Junpeng Lyu¹, Hai Mei^1,2, Liping Zu¹, Lisheng Liu^1,2,*, Liangliang Chu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 391-410, 2024, DOI:10.32604/cmes.2023.030614

Abstract This article proposes a modeling method for C/C-ZrC composite materials. According to the superposition of Gaussian random field, the original gray model is obtained, and the threshold segmentation method is used to generate the C-ZrC inclusion model. Finally, the fiber structure is added to construct the microstructure of the three-phase plain weave composite. The reconstructed inclusions can meet the randomness of the shape and have a uniform distribution. Using an algorithm based on asymptotic homogenization and finite element method, the equivalent thermal conductivity prediction of the microstructure finite element model was carried out, and the influence of component volume fraction… More >

Long Wang¹, Qingyun Shen¹, Gui Wang^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.11, pp. 2877-2890, 2023, DOI:10.32604/fdmp.2023.029206

Abstract Drill cuttings are broken bits of solid material removed from a borehole drilled by rotary, percussion, or auger methods and brought to the surface in the drilling mud. When these cuttings enter the annulus, they have an effect on the drilling fluid rheology and density, which is, in general, quite difficult to evaluate. By introducing an empirical correlation for the rheological properties of cuttings-laden drilling fluids, this study proposes a pressure-loss prediction method for an extended-reach well (ERW). After verifying the accuracy of this method, a case study is considered and a sensitivity analysis is conducted assuming a yield-power law… More >

Hang Cheng¹, Jiawen Fei¹, Jianfeng Wen^1,*, Shan-Tung Tu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09663

Abstract Alkaline water electrolysis system for hydrogen production has the characteristics of complex structure, fault coupling and state nonlinearity, coupled with the restriction by many factors such as data acquisition methods and analysis methods. The operation status cannot be fully characterized through current monitoring information. In order to solve the problems in health status assessment in the operation of alkaline water electrolysis system, a digital twin-driven predictive maintenance method is put forward to achieve the real-time monitoring of operation status and prediction of remaining useful life. In the study, a multi-disciplinary simulation model of the alkaline electrolysis system and a physical… More >

Difeng Zhu¹, Zhimou Zhu², Xuan Gong¹, Demao Ye¹, Chao Li^3,*, Jingjing Chen^4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3083-3100, 2023, DOI:10.32604/iasc.2023.040517

Abstract Traffic prediction is a necessary function in intelligent transportation systems to alleviate traffic congestion. Graph learning methods mainly focus on the spatiotemporal dimension, but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments. There exist two issues: 1) deep integration of the spatiotemporal information and 2) global spatial dependencies for structural properties. To address these issues, we propose a nonlinear spatiotemporal optimization method, which introduces hypergraph convolution networks (HGCN). The method utilizes the higher-order spatial features of the road network captured by HGCN, and dynamically integrates them with the historical data to… More >

Ying Bai^1,*, Xiaoti Ren², Hong Li¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2999-3018, 2023, DOI:10.32604/iasc.2023.040124

Abstract The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT… More >

Wenlu Ji¹, Yingqi Liao^1,*, Liudong Zhang²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618

Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled… More >

Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga whale optimization (BWO) algorithm is… More > Graphic Abstract

Yinghao Nie¹, Shan Tang^1,*, Gengdong Cheng^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.1, pp. 1-2, 2023, DOI:10.32604/icces.2023.09603

Abstract Advanced heterogeneous materials are widely used in many fields because of their excellent properties, especially those with hyperelastic properties and significant deformation behavior. Highly efficient numerical prediction methods of nonlinear mechanical properties of heterogeneous material provide essential tools for two-scale material and structural analysis, data-driven material design, and direct application in various engineering fields. Recently, the Clustering-based Reduced Order Model (CROM) methods [1-6] have proven effective in many nonlinear homogenization problems. However, some CROM methods would need help predicting significant large deformation behavior with more than 50% true strain. This presentation introduces the FEM-Cluster based Analysis (FCA: one of the… More >

Zhengmao Yang^1,*, Junjie Yang², Yang Chen³, Fulei Jing⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09229

Abstract Woven ceramic matrix composites (CMCs), exhibiting excellent thermomechanical properties at high temperatures, are promising as alternative materials to the conventional nickel-based superalloys in the hot section components of aero-engines. Therefore, understanding and predicting the lifetime of CMCs is critical. Fatigue prediction of woven CMCs currently involves long-term and costly testing. A feasible alternative is to use predictive modelling based on a deep understanding of the damage mechanisms. Therefore, this study develops a multiscale analysis modelling method for predicting the fatigue life of CMC materials at high temperature by investigating the thermomechanical fatigue damage evolution. To represent the global thermomechanical properties… More >

Wen Yee Wong¹, Khairunnisa Hasikin^1,*, Anis Salwa Mohd Khairuddin², Sarah Abdul Razak³, Hanee Farzana Hizaddin⁴, Mohd Istajib Mokhtar⁵, Muhammad Mokhzaini Azizan⁶

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1361-1384, 2023, DOI:10.32604/cmc.2023.038045

Abstract A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers included are AdaBoost, Support Vector… More >