Device and program for evaluating contamination effect

Abstract

A server device (40) comprises: an acquisition means (412) for acquiring the degree of contamination in a plurality of regions on the hull surface of a ship; and a calculation means (415) for calculating the frictional resistance of the entire hull by using a friction resistance coefficient corresponding to the degree of contamination in the plurality of regions.

Description

Contamination impact evaluation device and program

[0001] The present invention relates to a technique for assessing the effects of fouling on a ship hull.

[0002] Techniques for evaluating fouling on a ship's hull are known (for example, see Patent Document 1).

[0003] JP 2018-27740 A

[0004] One approach to assessing the impact of hull fouling is to perform a ship performance analysis using ship speed and horsepower. However, because the performance degradation of a ship identified through this performance analysis includes not only the impact of hull fouling but also other factors, this method does not allow for an accurate assessment of the impact of hull fouling. As an alternative, an Underwater Inspection (UWI) report may include images of the bow, middle, and stern of the ship's hull, along with the fouling status determined using these images. Therefore, it is possible to assess the impact of hull fouling based on this information contained in the UWI report. However, because the information contained in the UWI report only reveals the fouling status of a portion of the hull, this method does not allow for an understanding of the impact of fouling on the entire surface of the hull.

[0005] The present invention aims to make it possible to accurately grasp the effects of fouling on the entire surface of a hull.

[0006] One aspect of the present invention provides a fouling impact evaluation device that includes an acquisition means for acquiring the degree of fouling of multiple areas on the hull surface of a ship, and a calculation means for calculating the frictional resistance of the entire hull using a frictional resistance coefficient corresponding to the degree of fouling of the multiple areas.

[0007] The acquisition means may further acquire position information indicating the positions of the multiple regions on the hull, and the calculation means may calculate the frictional resistance of each of the multiple regions using the flow velocity at the position indicated by the position information of the region and the frictional resistance coefficient corresponding to the degree of contamination of the region, and calculate the frictional resistance of the entire hull by adding up the frictional resistances of the multiple regions.

[0008] The contamination impact evaluation device may further include a receiving means for receiving images of the plurality of areas photographed by an underwater moving body, a determining means for determining the degree of contamination of the plurality of areas using the images, and a mapping means for associating, for each of the plurality of areas, location information indicating the position of the area with the degree of contamination of the area.

[0009] The underwater vehicle may capture the images along weld lines on the hull, and the mapping means may identify the positions of the multiple regions on the hull based on the weld lines included in the images.

[0010] The mapping means may identify the locations of the plurality of regions on the hull based on the position of the underwater vehicle measured using an acoustic lighthouse.

[0011] The mapping means may identify the locations of the plurality of regions on the hull based on the position of the underwater vehicle measured using an inertial navigation system.

[0012] The multiple areas on the hull surface may be marked with marks indicating the positions of the multiple areas on the hull, and the mapping means may use the marks included in the image to identify the positions of the multiple areas on the hull.

[0013] The fouling impact evaluation device may further include a receiving means for receiving images of the plurality of areas photographed by the camera together with a fouling sample board held along the hull surface by the arm in an underwater moving body equipped with an arm and a camera, and a determining means for determining the degree of fouling of the plurality of areas based on the similarity between the fouling sample on the fouling sample board included in the image and each of the plurality of areas.

[0014] The fouling impact evaluation device may further include a storage means for storing a history of the frictional resistance of the entire hull or fuel efficiency calculated from the frictional resistance, and the time when maintenance work to reduce the frictional resistance of the hull was performed, and an analysis means for performing a trend analysis based on the history and the time to predict future changes in fuel efficiency when the maintenance work is performed at predetermined time intervals.

[0015] The fouling impact evaluation device further includes a receiving means for receiving input of an image of a target area between a first waterline in a first draft state of the hull and a second waterline in a second draft state different from the first draft state, photographed by an imaging means together with a fouling sample board, and a determining means for determining the degree of fouling of the target area based on the similarity between the fouling sample on the fouling sample board included in the image and the target area, and the calculation means may further use a frictional resistance coefficient corresponding to the degree of fouling of the target area to calculate the frictional resistance of the entire hull.

[0016] The calculation means may calculate an increase in friction resistance of the target area due to fouling in accordance with a difference in required horsepower between the first draft state and the second draft state.

[0017] Another aspect of the present invention provides a program for causing a computer to execute the steps of acquiring the degree of contamination of multiple areas on the surface of a ship's hull, and calculating the frictional resistance of the entire hull using a frictional resistance coefficient corresponding to the degree of contamination of the multiple areas.

[0018] According to the present invention, the influence of fouling on the entire surface of the hull can be grasped with high accuracy.

[0019] A diagram showing an example of an evaluation system according to an embodiment. A diagram showing an example of an image captured by an underwater drone. A diagram showing an example of the configuration of a server device. A flowchart showing an example of a process for quantitatively evaluating the impact of fouling. A diagram showing an example of mapping data. A diagram showing an example of a trend analysis result of the amount of fuel consumption increase due to the impact of fouling on the entire hull. A diagram showing an example of a flooded area of ​​a hull. A diagram showing an example of correlation information showing the relationship between boat speed and main engine horsepower. A diagram showing another example of correlation information showing the relationship between boat speed and main engine horsepower.

[0020] 1. Configuration Figure 1 is a diagram showing an example of an evaluation system 1 according to one embodiment. The evaluation system 1 evaluates the impact of hull fouling using images of the hull taken by an underwater drone 20 and provides information that contributes to determining whether to perform hull maintenance work. Note that the hull refers to the main body of the ship 10 excluding cargo and accessories, and does not include the propeller.

[0021] The evaluation system 1 includes an underwater drone 20, a control device 30, and a server device 40. The underwater drone 20 and the control device 30 are communicatively connected via a communication cable 2. The control device 30 and the server device 40 are communicatively connected via a network 3 such as the Internet. The server device 40 is also communicatively connected to a terminal device (not shown) mounted on the vessel 10 via the network 3 and a communication satellite 4. This allows the server device 40 to acquire outputs from various sensors mounted on the vessel 10.

[0022] The underwater drone 20 submerges while the ship 10 is moored and moves underwater along a predetermined path to capture images of multiple regions of the hull. More specifically, the underwater drone 20 captures images of multiple regions of the hull while moving along the hull's weld lines. The underwater drone 20 is an example of an underwater vehicle according to the present invention. The underwater drone 20 includes an arm 21, a camera 22, and a communication IF (interface) 23.

[0023] The arm 21 holds a fouling sample plate such as a rubato gauge along the surface of the hull. More specifically, the arm 21 presses the fouling sample plate against the surface of the hull using a pressing means such as a spring. This is because, when determining the degree of fouling of the hull using an image of the hull, the hull contained in the image is compared with the fouling sample plate, and therefore, the accuracy of determining the degree of fouling increases when the distance from the camera 22 to the hull and the distance from the camera 22 to the fouling sample plate are as equal as possible.

[0024] The camera 22 captures images of multiple areas of the hull along with the fouling sample board. The images captured by the camera 22 are, for example, video. However, the images captured by the camera 22 are not limited to video and may be, for example, still images captured at predetermined time intervals. In order to capture as comprehensive images of the hull as possible, it is preferable that the images be horizontally long panoramic images. The reason the hull is captured together with the fouling sample board is that when determining the degree of fouling of the hull using an image of the hull, the hull and the fouling sample board contained in this image are compared, so that the comparison can be made without being affected by the underwater environment, such as underwater brightness, light angle, water color, and water turbidity.

[0025] 2 is a diagram illustrating an example of an image 220 captured by the camera 22. The image 220 includes a certain area 221 of the hull surface and a fouling sample board 222. The fouling sample board 222 includes multiple fouling samples 223 with different degrees of fouling. As described above, the underwater drone 20 captures images while moving along the weld line 224 on the hull, and therefore the image 220 includes the weld line 224 formed on the hull surface.

[0026] The communication IF 23 transmits the image captured by the camera 22 to the control device 30 via the communication cable 2. Additional information such as the date and time of capture may be added to the image. Note that the image referred to here means a digital image.

[0027] The control device 30 is installed on the quay and controls the operation of the underwater drone 20 in response to operations by the operator. The control device 30 transfers images received from the underwater drone 20 to the server device 40 via the network 3.

[0028] The server device 40 is installed on land and managed and operated by the shipping company. The server device 40 evaluates the fouling effect on the hull of the ship 10 based on images taken by the underwater drone 20 and performs processing to provide information for determining whether maintenance work should be performed on the ship 10. The server device 40 is an example of a fouling effect evaluation device according to the present invention.

[0029] 3 is a diagram showing an example of the configuration of the server device 40. The server device 40 includes a processor 41, a memory 42, a storage 43, a communication IF 44, an input unit 45, and a display unit 46. The memory 42, the storage 43, the communication IF 44, the input unit 45, and the display unit 46 are each connected to the processor 41 via a bus.

[0030] The processor 41 controls each component of the server device 40 and performs various calculations by executing a program. Examples of the processor 41 include one or more central processing units (CPUs). The memory 42 is a computer-readable storage medium used as a work area for the processor 41. The memory 42 stores programs and data executed by the processor 41. Examples of the memory 42 include read-only memory (ROM) and random access memory (RAM). The storage 43 is a computer-readable storage medium that stores various data used by the processor 41. Examples of the storage 43 include a hard disk drive (HHD) and a solid state drive (SSD). The memory 42 or the storage 43 is an example of a storage means according to the present invention. The communication IF 44 is connected to the network 3 and performs data communication with other devices via the network 3 in accordance with a predetermined communication standard. The input unit 45 inputs signals to the processor 41 in response to user operations. Examples of the input unit 45 include a keyboard and a mouse. The display unit 46 displays various types of information under the control of the processor 41. An example of the display unit 46 is a liquid crystal display.

[0031] The processor 41 executes the programs stored in the memory 42 to function as a receiving means 411, an acquiring means 412, a determining means 413, a mapping means 414, a calculating means 415, a creating means 416, and an analyzing means 417. The receiving means 411, the acquiring means 412, the determining means 413, the mapping means 414, the calculating means 415, the creating means 416, and the analyzing means 417 are software modules realized by software working in cooperation with hardware resources.

