Optimum design of the carbon fiber thin-walled baffle for the space-based camera

2011 ◽  
Author(s):  
Yong Yan ◽  
Gu Song ◽  
An Yuan ◽  
Guang Jin
Keyword(s):  
Carbon Fiber ◽  
Optimum Design ◽  
Thin Walled

scholarly journals Optimum Design of Thin Walled Tube on the Mechanical Performance of Super Stud Bolt

2010 ◽  
Vol 4 (10) ◽  
pp. 1455-1466
Author(s):  
Yang XIAO ◽  
Nao-Aki NODA ◽  
Masahiro KUHARA ◽  
Kinjiro SAITO ◽  
Masato NAGAWA ◽  
...  
Keyword(s):  
Optimum Design ◽  
Mechanical Performance ◽  
Thin Walled Tube ◽  
Thin Walled

scholarly journals Study on Calculation of Bearing Capacity of Axially Loaded CFRP-Strengthened Cold-Formed Thin-Walled Lipped Channel Steel Columns

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yanan Sun ◽  
Pengfei Li ◽  
Guojin Qin
Keyword(s):  
Carbon Fiber ◽  
Bearing Capacity ◽  
Load Capacity ◽  
Slenderness Ratio ◽  
Steel Structures ◽  
Fiber Reinforced ◽  
Bonding Agents ◽  
Steel Column ◽  
Thin Walled ◽  
Carbon Fiber Reinforced

With the development of carbon fiber reinforced composites and the continuous improvement of the properties of bonding agents, scholars recommended using carbon fiber reinforced plastics (CFRP) to enhance cold-formed thin-walled C-shaped steel structures. It can provide a fast and effective way to strengthen and repair damaged steel structures. However, discussion on the bearing capacity calculation of cold-formed thin-walled C-section steel column strengthened by CFRP was limited. Also, the relevant influencing factors (the number of CFRP reinforcement layers), the orientation of CFRP (horizontal, vertical), and the location of CFRP reinforcement (web + flanges + lips, web + flanges, web, and flanges) were overlooked in calculating the bearing capacity of cold-formed thin-walled C-section steel column strengthened by CFRP. Then, the calculation result of the load capacity will be inaccurate. This work, therefore, studied the effects of CFRP reinforcement layers, CFRP direction, and CFRP reinforcement position on the ultimate load of CFRP-strengthened cold-formed thin-walled C-section steel column. A three-dimensional (3D) finite element model of cold-formed thin-walled steel strengthened by CFRP was established to discuss the bearing capacity under axial compression. Furthermore, a method for calculating the bearing capacity of the CFRP-strengthened cold-formed thin-walled C-section steel column was proposed based on the direct strength methods (DSM). The results indicate that not only the slenderness ratio, section size, and length of members but also the number of CFRP reinforcement layers and orientation of CFRP have an impact on the calculation of bearing capacity. The equation modified in this work has excellent accuracy and adaptability. Predicting the bearing capacity of reinforced members is necessary to give full play to the performance of CFRP accurately. Thus, the methods proposed can provide a reference value for practical engineering.

Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods

2020 ◽  
pp. 85-103
Author(s):  
Melda Yucel ◽  
Aylin Ece Kayabekir ◽  
Sinan Melih Nigdeli ◽  
Gebrail Bekdaş
Keyword(s):  
Machine Learning ◽  
Carbon Fiber ◽  
Optimum Design ◽  
Prediction Models ◽  
Shear Capacity ◽  
Fiber Reinforced ◽  
Error Performance ◽  
Carbon Fiber Reinforced Polymers ◽  
Design Variables ◽  
Carbon Fiber Reinforced

In this chapter, an application for demonstrating prediction success and error performance of ensemble methods combined via various machine learning and artificial intelligence algorithms and techniques was performed. For this reason, two single method was selected and combination models with Bagging ensemble was constructed and operated in the direction optimum design of concrete beams covering with carbon fiber reinforced polymers (CFRP) by ensuring the determination of design variables. The first part was optimization problem and method composing from an advanced bio-inspired metaheuristic called Jaya algorithm. Machine learning prediction methods and their operation logics were detailed. Performance evaluations and error indicators were represented for prediction models. In the last part, performed prediction applications and created models were introduced. Also, obtained prediction success for main model generated with optimization results was utilized to determine the optimum predictions about test models.

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