کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5474151 1520650 2017 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An assessment of anchor handling vessel stability during anchor handling operations using the method of artificial neural networks
ترجمه فارسی عنوان
ارزیابی استحکام باند لنگر هنگام انجام عملیات دستکاری لنگر با استفاده از روش شبکه های عصبی مصنوعی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی دریا (اقیانوس)
چکیده انگلیسی


- A mathematical model for estimating an anchor handling vessel's static heeling during anchor handling operation is proposed.
- This paper can be used as a basis for establishing a reliable on-board monitoring system for assessing stability margin.
- By having an awareness of the vessel's stability margin well in advance the operator can execute correct control strategies.
- The proposed on-board monitoring system is possible to establish in a cost[HYPHEN]effective manner.
- By having this system accidents like the Bourbon Dolphin vessel can be minimized.
- By having this system (on-board of anchor handling vessels) accidents like the Bourbon Dolphin vessel can be minimized.
- The framework presented in this paper can be extended for other marine operations such as fish trawling, pipe laying, and crane operation.

The risk of vessel capsizing is inherent to anchor handling operations (AHOs). Lessons learned from the Bourbon Dolphin accident reveal that the large static heeling angle could not be prevented due to the lack of awareness of the vessel's stability status, which can be improved with the help of a suitable on-board monitoring system. Therefore, an on-board monitoring system is proposed for assessing stability in terms of the static heeling angle. However, a complete mathematical model is not available for estimating a static heeling angle as a function of operational parameters. Therefore, an artificial neural network (ANN)-based functional relationship has been established between the operational parameters and the static heeling angle. Furthermore, a parametric study has been performed to investigate the effect of neural network topology on network performance. The results show that an ANN topology that contains one hidden-layer is efficient enough to predict a static heeling angle. The correlation coefficient between the ANN model predictions and the target values is 0.999. This result shows that the ANN provides an accurate estimate of the static heeling angle as a function of the operational parameters. Therefore, the proposed mathematical model can be used for assessing a vessel's stability during AHOs.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ocean Engineering - Volume 140, 1 August 2017, Pages 292-308
نویسندگان
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