Yingge Ni¹, Peicheng Li², Hui Zhao¹, Shengjun Qiao¹

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.3, pp. 1-15, 2024, DOI:10.23967/j.rimni.2024.06.004 - 12 July 2024

Abstract The structural nonlinear aeroelastic analysis of folding wings is conducted by integrating the modified Doublet Lattice Method in this paper. Firstly, a modified Doublet Lattice Method is investigated to relax the aspect ratio limitation of aerodynamic elements, and its correctness is verified through numerical examples. Secondly, the rational function approximation method is discussed, and the modified Doublet Lattice Method is approximated using the minimum state method to obtain the unsteady aerodynamic forces in the time domain. Finally, the aeroelastic characteristics of a folding wing with free-play and friction nonlinearity are studied by integrating time-domain unsteady More >

Javaid Ali¹, Armando Ciancio², Kashif Ali Khan³, Nauman Raza^4,5, Haci Mehmet Baskonus^6,*, Muhammad Luqman¹, Zafar-Ullah Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2275-2296, 2024, DOI:10.32604/cmes.2024.046923 - 08 July 2024

Abstract This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic (CCE) model. The underlying CCE model lacks a closed-form exact solution. Numerical solutions obtained through traditional finite difference schemes do not ensure the preservation of the model’s necessary properties, such as positivity, boundedness, and feasibility. Therefore, the development of structure-preserving semi-analytical approaches is always necessary. This research introduces an intelligently supervised computational paradigm to solve the underlying CCE model’s physical properties by formulating an equivalent unconstrained optimization problem. Singularity-free safe Padé rational functions approximate the mathematical More >

Ying Qu¹, Jinhao Wang¹, Xueting Cheng¹, Jie Hao¹, Weiru Wang¹, Zhewen Niu², Yuxiang Wu^2,*

Energy Engineering, Vol.121, No.7, pp. 1847-1863, 2024, DOI:10.32604/ee.2024.048300 - 11 June 2024

Abstract The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of… More >

Radwan Abu-Gdairi¹, R. Mareay^2,*, M. Badr³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647 - 25 April 2024

Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life More >

Debiao Meng^1,2,*, Yipeng Guo^1,2, Yihe Xu³, Shiyuan Yang^1,2,*, Yongqiang Guo⁴, Lidong Pan⁴, Xinkai Guo²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2329-2359, 2024, DOI:10.32604/cmes.2024.047507 - 11 March 2024

Abstract The escalating need for reliability analysis (RA) and reliability-based design optimization (RBDO) within engineering challenges has prompted the advancement of saddlepoint approximation methods (SAM) tailored for such problems. This article offers a detailed overview of the general SAM and summarizes the method characteristics first. Subsequently, recent enhancements in the SAM theoretical framework are assessed. Notably, the mean value first-order saddlepoint approximation (MVFOSA) bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation (MVSOSA); the latter serves as an auxiliary approach to the former. Their distinction is rooted in the varying expansion orders… More >

Kai Long¹, Ayesha Saeed¹, Jinhua Zhang², Yara Diaeldin¹, Feiyu Lu¹, Tao Tao³, Yuhua Li^1,*, Pengwen Sun⁴, Jinshun Yan⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 43-67, 2024, DOI:10.32604/cmes.2023.031538 - 30 December 2023

Abstract This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures. These structures, commonly encountered in engineering applications, often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables. As a result, sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges. Over the past several decades, topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds. In comparison… More >

Dongmei Huang¹, Dang Hong², Wei Li^1,*, Guidong Yang¹, Vesna Rajic³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 509-524, 2024, DOI:10.32604/cmes.2023.029215 - 22 September 2023

Abstract In this paper, the bifurcation properties of the vibro-impact systems with an uncertain parameter under the impulse and harmonic excitations are investigated. Firstly, by means of the orthogonal polynomial approximation (OPA) method, the nonlinear damping and stiffness are expanded into the linear combination of the state variable. The condition for the appearance of the vibro-impact phenomenon is to be transformed based on the calculation of the mean value. Afterwards, the stochastic vibro-impact system can be turned into an equivalent high-dimensional deterministic non-smooth system. Two different Poincaré sections are chosen to analyze the bifurcation properties and… More >

Héctor Rodrigo Amezcua Rivera¹, Amado Gustavo Ayala Milián¹

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.2, pp. 1-11, 2023, DOI:10.23967/j.rimni.2023.06.002 - 20 June 2023

Abstract n this paper, a computationally efficient strategy for the approximation of the non- linear behaviour of structures through the finite element method is proposed. This proposal is based on the execution of a set of linear analyses in which the strength of the elements where the damage occurs is sequentially degraded and, in addition, complemented with a stabilized reduced numerical integration scheme for solid finite elements. Thus, the stiffness matrix only contains information of one integration point and, consequently, the stresses are computed only at that point. Also, due to the stabilization, it is possible More >

Tianshun Xing, Jianjun Chen^*, Taihua Xu, Yan Fan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874 - 29 April 2023

Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which… More >

Amarendra Reddy Panyala^1,2, M. Baskar^3,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050 - 03 April 2023

Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >