WO2020025745A1 - Amélioration de l'efficacité de voyage d'un navire - Google Patents

Amélioration de l'efficacité de voyage d'un navire Download PDF

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Publication number
WO2020025745A1
WO2020025745A1 PCT/EP2019/070760 EP2019070760W WO2020025745A1 WO 2020025745 A1 WO2020025745 A1 WO 2020025745A1 EP 2019070760 W EP2019070760 W EP 2019070760W WO 2020025745 A1 WO2020025745 A1 WO 2020025745A1
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Prior art keywords
vessel
parameters
efficiency
determining
speed
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PCT/EP2019/070760
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English (en)
Inventor
Lily RACHMAWATI
Justin N NORMAN
Lars Ove SILSETH
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Kongsberg Maritime CM AS
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Publication of WO2020025745A1 publication Critical patent/WO2020025745A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B79/00—Monitoring properties or operating parameters of vessels in operation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B49/00—Arrangements of nautical instruments or navigational aids
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B71/00—Designing vessels; Predicting their performance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63J—AUXILIARIES ON VESSELS
    • B63J99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0005—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots with arrangements to save energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T70/00—Maritime or waterways transport
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T70/00—Maritime or waterways transport
    • Y02T70/10—Measures concerning design or construction of watercraft hulls

Definitions

  • Vessels such as ships, fulfil many purposes, such as passenger transportation, goods transportation, transportation of oil and chemicals, cable laying, and fish trawling.
  • Each ship has its own characteristics, such as size, weight, shape, engine configuration, and tank sizes.
  • the weight changes due to loading and unloading of for instance goods, ballast water, and fuel. This in turn causes a change in the hydrodynamic profile of the ship, both in terms of the draft of the ship, and in the ship's profile above water. Currents and winds will have a different effect of the ship depending on the load.
  • the ship can be designed to have more advantageous characteristics, such as a better hydrodynamic profile, an improved light
  • the modification of the flow introduced by the presence of the hull and the positioning of the propeller near the stern means that the propeller operates not in a uniform free stream as in open water tank test but in a complex wake field. In return the propeller also exerts a pressure field on the hull, sucking water back towards it with a mean effect of thrust deduction. Not only is it not feasible to perform a tank test for every possible scenario; it is impossible. Environmental factors, such as currents and wake field influence, are impossible to comprehensively test for in advance.
  • Vessel resistance is another important quantity that cannot be precisely computed from first principles in all practical sea states and vessel conditions.
  • Vessel resistance includes frictional, residual and air resistance, all of which vary with the square of vessel speed at low speed and in fair weather. Frictional resistance may account for anywhere between 45-90% of total resistance and depends on the size and roughness of the wetted area, the ship speed and the hull form. The vessel frictional resistance changes with time in part due to fouling of propeller and hull. Fouling leads to an increased surface roughness, which translates into a
  • the second source of resistance is caused by waves and eddies created as the vessel moves through water.
  • wave resistance increases with the square of the ship speed and contributes something like 8-25% of resistance, but it increases much more rapidly at higher speeds and creates a speed barrier where increase in propulsion power gets converted into wave energy instead of propulsion.
  • the residual resistance can account for 60% of total resistance.
  • Air resistance typically the smallest component, accounts for approximately 2-10% of total ship resistance, depending on the apparent wind speed and direction, the ship speed, and the cross-sectional area of the ship above waterline.
  • Embodiments of the present invention address at least some of the issues described above.
  • the present invention provides a method for optimizing an efficiency of a vessel on a voyage, the vessel being controllable by changing a set of adjustable control parameters via a set of control levers, the efficiency of the vessel being represented by a set of one or more efficiency parameters.
  • the method comprises: storing a set of historical operating data sets, each historical operating data set comprising operating values for the set of adjustable control parameters and corresponding operating values for a set of vessel performance parameters, and optionally a set of environmental parameters affecting the vessel, providing, based on the historical operating data sets, a current data- driven model describing the one or more efficiency parameters as corresponding one or more functions, each function depending on one or more of the adjustable control parameters and one or more of the vessel performance parameters and optionally one or more of the environmental parameters, determining that the vessel has reached a first steady state of travel, determining current values of the set of efficiency parameters at the first steady state of travel, determining, using the current data-driven model, one or more candidate sets of adjustable control parameter values having improved efficiency parameter values compared to the current values of the set of efficiency
  • control lever shall be construed broadly as means for changing the corresponding control parameter.
