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Title: Ship operational efficiency : performance models and uncertainty analysis
Author: Aldous, L. G.
ISNI:       0000 0004 8503 724X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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There are increasing economic and environmental incentives for ship owners and operators to develop tools to optimise operational decisions, particularly with the aim of reducing fuel consumption and/or maximising profit. Examples include real time operational optimisation, maintenance triggers and evaluating technological interventions. Performance monitoring is also relevant to fault analysis, charter party analysis, vessel benchmarking and to better inform policy decisions. The ship onboard systems and systems in which they operate are complex and it's common for data modelling and analysis techniques to be employed to help extract trends. All datasets and modelling procedures have an inherent uncertainty and to aid the decision maker, the uncertainty can be quantified in order to fully understand the economic risk of a decision. An unacceptable risk requires further investment in data quality and data analysis techniques. The data acquisition hardware, processing and modelling techniques together comprise the data acquisition strategy. This thesis presents three models which are deployed to measure the ship's performance. A method is developed to systematically evaluate the relative performance of each model. Model uncertainty is one of four uncertainties identified as being relevant to the ship performance measurement. This thesis details and categorises each source and presents a robust method, based on the framework of the "Guide to Uncertainty in Measurement using Monte Carlo Methods", to quantify the overall uncertainty in the ship performance indicator. The method is validated using a continuous monitoring dataset collected from onboard an in-service ship. This method enables uncertainty to be quantifiably attributed to each source and a sensitivity analysis highlights the relative significance of each. The two major data acquisition strategies, continuous monitoring, CM and noon reported, NR are compared in combination with the other data acquisition parameters to inform the appropriate strategy for the required application and where further investment is required. This work has demonstrated that there is a ten-fold improvement in uncertainty achieved using a continuous monitoring set relative to a noon report dataset. If noon report data were collected perfectly, without the influence of human error, then uncertainties of the 5% level are achievable. The significant data acquisition parameters that improve precision are speed sensor precision and sample size. The equivalent that improve bias are speed sensor trueness and sample averaging frequency.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available