Chuandong Lin^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI: 10.32604/icces.2025.010544

Abstract Multiphase flows with thermodynamic nonequilibrium effects are encountered in various engineering and natural systems, such as bubbly flows, droplet-laden flows, and phase change processes. To accurately model and simulate such complex flows, a Discrete Boltzmann Method (DBM) is introduced in this report. The DBM is a kinetic-based approach that can capture the dynamics of multiple phases and their interactions, including phase change, mass transfer, and energy exchange. The method is validated through simulations of multiphase flows with phase change, showing good agreement with analytical solutions. The capability of the DBM to handle thermodynamic nonequilibrium effects… More >

Xinlong Wang¹, Xiaohang Qu¹, Chuandong Lin^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.011311

Abstract The lattice Boltzmann method (LBM) has been extensively utilized in various fields, including the droplet dynamics [1–4]. At present, significant challenges persist in accurately resolving interfacial dynamics during droplet collisions—including deformation [5], breakup process [6] and capturing microscale details [7] of contact line motion during droplet-wall interactions. In this work, the non-orthogonal multiple relaxation time lattice Boltzmann method is used to study droplets impacting superhydrophobic walls with different characteristic of surface protrusion. The horizontal displacement, maximum spreading length, and the contact time are probed in the process of droplet collisions under various conditions of Weber More >

Nawaf N. Hamadneh¹, Waqar A. Khan², Waqar Ashraf³, Samer H. Atawneh⁴, Ilyas Khan^5,*, Bandar N. Hamadneh⁶

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2787-2796, 2021, DOI:10.32604/cmc.2021.013228 - 28 December 2020

Abstract In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. More >