  • the propeller speed is controlled, at least in part, using a physical lever, whereas on other vessels, the propeller speed is adjusted by interacting with a touch-sensitive screen displaying for instance an image resembling a physical propeller speed lever.
  • the optimization method for determining one or more candidate sets of adjustable control parameter values having improved efficiency parameter values compared to the current values of the set of efficiency parameters explores the space of possible control lever values to find a combination that yields more optimal performance as evaluated by underlying cost functions/computational models.
  • the set of one or more efficiency parameters may for instance comprise one or more of: a fuel efficiency, a mission efficiency, a life cycle cost index, a motion index (signifying for instance a degree of rolling around one or more axes of the vessel during the voyage).
  • the combinator curve is the optimum combination of pitch and shaft speed, or at least is thought to be optimal, with respect to fuel consumption.
  • Combinator curves may be tuned from time to time and, an in special cases, trained crew members may input a value for propeller pitch directly into the ship's control system deviating from the combinator curve.
  • the combinator curve is usually nothing more than a static recommendation that tells the crew which combination of propeller pitch and propeller RPM achieves the best fuel efficiency. Selecting operating conditions that are not on the combinator curve requires knowledge that is not available. In practice, a crew will not choose to deviate from the combinator curve except under exceptional conditions.
  • the combinator curve is not optimal when propeller(s) and engines are at different states from the state at which the propeller efficiency was initially measured to derive the combinator curve(s), due for instance to ageing and fouling.
  • the combinator curve no longer represents efficient pairing of propeller pitch and RPM. Changes in sea state also changes the propeller efficiency.
  • the propeller speed and pitch are allowed to vary independently. In other words, the optimization explores propeller pitch and RPM combinations not just on the combinator curve, but also away from the combinator curve.
  • embodiments of the invention that involve the propeller as an adjustable control parameter can be seen as providing an adaptive combinator curve.
  • the method not only allows the discovery of new such minima in general, it also specifically allows for the discovery of minima associated with settings that are not on the combinator curve.
  • Thrust is provided for instance by one or more diesel engines and/or electric motors through for instance a single controllable-pitch propeller.
  • the resistance encountered by the ship which is a complex function for instance of ship
  • Thrust and Torque are both functions of the advance speed, propeller diameter, angular speed, and fluid and flow characteristics. These functions are complex enough to defy a closed form mathematical expression, and analysis is performed by empirically identifying thrust and torque coefficient curves as functions of pertinent variables like advance speed, etc.
  • the propeller behaviour needs to be captured with multiple thrust and torque coefficient curves, which tend to have the same general trend across different pitch values in a large range of propeller speed and advance speed but sufficiently different in absolute magnitudes.
  • Optimizing propeller performance in open water is equivalent to identifying local and/or global optima in the propeller efficiency hyperplane, a tractable
  • the shaft power required increases at a higher rate as advance speed of propeller in water increases. This is attributable to drag increasing with squared velocity and other factors. Looking at marginal rates of return (i.e. knot per kilowatt supplied), going from a lower speed to a higher speed is "expensive”. Furthermore, a minimum ratio of power/speed is obtained at the lowest advance speed and running at the highest pitch.
  • the amount of power required to produce the same advance speed in identical load can vary significantly for propellers with variable pitch. At higher speed values, there are fewer combinations of propeller RPM and pitch, and consequently less room for optimizing performance. At lower speed values, there are more combinations, and power intake could vary
  • the set of adjustable control parameters may for instance comprise at least one or more of: a propeller speed, a propeller pitch, a power train setting, an engine part load, a commanded vessel heading, a vessel hull trim.
  • the set of vessel performance parameters may for instance comprise one or more of: a fuel flow rate, an actual vessel heading, a vessel speed over ground, a vessel course over ground, a shaftline torque, a vessel roll angle dependent parameter, a vessel pitch angle dependent parameter.
  • the preferred candidate set is applied only subject to an improvement criterion being fulfilled.
