Chao Zhang¹, Shang-Xi Lai¹, Hua-Ping Wang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 25-54, 2025, DOI:10.32604/sdhm.2024.053662 - 15 November 2024

Abstract Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure. Therefore, it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring (SHM) system, so as to provide a scientific basis for structural damage identification and dynamic model modification. In view of this, this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters. The paper primarily introduces data-driven modal parameter recognition methods… More >

Zhi Li¹, Yufei Zhao², Yueming Ji², Hanwen Gu², Zaibin Jiao^2,*

Energy Engineering, Vol.121, No.12, pp. 3899-3927, 2024, DOI:10.32604/ee.2024.055899 - 22 November 2024

Abstract By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement, information communication, and other fields, the digital DC distribution network can efficiently and reliably access Distributed Generator (DG) and Energy Storage Systems (ESS), exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play (PnP) operations. However, during device plug-in and -out processes, improper system parameters may lead to small-signal stability issues. Therefore, before executing PnP operations, conducting stability analysis and adjusting parameters swiftly is crucial. This study introduces a four-stage strategy for parameter optimization to enhance… More >

Jian Su¹, Haijian Shao^1,2,*, Xing Deng¹, Yingtao Jiang²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2219-2242, 2024, DOI:10.32604/cmc.2024.057604 - 18 November 2024

Abstract The rapidly advancing Convolutional Neural Networks (CNNs) have brought about a paradigm shift in various computer vision tasks, while also garnering increasing interest and application in sensor-based Human Activity Recognition (HAR) efforts. However, the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems. This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain, which reduces the model’s depth and accelerates activity inference. Unlike traditional pruning methods that focus on the spatial domain and the importance of filters, this… More >

Alonso Alvarez, Borja Bordel Sánchez^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2935-2966, 2024, DOI:10.32604/cmc.2024.056990 - 18 November 2024

Abstract Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering. Despite the impact of these transformations on organizations, they have not been extensively studied in academia. We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations, including organizations undergoing transformations (“final organizations”) and companies supporting these processes (“consultants”). The study aims to understand the motivations, objectives, and factors driving and challenging these transformations. Over 700 responses were collected to the question and categorized into 32 objectives. The findings show More >

Yang Chen¹, Baobin Xie¹, Weizheng Lu¹, Jia Li^1,*, Qihong Fang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011624

Abstract In order to accurately predict and evaluate the mechanical properties of multi-principal element alloys (MPEAs), some new models and simulation methods need to be developed to solve the problems caused by its unique natural characteristics, such as severe lattice distortion. The existing models are based on the development of low concentration alloys, and cannot be well applied to MPEAs. Here, we develop i) the random field theory informed discrete dislocation dynamics simulations based on high-resolution transmission electron microscopy, to systematically clarify the role of heterogeneous lattice strain on the complex interactions between the dislocation loop… More >

Yuanzhen Hou¹, YinBo Zhu¹, Heng-an Wu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011353

Abstract Traditional materials are emerging increasingly severe problems such as environmental pollution, non-renewability, and resource waste. As the most abundant natural biomass in nature, nanocellulose materials are expected to become a new generation of green, biodegradable, high-performance structural materials and contribute to sustainable development. Starting from the intrinsic relationship between hydrogen bonding network and microstructure deformation in nanocellulose, we performs the bottom-up multiscale mechanics methods, combing theoretical modeling, experimental characterization and material preparation, to reveal the physical mechanism and key characteristic parameters of the microstructure-regulated mechanical behaviors of nanocellulose materials, further establishing the cross-scale relationship between… More >

Rongzhuang Song¹, Yinbo Zhu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.012598

Abstract Kink defects are prevalent in nanocellulose. The existence of diverse kink patterns lacking molecular-scale resolution has caused uncertainty regarding the mechanisms governing the formation of different kinks in nanocellulose, including both reversible and irreversible kinks. The constraints resulting from these limitations often lead to significant confusion in exploring the structure-property relationships of nanocellulose. By integrating AFM experiments with molecular dynamics simulations, we examined the microstructure-dependent kink deformations in nanocellulose (Iβ phase) and the resultant local microstructural damages. In atomic force microscopy images, bent nanofibrils typically display minor curvatures, whereas kinked nanofibrils exhibit pronounced sharp bends,… More >

Chang-an Li¹, Guoliang Qin^1,*, Hao Wang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012370

Abstract The interactions among thermal history, plastic deformation and stress in inertia friction welding (IFW) under different welding parameters have been widely considered a crucial issue and still not fully understood. A novel 3D fully coupled finite element model based on a plastic friction pair was developed to simulate the IFW process of a Ni-based superalloy and reveal the omnidirectional thermo-mechanical coupling mechanism under different welding conditions. The numerical model successfully simulated the deceleration, deformation processes, and peak torsional moments in IFW and captured the evolution of temperature, plastic deformation, and stress. The simulated results were… More >

Ilsun You^1,*, Xiaofeng Chen², Vishal Sharma³, Gaurav Choudhary⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1907-1909, 2024, DOI:10.32604/cmes.2024.058939 - 31 October 2024

Abstract This article has no abstract. More >

Chaofan Yao, Huanhuan Cao, Zhanyuan Xu^*, Lichun Bai^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2107-2119, 2024, DOI:10.32604/cmes.2024.055743 - 31 October 2024

Abstract Shear deformation mechanisms of diamond-like carbon (DLC) are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance, which further influences the improvement of the friction and wear performance of DLC. This study aims to investigate this issue utilizing molecular dynamics simulation and machine learning (ML) techniques. It is indicated that the changes in the mechanical properties of DLC are mainly due to the expansion and reduction of sp³ networks, causing the stick-slip patterns in shear force. In addition, cluster analysis showed that the sp²-sp³ transitions arise… More >