CMES-Computer Modeling in Engineering & Sciences

About the Journal

This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales (picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.

Indexing and Abstracting

Science Citation Index (Web of Science): 2019 Impact Factor 0.805; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2019): 1.0; SNIP (Source Normalized Impact per Paper 2019): 0.499; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; CompuMath Citation Index; INSPEC Databases; Mathematical Reviews; MathSci Net; Mechanics; Science Alert; Science Navigator; Zentralblatt fur Mathematik; Portico, etc...

  • Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search
  • Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More
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  • Dynamical Stability of Cantilevered Pipe Conveying Fluid with Inerter-Based Dynamic Vibration Absorber
  • Abstract Cantilevered pipe conveying fluid may become unstable and flutter instability would occur when the velocity of the fluid flow in the pipe exceeds a critical value. In the present study, the theoretical model of a cantilevered fluid-conveying pipe attached by an inerter-based dynamic vibration absorber (IDVA) is proposed and the stability of this dynamical system is explored. Based on linear governing equations of the pipe and the IDVA, the effects of damping coefficient, weight, inerter, location and spring stiffness of the IDVA on the critical flow velocities of the pipe system is examined. It is shown that the stability of… More
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  • Modeling of an Internal Stress and Strain Distribution of an Inverted Staggered Thin-Film Transistor Based on Two-Dimensional Mass-Spring-Damper Structure
  • Abstract Equipped with a two-dimensional topological structure, a group of masses, springs and dampers can be demonstrated to model the internal dynamics of a thin-film transistor (TFT). In this paper, the two-dimensional Mass-Spring-Damper (MSD) representation of an inverted staggered TFT is proposed to explore the TFT’s internal stress/strain distributions, and the stress-induced effects on TFT’s electrical characteristics. The 2D MSD model is composed of a finite but massive number of interconnected cellular units. The parameters, such as mass, stiffness, and damping ratios, of each cellular unit are approximated from constitutive equations of the composite materials, while the electrical properties of the… More
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  • LES Investigation of Drag-Reducing Mechanism of Turbulent Channel Flow with Surfactant Additives
  • Abstract In this work, the drag-reducing mechanism of high-Reynoldsnumber turbulent channel flow with surfactant additives is investigated by using large eddy simulation (LES) method. An N-parallel finitely extensible nonlinear elastic model with Peterlin’s approximation (FENE-P) is used to describe the rheological behaviors of non-Newtonian fluid with surfactant. To close the filtered LES equations, a hybrid subgrid scale (SGS) model coupling the spatial filter and temporal filter is applied to compute the subgrid stress and other subfilter terms. The finite difference method and projection algorithm are adopted to solve the LES governing equations. To validate the correctness of our LES method and… More
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  • Blood Flow Through a Catheterized Artery Having a Mild Stenosis at the Wall with a Blood Clot at the Centre
  • Abstract The blood flow through a catheterized artery having a mild stenosis at the wall together with a blood clot at the centre is studied in the current investigation. Stenosis can occur in vessels carrying blood to brain (i.e., Carotid arteries), Renal arteries that supply blood to kidneys etc. The flow is refined in such vessels by application of catheter. We have used a Newtonian viscous fluid model and also distinct shapes of stenosis, (i.e., symmetric and non-symmetric shapes) are considered for this study. The entropy generation together with viscous dissipation is also taken into account for a complete description of… More
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  • A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models
  • Abstract The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Conventional Neural Networks) are provided to compare with the classification–detection approaches based on… More
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  • Inverse Construction Methods of Heterogeneous NURBS Object Based on Additive Manufacturing
  • Abstract According to the requirement of heterogeneous object modeling in additive manufacturing (AM), the Non-Uniform Rational B-Spline (NURBS) method has been applied to the digital representation of heterogeneous object in this paper. By putting forward the NURBS material data structure and establishing heterogeneous NURBS object model, the accurate mathematical unified representation of analytical and free heterogeneous objects have been realized. With the inverse modeling of heterogeneous NURBS objects, the geometry and material distribution can be better designed to meet the actual needs. Radical Basis Function (RBF) method based on global surface reconstruction and the tensor product surface interpolation method are combined… More
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  • A Simplified Model for Buckling and Post-Buckling Analysis of Cu Nanobeam Under Compression