  • An improvement criterion may for instance comprise a requirement that a change in an adjustable control parameter value must exceed a minimum absolute amount or exceed a minimum percentage amount or other relative amount relative to a current value of the adjustable control parameter value. For instance, a change in main engine RPM must exceed 1% or must exceed 5 RPM before the control levers are adjusted.
  • the set of one or more efficiency parameters comprises a fuel efficiency parameter
  • the current data-driven model comprises: - a fuel flow function relating a fuel flow of the vessel to a first set of one or more of the adjustable control parameters, and a speed function relating a speed of the vessel to a second set of one or more of the adjustable control parameters, and the fuel efficiency parameter is determined based on the fuel flow function and the speed function, such as a ratio between the fuel flow function and the speed function.
  • the method further comprises: - determining a predicted vessel performance parameter value using the current data-driven model and current values of the adjustable control parameters and optionally current values of the environmental parameters, determining an actual vessel performance parameter value for comparison with the predicted vessel performance parameter value, - determining that a discrepancy between the actual value and the predicted value of the vessel performance parameter exceeds a tolerance threshold and in response: i. providing, based on the amended set of historical operating data sets, an updated data-driven model describing the one or more efficiency parameters as corresponding one or more functions, each function depending on one or more of the adjustable control parameters and one or more of the vessel performance parameters and optionally one or more of the environmental parameters.
  • Such embodiments test the current data-driven model to determine whether it predicts the performance parameters with sufficient accuracy.
  • the current data-driven model is replaced by an updated data-driven model.
  • the updated data-driven model is based on historical data that emphasizes more recent historical data compared to the current data-driven model.
  • the updated data-driven model then becomes the current data-driven model. By emphasizing more recent historical data, the updated data-driven model better reflects the recently observed behaviour of the vessel.
  • the present invention provides a current data-driven model and provides for updating the data-driven model when the current data-driven model leads to an excessive discrepancy between the actual value and the predicted value of a vessel performance parameter.
  • the updated data-driven model automatically accounts for changes in vessel behaviour, whatever the source(s) of the change.
  • embodiments that include updating the data-driven model can account very well for the sea state and other environmental parameters.
  • providing the updated data-driven model may comprise the step of reducing an influence of older historical data sets relative to newer historical data sets. This may be achieved by partly decreasing the weight of older historical data relative to newer historical data, and/or by entirely removing older historical data.
  • at least one of the one or more functions depends on the set of environmental parameters, and the set of environmental parameters comprises one or more of: an apparent wind speed, an apparent wind direction, a wave height, a wave direction relative to a hull of the vessel, a true wind speed, a vessel physical condition. The inventors found that including one or more of these parameters can improve the model's predictive capabilities significantly.
  • Determining that the vessel has reached a steady state of travel may alternatively or additionally include one or more of: determining that a propeller speed does not vary more than allowed by a predefined steady-state propeller speed criterion, determining that a vessel heading does not vary more than allowed by a predefined steady-state vessel heading criterion, determining that a course-over-ground does not vary more than allowed by a predefined steady-state course-over-ground criterion, - determining that a propeller torque does not vary more than allowed by a predefined steady-state propeller torque criterion, determining that a fuel flow does not vary more than allowed by a predefined steady-state fuel flow criterion.
  • absolute or relative variations might be used as criteria.
  • the set of efficiency parameters comprises at least two efficiency parameters
  • the step of determining one or more candidate sets comprises determining at least two non-dominated candidate sets.
  • the preferred candidate is selected based on a preference of one efficiency parameter in the set of efficiency parameters over all other efficiency parameters in the set of efficiency parameters. Note that this is not the same as including only one efficiency parameter in the first place.
  • a second aspect of the invention provides digital computing and storage hardware configured specifically to perform a method in accordance with the first aspect of the invention.
  • a third aspect of the invention provides a computer-readable storage medium comprising program instructions that, when executed on suitable digital computing and storage hardware, cause the digital computing and storage hardware to perform a method in accordance with the first aspect of the invention.
  • Figure 1 illustrates an example of relationships between adjustable control parameters, performance parameters and environmental parameters.
  • Figure 2 is a flow chart illustrating a method in accordance with an embodiment of the invention.
  • Figure 3 illustrates an allowed space for exploring combined values of propeller pitch and propeller RPM during optimization.