[0032] The receiving means 411 receives images of multiple areas of the hull captured by the underwater drone 20. More specifically, when images of multiple areas of the hull are transmitted from the underwater drone 20, the receiving means 411 receives the images via the control device 30.

[0033] The acquiring means 412 acquires the degree of contamination of a plurality of areas on the hull surface and position information indicating the positions of these areas on the hull, using the images received by the receiving means 411. The acquiring means 412 includes a determining means 413 and a mapping means 414.

[0034] The determining means 413 uses the images received by the receiving means 411 to determine the degree of contamination of each of the multiple meshes obtained by dividing the hull surface. More specifically, the determining means 413 divides the hull surface into multiple meshes, with each mesh representing a region of the hull surface included in one image. The number of meshes depends on the size of the hull, but is, for example, 100 to 1,000. If the image is a video, one frame constituting the video may be treated as one image. The determining means 413 determines the degree of contamination of each mesh based on the similarity between the region of the hull surface included in the image and each of the contamination samples on the contamination sample board. The degree of contamination may be expressed as a letter representing a grade such as "A" to "F," or may be expressed as a qualitative evaluation such as "Good," or may be expressed numerically.

[0035] The mapping means 414 creates mapping data in which, for each of the multiple meshes of the hull, position information indicating the position of that mesh on the hull and the degree of contamination of that mesh determined by the determination means 413 are mapped. More specifically, the mapping means 414 identifies the position of each mesh on the hull by comparing the weld lines included in the image received by the receiving means 411 with the steel plate layout diagram of the hull stored in the storage 43. Next, the mapping means 414 associates, for each of the multiple meshes of the hull, position information indicating the position of that mesh and the degree of contamination of that mesh. In this way, mapping data is created. The mapping means 414 then stores the created mapping data in the storage 43.

[0036] The calculation means 415 calculates the frictional resistance of the entire hull based on the mapping data stored in the storage 43. More specifically, the calculation means 415 first calculates the frictional resistance of each of the multiple meshes of the hull using the flow velocity at the position indicated by the mesh position information included in the mapping data, the mesh area, and a frictional resistance coefficient corresponding to the mesh's contamination level. The correspondence between the contamination level and the frictional resistance coefficient is obtained by measuring the frictional resistance coefficients of soiled samples with multiple contamination levels, and is stored in a correspondence table and pre-stored in the storage 43. The higher the contamination level, the larger the frictional resistance coefficient. Note that the correspondence table may be appropriately modified according to the correspondence between the contamination level and the frictional resistance coefficient measured on an actual ship. Next, the calculation means 415 calculates the frictional resistance of the entire hull by adding up the frictional resistances of the multiple meshes of the hull. The calculation means 415 then stores the calculated frictional resistance of the entire hull in a ship database stored in the storage 43. The frictional resistance of the entire hull indicates the influence of contamination on the entire surface of the hull. If the frictional resistance of the entire hull increases, the amount of fuel required to travel at the same speed increases, so it is preferable to perform maintenance work such as hull cleaning to reduce the frictional resistance of the hull and eliminate the effects of this fouling.

[0037] The creation means 416 creates profitability information based on the frictional resistance of the entire hull stored in the storage 43. More specifically, the creation means 416 calculates the payback period for the maintenance work using the increase in fuel consumption due to the effect of hull fouling calculated from the frictional resistance of the entire hull, and creates a profitability information report including this payback period. Note that in this application, fuel consumption means the fuel consumption rate, and more specifically, means the distance that the ship 10 can navigate with a unit of fuel.

[0038] The analysis means 417 performs trend analysis based on the history of the frictional resistance of the entire hull or the fuel efficiency calculated from the frictional resistance stored in the storage 43. More specifically, the analysis means 417 performs trend analysis to predict future changes in fuel efficiency when hull maintenance work is performed at predetermined time intervals, based on the history of the frictional resistance or fuel efficiency of the entire hull and the history of hull maintenance work.

[0039] 2. Operation 2.1 Quantitative Evaluation of the Impact of Fouling Figure 4 is a flowchart illustrating a process for quantitatively evaluating the impact of fouling. Each time the ship 10 is moored at a quay and cargo handling is performed, fouling monitoring is performed during the cargo handling. However, fouling monitoring does not necessarily have to be performed every time the ship 10 is moored at a quay and cargo handling is performed. For example, fouling monitoring may be performed only when the ship is moored at a quay that has equipment for fouling monitoring, or may be performed at predetermined intervals between cargo handling operations, or may be performed while the ship is anchored offshore or while drifting with its engine stopped.

[0040] In contamination monitoring, the underwater drone 20 moves underwater, for example, along the vertical weld line closest to the bow of a plurality of vertically extending weld lines (hereinafter referred to as "vertical weld lines"), tracing the upper starboard side, lower starboard side, bottom, lower port side, and upper port side in that order, then moves to the next vertical weld line closest to the bow and moves in the opposite direction along this vertical weld line, moving back and forth along each vertical weld line while moving in the order from closest to the bow. Alternatively, the underwater drone 20 moves underwater, for example, along the horizontal weld lines closest to the starboard side of a plurality of horizontal weld lines (hereinafter referred to as "horizontal weld lines"), tracing the horizontal weld line closest to the starboard side in the direction from bow to stern, then moves to the next horizontal weld line closest to the starboard side and moves in the opposite direction along this horizontal weld line, moving back and forth along each horizontal weld line while moving in the order of upper starboard side, lower starboard side, bottom, lower port side, and upper port side.

