with an optimally blended winglet can break up the vortex that occurs at the wingtip, while on a baseline plane, the vortex that occurs gets more extensive as the angle of attack increases.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | blended ,winglet, UAV, BPNN, GA, stabilitas statis, blended winglet, UAV, BPNN, GA, static stability |
| Subjects: | T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL521 Aerodynamics, Hypersonic. T Technology > TS Manufactures > TS170 New products. Product Development T Technology > TS Manufactures > TS171 Product design |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21101-(S2) Master Thesis |
| Depositing User: | Fungky Dyan Pertiwi |
| Date Deposited: | 14 Feb 2022 05:58 |
| Last Modified: | 01 Nov 2022 04:09 |
| URI: | http://repository.its.ac.id/id/eprint/93975 |
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