基于计算流体力学和人工神经网络对断阶式滑行艇的水动力性能预测

In the present paper,the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics(CFD)analysis.For this purpose,the hydrodynamic resistances of without step,one-step,and two-step hulls of Cougar planing craft are evaluated under different distances of the second step and LCG from aft,weight loadings,and Froude numbers(Fr).Our CFD results are appropriately validated against our conducted experimental test in National Iranians Marine Laboratory(NIMALA),Tehran,Iran.Then,the hydrodynamic resistance of intended planing crafts under various geometrical and physical conditions is predicted using artificial neural networks(ANNs).CFD analysis shows two different trends in the growth rate of resistance to weight ratio.So that,using steps for planing craft increases the resistance to weight ratio at lower Fr and decreases it at higher Fr.Additionally,by the increase of the distance between two steps,the resistance to weight ratio is decreased and the porpoising phenomenon is delayed.Furthermore,we obtained the maximum mean square error of ANNs output in the prediction of resistance to weight ratio equal to 0.0027.Finally,the predictive equation is suggested for the resistance to weight ratio of stepped planing craft according to weights and bias of designed ANNs.

Using artificial neural network to predict velocity of sound in liquid water as a function of ambient temperature, electrical and magnetic fields

One of the main thermophysical properties of liquid water is velocity of sound.However,the effect of different externalities on velocity of sound in liquid water is not well known.Therefore,in current study,by designing an artificial neural network(ANN)velocity of sound in liquid water under different externalities is predicted.Selected externalities are ambient temperature from 272.65 K to 348.43 K,different electrical fields in range of 0 V/m to 4.03E+9 V/m and magnetic fields in range of 0-10.0594 T.To prepare of reference dataset for entry to ANN,numerical and experimental data as macroscopic reference data are extracted from microscopic characteristic of water HB strength.In order to achieve an appropriate ANN,ANN architecture sensitivity analysis is conducted by using an iterative algorithm.Learning procedure in the selected feed-forward back propagation ANN is done by hyperbolic transfer functions.Also,Levenberg-Marquardt algorithm is utilized for the optimization process.ANNs output showed that the maximum MSE in prediction of velocity of sound is 0.00066.Also,the minimum of correlation coefficient in prediction of velocity of sound is 0.99131.Based on the ANNs outputs,weights and bias,an equation to predict of velocity of sound in liquid water under intended externalities is proposed.Also,according to weight sensitivity analysis input of electrical fields with 63%relative importance percentage has a grater impression on the response variable of velocity of sound in liquid water.©2016 Shanghai Jiaotong University.Published by Elsevier B.V.

On hydrodynamic analysis of stepped planing crafts

Using transverse steps is one of the adopted strategies to improve the hydrodynamic characteristics of planing crafts.In the present paper,hydrodynamic characteristics of stepped planing crafts are numerically investigated.For this purpose,we analyzed the effects of five different types of transverse steps on lift to drag ratio,resistance,trim angle and sinkage of high speed planing crafts.Contours of pressure distribution on the bottom of crafts,water volume fraction and free-surface are also presented at different hull velocities.Computed numerical results are properly validated against our conducted towing tank experimental tests.