Data-driven Ship Performance Models - - Emphasis on Energy Efficiency and Fatigue Safety

Due to digitalization in the maritime industry, a huge amount of ship operation-related data has been collected. The main objective of this thesis is to exploit machine learning/big data analytics to build data-driven ship performance models, focusing on speed-power relationship modeling, and fatigue accumulation assessment during a ship’s operation at sea.The speed-power performance models are established in three different ways: 1) semi-empirical white-box models, 2) machine learning black-box methods, and 3) physics-informed grey-box models. The white-box models include improved semi-empirical formulas for ship added resistance due to head waves, and further developed formulas in arbitrary wave headings. Validation studies using three case study ships show good agreement between the speed predictions by the white-box models and the long-term averages of full-scale measurements. Different supervised machine learning methods’ capabilities have been compared for black-box modeling. The XGBoost algorithm is found to have the most reliable predictive ability, with the highest efficiency suitable for onboard devices. The novel grey-box models are proposed by considering the physical principles in model tests and big data information from real sailing. It has been demonstrated that the proposed grey-box models can improve prediction accuracy by approximately 30% for ship speed estimation and provides 50% less cumulative error of sailing time than the black-box methods.The impact of voyage optimization-aided operations on the encountered wave conditions and ship fatigue damage is investigated in this thesis. By recommending appropriate routes, voyage optimization can greatly extend the fatigue life of a ship by at least 50%. The machine learning techniques are also applied to a ship’s fatigue assessment. The results indicate that the proposed data-driven fatigue assessment model could increase accuracy by approximately 70% for the case study vessel compared to other prominent spectral methods

Development of speed-power performance models for ship voyage optimization

Various measures, such as voyage optimization, performance monitoring and ship cleaning schedules, have been developed to help increase the energy efficiency of shipping operations. One of the most important elements needed for these measures is a reliable ship speed-power model. Many research efforts have been devoted to developing such models to describe a ship’s energy performance for head-to-beam seas, which are important for ship design purposes. For measures to increase the energy efficiency of a ship’s operations, speed-power performance models for other heading angles are of equal importance but are rarely investigated. Therefore, the overall objective of this thesis is to develop speed-power models for arbitrary wave headings that are especially applicable for ship voyage optimization. First, a semi-empirical model is proposed based on experimental tests. Then, a machine learning model (black box) is developed based on a large amount of full-scale measurement data.For the semi-empirical model, formulas to estimate a ship’s added resistance in head waves are developed to effectively describe a ship’s hull forms and other main characteristics. The formulas are then extended to estimate the impacts of wave headings from different angles, and these are verified by experimental model tests. A significant wave height-based correction factor is proposed to consider the nonlinear effect on a ship’s resistance and power increase due to irregular waves. For the machine learning-based model, the XGBoost algorithm is used to establish the model based on full-scale measurements of a PCTC. The input features include parameters related to ship operation profiles, metocean conditions, and motion responses.For the three case study ships, the discrepancy between power predictions and the actual values is reduced from more than 40% using today’s well-recognized methods to approximately 5% using the semi-empirical model proposed in this thesis. The machine learning model can further reduce the discrepancy to less than 1%. It is also demonstrated that the improved models can help to effectively optimize a ship’s voyage planning to reduce fuel consumption

A Practical Speed Loss Prediction Model at Arbitrary Wave Heading for Ship Voyage Optimization

This paper proposes a semi-empirical model to predict a ship’s speed loss at arbitrary wave heading. In the model, the formulas that estimate a ship’s added resistance due to waves attacking from different heading angles have been further developed. A correction factor is proposed to consider the nonlinear effect due to large waves in power estimation. The formulas are developed and verified by model tests of 5 ships in regular waves with various heading angles. The full-scale measurements from three different types of ships, i.e., a PCTC, a container ship, and a chemical tanker, are used to validate the proposed model for speed loss prediction in irregular waves. The effect of the improved model for\ua0speed loss prediction\ua0on a ship’s voyage optimization is also investigated. The results indicate that a ship’s voyage optimization solutions can be significantly affected by the prediction accuracy of speed loss caused by waves

A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements

This paper proposes a semi-empirical model to estimate a ship’s speed loss at head sea. In the model, the formulas to estimate a ship’s added resistance due to waves have been further developed to better consider the ship hull forms, in addition to other main particulars. Based on the model experimental tests of 11 ships in regular head waves, the new formulas have more flexible forms and can better fit the test results than other similar models. In addition, this model proposes a significant wave height based correction factor multiplied to the conventional integration to compute wave resistance in irregular waves. This factor is supposed to consider the impact of coupled ship motions in high waves on a ship’s added resistance due to waves. The model is validated by the full-scale measurement from two vessels, a PCTC and a chemical tanker. The encountered weather conditions along the sailing routes are extracted from the reanalysis metocean data. The results indicate that the proposed model can provide quite accurate predictions of ship speed loss in head sea operations

Scale invariant distribution functions in quantum systems with few degrees of freedom

Scale invariance usually occurs in extended systems where correlation functions decay algebraically in space and/or time. Here we introduce a new type of scale invariance, occurring in the distribution functions of physical observables. At equilibrium these functions decay over a typical scale set by the temperature, but they can become scale invariant in a sudden quantum quench. We exemplify this effect through the analysis of linear and non-linear quantum oscillators. We find that their distribution functions generically diverge logarithmically close to the stable points of the classical dynamics. Our study opens the possibility to address integrability and its breaking in distribution functions, with immediate applications to matter-wave interferometers.Comment: 8+10 pages. Scipost Submissio

Voyage optimization combining genetic algorithm and dynamic programming for fuel/emissions reduction

Deterministic optimization algorithms generate optimal routes/paths and speeds along ship voyages. However, a ship can rarely follow pre-defined speeds because dynamic sea environments lead to continuous speed variation. In this paper, a voyage optimization method is proposed to optimize ship engine power to reduce fuel and air emissions. It is a combination of dynamic programming and genetic algorithm to solve voyage planning in three-dimensions. In this method, the engine power is discretized into several levels. The potential benefit of using this algorithm is investigated by a medium-size chemical tanker. A ship\u27s actual sailing is used to demonstrate benefits of the proposed method. On average 3.4% of fuel-saving and emission reduction can be achieved than state-of-the-art deterministic methods. If compared with the actual full-scale measurements, on average 5.6% reduction of fuel consumption and GHG emissions (about 275 tons) can be expected by the proposed method for the six case study voyages

The Atiyah class of generalized holomorphic vector bundles

For a generalized holomorphic vector bundle, we introduce the Atiyah class, which is the obstruction of the existence of generalized holomorphic connections on this bundle. Similar to the holomorphic case, such Atiyah classes can be defined by three approaches: the $\rm{\check{C}}$ech cohomology, the extension class of the first jet bundle as well as the Lie pair