  • Abstract Both of Buckling and post-buckling are fundamental problems of geometric nonlinearity in solid mechanics. With the rapid development of nanotechnology in recent years, buckling behaviors in nanobeams receive more attention due to its applications in sensors, actuators, transistors, probes, and resonators in nanoelectromechanical systems (NEMS) and biotechnology. In this work, buckling and post-buckling of copper nanobeam under uniaxial compression are investigated with theoretical analysis and atomistic simulations. Different cross sections are explored for the consideration of surface effects. To avoid complicated high order buckling modes, a stressbased simplified model is proposed to analyze the critical strain for buckling, maximum deflection,… More
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  • Dynamic Characteristics Analysis of Ice-Adhesion Transmission Tower-Line System under Effect of Wind-Induced Ice Shedding
  • Abstract The tower line system will be in an unsafe status due to uniform or uneven fall of ice coating which is attached to the surface of tower and lines. The fall of ice could be caused by wind action or thermal force. In order to study the dynamic characteristics of the self-failure of the transmission line under the action of dynamic wind load, a finite element model of the two-span transmission tower line system was established. The birth and death element methods are used to simulate the icing and shedding of the line. Tensile failure strength is the shedding criterion… More
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  • An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing
  • Abstract Nowadays, succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers. Hence, to secure both data and keys ensuring secured data storage and access, our proposed work designs a Novel Quantum Key Distribution (QKD) relying upon a non-commutative encryption framework. It makes use of a Novel Quantum Key Distribution approach, which guarantees high level secured data transmission. Along with this, a shared secret is generated using Diffie Hellman (DH) to certify secured key generation at reduced time complexity. Moreover, a non-commutative approach is used, which effectively allows the users to store… More
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  • Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm
  • Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More
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  • Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames
  • Abstract Steel frames equipped with buckling restrained braces (BRBs) have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces. Therefore, special attention has been paid to the seismic risk assessment (SRA) of such structures, e.g., seismic fragility analysis. Conventional approaches, e.g., nonlinear finite element simulation (NFES), are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures. In this study, a machine learning (ML)- based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions. An optimal artificial neural network model can be trained using calculated damage… More
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  • A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon
  • Abstract The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the… More
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  • Numerical Simulation of Bone Remodeling Coupling the Damage Repair Process in Human Proximal Femur
  • Abstract Microdamage is produced in bone tissue under the long-term effects of physiological loading, as well as age, disease and other factors. Bone remodeling can repair microdamage, otherwise this damage will undermine bone quality and even lead to fractures. In this paper, the damage variable was introduced into the remodeling algorithm. The new remodeling algorithm contains a quadratic term that can simulate reduction in bone density after large numbers of loading cycles. The model was applied in conjunction with the 3D finite element method (FEM) to the remodeling of the proximal femur. The results showed that the initial accumulation of fatigue… More
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  • Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things
  • Abstract Internet of Things (IoT) is a network that connects things in a special union. It embeds a physical entity through an intelligent perception system to obtain information about the component at any time. It connects various objects. IoT has the ability of information transmission, information perception,andinformationprocessing.Theairqualityforecastinghasalways been an urgent problem, which affects people’s quality of life seriously. So far, many air quality prediction algorithms have been proposed, which can be mainly classifed into two categories. One is regression-based prediction, the other is deep learning-based prediction. Regression-based prediction is aimed to make use of the classical regression algorithm and the various… More
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  • 3D Multilayered Turtle Shell Models for Image Steganography
  • Abstract By embedding secret data into cover images, image steganography can produce non-discriminable stego-images. The turtle shell model for data hiding is an excellent method that uses a reference matrix to make a good balance between image quality and embedding capacity. However, increasing the embedding capacity by extending the area of basic structures of the turtle shell model usually leads to severe degradation of image quality. In this research, we innovatively extend the basic structure of the turtle shell model into a three-dimensional (3D) space. Some intrinsic properties of the original turtle shell model are well preserved in the 3D version.… More
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