Numerical characterization of distributed dynamic systems using tools of intelligent computing and generalized dimensional analysis

Bellamine, F.H.; Elkamel, Ali

doi:10.1016/j.amc.2006.05.002

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…The Gaussian method makes the calculation of the integral process easy and quick, which is its biggest advantage. We can get the fitting Gaussian function related to the measured data [24][25][26][27][28][29]. The data in Table 3 were fitted by the Gaussian function and the results are shown as Table 6.…”

Section: Gaussian Fitting Calculationmentioning

confidence: 99%

Calculation of Peak Particle Velocity Caused by Blasting Vibration in Step Topography

Yang¹

2018

J. Eng. Technol. Sci.

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High ground vibrations not only adversely affect the integrity of the structures in a mine area but also create inconvenience for the nearby population. In order to protect the Sanyou Mine slope in Tangshan, China from blasting vibration, the peak particle velocity in step topography must be accurately calculated. At present, the reflection coefficient of the stress wave at free interface is not considered in the equation for calculating the peak particle velocity in step topography. Therefore the accuracy of the peak particle velocity calculation is decreased in the side direction when the reflection coefficient changes. In this study, a 3D finite element analysis was employed for modeling of the blasting vibration. A series of field-testing experiments was conducted to measure the peak particle velocity. Then the reflection coefficient of the stress wave was calculated. Based on this, the principle of the peak particle velocity in step topography was explained. In addition, the application range of the equation in step topography was determined and a new equation for peak particle velocity calculation in step topography is proposed based on the numerical simulation analysis and field-testing experiment.

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Section: Gaussian Fitting Calculationmentioning

confidence: 99%

Calculation of Peak Particle Velocity Caused by Blasting Vibration in Step Topography

Yang¹

2018

J. Eng. Technol. Sci.

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“…Neural network approaches [1][2][3][4] are used to avoid long simulation times, particularly when evaluating changes in the physical properties of such systems or processes. Once neural networks are trained, the computation time of the modeled outputs is negligible and is orders of magnitude faster than any single fine mesh simulation.…”

Section: Introductionmentioning

confidence: 99%

Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge

Bellamine

Almansoori

Elkamel

2015

Applied Mathematics and Computation

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“…If only some parameters of PDEs are unknown, then these parameters can be estimated from the process data. − In many cases, the structure and the parameters of the system could be both unknown; then the identification approaches are used for modeling DPSs based on input/output data. − Time/space separation method and time/space discretization method both can be used for PDE unknown DPS model identification based on input and output data. ,− Time/space discretization method usually assumes that the local dynamics is the same at different spatial locations, and local models can be established based on the identification theory of a lattice dynamic system. ,, They achieve good predictions for many DPSs if the spatial regions are partitioned properly. However, the model dimension may be high because it is determined by the number of local models.…”

Section: Introductionmentioning

confidence: 99%

Embedded Interval Type-2 T-S Fuzzy Time/Space Separation Modeling Approach for Nonlinear Distributed Parameter System

Wang

et al. 2011

Ind. Eng. Chem. Res.

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Challenging modeling problems include how to obtain a simple and accurate model for a partial differential equation (PDE) unknown nonlinear distributed parameter system (DPS). In this paper, a time/space separation modeling approach based on the interval type-2 T-S fuzzy model (IT2 T-S model) is proposed for nonlinear DPS. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by a linear time/space separation method. Second, the interval type-2 T-S fuzzy model is determined from the low-dimensional time series to reconstruct the system dynamics through the spatial basis functions. As the IT2 T-S model employs an interval linear model in the consequent part, the relationship between the input and spatial output can be determined by linear expressions through type reduction and linear time/space reconstruction. Thus, the obtained model is suitable for control design. The simulation presents the accuracies and effectiveness of the proposed modeling methodologies.

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Numerical characterization of distributed dynamic systems using tools of intelligent computing and generalized dimensional analysis

Cited by 16 publications

References 11 publications

Calculation of Peak Particle Velocity Caused by Blasting Vibration in Step Topography

Calculation of Peak Particle Velocity Caused by Blasting Vibration in Step Topography

Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge

Embedded Interval Type-2 T-S Fuzzy Time/Space Separation Modeling Approach for Nonlinear Distributed Parameter System

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