ParametricVibration Analysis of Pipes Conveying Fluid by Nonlinear Normal Modes and a Numerical Iterative Approach

Nonlinear normal modes and a numerical iterative approach are applied to study the parametric vibrations of pipes conveying pulsating fluid as an example of gyroscopic continua.The nonlinear non-autonomous governing equations are transformed into a set of pseudo-autonomous ones by employing the harmonic balance method.The nonlinear normal modes are constructed by the invariant manifold method on the state space and a numerical iterative approach is adopted to obtain numerical solutions,in which two types of initial conditions for the modal coefficients are employed.The results show that both initial conditions can lead to fast convergence.The frequency-amplitude responses with some modal motions in phase space are obtained by the present iterative method.Quadrature phase difference and traveling waves are found in the time-domain complex modal analysis.

ISAR Imaging of Missing Data Based on the Iterative Adaptive Approach

Through the coherent accumulation of target echoes, inverse synthetic aperture radar （ISAR） imaging achieves high azimuth resolution. However, because of the instability of the radar system, the echoes of the 1SAR will be randomly lost. The conventional FFT processing methods can cause image blur and high sidelobes or other issues. A novel algorithm for ISAR missing-data imaging based on the Iterative Adaptive Approach （IAA） is proposed. The algorithm enjoys global convergence properties and does not need to set the parameters in advance. The missing-data ISAR imaging results for simulated and measured data illustrate the effectiveness of the algorithm.

Extreme Wave Simulation with Iterative Adaptive Approach in Numerical Wave Flume

Extreme wave is highly nonlinear and may occur due to diverse reasons unexpectedly.The simulated results of extreme wave based on wave focusing,which were generated using high order spectrum method,are presented.The influences of the steepness,frequency bandwidth as well as frequency spectrum on focusing position shift were examined,showing that they can affect the wave focusing significantly.Hence,controlled accurate generation of extreme wave at a predefined position in wave flume is a difficult but important task.In this paper,an iterative adaptive approach is applied using linear dispersion theory to optimize the control signal of the wavemaker.The performance of the proposed approach is numerically investigated for a wide variety of scenarios.The results demonstrate that this approach can reproduce accurate wave focusing effectively.

Theoretical Computation of Nonlinear System Equations of Heavier Pellets Movements for Two Phase Flow

This paper presents nonlinear ordinary differential equations (ODES) of the heavier pellets movement for two phase flow, which actually represent a system of equations. The usual methods of solution such as Runge -Kutta method and it's datum results are discussed. This paper solves ODES of general form using variable mesh-length, linearizing the nonlinear terms by finite analysis method, fuilding an iteration sequence, and amending the nonlinear terms by iteration . The conditions of convergent operation of iteration solution is checked. The movement orbit and velocity of the pellets are calculated. Analysis of research results and it's application examples are illustrated.

Linearly and Quadratically Separable Classifiers Using Adaptive Approach

This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in R n.In each iteration,a subset of the sampling data （n-points,where n is the number of features） is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.