基于传感器测量的生物污垢对船舶轴功率影响评估

IF 1.4 Q3 ENGINEERING, MARINE
H. Bakka, Hanne Rognebakke, I. Glad, Ingrid Hobæk Haff, E. Vanem
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引用次数: 0

摘要

船体和螺旋桨上的海洋生物污垢增加了船舶在水中移动的阻力,降低了船舶的推进效率。由于很少测量生物量,因此很难估计结垢的影响。在本文中,我们提出了一个新的数据驱动模型,用于大型集装箱船的总轴功率使用,以估计未观察到的结垢影响。由于物理模型和机器学习模型的局限性,我们为此开发了一个贝叶斯广义加性模型。我们讨论了模型的代表性训练数据的问题。此外,我们将数据子集和子采样到一个具有代表性的样本中。通过样本外预测质量、物理适当性和残差的自相关来比较模型。贝叶斯广义加性模型与使用集成嵌套拉普拉斯近似的计算推理相结合,给出了生物淤积效应随时间变化的稳健估计。它还允许将轴的总功率使用分解为速度、天气和其他条件的影响。该模型可用于了解不同船体和螺旋桨处理的有效性和时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating the effect of biofouling on ship shaft power based on sensor measurements
ABSTRACT Marine biofouling on a ship's hull and propeller increases the resistance of the ship moving through water and reduces the propulsion efficiency of the ship. Estimating the effect of fouling is difficult, as the biomass is rarely measured. In this paper, we present a new data-driven model for the total shaft power use of a large containership, in order to estimate the unobserved effect of fouling. Due to the limitations of both physical models and machine learning models, we develop a Bayesian generalized additive model for our purpose. We discuss issues of representative training data for the model. Further, we subset and subsample the data to a representative sample. Models are compared by out-of-sample predictive quality, physical appropriateness, and through autocorrelation of residuals. The Bayesian generalized additive model combined with computational inference using integrated nested Laplace approximations gives a robust estimate of the biofouling effect over time. It also allows a decomposition of the total shaft power use into effects of speed, weather, and other conditions. This model can be used to understand the effectiveness and timing of different hull and propeller treatments.
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来源期刊
Ship Technology Research
Ship Technology Research ENGINEERING, MARINE-
CiteScore
4.90
自引率
4.50%
发文量
10
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