Jian Wang^1,*, Yonggang Zheng¹, Hongwu Zhang¹, Hongfei Ye¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.09761

Abstract Water is the most ubiquitous fluid in nature and widely exists in the micro/nanoconfinement of leafstalks, shale, bones, etc. The complex relation of the properties and behaviours of water to the temperature, pressure and confinement size enhances the difficulty in the accurate simulation, such as the supercooled state of pure water below the freezing point. As a powerful tool, molecular dynamics simulation is adequate for investigating the relevant properties and behaviours. However, accurately calculating the physical properties of liquid and supercooled water is quite challenging by molecular simulations owing to limited model parameters. Machine learning (ML) techniques and temperature-dependent parameters… More >

Pingfan Li, Jiumei Xiao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 999-1016, 2024, DOI:10.32604/cmes.2023.044059

Abstract Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties. However, issues such as high initial stress and low energy-absorbing efficiency arise. This study proposes a novel energy-absorbing structure in which a straight tube is combined with a conical tube and a bamboo-inspired bulkhead structure is introduced. This configuration allows the conical tube to flip outward first and then fold together with the straight tube. This deformation mode absorbs more energy and less peak force than the conical tube sinking and flipping inward. Through finite element numerical simulation, the specific energy absorption capacity of the… More >

Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

Shuqin Zhang, Xinyu Su^*, Yunfei Han, Tianhui Du, Peiyu Shi

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526

Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines the knowledge graph and the… More >

Tamal Kumar Kundu¹, Dinesh Kumar Anguraj^1,*, S. V. Sudha^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3289-3304, 2023, DOI:10.32604/cmc.2023.039515

Abstract A theoretical methodology is suggested for finding the malaria parasites’ presence with the help of an intelligent hyper-parameter tuned Deep Learning (DL) based malaria parasite detection and classification (HPTDL-MPDC) in the smear images of human peripheral blood. Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy. The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset. The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network (DNN) with the help of the contrastive divergence method to pre-train.… More >

Ahmad F Subahi^*, Areej Athama

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3801-3816, 2023, DOI:10.32604/cmc.2023.034522

Abstract With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs face high admission demand. In… More >

Xiangman Zhou^1,*, Jingping Qin¹, Zichuan Fu¹, Min Wang¹, Youlu Yuan¹, Junjian Fu¹, Haiou Zhang², Seyed Reza Elmi Hosseini^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2383-2401, 2024, DOI:10.32604/cmes.2023.029249

Abstract Finite element (FE) coupled thermal-mechanical analysis is widely used to predict the deformation and residual stress of wire arc additive manufacturing (WAAM) parts. In this study, an innovative single-layer multi-bead profile geometric modeling method through the isosceles trapezoid function is proposed to build the FE model of the WAAM process. Firstly, a straight-line model for overlapping beads based on the parabola function was established to calculate the optimal center distance. Then, the isosceles trapezoid-based profile was employed to replace the parabola profiles of the parabola-based overlapping model to establish an innovative isosceles trapezoid-based multi-bead overlapping geometric model. The rationality of… More >

Vijayakumar Thulasi^*, Thundil Karuppa Raj Rajagopal

Frontiers in Heat and Mass Transfer, Vol.4, No.2, pp. 1-5, 2013, DOI:10.5098/hmt.v4.2.3007

Abstract Researchers across the world are exploring the potential of using diethyl ether as an alternate fuel to meet the stringent emission norms due to the high oxygen content in the fuel. The spray characteristics of any injected fuel are highly influenced by its physical properties. Due to high injection pressure in CI engines the fuel tends to cavitate inside the nozzle greatly. The change in fuel properties will affect the cavitating behavior of the fuel. In this paper computational technique is used to study and compare the internal flow characteristics of a fuel injector for different blends of diethyl ether… More >

Shigenao Maruyama^a, Masud Behnia^b, Masasazumi Chisaki^c, Takuma Kogawa^c,*, Junnosuke Okajima^a, Atsuki Komiya^a

Frontiers in Heat and Mass Transfer, Vol.4, No.2, pp. 1-6, 2013, DOI:10.5098/hmt.v4.2.3002

Abstract The diffusion process of deep seawater drawn up by a vertical pipe deployed in the ocean is investigated. This vertical pipe is based on the principal of perpetual salt fountain. Numerical simulations of seawater upwelling from the pipe are performed based on experiments conducted in the Mariana trench region. Two turbulence modeling approaches were examined: k-ε model and Large Eddy Simulations (LES). The results in both models show that diffusion of the deep seawater diffusion after ejection from the pipe. The LES results show a 50% lower vertical penetration compared to the k-ε model as well as well as predicting… More >

Zan Wu^a, Zhaozan Feng^b, Bengt Sundén^a,*, Lars Wadsö^c

Frontiers in Heat and Mass Transfer, Vol.5, pp. 1-10, 2014, DOI:10.5098/hmt.5.18

Abstract Thermal conductivity and rheology behavior of two aqueous nanofluids, i.e., alumina and multi-walled carbon nanotube (MWCNT) nanofluids, were experimentally investigated and compared with previous analytical models. Information about the possible agglomeration size and interfacial thermal resistance in the nanofluids were obtained and partially validated. By incorporating the effects of interfacial thermal resistance, a revised model was found to accurately reproduce the experimental data based on the agglomeration size extracted from the rheology analysis. In addition, the thermal conductivity change of the alumina/water nanofluid with elapsed time was investigated. Thermal conductivity measurements were also conducted for alumina/water and MWCNT/water nanofluid mixtures. More >