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  • Open Access


    Numerical Stability and Accuracy of Contact Angle Schemes in Pseudopotential Lattice Boltzmann Model for Simulating Static Wetting and Dynamic Wetting

    Dongmin Wang1,2,*, Gaoshuai Lin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 299-318, 2023, DOI:10.32604/cmes.2023.027280

    Abstract There are five most widely used contact angle schemes in the pseudopotential lattice Boltzmann (LB) model for simulating the wetting phenomenon: The pseudopotential-based scheme (PB scheme), the improved virtual-density scheme (IVD scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the fluid layer density above the wall (MPB-C scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the weighted average density of surrounding fluid nodes (MPB-W scheme) and the geometric formulation scheme (GF scheme). But the numerical stability and accuracy of the schemes for wetting simulation remain… More >

  • Open Access


    An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model

    G. Naveen Sundar1, Stalin Selvaraj2, D. Narmadha1, K. Martin Sagayam3, A. Amir Anton Jone3, Ayman A. Aly4, Dac-Nhuong Le5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 31-46, 2022, DOI:10.32604/cmes.2022.019914

    Abstract Hepatocellular carcinoma (HCC) is one major cause of cancer-related mortality around the world. However, at advanced stages of HCC, systematic treatment options are currently limited. As a result, new pharmacological targets must be discovered regularly, and then tailored medicines against HCC must be developed. In this research, we used biomarkers of HCC to collect the protein interaction network related to HCC. Initially, DC (Degree Centrality) was employed to assess the importance of each protein. Then an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target More >

  • Open Access


    Experimental Performance Evaluation and Artificial-Neural-Network Modeling of ZnO-CuO/EG-W Hybrid Nanofluids

    Yuling Zhai*, Long Li, Zihao Xuan, Mingyan Ma, Hua Wang

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 629-646, 2022, DOI:10.32604/fdmp.2022.017485

    Abstract The thermo-physical properties of nanofluids are highly dependent on the used base fluid. This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG (ethylene glycol)-W (water) hybrid nanofluids with mass concentration and temperatures in the ranges 1-5 wt.% and 25-60°C, respectively. The characteristics and stability of these mixtures were estimated by TEM (transmission electron microscopy), visual observation, and absorbance tests. The results show that 120 min of sonication and the addition of PVP (polyvinyl pyrrolidone) surfactant can prevent sedimentation for a period reaching up to 20 days. The… More >

  • Open Access


    Performance Evaluation of Small-channel Pulsating Heat Pipe Based on Dimensional Analysis and ANN Model

    Xuehui Wang1, Edward Wright1, Zeyu Liu1, Neng Gao2,*, Ying Li3

    Energy Engineering, Vol.119, No.2, pp. 801-814, 2022, DOI:10.32604/ee.2022.018241

    Abstract The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips, as it is characterised by excellent heat transfer ability and flexibility for miniaturisation. To boost the application of PHP, reliable heat transfer performance evaluation models are especially important. In this paper, a heat transfer correlation was firstly proposed for closed PHP with various working fluids (water, ethanol, methanol, R123, acetone) based on collected experimental data. Dimensional analysis was used to group the parameters. It was shown that the average absolute deviation (AAD) and correlation coefficient More >

  • Open Access


    A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

    Tanzila Saba1, Mirza Naveed Shahzad2,*, Sonia Iqbal2,3, Amjad Rehman1, Ibrahim Abunadi1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4785-4801, 2022, DOI:10.32604/cmc.2022.022309

    Abstract Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’ arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists’ arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In… More >

  • Open Access


    Exploring the Approaches to Data Flow Computing

    Mohammad B. Khan1, Abdul R. Khan2,*, Hasan Alkahtani2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2333-2346, 2022, DOI:10.32604/cmc.2022.020623

    Abstract Architectures based on the data flow computing model provide an alternative to the conventional Von-Neumann architecture that are widely used for general purpose computing. Processors based on the data flow architecture employ fine-grain data-driven parallelism. These architectures have the potential to exploit the inherent parallelism in compute intensive applications like signal processing, image and video processing and so on and can thus achieve faster throughputs and higher power efficiency. In this paper, several data flow computing architectures are explored, and their main architectural features are studied. Furthermore, a classification of the processors is presented based… More >

  • Open Access


    Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

    Nawaf N. Hamadneh1, Waqar A. Khan2, Waqar Ashraf3, Samer H. Atawneh4, Ilyas Khan5,*, Bandar N. Hamadneh6

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

    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 >

  • Open Access


    Assessment and Computational Improvement of Thermal Lattice Boltzmann Models Based Benchmark Computations

    R. Djebali1, M. El Ganaoui2

    CMES-Computer Modeling in Engineering & Sciences, Vol.71, No.3, pp. 179-202, 2011, DOI:10.3970/cmes.2011.071.179

    Abstract The Lattice Boltzmann method (LBM) became, today, a powerful tool for simulating fluid flows. Its improvements for different applications and configurations offers more flexibility and results in several schemes such as in presence of external/internal forcing term. However, we look for the suitable model that gives correct informations, matches the hydrodynamic equations and preserves some features like coding easily, preserving computational cost, stability and accuracy. In the present work, high order incompressible models and equilibrium distribution functions for the advection-diffusion equations are analyzed. Boundary conditions, acceleration, stability and preconditioning with initial fields are underlined which… More >

  • Open Access


    A Thermal Lattice Boltzmann Model for Flows with Viscous Heat Dissipation

    Hao-Chueh Mai1, Kuen-Hau Lin1, Cheng-Hsiu Yang1, Chao-An Lin1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.61, No.1, pp. 45-62, 2010, DOI:10.3970/cmes.2010.061.045

    Abstract A thermal BGK lattice Boltzmann model for flows with viscous heat dissipation is proposed. In this model, the temperature is solved by a separate thermal distribution function, where the equilibrium distribution function is similar to its hydrodynamic counterpart, except that the leading quantity is temperature. The viscous dissipation rate is obtained by computing the second-order moments of non-equilibrium distribution function, which avoids the discretization of the complex gradient term, and can be easily implemented. The proposed thermal lattice Boltzmann model is scrutinized by computing two-dimensional thermal Poiseuille flow, thermal Couette flow, natural convection in a More >

  • Open Access


    ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

    Yuvaraj P1, A Ramachra Murthy2, Nagesh R Iyer3, S.K. Sekar4, Pijush Samui5

    CMC-Computers, Materials & Continua, Vol.41, No.3, pp. 193-214, 2014, DOI:10.3970/cmc.2014.041.193

    Abstract This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (Gf), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique… More >

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