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Pressure and velocity cross-spectrum of normal modes in low-frequency acoustic vector field of shallow water and its application

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Journal of Systems Engineering and Electronics 2015年第2期26卷 241-249页

作者： Yun Yu Qing Ling Jiang Xu naval academy of armament

The pressure and horizontal particle velocity combined descriptions in the very low frequency acoustic field of shallow wa- ter integrated with the concept of effective depth of Pekeris wave- guide is proposed, especially the active component of the pressure and horizontal particle velocity cross-spectrum, also called ho- rizontal complex cross acoustic intensity, when only two normal modes are trapped in the waveguide. Both the approximate theo- retic analysis and the numerical results show that the sign of the horizontal complex cross acoustic intensity active component is independent of the range when vertically deployed receiving dual sensors are placed in appropriate depths, the sum of which is equal to the waveguide effective depth, so it can be used to tell whether the sound source is near the surface or underwater; while the range rate is expected to be measured by utilizing the sign distribution characteristic of the reactive component. The further robustness analysis of the depth classification algorithm shows that the existence of shear waves in semi infinite basement and the change of acoustic velocity profiles have few effects on the application of this method, and the seabed attenuation will limit the detection range, but the algorithm still has a good robustness in the valid detection range.

关键词： low frequency acoustic vector field,effective depth,complex cross acoustic intensity,active component,target depth classificatio

The design of propeller and propeller boss cap fins(PBCF) by an integrative method

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Journal of Hydrodynamics 2014年第4期26卷 586-593页

作者：马骋蔡昊鹏钱正芳陈科 Institute of naval academy of armament Department of Ship Engineering naval University of Engineering

Generally, after a marine propeller design, the propeller boss cap fins （PBCF） design concerns with an optimal selection of model test results, without a due consideration of the interaction between the PBCF and the propeller. In this paper, the PBCF and the propeller are considered as a whole system with their design as an integrative process, in which the concept of the increased loading in the blade root is incorporated. The load distribution on the blade becomes well-proportioned due to the increased loading in the blade root, and it is advantageous to the reduction of the vibratory force and the blade tip vortex. The blade root area is stronger in withstanding forces, and is not easy to be vibrated, therefore, the increased loading there is beneficial to the noise reduction. The disadvantage of the increased loading in the blade root is the generation of the hub vortex behind the boss cap, but the hub vortex can be broken up by the energy saving hydrodynamic mechanism of the PBCF. The integrative design method introduced in this paper can provide a higher efficiency for propellers under the same design conditions. In this paper, an integrative propeller and PBCF design method including the theoretical design and the numerical optimization design is proposed, based on the potential flow theory, the CFD tools, the improved particle swarm optimization algorithm, and the model tests. A propeller with the PBCF is designed based on the method of integrated increased loading in the blade root for a cargo vessel in this paper. The cavitation tunnel model test results show that the propeller and the PBCF thus designed enjoys a higher efficiency, and the design method is effective, reliable and practical.

关键词： propeller boss cap fins （PBCF） integrative design increased loading in blade root

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Applied Mathematics and Mechanics(English Edition) 2011年第12期32卷 1491-1504页

作者：张振华汪玉张立军袁建红赵海峰 Department of Ship and Ocean Engineering Naval University of Engineering naval academy of armament

The final anomalous sag distortion of the ship girder subjected to the near field underwater explosion （undex） below the middle ship is studied. The sinking exercise of Spruance class destroyer DD973 sunk by one MK48 torpedo is first presented, and a simulation model is established. The exponential attenuation phase, the reciprocal attenuation phase, the post reciprocal attenuation phase, and the negative pressure phase of the undex load are precisely applied in this model. The fluid-solid interaction, the added water mass, the gravity, and the change of buoyancy are also taken into account. The similarity theory is then used to analyze the dynamic response of the ship girder. Similarity parameters and theory prediction formulae of the dynamic response of the ship girder are presented, The physical meaning and influences of these similarity parameters are analyzed.

关键词： underwater explosion （undex） ship girder near field anomalous response,similarity analysis

Neural Network Prediction Model for Ship Hydraulic Pressure Signal Under Wind Wave Background

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Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 224-227页

作者：李松张春华石敏 naval academy of armament Unit 61741

The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.

