A multiscale analysis strategy with physical modified-micromechanics of failure (MMF3) criterion was proposed to analyze the failure behaviors of carbon fiber reinforced plastic (CFRP) laminates. The quantitative relationship between the macro- and micro- stresses was determined considering two typical fiber distributions. Thermal residual stress was taken into account in the stress transformation. The failures were defined and the properties of damaged elements were degraded at the constituent level. The back-calculation method based on the iteration algorithm was proposed to determine the micro strength with macro mechanical tests. A series of off-axis loading tests were conducted to verify the established multiscale models. The predicted strength was also compared with the results using micromechanics of failure (MMF) criterion to present accuracy improvements. Thermal residual stress was found to affect the strength by contributing to the matrix damage status. Meanwhile, sensitivity analysis was provided for the matrix-dominant micro strength to investigate its physical meaning. Results suggest that the micro tensile and compressive strength of the matrix influenced the off-axis tensile and compressive strengths respectively, with relative large off-axis angles, while the micro shear strength of the matrix dominated when the off-axis angles were relative small.
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http://dx.doi.org/10.3390/ma11112255 | DOI Listing |
Sci Rep
December 2024
College of Information Engineering, SuQian University, SuQian, 223800, China.
The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings.
View Article and Find Full Text PDFJ Biomech
December 2024
PoliTo(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
In-stent restenosis represents a major cause of failure of percutaneous coronary intervention with drug-eluting stent implantation. Computational multiscale models have recently emerged as powerful tools for investigating the mechanobiological mechanisms underlying vascular adaptation processes during in-stent restenosis. However, to date, the interplay between intervention-induced inflammation, drug delivery and drug retention has been under-investigated.
View Article and Find Full Text PDFFundam Res
November 2024
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems from environmental conditions. The design of electronic packaging is highly complex and requires the consideration of multi-physics phenomena, such as thermal transport, electromagnetic fields, and mechanical stress. This review presents a comprehensive overview of the multiphysics coupling of electric, magnetic, thermal, mechanical, and fluid fields, which are crucial for assessing the performance and reliability of electronic devices.
View Article and Find Full Text PDFJ Environ Manage
December 2024
School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address:
This study delves into the multi-scale temporal and spatial variations of soil heat flux (G) within riparian zones and its correlation with net radiation (Rn) across six riparian woodlands in Shanghai, each characterized by distinct vegetation types. The objective is to assess the complex interrelations between G and Rn, and how these relationships are influenced by varying vegetation and seasons. Over the course of a year, data on G and Rn is collected to investigate their dynamics.
View Article and Find Full Text PDFSci Rep
December 2024
College of Electrical Engineering, Anhui Polytechnic University, Wuhu, 241000, Anhui, China.
The quantity of cable conductors is a crucial parameter in cable manufacturing, and accurately detecting the number of conductors can effectively promote the digital transformation of the cable manufacturing industry. Challenges such as high density, adhesion, and knife mark interference in cable conductor images make intelligent detection of conductor quantity particularly difficult. To address these challenges, this study proposes the YOLO-cable model, which is an improvement made upon the YOLOv10 model.
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