  • Figure 4 is a flow chart illustrating a method in accordance with another embodiment of the invention.
  • Figure 5 schematically illustrates dominated and non-dominated solutions, as well as selection of preferred solutions in dependence of which efficiency parameter is preferred among multiple efficiency parameters.
  • the following example refers to a vessel that has one diesel main engine, two constant speed auxiliary engines (referred to as AUX1 and AUX2 below), a controllable pitch propeller and bow thrusters.
  • the vessel can be operated in six power modes:
  • PTO 60Hz mode the main engine runs at MCR (maximum continuous rating) speed and supplies propulsion power as well as hotel loads.
  • PTO 50-60Hz mode the main engine can be operated from 625 rpm to 750 rpm, supplying power to the propeller as well as the switchboard and the rest of the vessel through a rotating converter.
  • Full Combinator mode the main engine operates at the largest range of speed variation (450 to 750 rpm) to power propulsion while the auxiliary engines provides power for the rest of the vessel.
  • PTO Limited Combinator split
  • auxiliary engines supply hotel load while the main engine provides propulsion and thruster power.
  • PTI Boost mode provides the largest possible propulsion power with the main engine running at MCR speed and auxiliary engines adding to the propulsion power through an Active Front End (AFE).
  • AFE Active Front End
  • the vessel also has a number of fresh water tanks, diesel oil tanks and urea tanks with varying capacity. To adjust the trim of the vessel and the resistance in water, distribution of diesel oil in the appropriate tanks can be varied.
  • the data-driven model considers the vessel to be completely characterised by the following parameters:
  • Fig. 1 schematically illustrates relationships in an exemplary data-driven model, relating adjustable control parameters, performance parameters and environmental parameters to one another. Arrows point from inputs to outputs.
  • Main Engine RPM (ME RPM)
  • ME RPM Main Engine RPM
  • the chart may be used to track the effect of changing a control lever as well as trace back the control levers to be varied to achieve a change in a dependent variable.
  • Adjustable control parameters are shown in simple square boxes in the diagram.
  • Propeller RPM, propeller pitch, Heading, and Main Engine load (“ME load”) are examples of adjustable control parameters ("control levers").
  • the model also includes performance variables, which depend on the control levers, and in some cases on non-controllable variables. These are shown in ovals.
  • data are stored in order to record how the vessel performed under specific conditions. Speed over ground, wind direction, apparent wind speed, fuel rates, and so on, are stored together with the settings of the adjustable control parameters at those times.
  • These historical data sets form the basis for the data-driven model.
  • the data-driven model is established by adjusting parameters in the underlying computational models to best fit the historical data, or at least some of it, namely that which is considered important. This can be selected either automatically or at least partially manually.
  • the most recent historical data sets are used when fitting the parameters of the computational models, as these best reflect the recent performance of the vessel and therefore, all else being equal, will result in a data-driven model which has a strong predictive power.
  • Fig. 2 illustrates an embodiment of the invention.
  • Historical data sets are stored during a voyage, as illustrated by step 201.
  • the historical data sets are stored in a database 202, preferably a database stored on a computer system.
  • a current data-driven model 204 is determined.
  • the vessel is in a steady state, i.e. that the vessel is not in the process of, as examples, significantly slowing down or speeding up or changing heading.
  • current values of the one or more efficiency parameters are determined.
  • one or more adjustable control parameter candidate sets are determined, which, if applied, will (or at least should, based on the model's strength in view of current conditions) result in an improved efficiency.
  • a particular candidate set is selected. If there are more than one candidate set, a preferred candidate set may be selected based on a criterion or criteria, or it may be selected randomly.
  • the preferred candidate set is applied. As a result, the vessel should exhibit an impoved efficiency.
  • Design data e.g. from propeller open water test or tank tests, a diesel engine map and an auxiliary engine datasheet, offer some information on areas unexplored by the historical operation of the vessel.
  • Data-driven models based on historical data are invoked only to evaluate a candidate solution in the optimization if it involves control lever values within a set percentage around past data values. Beyond the set percentage, data driven models based on design data may for instance be invoked. This is illustrated in Fig. 3.
  • Combinations of propeller RPM and pitch available are limited in comparison to the possible set of values.