[0041] During this movement, the underwater drone 20 holds the soiling sample board 222 along the surface of the hull with the arm 21, and takes images of multiple areas of the hull together with the soiling sample board 222 with the camera 22. The underwater drone 20 transmits the images taken by the camera 22 to the control device 30 via the communication IF 23. When the control device 30 transfers these images to the server device 40, the processing of step S11 begins.

[0042] In step S11, the receiving means 411 receives the image transmitted from the underwater drone 20 via the control device 30 and stores it in the storage 43.

[0043] In step S12, the determination means 413 determines the degree of contamination of each mesh of the hull based on the image acquired in step S11. In the example shown in Figure 2, the determination means 413 uses image analysis to calculate the similarity between the hull surface region 221 included in the image and each contamination sample 223. For example, if the contamination sample 223 with the highest similarity exhibits a contamination level of "E", the determination means 413 determines this contamination level of "E" as the contamination level of that mesh.

[0044] In step S13, the mapping means 414 maps, onto each of the multiple meshes of the hull, position information indicating the position of that mesh and the degree of contamination of that mesh determined in step S12. More specifically, the mapping means 414 identifies the position of each mesh on the hull by comparing the weld lines included in each image acquired in step S11 with the steel plate layout diagram of the hull stored in storage 43. For example, if the tenth vertical weld line counting from the stern on the starboard side of the hull and the fiftieth horizontal weld line counting from the bottom of the ship intersect in image 220 shown in Figure 2, the position where these weld lines 224 intersect in the steel plate layout diagram is identified as the position of the mesh photographed in this image.

[0045] Furthermore, if the image acquired in step S11 includes a mark painted on the hull, the mapping means 414 may take this mark into account when determining the position of each mesh on the hull. For example, if the image includes the letters "FPT" indicating the location of a tank, the mapping means 414 may determine the location on the hull where these letters are painted as the position of the mesh. Marks for determining the position of the corresponding mesh may be attached to the hull in advance at all intersections of the weld lines. After determining the positions of all meshes in this manner, the mapping means 414 creates mapping data that associates the position information of each mesh with the degree of contamination of that mesh for each of the multiple meshes on the hull, and stores the mapping data in the storage 43.

[0046] FIG. 5 is a diagram illustrating mapping data 500. The mapping data 500 may be a three-dimensional diagram, or may be a two-dimensional diagram representing each of the starboard and port sides. In the mapping data 500, the position of a mesh is represented by the arrangement of meshes on the hull surface. Each mesh is represented by a color corresponding to the degree of fouling. In FIG. 5, different colors are represented by different hatching. Note that the mapping data 500 is not limited to the example shown in FIG. 5, and may be a development diagram of the hull, or a table that associates position information of each mesh with the degree of fouling.

[0047] If the images captured by the underwater drone 20 do not cover the entire hull, the meshes for which the degree of contamination has been determined will be distributed discretely. In such a case, the mapping means 414 may interpolate between the meshes using a known method such as spline interpolation.

[0048] 4, in step S14, the calculation means 415 calculates the friction resistance of the entire hull based on the mapping data created in step S13. First, the calculation means 415 calculates the friction resistance D of each mesh of the hull using the following equation (1): f Calculate the following. f = 1 / 2ρV 2 S×C f ...(1)

[0049] where ρ is the specific gravity of water, V is the flow velocity in the mesh, S is the area of ​​the mesh, and C f is the friction resistance coefficient of the mesh. The specific gravity ρ of water is a constant. The flow velocity V is estimated by CFD (Computational Fluid Dynamics). Alternatively, the flow velocity V may be estimated from the wear rate of the paint film on the actual ship 10. The flow velocity V varies depending on the position on the hull. Therefore, the flow velocity at the position indicated by the position information of the target mesh is used as the flow velocity V. For example, if the shape of the mesh is a rectangle with a length of 1 m and a width of 5 m, the area S is 1 m x 5 m = 5 m. 2 The friction resistance coefficient C fFor example, if the contamination level of the target mesh is "E", the friction resistance coefficient associated with the contamination level "E" in the correspondence table stored in the storage 43 is used.

[0050] In addition, since the image taken by the underwater drone 20 does not cover the entire hull, the calculation means 415 calculates the friction resistance D f When the meshes for which the values ​​are calculated are distributed discretely, the meshes may be interpolated using a known method such as spline interpolation.

[0051] Next, the calculation means 415 calculates the friction resistance D of all the meshes of the hull. f For example, if the hull is divided into 1000 meshes, the calculation means 415 calculates the friction resistance D f The friction resistance of the entire hull is calculated by adding

[0052] Furthermore, the calculation means 415 may calculate the effect of the increase in frictional resistance of the propeller by subtracting the increase in horsepower required to maintain the same vessel speed due to the increase in frictional resistance of the entire vessel 10 from the increase in horsepower required to maintain the same vessel speed due to the increase in frictional resistance of the entire vessel 10, including the hull and the propeller. The frictional resistance of the entire vessel 10 may be calculated using a known method using, for example, vessel speed and horsepower.

