Unlike previous numerical techniques, this study
utilizes artificial neural networks (ANNs) to predict optimal ion
mobility at specific applied voltages. The finite difference
method (FDM) integrated with the full multigrid method (FMG)
is employed to train the ANN model. This model is designed to
predict ion mobility and optimize computational grids for a
given precipitator design. Traditionally, the FDM-FMG method
required several iterations to achieve convergence of the
potential error, with the computed current density aligning with
experimental data. This approach, however, proved
computationally expensive. The incorporation of ANNs
significantly reduces the computational efforts of the FDMFMG
method, enhancing overall performance by minimizing
the time required for convergence. This leads to a more efficient
numerical process, particularly in large-scale simulations. The
proposed method has been rigorously validated against
previously published experimental results, demonstrating
excellent agreement and showcasing its potential for improving
computational efficiency in ion mobility prediction within
electrostatic precipitators. |
