Haitao Liu^1,2,*, Jiaming Wang¹, Xiuliang Zhang¹, Yanji Jiang², Qian Xiao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2747-2772, 2024, DOI:10.32604/cmes.2024.047129 - 11 March 2024

Abstract The expansion chamber serves as the primary silencing structure within the exhaust pipeline. However, it can also act as a sound-emitting structure when subjected to airflow. This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow. A fluid simulation model is established, utilizing the Large Eddy Simulation (LES) method to calculate the unsteady flow within the expansion chamber. The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity, exhibiting a high… More > Graphic Abstract

Xiaozhou Xia¹, Changsheng Qin¹, Guangda Lu², Xin Gu^1,*, Qing Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2257-2276, 2024, DOI:10.32604/cmes.2023.031238 - 15 December 2023

Abstract Accurate simulation of the cracking process caused by rust expansion of reinforced concrete (RC) structures plays an intuitive role in revealing the corrosion-induced failure mechanism. Considering the quasi-brittle fracture of concrete, the fracture phase field driven by the compressive-shear term is constructed and added to the traditional brittle fracture phase field model. The rationality of the proposed model is verified by a mixed fracture example under a shear displacement load. Then, the extended fracture phase model is applied to simulate the corrosion-induced cracking process of RC. The cracking patterns caused by non-uniform corrosion expansion are… More >

Zi Han^1,*, Zhentian Huang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 245-272, 2024, DOI:10.32604/cmes.2023.029708 - 22 September 2023

Abstract In the context of global mean square error concerning the number of random variables in the representation, the Karhunen–Loève (KL) expansion is the optimal series expansion method for random field discretization. The computational efficiency and accuracy of the KL expansion are contingent upon the accurate resolution of the Fredholm integral eigenvalue problem (IEVP). The paper proposes an interpolation method based on different interpolation basis functions such as moving least squares (MLS), least squares (LS), and finite element method (FEM) to solve the IEVP. Compared with the Galerkin method based on finite element or Legendre polynomials,… More > Graphic Abstract

Yujing Ma^1,*, Leilei Chen^2,3, Haojie Lian^3,4, Zhongwang Wang^2,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.28, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.010585

Abstract An efficient method of moments (MoM) based on polynomial chaos expansion(PCE) is applied to quickly calculate the electromagnetic scattering problems. The triangle basic functions are used to discretize the surface integral equations. The PCE is utilized to accelerate the MoM by constructing a surrogate model for univariate and bivariate analysis[1]. The mathematical expressions of the surrogate model for the radar cross-section (RCS) are established by considering uncertain parameters such as bistatic angle, incident frequency, and dielectric constant[2,3]. By using the example of a scattering cylinder with analytical solution, it is verified that the MoM accelerated More >

Yujing Ma¹, Zhongwang Wang², Xiaohui Yuan¹, Leilei Chen^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-2, 2023, DOI:10.32604/icces.2023.09662

Abstract The paper presents a novel fast calculation method for broadband Electromagnetic Scattering analysis. In this work, the isogeometric boundary element method is used to solve Helmholtz equations for the electromagnetic scattering problems. The non-uniform rational B-splines are employed to construct structural geometries and discretize electric and magnetic field integral equations [1,2]. To avoid timeconsuming multi-frequency calculations, the series expansion method is used to decouple the frequencydependent terms from the integrand in the boundary element method [3,4]. The second-order Arnoldi (SOAR) method is applied to construct a reduced-order model that retains the essential structures and key More >

Xuhang Lin¹, Haibo Chen^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09388

Abstract As an important and useful tool for reducing noise, the sound barrier is of practical significance. The sound barrier has different noise reduction effects for different sizes, shapes and properties of the sound absorbing material. Liu et al. [1] have performed shape optimization of sound barriers by using isogeometric boundary element method and method of moving asymptotes (MMA). However, in engineering practice, it is difficult to determine some parameters accurately such as material properties, geometries, external loads. Therefore, it is necessary to consider these uncertainty conditions in order to ensure the rationality of the numerical… More >

Muhammad Hameed Siddiqi^1,*, Asfandyar Khan², Muhammad Bilal Khan², Abdullah Khan², Madallah Alruwaili¹, Saad Alanazi¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2219-2239, 2023, DOI:10.32604/csse.2023.040212 - 28 July 2023

Abstract The use of the internet is increasing all over the world on a daily basis in the last two decades. The increase in the internet causes many sexual crimes, such as sexual misuse, domestic violence, and child pornography. Various research has been done for pornographic image detection and classification. Most of the used models used machine learning techniques and deep learning models which show less accuracy, while the deep learning model ware used for classification and detection performed better as compared to machine learning. Therefore, this research evaluates the performance analysis of intelligent neural-based deep… More >

Haoran Xu¹, Shuai Chen^2,*, Xue Song^1,3, Jingying Wang^1,*

Molecular & Cellular Biomechanics, Vol.20, No.1, pp. 15-22, 2023, DOI:10.32604/mcb.2023.029107 - 20 June 2023

Abstract Clinically, it is common for Class III patients with maxillary skeletal deficiency, which may result in a variety of adverse consequences. Protraction headgear and rapid maxillary expansion (PE) is an effective treatment, but its effect on upper airway hydrodynamics has not been reported. The main purpose of this study was to evaluate the changes of the flow in the upper airway after PE by computational fluid dynamics (CFD). The sample includes fifteen patients (6 males, 9 females, age 11.00 ± 1.00) and the paired T-test was used to analyze the differences between the measured data… More > Graphic Abstract

SHU ZHAO^1,*, SHIXIN YE²

BIOCELL, Vol.47, No.6, pp. 1377-1388, 2023, DOI:10.32604/biocell.2023.023874 - 19 May 2023

Abstract Background: This paper aims to establish a light-controlled phosphorylation detection method at the Y785 site of tropomyosin receptor kinase A (TrkA) receptor in mammalian cells by using genetic code expansion technology and detecting the effects of optical activation of this site on the downstream MAPK/ERK pathway. The study is based on the current situation that the regulatory mechanism of TrkA phosphorylation has not been fully elucidated. Methods: Two photosensitive unnatural amino acids, p-azido-L-phenylalanine (AzF) and photo-caged tyrosine (ONB) were introduced into the TrkA-Y785 site by genetic code expansion technology and site-directed mutagenesis. Western blotting and laser More >

Shujing Sun^1,3, Jiale Wu², Jian Yao¹, Yang Cheng⁴, Xin Zhang¹, Zhihua Lu³, Pengjiang Qian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 923-938, 2023, DOI:10.32604/cmes.2023.027356 - 23 April 2023

Abstract Many existing intelligent recognition technologies require huge datasets for model learning. However, it is not easy to collect rectal cancer images, so the performance is usually low with limited training samples. In addition, traditional rectal cancer staging is time-consuming, error-prone, and susceptible to physicians’ subjective awareness as well as professional expertise. To settle these deficiencies, we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3. First, a novel deep learning model (RectalNet) is constructed based on residual learning, which combines the squeeze-excitation with the asymptotic output layer and new More >