Applying the Artificial Neural Network to Estimate the Drag Force for an Autonomous Underwater Vehicle
ABSTRACT
This paper offer an artificial neural network (ANN) model to calculate drag force on an axisymmetric underwater vehicle by obtaining dataset from a computational fluid dynamic analysis. First, great effort was done to calculate the pressure and viscous data forces by increasing the precision and numerical data in order to extend and raise quality of dataset. In this step, numerous different geometry models (configurations of axisymmetric body) were designed, examined and evaluated input parameters including: diameter of body, diameter of nose disc, length of body, length of nose and velocity whereas outputs contain pressure and viscous forces. This dataset was used to train the ANN model. Feed-forward neural network (FFNN) is selected which is more common and suitable in this field’s study. A three-layer neural network was opted and after training this network, the results showed good agreement with CFD data. This study shows that applying the ANN model helps to reach final purpose in the least time and error, in addition a variety of tests can be performed to have a desired design in this way.
This paper offer an artificial neural network (ANN) model to calculate drag force on an axisymmetric underwater vehicle by obtaining dataset from a computational fluid dynamic analysis. First, great effort was done to calculate the pressure and viscous data forces by increasing the precision and numerical data in order to extend and raise quality of dataset. In this step, numerous different geometry models (configurations of axisymmetric body) were designed, examined and evaluated input parameters including: diameter of body, diameter of nose disc, length of body, length of nose and velocity whereas outputs contain pressure and viscous forces. This dataset was used to train the ANN model. Feed-forward neural network (FFNN) is selected which is more common and suitable in this field’s study. A three-layer neural network was opted and after training this network, the results showed good agreement with CFD data. This study shows that applying the ANN model helps to reach final purpose in the least time and error, in addition a variety of tests can be performed to have a desired design in this way.
Cite this paper
Yari, E. , Ayoobi, A. and Ghassemi, H. (2014) Applying the Artificial Neural Network to Estimate the Drag Force for an Autonomous Underwater Vehicle. Open Journal of Fluid Dynamics, 4, 334-346. doi: 10.4236/ojfd.2014.43025.
Yari, E. , Ayoobi, A. and Ghassemi, H. (2014) Applying the Artificial Neural Network to Estimate the Drag Force for an Autonomous Underwater Vehicle. Open Journal of Fluid Dynamics, 4, 334-346. doi: 10.4236/ojfd.2014.43025.
References
[1] Carmichael, B.H. (1966) Underwater Vehicle Drag Reduction Through Choice of Shape. AIAA Paper 66-657. [2] Parsons, J.S. and Goodsont, R.E. (1974) Shaping of Axisymmetric Bodies for Minimum Drag in Incompressible Flow. AIAA. Journal of Hydronautics, 8, NO. 3. [3] Freudenthal, J.L. (2002) Experimental Studies on the Drag of an Axisymmetric Hull. Thesis in Aerospace Engineering, Mississippi. [4] Paster, D., Raytheon, Co. and Portsmouth, R.I. (2003) Importance of Hydrodynamic Considerations for Underwater Vehicle Design. IEEE Oceans, 18, 1413-1422. [5] Baker, C. (2004) Estimating Drag Force on Submarine Hulls. Report DRDC Atlantic CR2004-125, Defence R&D Canada-Atlantic. [6] Zak, B. (2005) The Neural Model of Dynamics of Unmanned Underwater Vehicle. EUSFLAT-LFA Conference, Gdynia, 506-511. [7] Shi, Y., Qian, W., Yan, W. and Li, J. (2007) Adaptive Depth Control for Autonomous Underwater Vehicles Based on Feed forward Neural Networks. International Journal of Computer Science & Applications, 4, 107-118. [8] Karim Md., M., Rahman Md., M. and Alim Md., A. (2008) Numerical Computation of Viscous Drag for Axisymmetric Underwater Vehicles. Journal of Mechanical and Industrial Engineering, 9-21. [9] Alvarez, A., Bertram, V. and Gualdesi, L. (2009) Hull Hydrodynamic Optimization of Autonomous Underwater Vehicles Operating at Snorkeling Depth. Ocean Engineering, 36, 105-112.
http://dx.doi.org/10.1016/j.oceaneng.2008.08.006 [10] Suman, K.N.S., Rao, D.N., Das, H.N. and Kiran, G.B. (2010) Hydrodynamic Performance Evaluation of an Ellipsoidal Nose for a High Speed under Water Vehicle. Jordan Journal of Mechanical and Industrial Engineering, 4, 641-652. [11] Hakeem, M.A., Kamil, M. and Arman, I. (2008) Prediction of Temperature Profiles Using Artificial Neural Networks in a Vertical Thermosiphon Reboiler. Applied Thermal Engineering, 28, 1572-1579. [12] Cacciola, M., Megali, G., Fiasché, M., Versaci, M. and Morabito, F.C. (2010) A Comparison between Neural Networks and k-Nearest Neighbours for Blood Cells Taxonomy. Memetic Computing, 2, 237-246.
http://dx.doi.org/10.1007/s12293-010-0043-6 [13] Chehreh Chelgani, S., Shahbazi, B. and Rezai, B. (2010) Estimation of Froth Flotation Recovery and Collision Probability Based on Operational Parameters Using an Artificial Neural Network. International Journal of Minerals, Metallurgy and Materials, 17, 526-534.