[0053] In step S15, the calculation means 415 uses the friction resistance calculated in step S14 to calculate the increase in fuel consumption due to the influence of hull fouling according to the following formula (2): Increase in fuel consumption due to the influence of hull fouling = Increase in horsepower required to maintain the same ship speed due to the influence of hull fouling × Fuel consumption rate = Increase in friction resistance due to the influence of hull fouling × Ship speed × Fuel consumption rate (2)

[0054] Here, the fuel consumption rate is the amount of fuel consumed per unit horsepower, and its unit is, for example, g / kWh. The increase in frictional resistance due to the influence of hull fouling can be obtained by calculating the difference between the frictional resistance of the entire ship calculated in step S14 and a reference value such as an initial value. The ship speed is the planned speed of the ship 10 or the actual speed of the ship 10. The ship speed may be input by the operator operating the input unit 45, or may be obtained from a sensor installed on the ship 10.

[0055] In addition, the calculation means 415 may calculate the increase in fuel consumption due to the influence of propeller fouling by using the increase in frictional resistance of the propeller instead of the increase in frictional resistance due to the influence of hull fouling in the above formula (2).

[0056] In step S16, the calculation means 415 stores the friction resistance of the entire hull calculated in step S14 and the increase in fuel consumption due to the effect of hull fouling calculated in step S15 in a ship database stored in the storage 43. In the ship database, the friction resistance of the entire hull and the increase in fuel consumption due to the effect of hull fouling may be stored in association with the identification information of the ship 10 and the date and time when fouling monitoring was carried out. The date and time when the fouling monitoring was carried out may be the date and time of shooting added to the image of the hull.

[0057] Each time the ship 10 is loaded or unloaded, the frictional resistance of the entire hull and the increase in fuel consumption due to the effect of hull fouling are calculated and stored in the ship database, thereby chronologically accumulating a history of the frictional resistance of the entire hull and the increase in fuel consumption due to the effect of hull fouling. The ship database also stores a history of maintenance work input by the operator through operation of the input unit 45. This maintenance work history includes the type of maintenance work and the time when the maintenance work was performed.

[0058] The information stored in the ship database may be provided by the shipping company to a management company that performs maintenance work on the ship 10. Examples of methods for providing this information include sending it to a terminal device of the management company and mailing a printed copy printed on a printer (not shown).

[0059] 2.2 Providing profitability information for maintenance work The creation means 416 provides profitability information for maintenance work based on the ship database stored in the storage 43, in order to support the management company's personnel in making decisions about carrying out maintenance work. More specifically, the creation means 416 uses the increase in fuel consumption due to the impact of hull fouling stored in the ship database to calculate the payback period using the following formula (3): Payback period = Maintenance work cost / Increase in fuel consumption due to the impact of hull fouling (3)

[0060] Here, if the unit of increase in fuel efficiency due to hull fouling is ton / day, the unit of the payback period will be day, and if the unit of increase in fuel efficiency due to hull fouling is ton / year, the unit of the payback period will be year. Maintenance work costs are the costs required for maintenance work and are determined in advance for each management company that carries out the maintenance work. Maintenance work costs may also include opportunity costs associated with the maintenance work. Since the increase in fuel efficiency due to hull fouling is eliminated by carrying out maintenance work, the period over which the maintenance work costs can be recovered can be obtained using the above formula (3).

[0061] The creation means 416 creates a report including the payback period. The creation means 416 may also compare the payback period with a threshold value to determine whether or not maintenance work should be performed, and include the determination result in the report. This report is provided by the shipping company to a management company that performs maintenance work on the ship 10. Examples of the method of providing the report include sending it to a terminal device of the management company or mailing a printed copy printed on a printer (not shown). A management company staff member can determine whether or not to perform maintenance work by comparing the payback period included in this report with a threshold value set for each management company. For example, a management company staff member may determine to perform hull cleaning if the payback period, during which the cost of hull cleaning can be recovered by reducing the amount of fuel consumption increase due to hull fouling, is less than 1.5 years.

[0062] 2.3 Trend Analysis The analysis means 417 performs trend analysis based on the ship database stored in the storage 43. More specifically, the analysis means 417 predicts future changes in fuel efficiency increase due to the effects of hull fouling when hull maintenance work is performed at predetermined time intervals, based on the changes over time in fuel efficiency increase due to the effects of hull fouling in the past stored in the ship database and the timing of maintenance work. Note that, while an example of predicting future fuel efficiency increase due to the effects of hull fouling is described here, future fuel efficiency itself may also be predicted.

[0063] Figure 6 is a diagram illustrating the results of a trend analysis of the increase in fuel efficiency due to the influence of hull fouling, showing the effect of fouling on the entire hull. In Figure 6, the horizontal axis represents time (years) and the vertical axis represents the increase in fuel efficiency (%) due to the influence of hull fouling. Note that in the example shown in Figure 6, the increase in fuel efficiency (%) due to the influence of hull fouling is used, but the increase in frictional resistance due to the influence of hull fouling may be used instead of the increase in fuel efficiency (%) due to the influence of hull fouling.