  • the area illustrates propeller RPM of 80 to 140 and pitch of 0.8 to 1.5 in p/d. All combinations are in principle allowed.
  • the dots that form curve-like structures in the plot) signify operating points found in historical data sets. A 7% region beyond (in all directions) the support of historical data sets is illustrated as dark areas in the plot. Available test data from the design phase may be utilized beyond that boundary.
  • Propeller open water test results may be employed to predict shaftline torque and power, vessel speed and speed over ground when the propeller rpm and pitch are beyond the specified boundary around historical data.
  • the diesel engine fuel map may be employed in lieu of the historical data driven model for main engine fuel consumption for points beyond historical data, and datasheets for auxiliary engines 1 and 2 may be employed at all times.
  • this design data should not be expected to closely represent operational behaviour of the vessel, as the tank test result represents open water behaviour for a stock propeller. If substituted with more specific information, e.g. self-propelled towing test for the right hull shape and the actual propeller instead of the stock propeller, the resulting models would be closer to the true behaviour. However, the model points in the right general direction.
  • design data supplementing historical data is generally only available for a very limited number of variables. Motion parameters, such as roll and pitch, may be predicted using models derived from historical data.
  • Selected embodiments of the invention provide online learning of the data-driven model. These are needed for the computational models to implicitly capture the factors just described and no data is available to indicate when they vary and how their variation affect for example shaft torque. The rate at which these models need to be updated would differ: Shaftline Torque for a given speed, propeller RPM and pitch, may not change as quickly as the Course Over Ground for a given heading.
  • inventions of the invention are to find controllable parameters that optimize one or more efficiencies. In some embodiments, this means finding values for the adjustable control parameters, X, that minimize cost functions, F,
  • the adjustable control parameters representing the efficiency parameters.
  • the adjustable control parameters representing the efficiency parameters.
  • X ⁇ x t , X 2 , x 3 , X 4 , x 5 , x 6 , Ch, X 8 , * 9> *10, *ll) are x 4 : main engine rotational speed (in rpm)
  • F 4 (X) LCC(X) where LCC ⁇ X ) is a catch-all life cycle cost function.
  • the life cycle cost might represent the rate of wear of the main engine. The main engine wears more quickly the higher the power output.
  • the optimization is a numerical search procedure and can yield values of X that are not allowed.
  • the propeller pitch might be limited to values between 0 and 1.4.
  • the search process takes such contraints into account by discarding those values of X that do not fulfill all constraints.
  • propeller rpm must be below 130.
  • the engines also have a limited output power capability, and therefore constraints exist for those parameters as well.
  • the heading must be in the range from 0 to 360, which is therefore also imposed when determining whether a solution X is valid.
  • the optimization method finds solutions to optimize more than one efficiency parameter at the same time. In practice this involves finding a set of recommended operating points that represent trade off in the performance metrics, with an option to prioritize one objective over another, and in practice typically within a user specified deadline in order to obtain a solution within a reasonable amount of time.
  • a multi-objective optimization problem involves the optimization of a set of conflicting objectives such that there is no single solution that outperforms all other solutions in all objectives. Solutions to a multi objective problem are optimal in the sense of Pareto optimality, i.e. a solution is optimal if improvement in one objective implies degradation in at least one other objective.
  • a multi-objective optimization problem has a non-unique set of solutions which represent trade-offs between the objective values and form a non-dominated front in the objective space.
  • solution A is better in all objectives than solution B, and solution A and C are non-dominated (i.e. they represent trade off in objective values and they are incomparable as there is no basis for deciding that A is better than C). That does not necessarily mean that solution C is better than solution B.
  • Solution B cannot possibly dominate solution C but the two can be non-dominated.
  • Typical approaches to multi- objective optimization include:
  • optimization algorithm aims to find a set of non-dominated solutions lying as close as possible to the theoretical Pareto front in the objective space. Apart from proximity to the optimal front, a uniform distribution of solutions in the objective space is usually considered good, as it gives a useful set of alternatives representing trade off in objectives to choose from.
  • Pareto dominance based search is typically achieved by ranking solutions based on pair-wise dominance relation, and archiving the best non-dominated solutions found so far, as a basis for further exploration in the search space.
  • Solution A dominates solution B if solution A performs at least as well as B in all objective functions and outperforms B in at least one objective.
