936 resultados para Nonlinear Dynamic Responses


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The nonlinear behavior varying with the instantaneous response was analyzed through the joint time-frequency analysis method for a class of S. D. O. F nonlinear system. A masking operator an definite regions is defined and two theorems are presented. Based on these, the nonlinear system is modeled with a special time-varying linear one, called the generalized skeleton linear system (GSLS). The frequency skeleton curve and the damping skeleton curve are defined to describe the main feature of the non-linearity as well. Moreover, an identification method is proposed through the skeleton curves and the time-frequency filtering technique.

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Most of the existing mathematical models for analyzing the dynamic response of TLP are based on explicit or implicit assumptions that motions (translations and rotations) are small magnitude. However, when TLP works in severe adverse conditions, the a priori assumption on small displacements may be inadequate. In such situation, the motions should be regarded as finite magnitude. This paper will study stochastic nonlinear dynamic responses of TLP with finite displacements in random waves. The nonlinearities considered are: large amplitude motions, coupling the six degrees-of-freedom, instantaneous position, instantaneous wet surface, free surface effects and viscous drag force. The nonlinear dynamic responses are calculated by using numerical integration procedure in the time domain. After the time histories of the dynamic responses are obtained, we carry out cycle counting of the stress histories of the tethers with rain-flow counting method to get the stress range distribution.

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This paper investigates the effects of structure parameters on dynamic responses of submerged floating tunnel (SFT) under hydrodynamic loads. The structure parameters includes buoyancy-weight ratio (BWR), stiffness coefficients of the cable systems, tunnel net buoyancy and tunnel length. First, the importance of structural damp in relation to the dynamic responses of SFT is demonstrated and the mechanism of structural damp effect is discussed. Thereafter, the fundamental structure parameters are investigated through the analysis of SFT dynamic responses under hydrodynamic loads. The results indicate that the BWR of SFT is a key structure parameter. When BWR is 1.2, there is a remarkable trend change in the vertical dynamic response of SFT under hydrodynamic loads. The results also indicate that the ratio of the tunnel net buoyancy to the cable stiffness coefficient is not a characteristic factor affecting the dynamic responses of SFT under hydrodynamic loads.

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The recent progress of submerged floating tunnel (SFT) investigation and SFT prototype (SFTP) project in Qiandao Lake (Zhejiang Province, P.R. China) is the background of this research. Structural damping effect is brought into present computation model in terms of Rayleigh damping. Based on the FEM computational results of SFTPs as a function of buoyancy-weight ratio (BWR) under hydrodynamic loads, the effect of BWR on the dynamic response of SFT is illustrated. In addition, human comfort index is adopted to discuss the comfort status of the SFTP.

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This dissertation is concerned with the problem of determining the dynamic characteristics of complicated engineering systems and structures from the measurements made during dynamic tests or natural excitations. Particular attention is given to the identification and modeling of the behavior of structural dynamic systems in the nonlinear hysteretic response regime. Once a model for the system has been identified, it is intended to use this model to assess the condition of the system and to predict the response to future excitations.

A new identification methodology based upon a generalization of the method of modal identification for multi-degree-of-freedom dynaimcal systems subjected to base motion is developed. The situation considered herein is that in which only the base input and the response of a small number of degrees-of-freedom of the system are measured. In this method, called the generalized modal identification method, the response is separated into "modes" which are analogous to those of a linear system. Both parametric and nonparametric models can be employed to extract the unknown nature, hysteretic or nonhysteretic, of the generalized restoring force for each mode.

In this study, a simple four-term nonparametric model is used first to provide a nonhysteretic estimate of the nonlinear stiffness and energy dissipation behavior. To extract the hysteretic nature of nonlinear systems, a two-parameter distributed element model is then employed. This model exploits the results of the nonparametric identification as an initial estimate for the model parameters. This approach greatly improves the convergence of the subsequent optimization process.

The capability of the new method is verified using simulated response data from a three-degree-of-freedom system. The new method is also applied to the analysis of response data obtained from the U.S.-Japan cooperative pseudo-dynamic test of a full-scale six-story steel-frame structure.

The new system identification method described has been found to be both accurate and computationally efficient. It is believed that it will provide a useful tool for the analysis of structural response data.

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This work considers the isomorphous optically active crystals NaClO3 and NaBrO3. The connection between their second-order nonlinear optical (NLO) responses and chemical bond structures is established, starting from the experimental optical activities. The calculation reproduces the well-known experimental fact that crystals of NaClO3 and NaBrO3 with similar structures have different signs of optical rotation and of second harmonic generation (SHG). Unlike previous bond charge models, the method may include more than one type of bond in the calculation, and therefore may be used to study the optical activity and nonlinear optical properties of more general crystals. (C) 1998 Elsevier Science B.V. All rights reserved.

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The impact of the seasonal deposition of phytoplankton and phytodetritus on surface sediment bacterial abundance and community composition was investigated at the Western English Channel site L4. Sediment and water samples were collected from January to September in 2012, increasing in frequency during periods of high water column phytoplankton abundance. Compared to the past two decades, the spring bloom in 2012 was both unusually long in duration and contained higher than average biomass. Within spring months, the phytoplankton bloom was well mixed through the water column and showed accumulations near the sea bed, as evidenced by flow cytometry measurements of nanoeukaryotes, water column chlorophyll a and the appearance of pelagic phytoplankton at the sediment. Measurements of chlorophyll and chlorophyll degradation products indicated phytoplankton material was heavily degraded after it reached the sediment surface: the nature of the chlorophyll degradation products (predominantly pheophorbide, pyropheophorbide and hydroxychlorophyllone) was indicative of grazing activity. The abundance of bacterial 16S rRNA genes g−1 sediment (used as a proxy for bacterial biomass) increased markedly with the onset of the phytoplankton bloom, and correlated with measurements of chlorophyll at the surface sediment. Together, this suggests that bacteria may have responded to nutrients released via grazing activity. In depth sequencing of the 16S rRNA genes indicated that the composition of the bacterial community shifted rapidly through-out the prolonged spring bloom period. This was primarily due to an increase in the relative sequence abundance of Flavobacteria.

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This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.