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Vessel Power Prediction and Its Impact on Emission Inventory

推进 螺旋桨 船体 牵引 功率(物理) 海洋工程 发动机功率 工程类 航空航天工程 物理 量子力学
作者
Fangfang Wang,Han Bao
标识
DOI:10.1115/msec2009-84220
摘要

Current practice of emission estimate for ocean-going vessels largely relies on the conventional propeller law for determining power consumption. This practice tends to underestimate the actual emission when sea states and winds are ignored. This paper presents an evaluation of two approaches on the prediction of power of a container vessel. The first approach estimates vessel power as a function of the vessel speed according to the propeller law. While the propeller law approach is cost-effective and time-saving in computing vessel propulsion power, it generally under-estimates vessel propulsion power due to the omission of many other influencing factors including vessel course, engine model, ocean states and weather conditions. The second approach derives vessel propulsion power as a function of the vessel speed and resistance forces. The propulsion power required for a particular vessel behavior is determined based on vessel towing resistance, added resistance from waves and winds, and a variety of propeller and hull dependent efficiencies. Because of the incorporation of external factors, this approach should be more accurate than the propeller law in reflecting the actual vessel power requirement. Comparative analysis is conducted among the two estimate results and real measurement data on engine power output. The results clearly show that power estimated from the propeller law underestimate the vessel propulsion power and the gap increases much faster for higher vessel speeds. Power estimate from the second approach provides more accurate results as they greatly match the measured power values. The ups and downs of the prediction results precisely reflect real power variation along with speed changes. Improved power prediction leads to more reliable emission inventory calculation. However, the improvement of accuracy should be balanced with the increased requirement on data sources and computing efforts.

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