[0064] In Figures 6(a) to 6(c), the change in fuel consumption increase due to the impact of past hull fouling is the same, but the future maintenance work patterns are different. Figure 6(a) shows the results of trend analysis when pattern A, in which hull cleaning will be performed every year in the future, is adopted. Figure 6(b) shows the results of trend analysis when pattern B, in which hull cleaning will be performed every 1.5 years in the future, is adopted. Figure 6(c) shows the results of trend analysis when pattern C, in which hull cleaning will be performed every two years in the future, is adopted.

[0065] A method for generating the trend analysis results shown in FIG. 6 will now be described in detail. A graph of the solid line portion of the broken line L1, L2, or L3 is generated from time-series data of the increase in fuel efficiency (%) due to the influence of hull fouling in the past obtained from the ship database. More specifically, the graph of the solid line portion of the broken line L1, L2, or L3 is generated by connecting points representing the increase in fuel efficiency (%) due to the influence of hull fouling at multiple points in time, represented by black circles in FIG. 6 . In this example, hull cleaning was performed at past times P1 and P2. When hull cleaning is performed, the increase in fuel efficiency (%) due to the influence of hull fouling decreases. Therefore, it is preferable to perform a process for quantitatively evaluating the influence of fouling shown in FIG. 4 at least before and after these hull cleanings to calculate the increase in fuel efficiency due to the influence of hull fouling. This results in points Q1 and Q2 representing the increase in fuel efficiency (%) due to the influence of hull fouling before and after the hull cleaning at time P1, and points Q3 and Q4 representing the increase in fuel efficiency (%) due to the influence of hull fouling before and after the hull cleaning at time P2.

[0066] Trend line L11 is obtained by connecting points Q2 and Q4, which indicate the increase in fuel consumption (%) due to hull fouling after hull cleaning at times P1 and P2, and the origin using curve approximation or other methods in the solid line portions of broken line L1, L2, or L3. The lower limit of the increase in fuel consumption due to hull fouling after hull cleaning gradually increases over time due to the accumulation of deterioration that cannot be repaired by hull cleaning, such as unevenness in the hull steel plates. This trend line L11 shows the trend of the increase in fuel consumption (%) due to hull fouling caused by factors other than hull fouling. Furthermore, trend line L15 is obtained by connecting the positive slopes in the solid line portions of broken line L1, L2, or L3. This trend line L15 shows the trend of the increase in fuel consumption (%) due to hull fouling.

[0067] Using trend line L11 and trend line L15, it is possible to predict the trend of the increase in fuel consumption (%) due to the influence of hull fouling when hull cleaning is performed at predetermined intervals in the future, such as every year, every 1.5 years, or every two years. For example, if hull cleaning is performed every year, the increase in fuel consumption (%) due to the influence of hull fouling is predicted as shown by the two-dot dashed line portion of polygonal line L1 in Figure 6(a). If hull cleaning is performed every 1.5 years, the increase in fuel consumption (%) due to the influence of hull fouling is predicted as shown by the two-dot dashed line portion of polygonal line L2 in Figure 6(b). If hull cleaning is performed every two years, the increase in fuel consumption (%) due to the influence of hull fouling is predicted as shown by the two-dot dashed line portion of polygonal line L3 in Figure 6(c).

[0068] For each of the trend analysis results shown in Figures 6(a) to 6(c), the frequency of hull cleaning can be determined by calculating (cost reduction due to improved fuel efficiency - cost increase due to hull cleaning) over the entire remaining lifetime of the vessel 10.

[0069] The dashed-two-dot line portion of the broken line L1 shown in Figure 6(a) represents the predicted change in fuel consumption increase due to the impact of hull fouling if hull cleaning is performed at annual intervals in the future. In the example shown in Figure 6(a), hull cleaning is scheduled to be performed at periods P3 to P5, which are one year apart in the future. The predicted total fuel consumption increase due to the impact of hull fouling if hull cleaning is performed at annual intervals in this way is represented by the area of ​​the shape enclosed by the dashed-two-dot line portion of the broken line L1 and the horizontal axis.

[0070] The dashed-two-dot line portion of the broken line L2 shown in Figure 6(b) represents the predicted change in fuel consumption increase due to the impact of hull fouling if hull cleaning is performed every 1.5 years in the future. In the example shown in Figure 6(b), hull cleaning is scheduled to be performed at times P6 and P7, which are 1.5 years apart in the future. The predicted total fuel consumption increase if hull cleaning is performed every 1.5 years in this way is represented by the area of ​​the shape enclosed by the dashed-two-dot line portion of the broken line L2 and the horizontal axis.

[0071] The dashed-two-dot line portion of the broken line L3 shown in Figure 6(c) represents the predicted change in fuel consumption increase due to the impact of hull fouling if hull cleaning is performed every two years in the future. In the example shown in Figure 6(c), hull cleaning is scheduled to be performed at times P8 and P9, which are two years apart in the future. The predicted total fuel consumption increase if hull cleaning is performed every two years in this way is represented by the area of ​​the shape enclosed by the dashed-two-dot line portion of the broken line L3 and the horizontal axis.

