Mingxuan Xu, Tao Jin, Weihong Kong, Yazhi Li, Xing Shen, Cheng Liu and Tianyang Zhu
This study aims to assess the vibrational behavior of a large transport airship based on finite element (FE) simulation and modal testing of its scaled model.
Abstract
Purpose
This study aims to assess the vibrational behavior of a large transport airship based on finite element (FE) simulation and modal testing of its scaled model.
Design/methodology/approach
A full-size parametric FE model of the airframe was established according to the structural layout of the composite beam-cable airframe of the airship, and vibrational analysis of the airframe was conducted. The influence of cable pre-tension load on the inherent properties of the airframe was investigated. Based on the simplification of the full-size FE model, scaled numerical and test models of the airframe, with a geometric scale factor of 1:50, were established and built.
Findings
The simulation and test results of the scaled models indicated that the mode shapes of the full-size and scaled models were similar. The natural frequencies of both the full-size and scaled models complied with the theoretical similarity relation of the frequency response.
Originality/value
This study demonstrated that the vibrational test results of the scaled model with very large scaling can be used to characterize the modal properties of the beam-cable airframe of a large transport airship.
Details
Keywords
Qing-Wen Zhang, Pin-Chao Liao, Mingxuan Liang and Albert P.C. Chan
Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality…
Abstract
Purpose
Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.
Design/methodology/approach
The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.
Findings
The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.
Practical implications
The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.
Originality/value
Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.
Details
Keywords
Qichang Duan, Mingxuan Mao, Pan Duan and Bei Hu
The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of…
Abstract
Purpose
The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed.
Design/methodology/approach
In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching.
Findings
The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm.
Research limitations/implications
Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved.
Practical implications
The proposed algorithm can be used to many practical engineering problems such as tracking problems.
Social implications
The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent.
Originality/value
The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.