Linear and quadratic damping coefficients of a single module of a very large floating structure over variable bathymetry:Physical and numerical free-decay experiments

The linearity assumption is widely used when acquiring the hydrodynamic coefficients of a floating structure.However,the linear damping is frequently underestimated,especially for the natural frequency.To investigate the sloping seafloor effects on the damping terms of a single module of a semi-submersible Very Large Floating Structure(VLFS),this paper revisits the conventional formulation and further proposes the direct integration method for obtaining the linear and quadratic damping coefficients from free-decay tests.Numerical free-decay simulations of the single module over variable bathymetry are carried out by the CFD numerical tank.Corresponding model tests are also implemented to verify and validate against the numerical solutions.The effects of the sloping seafloor,as well as the water depth,on the hydrodynamic coefficients are investigated based on the validated CFD modeling.Both numerical and experimental results indicate that the acquisition of the linear and quadratic damping coefficients is sensitive to the data-processing and identification approaches.For the case studied in present paper,the identification errors introduced by the conventional method are 1.5%while they are 0.5%using the direct integration method.The quadratic damping coefficient for heave mode decreases about 10.4%when the sloping angle increases from 0 to 6 deg.

Vibration response analysis of 2-DOF locally nonlinear systems based on the theory of modal superposition

Many important vibration phenomena which simultaneously contain quadratic nonlinear stiffness and damping exist in the complicated vibrating systems under practical circumstances. In this paper, we established a 2-degree-of-freedom (DOF) nonlinear vibration model for such a system, deduced the differential equations of motion which govern its dynamics, and worked out the solutions for the governing equations by the principle of superposition of nonlinear normal modes (NLNM) based on Shaw’s theory of invariant manifolds. We conducted numerical simulations with the established model, using superposition of nonlinear normal modes and direct numerical methods, respectively. The obtained results demonstrate the feasibility of the proposed method in that its calculated data varies in a similar tendency to that of the direct numerical solutions.

Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion

On highways,vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles.To ensure their safety,predicting the sideslip trajectories of such vehicles is crucial.However,the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction.Hence,this study uses a physical model-based approach to predict vehicle sideslip trajectories.Nevertheless,the traditional physical model-based method relies on constant input assumption,making its long-term prediction accuracy poor.To address this challenge,this study presents the time-series analysis and interacting multiple model-based(IMM)sideslip trajectory prediction(TSIMMSTP)method,which encompasses time-series analysis and multi-physical model fusion,for the prediction of vehicle sideslip trajectories.Firstly,we use the proposed adaptive quadratic exponential smoothing method with damping(AQESD)in the time-series analysis module to predict the input state sequence required by kinematic models.Then,we employ an IMM approach to fuse the prediction results of various physical models.The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories.The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios,and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.