http://dx.doi.org/10.1007/s12613-010-0353-1 [14] Hoskins, J.C. and Himmelblau, D.M. (1988) Artificial Neural Network Models of Knowledge Representation in Chemical Engineering. Computers & Chemical Engineering, 12, 881-890.
http://dx.doi.org/10.1016/0098-1354(88)87015-7 [15] Bhat, N. and McAvoy, T. (1989) Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems. American Control Conference, Pittsburgh, 21-23 June 1989, 1336-1341. [16] Afkhami, A., Abbasi-Tarighat, M. and Bahramb, M. (2008) Artificial Neural Networks for Determination of Enantiomeric Composition of α-Phenylglycine Using UV Spectra of Cyclodextrin Host-Guest Complexes: Comparison of Feed-Forward and Radial Basis Function Networks. Talanta, 75, 91-98. [17] Hagan, M.T. and Menhaj, M. (1994) Training Feed-Forward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5, 989-993.
http://dx.doi.org/10.1109/72.329697 [18] Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996) Neural Network Design. PWS Publishing, Boston. [19] Cybenko, G. (1989) Approximation by Superpositions of a Sigmoid Function. Mathematics of Control, Signals and Systems, 2, 303-314.
[1] Carmichael, B.H. (1966) Underwater Vehicle Drag Reduction Through Choice of Shape. AIAA Paper 66-657. [2] Parsons, J.S. and Goodsont, R.E. (1974) Shaping of Axisymmetric Bodies for Minimum Drag in Incompressible Flow. AIAA. Journal of Hydronautics, 8, NO. 3. [3] Freudenthal, J.L. (2002) Experimental Studies on the Drag of an Axisymmetric Hull. Thesis in Aerospace Engineering, Mississippi. [4] Paster, D., Raytheon, Co. and Portsmouth, R.I. (2003) Importance of Hydrodynamic Considerations for Underwater Vehicle Design. IEEE Oceans, 18, 1413-1422. [5] Baker, C. (2004) Estimating Drag Force on Submarine Hulls. Report DRDC Atlantic CR2004-125, Defence R&D Canada-Atlantic. [6] Zak, B. (2005) The Neural Model of Dynamics of Unmanned Underwater Vehicle. EUSFLAT-LFA Conference, Gdynia, 506-511. [7] Shi, Y., Qian, W., Yan, W. and Li, J. (2007) Adaptive Depth Control for Autonomous Underwater Vehicles Based on Feed forward Neural Networks. International Journal of Computer Science & Applications, 4, 107-118. [8] Karim Md., M., Rahman Md., M. and Alim Md., A. (2008) Numerical Computation of Viscous Drag for Axisymmetric Underwater Vehicles. Journal of Mechanical and Industrial Engineering, 9-21. [9] Alvarez, A., Bertram, V. and Gualdesi, L. (2009) Hull Hydrodynamic Optimization of Autonomous Underwater Vehicles Operating at Snorkeling Depth. Ocean Engineering, 36, 105-112.
http://dx.doi.org/10.1016/j.oceaneng.2008.08.006 [10] Suman, K.N.S., Rao, D.N., Das, H.N. and Kiran, G.B. (2010) Hydrodynamic Performance Evaluation of an Ellipsoidal Nose for a High Speed under Water Vehicle. Jordan Journal of Mechanical and Industrial Engineering, 4, 641-652. [11] Hakeem, M.A., Kamil, M. and Arman, I. (2008) Prediction of Temperature Profiles Using Artificial Neural Networks in a Vertical Thermosiphon Reboiler. Applied Thermal Engineering, 28, 1572-1579. [12] Cacciola, M., Megali, G., Fiasché, M., Versaci, M. and Morabito, F.C. (2010) A Comparison between Neural Networks and k-Nearest Neighbours for Blood Cells Taxonomy. Memetic Computing, 2, 237-246.
http://dx.doi.org/10.1007/s12293-010-0043-6 [13] Chehreh Chelgani, S., Shahbazi, B. and Rezai, B. (2010) Estimation of Froth Flotation Recovery and Collision Probability Based on Operational Parameters Using an Artificial Neural Network. International Journal of Minerals, Metallurgy and Materials, 17, 526-534.
http://dx.doi.org/10.1007/s12613-010-0353-1 [14] Hoskins, J.C. and Himmelblau, D.M. (1988) Artificial Neural Network Models of Knowledge Representation in Chemical Engineering. Computers & Chemical Engineering, 12, 881-890.
http://dx.doi.org/10.1016/0098-1354(88)87015-7 [15] Bhat, N. and McAvoy, T. (1989) Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems. American Control Conference, Pittsburgh, 21-23 June 1989, 1336-1341. [16] Afkhami, A., Abbasi-Tarighat, M. and Bahramb, M. (2008) Artificial Neural Networks for Determination of Enantiomeric Composition of α-Phenylglycine Using UV Spectra of Cyclodextrin Host-Guest Complexes: Comparison of Feed-Forward and Radial Basis Function Networks. Talanta, 75, 91-98. [17] Hagan, M.T. and Menhaj, M. (1994) Training Feed-Forward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5, 989-993.
http://dx.doi.org/10.1109/72.329697 [18] Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996) Neural Network Design. PWS Publishing, Boston. [19] Cybenko, G. (1989) Approximation by Superpositions of a Sigmoid Function. Mathematics of Control, Signals and Systems, 2, 303-314.