  • Pareto ranking schemes assign a solution quality metric, akin to the cost function, based on the pair-wise dominance relations.
  • Pareto rank is also a population-dependent metric, in that the rank of a solution is evaluated relative to other solutions present as alternatives and would change with variation in the alternatives.
  • Multi-objective optimization algorithms that aim to find a set of non-dominated solutions are typically
  • Fig. 5 schematically illustrates a number of solutions and the values of two efficiency parameters, f lr and f 2 .
  • solutions 501 are preferred.
  • Solution 502 in particular provides the lowest value of f 2 subject to the lowest value of f t .
  • Solution 502 is therefore preferred in case efficiency parameter f t is critical.
  • efficiency parameter f 2 is of some importance, solution 504 might be the preferred solution among solutions 503.
  • solution 504 does not provide the lowest value of f lr it may be the preferred solution because what is lost in terms of efficiency in respect of is more than gained in terms of the increased efficiency of f 2 .
  • Solutions 505 are dominated because solution 506 is better in terms of both f lr and f 2 . Therefore none of these would be selected as a preferred solution and their corresponding control lever settings would not be applied. No other solutions in Fig. 5 are dominated.
  • solutions 507 are preferred.
  • Solution 508 in particular provides the lowest value of f t subject to the lowest value of f 2 .
  • Solution 508 is therefore preferred in case efficiency parameter ⁇ is critical. If efficiency parameter is of some importance, solution 509 might be the preferred solution. Although solution 509 does not provide the lowest value of f 2 it may be the preferred solution because what is lost in terms of efficiency in respect of f 2 is more than gained in terms of the increased efficiency of f t .
  • efficiency parameters are generally optimized, whether it be by minimization or by maximization, or a combination.
  • Fig. 4 illustrates updating the current data-driven model.
  • the method will applied in conjunction with the method illustrated in Fig. 2 and described above. Therefore, the method is illustrated as connecting to A, labelled "215", of Fig. 2.
  • a vessel performance parameter is predicted in step 403.
  • performance parameters include for instance Speed, Main Engine fuel flow rate, Speed over Ground (“SoG”), Course over Ground (“CoG”), and hull attitude.
  • SoG Speed over Ground
  • CoG Course over Ground
  • hull attitude an actual value of the performance parameter is obtained in step 405.
  • the current data- driven model is updated as shown in step 410 resulting in an updated data-driven model which then replaces the current data-driven model as illustrated in Fig. 4.
  • the updating is based on the currently stored historical data sets 202, which have typically been amended by new operating data sets compared to the time when the current data-driven model, before being updated, was determined. These data sets reflect all of the parameters discussed above. On a relatively short time scale, environmental factors, such as wind speed and/or direction and sea state, may have changed. As discussed, these affect the efficiency of the vessel in a way that may favour a different combination of propeller RPM and pitch compared to the combination that were favoured when the last data-driven model was determined. On a longer time scale, fouling, changing load, and so on affect the efficiency.
  • the updated data-driven model 404 better predicts control parameter values that will improve the efficiency of the vessel, which is the very purpose of the data-driven model.
  • a persistent media e.g. a disk.
  • a subset of those data sets are used to determine the data-driven model.
  • the subset includes those data sets that are considered relevant for the situation at hand, e.g. resembles current condition or more recent. Therefore, the embodiment in Fig. 4 illustrates a step 409 of selecting historical data sets for providing the updated data-driven model.
  • the simplest approach is to simply use a number of the latest historical data sets.
  • older historical data sets may be included in the subset because of a particular relevance. For instance, if wave conditions have changed in the course of hours, for instance due to a change in heading, there will be recent historical data sets for different wave conditions. If the waves are strong, they will have a large influence in the optimization. If the vessel now returns to the heading that were applied some hours ago, the historical data sets from that time may give a better prediction of optimal control lever settings.
  • S' may contain duplicates of members of S that are particularly relevant to the current condition, i.e. members that are emphasized. Members that are de emphasized may have lower weightage or not included at all. These for example could be datapoints that conflict with the most recent datapoint (e.g. same propeller pitch and rpm but different torque).
  • the subset used for determining the data-driven model typically resides in active memory/RAM of the computer system involved in establishing the data-driven model based on the historical data sets.

