【作者】

Panos Theodossopoulos

【摘要】

该论文已在赫尔辛基举行的第28届CIMAC大会上发表，论文的版权归CIMAC所有。Retaining and improving the performance and efficiency of shipboard power plants, as well as performing condition monitoring towards efficient fault diagnosis and asset management, requires Monitoring (measuring) and Evaluation (benchmarking). On the monitoring side, any in-service measurements may occasionally prove to be untrustworthy, taking into account sensor and recording accuracy issues. Performance evaluation and fault diagnosis rely on dependable and accurate benchmark/reference, against which measurements can be compared. The core of the novel methodology, presented in this paper, involves the use of a thermodynamic simulation model for each specific shipboard engine. This model is tuned to be an exact replica of the actual engine in operation, reflecting the physical relationships of all primary parameters (temperatures, pressures, rpm) and resultant values (torque, fuel consumption, emissions etc.). Once tuning is performed, the model predicts engine performance as influenced by ambient conditions, load, speed and fuel, at any operating point. Based on the premise that the operating envelope of the engine is known, a great number of simulations are performed a-priori, for combinations of all possible engine settings, ambient conditions and fuels. Thus an engine performance hyper-map (Engine Hyper Cube) is generated. This hyper-map database can provide the “expected” values of performance parameters at any engine operating condition. These “expected” values are then compared to the “measured” values offering diagnostics based on the residual differences between the two. Euronav Shipping Company has installed Engine Hyper Cube models, as produced by Propulsion Analytics, for seven Suezmax tankers (sister ships). The ships’ main engines have various measuring and data acquisition systems installed onboard, with the Engine Hyper Cube methodology capable of working with any such system and/or sensor. To ascertain the accuracy of the methodology and the predictive potential, a single blind validation was performed where the engine settings from service performance reports for some years in the past, were input into the Engine Hyper Cube software. Any observed swing in residuals (measured-expected) in the timeline, were then compared with the known engine maintenance events. The results indicated recognizable shift in performance, following maintenance events in the ship’s records, confirming the validity and accuracy of the Engine Hyper Cube methodology. Another case is presented, where a fuel injection problem was investigated. An in-depth analysis using measured cylinder pressure diagrams compared with pressure trace predictions and the use of heat release analysis pinpointed the cylinder with fuel injection issues. The methodology also allows the shipping company to perform optimization studies (e.g., VIT optimization) as well as execute a number of ‘what-if’ scenarios for examining how the vessel engine performs in regimes it had not operated in the past. One such case is also presented. The shipping company is using these methodologies and technologies for monitoring and evaluation, aiming at optimum vessel operation.

【会议名称】

第28届CIMAC会议

【会议地点】

芬兰 赫尔辛基

【下载次数】

2

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