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A particle formation mechanism is proposed on the basis of a multiscale analysis of the plasma reactor accounting for: heat and mass balance, RTD measurement and internal flow modelling and particle characterisation by TEM.
In this paper, we combine fuzzy modelling and Particle Swarm Optimisation (PSO) to address the multi-objective optimal alloy design problem.
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SM was developed by hybridising desirability model and particle swarm optimization (PSO).
The toolkit provides the fundamental physics models and particle tracking algorithms that track each particle in space and time.
The former needs a particle turbulence model and particle stress model, whereas the latter needs a fluid instantaneous velocity model and interparticle collision model.
Particle growth is incorporated in the model, and particle size distributions are easily obtained.
A stochastic modeling and particle filtering-based algorithm has been proposed by using a state-space model to solve nonlinear and non-Gaussian signals [12].
In areas where not enough real observed data are available, a hydrogeological model and particle tracking method can be used for the monitoring network optimization.
Computational fluid dynamics (CFD) with standard k-ε turbulence model and particle trajectory model is used to simulate the internal flow when fracturing fluid flows across the ball seat.
Moreover, a new hybrid approach based on analytical modeling and Particle Swarm Optimization (PSO) is proposed to achieve improved photoelectric behavior at zero bias that can ensure favorable self-powered MSM-based UV-PD.
Moreover, a new hybrid approach based on analytical modeling and Particle Swarm Optimization (PSO) is proposed to determinate the optimal band-gap profile of the amended CZTS absorber layer to achieve further efficiency enhancement.
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