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Aeroelastic behavior of a slender body considering free fittings
M. Ehramianpour,H. Haddadpour,M. T. Ahmadian 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.9
This paper presents dynamic and vibration analysis of a flight vehicle with consideration of the free fitting between its two sections. Using the Lagrangian approach, a general analytical model is developed for a non-spinning elastic vehicle in planar motion. The model contains rigid body motions and bending deformations of two sections of the flight vehicle and a nonlinear rotational spring that models the freeplay between the two sections. To express bending deformation, the mode summation method is applied. It is shown that freeplay in the joints significantly affects the trajectory of the flight vehicle. Numerical examples reveal the effect of a joint’s nonlinearity on the trajectory and stability of the flight vehicle.
Ali A. Abbasi,G.R. Vossoughi,M.T. Ahmadian 한국통합생물학회 2012 Animal cells and systems Vol.16 No.2
In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w),dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Flu¨ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.
Abbasi, Ali A.,Vossoughi, G.R.,Ahmadian, M.T. The Korean Society for Integrative Biology 2012 Animal cells and systems Vol.16 No.2
In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.