Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural\nNetwork and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic\ndrag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous\nstudies,which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder,\nand rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using\nartificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used\nto predict the wall factor, which is more suitable for addressing the problem of small samples.The characteristic dimension was presented\nto describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes\nwas established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental\nresults.
Loading....