Modeling and Simulation of Draghead on Trailing Suction Hopper Dredger

Su, Zhen; Cao, Diandian; Sun, Jianyong

doi:10.1007/978-981-10-3187-8_71

Cited by 1 publication

(3 citation statements)

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“…These influence factors interact with each other. According to the actual operation experience of dredging construction personnel and the literature, [1,5,12] this paper chooses to build the suction density with mud pump speed, ship speed, high pressure flushing and steaming head angle and suction flow 5 inputs model prediction.…”

Section: Rake Head Production Analysis 21 Dredging Data Analysismentioning

confidence: 99%

“…Therefore, it is especially important to establish a suitable rake head model to study the inhalation density. [1] The known physical model of the rake head is set under ideal conditions, such as keeping the soil type unchanged, and the type of rake head used is unchanged. [2,3] However, in the actual dredging construction; the above conditions cannot be satisfied; in addition, the mud mixture formation process is complicated in the rake head, and the establishment of the physical model of the rake head also needs to consider many unknown parameters from the soil, such as permeability and on-site density.…”

Section: Introductionmentioning

confidence: 99%

“…Assuming that the number of ELM hidden layer nodes is L and the hidden layer node output function is k(x), then its model is: (1) where is the weight between the i-th single hidden layer node and the input node, is the threshold of the i-th hidden layer node in the network;…”

Section: Introduction To Related Algorithms 31 Extreme Learning Machinementioning

confidence: 99%

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Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

Hao

2020

MATEC Web Conf.

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The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

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Section: Rake Head Production Analysis 21 Dredging Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%