[0072] 6(a) to 6(c) show the relationship between the frequency of maintenance work performed over the life cycle of the ship 10 and the total increase in fuel consumption due to the impact of hull fouling. The more frequently maintenance work is performed, the smaller the total increase in fuel consumption due to the impact of hull fouling, and therefore the lower the fuel costs, but the more frequently maintenance work is performed, and therefore the maintenance work costs increase. The analysis means 417 may calculate the total cost savings based on the maintenance work costs required and the fuel cost savings achieved when hull cleaning is performed at intervals of one year, 1.5 years, and two years.

[0073] The results of the trend analysis shown in FIGS. 6( a) to 6(c) are provided by the shipping company to a management company that performs maintenance work on the ship 10. The total cost savings may also be provided to the management company along with the results of the trend analysis. Examples of how this is provided include sending the results to a terminal device of the management company or mailing a printed copy (not shown). A management company staff member can create an optimal maintenance work plan by comparing the results of the trend analysis shown in FIGS. 6(a) to 6(c). For example, if the total cost savings in the trend analysis results shown in FIG. 6(b) are the greatest, the management company staff member may create a maintenance work plan that involves performing maintenance work every 1.5 years.

[0074] Furthermore, when large-scale repairs such as sandblasting are performed, deterioration that cannot be repaired by hull cleaning, such as unevenness of the hull steel plates and peeling paint, is eliminated, thereby lowering the base amount of fuel consumption increase due to the effects of hull fouling. For example, if large-scale repairs and hull cleaning are performed together, the lower limit of the amount of fuel consumption increase due to the effects of hull fouling after these operations will be lower. Therefore, trend analysis may be performed taking into account the elimination of deterioration that cannot be repaired by hull cleaning due to the implementation of large-scale repairs.

[0075] 6(a) to 6(c) only show the results of a trend analysis of the increase in fuel efficiency due to the effect of hull fouling, which indicates the effect of fouling on the entire hull, but the results of a trend analysis of the increase in fuel efficiency due to the effect of propeller fouling and the results of a trend analysis of the increase in fuel efficiency due to the effect of fouling on the entire vessel 10, calculated using horsepower and vessel speed, may also be shown. Furthermore, the maintenance work patterns are not limited to those that differ only in the interval between hull cleaning, but may also differ in the interval between hull cleaning, propeller cleaning, major repairs, or at least two of these, or may differ in the timing of the next maintenance work, such as performing the next maintenance work immediately, in six months, or in one year.

[0076] According to the embodiment described above, the hull's frictional resistance is calculated using the hull's degree of fouling, which allows for a more accurate evaluation of the impact of hull fouling than when analyzing the performance of the vessel 10 using vessel speed and horsepower. Furthermore, mapping data is created based on images of multiple regions of the hull, mapping the position information and fouling levels of these regions. The frictional resistance of the entire hull is calculated based on this mapping data, allowing for an understanding of the impact of fouling on the entire hull surface. Calculating the frictional resistance of the entire hull in this manner allows for a quantitative evaluation of the impact of fouling on the entire hull surface, allowing management company personnel to make a logical decision on whether or not to perform maintenance work. Furthermore, performing maintenance work based on the calculated frictional resistance of the entire hull is expected to increase the effectiveness of the maintenance work. Increasing the effectiveness of maintenance work leads to a sense of satisfaction with the implementation of maintenance work, which is expected to lead to an increase in the rate at which maintenance work is performed.

[0077] Furthermore, since profitability information including the payback period is provided to the management company, the management company's personnel can make a profitable decision on whether or not to carry out maintenance work. Furthermore, since the management company is provided with the results of a trend analysis of the increase in fuel consumption due to the impact of hull fouling, the management company's personnel can know the frequency of maintenance work that will minimize the total cost over the life cycle of the ship 10. This allows the management company's personnel to create an optimal maintenance work plan.

[0078] Furthermore, because the frictional resistance of the entire hull and the frictional resistance of the propeller can be calculated separately, it is possible to distinguish between the effects of fouling on the hull and the effects of fouling on the propeller as the causes of performance degradation of the vessel 10. This makes it possible to carry out optimal maintenance work according to the cause of performance degradation of the vessel 10, such as performing hull cleaning if the cause of performance degradation of the vessel 10 is fouling on the hull, and performing propeller cleaning if the cause is fouling on the propeller.

[0079] 3. Modifications The above-described embodiment is an example of the present invention, and the present invention is not limited to this embodiment. The above-described embodiment may be modified as in the following modifications. Furthermore, two or more of the following modifications may be implemented in combination.

[0080] 3.1 Variation 1 In the above-described embodiment, the mapping means 414 may identify the position of each mesh on the hull using a method using acoustic beacons. The acoustic beacons are installed in at least three locations, including on a quay, the seabed, or the sea surface. The underwater drone 20 is equipped with a receiver that receives acoustic signals transmitted from the acoustic beacons, and measures the three-dimensional position of the underwater drone 20 using triangular positioning based on the acoustic signals received from the three acoustic beacons. As another example, the underwater drone 20 may measure the three-dimensional position of the underwater drone 20 using acoustic signals and depth sensor information received from two acoustic beacons, or an acoustic signal received from one acoustic beacon and its reception angle.

