In the present analysis, several parameters used in a numerical simulation are investigated in an integrated study to obtain their influence on the process and results of this simulation. The parameters studied are el...In the present analysis, several parameters used in a numerical simulation are investigated in an integrated study to obtain their influence on the process and results of this simulation. The parameters studied are element formulation, friction coefficient, and material model. Numerical simulations using the non-linear finite element method are conducted to produce virtual experimental data for several collision scenarios. Pattern and size damages caused by collision in a real accident case are assumed as real experimental data, and these are used to validate the method. The element model study performed indicates that the Belytschko-Tsay element formulation should be recommended for use in virtual experiments. It is recommended that the real value of the friction coefficient for materials involved is applied in simulations. For the study of the material model, the application of materials with high yield strength is recommended for use in the side hull structure.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
文摘In the present analysis, several parameters used in a numerical simulation are investigated in an integrated study to obtain their influence on the process and results of this simulation. The parameters studied are element formulation, friction coefficient, and material model. Numerical simulations using the non-linear finite element method are conducted to produce virtual experimental data for several collision scenarios. Pattern and size damages caused by collision in a real accident case are assumed as real experimental data, and these are used to validate the method. The element model study performed indicates that the Belytschko-Tsay element formulation should be recommended for use in virtual experiments. It is recommended that the real value of the friction coefficient for materials involved is applied in simulations. For the study of the material model, the application of materials with high yield strength is recommended for use in the side hull structure.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
