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Prediction of the Force on a Projectile in an Electromagnetic Launcher Coil with Multilayer Neural Network

Yıl 2018, Cilt: 1 Sayı: 3, 1 - 10, 18.12.2018

Adem Dalcalı Onursal Çetin Cemil Ocak Feyzullah Temurtaş

https://doi.org/10.35377/saucis.01.03.496233
Cited By: 4

Öz

The force on the projectile in the electromagnetic launchers
varies according to the the excitation value and the position of the projectile
in the winding. In this study, 3D model of coil and projectile used in
electromagnetic launchers have been created and analyzed by finite element
method. The force characteristic on the projectile has been obtained by
changing the excitation value of the winding and the position of the projectile
using parametric solution method. In finite element analysis, more accurate
analysis can be performed by defining smaller solution steps. However, the
analysis time is prolonged due to the increase in the number of variables.
Taking into consideration the duration of analysis, the force prediction has
been carried out using multilayer neural network models consisting of one
hidden layer and two hidden layers. Successful results have been obtained in
the force prediction studies with multilayer neural networks.

Anahtar Kelimeler

multilayer neural network force prediction electromagnetic launcher finite element method

Kaynakça

  • [1] K.S. Chandan and P. M. Rao, “A Mathematical Formulation of Inductance for Multipole Field Electromagnetic Launcher,” International Journal of Pure and Applied Mathematics, vol. 118, no. 24, pp. 1-13, 2018.
  • [2] V. Sarı, “Elektromanyetik Fırlatıcıların Farklı Manyetik Özellikteki Çekirdeklerle Performans Analizi,” Doktora Tezi, Erciyes Üniversitesi, Kayseri, 2015.
  • [3] E. İnger, “Elektromanyetik Fırlatıcı Sistemlerinin İrdelenmesi Ve Simülasyonu,” Doktora Tezi, Gazi Üniversitesi, Ankara, 2013.
  • [4] H. D. Fair, “Electromagnetic Launch Science and Technology in the United States Enters A New Era,” IEEE Transactions on Magnetics, vol. 41, no. 1, pp. 158-164, 2005.
  • [5] İ. Çoşkun, O. Kalender ve Y. Ege, “İndüksiyon Bobin Silahı İçin Uygun Stator Bobini Geometrisinin Araştırılıması,” BAÜ Fen Bilimleri Enstitüsü Dergisi, 8, s. 40-48, 2006.
  • [6] F. Daldaban ve V. Sarı, “Bir Relüktans Fırlatıcının Sonlu Elemanlar Yöntemi ile İncelenmesi,” Gazi Üniv. Müh. Mim. Fak. Der., cilt 30, no 4, s. 605-614, 2015.
  • [7] B. Jing, T. Liao, T. Jiang, L. Chen and X. Jia, “Optimal Design and Simulation of Combined Reluctance-induction Electromagnetic Launcher,” in Electromagnetics Research Symposium, 2017, pp. 1377-1381.
  • [8] D. V. Le, B. S. Go, M. G. Song, M. Park and I. K. Yu, “Design of an Electromagnetic Induction Coilgun Using the Taguchi Method,” IEEE Transactions On Plasma Science, vol. 46, no. 10, 2018.
  • [9] M. Akbaba and S. Q. Fakhro, "Field distribution and iron loss computation in reluctance augmented shaded-pole motors using finite element method," IEEE Transactions on Energy Conversion, vol. 7, no. 2, pp. 302–307, 1992.
  • [10] A. Dalcalı, ve M. Akbaba, “FEM Study of the Effects of Geometric Changes on the Variable Reluctance Shaded-Pole Motors Performance,” in 6th Paris International Conference on Recent Trends in Engineering and Technology, April 2017.
  • [11] M. Akbaba and S. Q. Fakhro, "An ımproved computational technique of the inductance parameters of the reluctance augmented shaded-pole motors using finite element method," IEEE Transactions on Energy Conversion, vol. 7, no. 2, pp. 308–314, 1992.
  • [12] A. Dalcalı. and M. Akbaba, “Comparison of 2D and 3D magnetic field analysis of single-phase shaded pole induction motors,” Engineering Science and Technology, an International Journal, vol. 19, pp. 1–7, 2016.
  • [13] S.L. HO and W.N. FU, “Review and Future Application of Finite Element Methods in Induction Motors,” Electric Machines & Power Systems, vol. 26, no. 2, pp. 111-125, 2007.
  • [14] W.N. FU “A Versatile Finite Element Model of Electric Machines,” Electric Power Components and Systems, vol. 31, no. 10, pp. 941-966, 2003.
  • [15] A. Saygın, C. Ocak, A. Dalcalı, O. Gürdal, S. Alantar and Y. Tarhan, “Influence of Pole Arc Offset on The Field and Output Parameters of Brushless DC Motors,” International Journal of Advancements in Electronics and Electrical Engineering, vol. 3, no. 1, pp. 47-51, 2014.
  • [16] O. Cetin and F. Temurtas “Classification of Magnetoencephalography Signals by Multilayer and Radial Based Artificial Neural Networks,” Elec Lett Sci Eng, vol. 14, no. 1, pp. 32–38, 2018.
  • [17] O. Cetin, F. Temurtas and S. Gulgonul, “An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function,” Dicle Medical Journal, vol.42, no. 2, p. 150–157, 2015.
  • [18] A. Gulbag and F. Temurtas, “A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures,” Sensors and Actuators B, vol. 121, no. 1, pp. 590–599, 2007.
  • [19] A, Gulbag and F. Temurtas, “A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems,” Sensors and Actuators B, vol. 115, no. 1, pp. 252–262, 2006.
  • [20] E. Dogan, “Suspended sediment load estimation in lower Sakarya river by using soft computational methods,” Elec Lett Sci Eng, vol. 1, pp. 22–32, 2005.

