Study On Intelligent Identification For Hydrodynamic Parameters Of Water-exit Body

Due to the impact of free-surface and cavitation bubbles,the moving process of water-exit body is a very strong unsteady hydrodynamics problem.The body will endure very strong unsteady hydrodynamic forces during the transient process;this is of great importance in engineering design.However,it has been thus far,both at home and abroad,a rather difficult task to investigate the unsteady hydrodynamic forces due to water-exit body.In this dissertation,by using modern identification techniques,we would like to contribute to such investigations by focusing on mathematical modeling and parameter identification of the above mentioned hydrodynamic system, based on related experimental data and the corresponding hydrodynamics equations.Whilst identification techniques in the field of linear aerodynamics parameter identification have been considerably developed,some difficult problems remain untackled on identification of nolinear aerodynamics parameter.However,the identification and estimation of nonlinear hydrodynamics parameters is still in its initial stage.Starting with the characteristics of parameters of hydrodynamics due to water-exit body,we conduct investigations on model identification,parameter estimation,experimental design,and system verification.First of all,we study the parameter modeling problem of unsteady hydrodynamics due to vertical water-exit body based on fuzzy system.Next,we use Particle Swarm Optimization(PSO) algorithm to investigate the simulated identification of the several parameters of the unsteady hydrodynamic forces due to a submerged body. The results we obtained are considerably consistent with the real values,this being a strong evidence for the practical value and inspiriting effects of intelligence techniques in hydrodynamics parameter identification. Nevertheless,for multi-dimensional complex hydrodynamic systems,further improvements need to be done on several issues such as optimization of nonlinear model structure and parameter estimation algorithms.On the aspect of model identification,we specially study the selection techniques for nonlinear model structure with linear parameters and the corresponding algorithms.We propose a new set of bi-orthogonal functions in Hilbert space,derive the corresponding forward and backward recursive equations,and prove that the proposed bi-orthogonal functions can generate the orthogonal projection of an arbitrary function onto the subspace spanned by a given set of functions.It is appropriate to stress that the proposed recursive method avoids the computation of inverse operations,does not need the Gram-Schmidt orthogonalization technique,and allows us to update the whole set of bi-orthogonal function when the dimension of the subspace is enlarged or decreased. Based on the proposed recursive equations,we further propose a new model structure selection technique,termed Stepwise Projection(SP) algorithm.SP is a bi-direction selection technique,which firstly selects an initial set of model items through forward selection technique,and then further selects the optimal items from the initial set by use of backward selection technique,and finally yields model structure and parameter estimations simultaneously. SP is applicable not only to system identification,but to a broad range of signal processing problems such as data compression and sparse approximation of signals.On the aspect of parameter estimation,due to introduce of identification rules,the hydrodynamics parameter identification problems can be converted to nonlinear constrained optimization problems.Therefore,we investigate specialized intelligent optimization algorithms suitable for hydrodynamics parameter identification. The general intelligent optimization algorithms,taking PSO and Differential Evolution(DE) for instances,are unconstrained optimization techniques and thereby lack an implicit constraint-handling mechanism.Therefore, they need extra technique(say,penalty function method) to handle constraints when they are used for soling constrained optimization problems.But this would result in some new difficulties.Inspired by the philosophy of PSO,we propose an improved swarm intelligence algorithm,called Differential Swarm(DS),which incorporates into its algorithmic mechanism the impact of constraints and has a certain ability to self-adaptively handle constraints.The performance of DS is experimented on the benchmark set of 24 functions for CEC06 and compared with other evolutionary algorithms.Experimental results show that DS performs considerably well in terms of optimization ability,simplicity,convergence rate,and generality,and is suitable for hydrodynamics parameter identification. On the aspect of experimental design,since the hydrodynamic forces due to water-exit body of six-degree freedom is over complicated,we devise relevant experiments to measure the hydrodynamic forces acting on constrained model exiting water obliquely.Moreover,by considering the physical characteristics of such constrained model at different stages of water-exit process,we conduct research on modeling and identification of steady hydrodynamics parameters of no cavity flow,and of the axial and normal unsteady hydrodynamics parameters.On the aspect of system verification,we predict the hydrodynamic forces of other water-exit experiments under similar condition by using the hydrodynamics parameters obtained through identification techniques.It has been shown that the predicted results are highly consistent with the experimental results,thus verifying the correctness of the hydrodynamics parameter model of water-exit body.