关键词： hydrodynamic pressure signal wavelet-transform radial basis function(RBF) neural network signal to noise ratio(SNR) predictive e

Numerical Reality Method of the Microburst Model

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Journal of Donghua University(English Edition) 2015年第4期32卷 597-601页

作者：陶杨韩维 naval academy of armament Department of Airborne Vehicle Engineering Naval Aeronautical and Astronautical University

An engineering microburst model to generate the microburst wind field for virtual flight simulation has been presented. The model is built as a finite viscosity vortex core model based on the vortex ring theory considering the air viscosity,and it can solve the problem of induced velocity discontinuity at the inner region near the vortex core. Moreover,the central axis velocity is obtained by turbulence free jet theory so as to avoid the singularity.The parameters in multiple-vortex-ring microburst model are determined by improved quantum genetic algorithm( QGA) based on immune and mutation operator,and the parameters optimization of the model under condition of different maximum vertical velocity are investigated. The results show that the microburst model is effective and accurate. The simulation results fit the preset value very well,and the error is controlled within 10^(- 7).

关键词： microburst vortex ring viscosity vortex core quantum genetic algorithm(QG A) immune and mutation operator multiplevortexring model

Chaff Cloud Jamming Suppression Based on Wavelet Transform

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Journal of Shanghai Jiaotong university(Science) 2011年第6期16卷 704-707页

作者：赵海波裴晋泽赵雷鸣胡光锐 naval academy of armament Department of Electronic Engineering Shanghai Jiaotong University

Radar target signals and chaff cloud jamming signals have different characters by the wavelet transform.The wavelet coefficients of radar target signals are highly correlated with its near-and-near-scale wavelet coefficients,however the correlativity between the wavelet coefficients of chaff cloud jamming signals and its nearand-near scale wavelet coefficients is less significant.Based on the binary-base discrete wavelet transform and the correlation algorithm,the method of target entropy to estimate standard variance of the jamming signals and each scale is proposed to ensure reasonable threshold,to suppress chaff cloud signals and finally to reconstruct mixed signals by the improved spatially selective noise filtration(ISSNF) method.The extensive simulation results show that the proposed method can availably suppress chaff cloud jamming and decontaminate target echo.

关键词： wavelet transform target entropy improved spatially selective noise filtration(ISSNF) jamming suppression

Calculation of fire extinguishment time with water mist in an enclosed room

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Journal of Thermal Science 2010年第6期19卷 561-566页

作者： Lijun Yang Jianbo Zhao Zhenyu Xu School of Astronautics Beijing University of Aeronautics and Astronautics Beijing 100191 China naval academy of armament Beijing 100161 China

The fire extinguishment time is a major factor to evaluate the efficiency of fire extinguishment with water mist. In this paper the fire extinguishment time with water mist in an enclosed room is calculated. Before adding water mist, the chemical kinetics plays the role in combustion, where a dimensionless math model is established by us-ing the Semenov theory. After adding water mist, the diffusion plays the role instead. Then another math model containing water mist and dominated by oxygen concentration is established. The fire temperature is integrated from Tm to extinguishment temperature TB and the extinguishment time can be obtained. The calculated values are compared with the experimental data under different conditions. The results show that this model can predict the fire extinguishment time accurately. Besides, this model also can be used to determine the critical water mist flux and evaluate which fire extinguishment mechanisms dominate the extinguishment.

关键词：细水雾灭火消防灭火计算值时间房间封闭数学模型化学动力学

Online Sequential Extreme Learning Machine Based Multilayer Perception with Output Self Feedback for Time Series Prediction

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Journal of Shanghai Jiaotong University(Science) 2013年第3期18卷 366-375页

作者：潘峰赵海波 School of Information Science and Technology Donghua University Institude of Aircraft Equipment Naval Academy of Armament

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital part for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases: Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.

关键词： time series prediction extreme learning machine (ELM) autoregression (AR) online sequential learning ELM (OS-ELM) recurrent neural network (RNN)

Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

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Journal of Shanghai Jiaotong university(Science) 2013年第4期18卷 434-442页

作者：潘峰赵海波刘华山 School of Information Science and Technology Donghua University Institute of Aircraft Equipment Naval Academy of Armament

This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.

关键词：计算机模式识别图像处理应用程序