Bir Elektromanyetik Fırlatıcı Bobininde Mermiye Etkiyen Kuvvetin Çok Katmanlı Sinir Ağı ile Kestirimi

Yıl 2018, Cilt: 1 Sayı: 3, 1 - 10, 18.12.2018

Adem Dalcalı Onursal Çetin Cemil Ocak Feyzullah Temurtaş

https://doi.org/10.35377/saucis.01.03.496233
Cited By: 4

Öz

Elektromanyetik
fırlatıcılarda merminin üzerindeki kuvvet, uyartım değeri ve merminin sargı
içerisindeki konumuna göre değişiklik göstermektedir. Bu çalışmada
elektromanyetik fırlatıcılarda kullanılan bobin ve merminin 3 boyutlu modeli
oluşturularak sonlu elemanlar metodu ile analizler gerçekleştirilmiştir.
Parametrik çözüm metodu kullanılarak, sargının uyartım değeri ve mermi konumu
değiştirilerek mermi üzerindeki kuvvet karakteristiği elde edilmiştir. Sonlu
elemanlar analizlerinde daha küçük çözüm adımları tanımlanarak daha hassas
analizler gerçekleştirilebilir. Bununla birlikte, değişkenlerin sayısındaki
artış nedeniyle analiz süresi uzamaktadır. Analiz süresi dikkate alınarak,
çalışmada kuvvet kestirimi tek gizli katmandan ve iki gizli katmandan oluşan
çok katmanlı sinir ağı modelleri kullanılarak gerçekleştirilmiştir. Çok
katmanlı sinir ağları ile yapılan kuvvet kestirimi çalışmalarında başarılı
sonuçlar elde edilmiştir.

Anahtar Kelimeler

çok katmanlı sinir ağı kuvvet kestirimi elektromanyetik fırlatıcı sonlu elemanlar analizi