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Abstract

L'invention concerne un procédé d'optimisation d'une efficacité d'un navire pendant un voyage, le navire pouvant être commandé par la modification d'un ensemble de paramètres de commande réglables par l'intermédiaire d'un ensemble de leviers de commande, l'efficacité du navire étant représentée par un ensemble d'un ou plusieurs paramètres d'efficacité, le procédé consistant à : stocker un ensemble d'ensembles de données d'exploitation historiques, chaque ensemble de données de fonctionnement historiques comprenant des valeurs de fonctionnement pour l'ensemble de paramètres de commande réglables, et des valeurs de fonctionnement correspondantes pour un ensemble de paramètres de performance de navire, et facultativement un ensemble de paramètres environnementaux affectant le navire; fournir, sur la base des ensembles de données de fonctionnement historiques, un modèle entraîné par des données actuelles décrivant le ou les paramètres d'efficacité en tant qu'une ou plusieurs fonctions correspondantes, chaque fonction dépendant d'un ou de plusieurs des paramètres de commande réglables et d'un ou plusieurs des paramètres de performance de navire et éventuellement d'un ou plusieurs des paramètres environnementaux; déterminer que le navire a atteint un premier état de déplacement constant; déterminer des valeurs actuelles de l'ensemble de paramètres d'efficacité au premier état de déplacement constant; déterminer, à l'aide du modèle entraîné par des données actuelles, un ou plusieurs ensembles candidats de valeurs de paramètres de commande ajustables ayant des valeurs de paramètres d'efficacité améliorées par rapport aux valeurs actuelles de l'ensemble de paramètres d'efficacité; sélectionner un ensemble candidat préféré parmi le ou les ensembles candidats; et appliquer l'ensemble candidat préféré de paramètres de commande réglables en réglant les leviers de commande sur les valeurs dans l'ensemble candidat préféré, en appliquant facultativement l'ensemble candidat préféré soumis à un critère d'amélioration.
PCT/EP2019/070760 2018-08-03 2019-08-01 Amélioration de l'efficacité de voyage d'un navire WO2020025745A1 (fr)

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CN114489042A (zh) * 2021-12-22 2022-05-13 广东技术师范大学 基于无人船的平衡状态的控制方法及控制装置
CN114954841A (zh) * 2022-06-24 2022-08-30 江苏科技大学 一种船舶动力匹配的船-机-桨功率实时匹配方法及设备
US11598282B1 (en) 2022-02-23 2023-03-07 Atlantic Towing Limited Systems and methods for optimizing vessel fuel consumption
EP4257476A1 (fr) * 2022-04-04 2023-10-11 thyssenkrupp Marine Systems GmbH Procédé de commande d'un navire équipé d'une hélice à pas variable

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CN114882736B (zh) * 2022-04-07 2023-11-24 武汉理工大学 可容忍风险船舶航路与海上区域建筑安全间距计算方法

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US20140336853A1 (en) * 2013-05-10 2014-11-13 ESRG Technology Group, LLC Methods for automatically optimizing ship performance and devices thereof

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US20100185471A1 (en) * 2009-01-16 2010-07-22 Henry Chen Analyzing voyage efficiencies
EP2669172A1 (fr) * 2012-06-01 2013-12-04 ABB Technology AG Procédé et système pour prédire la performance d'un navire
US20140336853A1 (en) * 2013-05-10 2014-11-13 ESRG Technology Group, LLC Methods for automatically optimizing ship performance and devices thereof

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114489042A (zh) * 2021-12-22 2022-05-13 广东技术师范大学 基于无人船的平衡状态的控制方法及控制装置
CN114489042B (zh) * 2021-12-22 2024-01-12 广东技术师范大学 基于无人船的平衡状态的控制方法及控制装置
US11598282B1 (en) 2022-02-23 2023-03-07 Atlantic Towing Limited Systems and methods for optimizing vessel fuel consumption
EP4257476A1 (fr) * 2022-04-04 2023-10-11 thyssenkrupp Marine Systems GmbH Procédé de commande d'un navire équipé d'une hélice à pas variable
CN114954841A (zh) * 2022-06-24 2022-08-30 江苏科技大学 一种船舶动力匹配的船-机-桨功率实时匹配方法及设备

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