Taha Yacine Trad^1,*, Kheireddine Choutri¹, Mohand Lagha¹, Souham Meshoul², Fouad Khenfri³, Raouf Fareh⁴, Hadil Shaiba⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4757-4786, 2024, DOI:10.32604/cmc.2024.055634 - 19 December 2024

Abstract The popularity of quadrotor Unmanned Aerial Vehicles (UAVs) stems from their simple propulsion systems and structural design. However, their complex and nonlinear dynamic behavior presents a significant challenge for control, necessitating sophisticated algorithms to ensure stability and accuracy in flight. Various strategies have been explored by researchers and control engineers, with learning-based methods like reinforcement learning, deep learning, and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems. This paper investigates a Reinforcement Learning (RL) approach for both high and low-level quadrotor control systems, focusing on attitude stabilization and position… More >

Long Huang^1,2,3,*, Junjia Zou³, Baoqing Liu¹, Zhijiang Jin^1,2, Jinyuan Qian¹

Frontiers in Heat and Mass Transfer, Vol.22, No.6, pp. 1613-1643, 2024, DOI:10.32604/fhmt.2024.058231 - 19 December 2024

Abstract This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers. The data were generated by experimentally validated Computational Fluid Dynamics (CFD) simulations of air-to-water microchannel heat exchangers. A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Gaussian Process Regression (GPR). These models are adeptly applied to predict air-side heat transfer performance with high precision, with ANN and GPR exhibiting notably superior accuracy. Additionally, this research further delves into… More >

Alexander Fedoseev^*, Mikhail Salnikov

Frontiers in Heat and Mass Transfer, Vol.22, No.6, pp. 1679-1694, 2024, DOI:10.32604/fhmt.2024.056999 - 19 December 2024

Abstract In order to research the process of boiling occurring on a porous surface, a model of multiple blocks was developed. The mathematical basis of these blocks is the lattice Boltzmann method in combination with heat transfer equation. The reported complex allows one to obtain the boiling curves for various wall superheats and to find the optimal parameters of a porous heater in terms of heat transfer enhancement. The porous heater structure is specified as a skeleton of square metal heaters located in the lower part of the computational domain. The calculations were performed for the… More > Graphic Abstract

Qian Mao¹, Umberto D’Ortona¹, Julien Favier^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.012535

Abstract The human airways are protected by two fluid layers, a periciliary layer (PCL) covering the epithelial surface and a mucus layer on top of the PCL. The cilia are almost immersed in the PCL and interact with the mucus through their tips. The mucus is often described as a yield stress and shear thinning fluid. The effect of these non-Newtonian properties on ciliary coordination and mucus transport was investigated using the Lattice-Boltzmann method. The non-Newtonian mucus was modelled using the Herschel-Bulkley model. Three mucus flow regimes were observed and analysed in a wide range of… More >

Jinhua Lu^1,*, Song Zhao¹, Pierre Boivin¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011180

Abstract The hybrid lattice Boltzmann model [1] for compressible flows uses the lattice Boltzmann method (LBM) to simulate the flow field and the finite volume scheme for the energy field. It inherits the good numerical stability and low dissipation [2] of LBM and avoids the complexity of solving all governing equations within the LBM framework. However, it still faces three issues. First, for compressible flows, the equilibrium distribution functions must exactly recover third-order moments, but it cannot be achieved for the simple DmQn (m dimensions and n discrete phase velocities) models involving only neighboring nodes [3],… More >

Haitao Wu¹, Shenglong Gu^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.011231

Abstract Subglacial water conveyance is the prevalent operational mode for cold-region channels during winter, necessitating the stability of ice covers during flow regulation. The coupling of Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) and Total Lagrangian Smoothed Particle Hydrodynamics (TLSPH) provides a robust computational framework for addressing the intricate fluid-structure interaction in channel-ice-water systems. This study employs WC-TLSPH to analyze the influence of flow variations on the stability of channel ice covers, determining the range of extreme hydraulic pressure changes sustainable by ice covers of varying widths and thicknesses. Results indicate that flow variations are a significant… More >

Ziying Zhang^1,*, Chu Li¹, Junwei Zhu¹, Qinghong Wu¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010912

Abstract The process of perfusion seeding of cells into a porous scaffold represents a pivotal initial stage in the development of tissue-engineered bones. The rheological behavior of the cell suspension plays a crucial role in influencing the transport and distribution of cells within the scaffold. Currently, there is limited understanding of the non-Newtonian rheology of cell suspensions in complex pores which differs significantly from simple channels or linear shear flow. In this study, we utilize our previously developed mesoscopic model of perfusion cell seeding to investigate the rheological behavior of cell suspensions at the cellular scale. More >

Aruna M. Pugalenthi^*, Khaoula Khlie

Journal of Renewable Materials, Vol.12, No.11, pp. 1879-1893, 2024, DOI:10.32604/jrm.2024.055747 - 22 November 2024

Abstract With growing environmental concerns and the depletion of oil reserves, the need to replace synthetic fibres with sustainable alternatives in composite materials has become increasingly urgent. This study investigates the potential of Leptadenia pyrotechnica fibre as a sustainable reinforcement material in hybrid composites alongside E-glass fibres. The primary objectives are to assess these hybrid composites’ mechanical properties, structural integrity, and performance. To achieve this, Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were employed to analyze the microstructure and chemical composition of the composites. At the same time, mechanical testing focused on properties such… More >

Ting Chen¹, Shuna Jiang², Xin Fan^3,*, Jianchuan Xia², Xiujuan Zhang², Chuanwen Luo³, Yi Hong³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2195-2217, 2024, DOI:10.32604/cmc.2024.056960 - 18 November 2024

Abstract In blockchain-based unmanned aerial vehicle (UAV) communication systems, the length of a block affects the performance of the blockchain. The transmission performance of blocks in the form of finite character segments is also affected by the block length. Therefore, it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems, especially in wireless environments involving UAVs. This paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during transmission. In our scheme, using a friendly jamming UAV… More >

Yameng Yin¹, Lieping Zhang^2,*, Xiaoxu Shi¹, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2769-2790, 2024, DOI:10.32604/cmc.2024.056791 - 18 November 2024

Abstract By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value… More >