Kaynakça

  • [1] K.S. Chandan and P. M. Rao, “A Mathematical Formulation of Inductance for Multipole Field Electromagnetic Launcher,” International Journal of Pure and Applied Mathematics, vol. 118, no. 24, pp. 1-13, 2018.
  • [2] V. Sarı, “Elektromanyetik Fırlatıcıların Farklı Manyetik Özellikteki Çekirdeklerle Performans Analizi,” Doktora Tezi, Erciyes Üniversitesi, Kayseri, 2015.
  • [3] E. İnger, “Elektromanyetik Fırlatıcı Sistemlerinin İrdelenmesi Ve Simülasyonu,” Doktora Tezi, Gazi Üniversitesi, Ankara, 2013.
  • [4] H. D. Fair, “Electromagnetic Launch Science and Technology in the United States Enters A New Era,” IEEE Transactions on Magnetics, vol. 41, no. 1, pp. 158-164, 2005.
  • [5] İ. Çoşkun, O. Kalender ve Y. Ege, “İndüksiyon Bobin Silahı İçin Uygun Stator Bobini Geometrisinin Araştırılıması,” BAÜ Fen Bilimleri Enstitüsü Dergisi, 8, s. 40-48, 2006.
  • [6] F. Daldaban ve V. Sarı, “Bir Relüktans Fırlatıcının Sonlu Elemanlar Yöntemi ile İncelenmesi,” Gazi Üniv. Müh. Mim. Fak. Der., cilt 30, no 4, s. 605-614, 2015.
  • [7] B. Jing, T. Liao, T. Jiang, L. Chen and X. Jia, “Optimal Design and Simulation of Combined Reluctance-induction Electromagnetic Launcher,” in Electromagnetics Research Symposium, 2017, pp. 1377-1381.
  • [8] D. V. Le, B. S. Go, M. G. Song, M. Park and I. K. Yu, “Design of an Electromagnetic Induction Coilgun Using the Taguchi Method,” IEEE Transactions On Plasma Science, vol. 46, no. 10, 2018.
  • [9] M. Akbaba and S. Q. Fakhro, "Field distribution and iron loss computation in reluctance augmented shaded-pole motors using finite element method," IEEE Transactions on Energy Conversion, vol. 7, no. 2, pp. 302–307, 1992.
  • [10] A. Dalcalı, ve M. Akbaba, “FEM Study of the Effects of Geometric Changes on the Variable Reluctance Shaded-Pole Motors Performance,” in 6th Paris International Conference on Recent Trends in Engineering and Technology, April 2017.
  • [11] M. Akbaba and S. Q. Fakhro, "An ımproved computational technique of the inductance parameters of the reluctance augmented shaded-pole motors using finite element method," IEEE Transactions on Energy Conversion, vol. 7, no. 2, pp. 308–314, 1992.
  • [12] A. Dalcalı. and M. Akbaba, “Comparison of 2D and 3D magnetic field analysis of single-phase shaded pole induction motors,” Engineering Science and Technology, an International Journal, vol. 19, pp. 1–7, 2016.
  • [13] S.L. HO and W.N. FU, “Review and Future Application of Finite Element Methods in Induction Motors,” Electric Machines & Power Systems, vol. 26, no. 2, pp. 111-125, 2007.
  • [14] W.N. FU “A Versatile Finite Element Model of Electric Machines,” Electric Power Components and Systems, vol. 31, no. 10, pp. 941-966, 2003.
  • [15] A. Saygın, C. Ocak, A. Dalcalı, O. Gürdal, S. Alantar and Y. Tarhan, “Influence of Pole Arc Offset on The Field and Output Parameters of Brushless DC Motors,” International Journal of Advancements in Electronics and Electrical Engineering, vol. 3, no. 1, pp. 47-51, 2014.
  • [16] O. Cetin and F. Temurtas “Classification of Magnetoencephalography Signals by Multilayer and Radial Based Artificial Neural Networks,” Elec Lett Sci Eng, vol. 14, no. 1, pp. 32–38, 2018.
  • [17] O. Cetin, F. Temurtas and S. Gulgonul, “An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function,” Dicle Medical Journal, vol.42, no. 2, p. 150–157, 2015.
  • [18] A. Gulbag and F. Temurtas, “A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures,” Sensors and Actuators B, vol. 121, no. 1, pp. 590–599, 2007.
  • [19] A, Gulbag and F. Temurtas, “A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems,” Sensors and Actuators B, vol. 115, no. 1, pp. 252–262, 2006.
  • [20] E. Dogan, “Suspended sediment load estimation in lower Sakarya river by using soft computational methods,” Elec Lett Sci Eng, vol. 1, pp. 22–32, 2005.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Adem Dalcalı 0000-0002-9940-0471

Onursal Çetin

Cemil Ocak

Feyzullah Temurtaş

Yayımlanma Tarihi 18 Aralık 2018
Gönderilme Tarihi 12 Aralık 2018
Kabul Tarihi 17 Aralık 2018
Yayımlandığı Sayı Yıl 2018Cilt: 1 Sayı: 3

Kaynak Göster

IEEE A. Dalcalı, O. Çetin, C. Ocak, ve F. Temurtaş, “Bir Elektromanyetik Fırlatıcı Bobininde Mermiye Etkiyen Kuvvetin Çok Katmanlı Sinir Ağı ile Kestirimi”, SAUCIS, c. 1, sy. 3, ss. 1–10, 2018, doi: 10.35377/saucis.01.03.496233.

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An Experimental Study on Sensorless Determination of the Projectile Position by Artificial Neural Network in Magnetic Launcher Systems
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 Kapak Resmi İndir

    Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science