[0081] The underwater drone 20 also measures the shooting direction of the camera 22 and the shooting distance to the hull surface that is the subject of the image capture. The underwater drone 20 adds position information indicating the three-dimensional position of the underwater drone 20, the shooting direction, and the shooting distance as additional information to the image captured by the camera 22 and transmits the image. The mapping means 414 identifies the position of each mesh on the hull based on the position information, shooting direction, and shooting distance of the underwater drone 20 that are added to the image. For example, the mapping means 414 identifies a position that is the shooting distance away from the position of the underwater drone 20 along the shooting direction as the position of the corresponding mesh on the hull. The method according to this modification can also identify the position of each mesh on the hull.

[0082] Furthermore, since the ship 10 is moored, its three-dimensional position does not change significantly, but there are changes in draft due to cargo volume, changes in water level, slight changes in bow-stern direction, etc., so the three-dimensional position of the ship 10 may be measured. In this case, the position of the underwater drone 20 relative to the ship 10 can be determined more accurately. Note that the three-dimensional position of the ship 10 can be measured using a method similar to the method for measuring the three-dimensional position of the underwater drone 20, for example.

[0083] In this variant, the underwater drone 20 may measure its three-dimensional position using a known method of estimating its own position, such as a method using an inertial navigation system, either alone or in combination with the method using the acoustic beacon described above.

[0084] 3.2 Modification 2 In the above-described embodiment, marks used to identify three-dimensional positions on the hull may be applied to multiple regions on the hull surface, for example, by applying uneven paint. For example, marks indicating the positions of the regions are applied to the hull surface at predetermined intervals along the weld lines. The mapping means 414 identifies the position of each mesh on the hull using the marks included in the image. According to this modification, the position of each mesh on the hull can be identified with high accuracy.

[0085] 3.3 Modification 3 In the above-described embodiment, the underwater drone 20 may be equipped with traveling wheels that travel on the surface of the hull. The underwater drone 20 may also measure its traveling distance and use the measured traveling distance to determine its position relative to the ship 10. The traveling distance may be measured, for example, by calculating it based on the number of wheel rotations or by measuring it using a laser rangefinder. The underwater drone 20 adds the measured traveling distance to the image and transmits it. The mapping means 414 identifies the position of each mesh on the hull based on the traveling distance added to the image. For example, the mapping means 414 identifies a position that is the traveling distance along a predetermined traveling path from the position where the underwater drone 20 starts traveling as the position of the corresponding mesh on the hull. The method according to this modification can also identify the position of each mesh on the hull.

[0086] 3.4 Variation 4 In the above-described embodiment, the underwater drone 20 does not necessarily need to capture the hull surface along with the contamination sample board. For example, the underwater drone 20 captures only the hull surface using the camera 22. Contamination sample images representing the contamination sample boards are stored in the storage 43. The mapping means 414 uses image analysis to calculate the similarity between each contamination sample included in the contamination sample images stored in the storage 43 and the hull surface area included in the image captured by the underwater drone 20, and determines the contamination level corresponding to the contamination sample with the highest similarity. The contamination sample image used to determine the contamination level preferably represents a contamination sample board for an underwater environment similar to that of the hull. For example, a contamination sample board for a similar imaging environment may be selected and used based on information such as the brightness at the time of underwater imaging. The method according to this variation can also determine the contamination level of the hull mesh.

[0087] 3.5 Modification 5 In the above-described embodiment, the calculation means 415 may calculate the frictional resistance of the propeller using the degree of contamination of the propeller. For example, the underwater drone 20 uses the camera 22 to take images of the propeller before and after the contamination has been removed. The determination means 413 analyzes these images to determine the degree of contamination of the propeller. For example, a large difference between the image of the propeller before and after the contamination has been removed indicates that the degree of contamination of the propeller is high. The calculation means 415 uses the degree of contamination of the propeller instead of the degree of contamination of the hull to calculate the frictional resistance of the propeller using the above-described formula (1). However, the mesh frictional resistance coefficient C f In this modification, a frictional resistance coefficient corresponding to the degree of fouling of the propeller is used instead of a frictional resistance coefficient corresponding to the degree of fouling of the hull mesh. According to this modification, it is possible to obtain more accurate frictional resistance of the propeller.

[0088] 3.6 Modification 6 In the above-described embodiment, the underwater drone 20 does not necessarily have to capture images of the hull surface. For example, a diver may dive underwater with a camera and use the camera to capture images of the hull surface. The images captured by the camera are transmitted to the server device 40. Images of the hull surface can also be captured using the method according to this modification.

[0089] 3.7 Modification 7 In the above-described embodiment, the configurations of the evaluation system 1, ship 10, underwater drone 20, control device 30, and server device 40 are examples and are not limited to these. The functions of one device may be distributed among multiple devices, or the functions of multiple devices may be collectively performed by one device. Furthermore, the operations of the evaluation system 1, ship 10, underwater drone 20, control device 30, and server device 40 are examples and are not limited to these. The order of the processing procedures of the evaluation system 1, ship 10, underwater drone 20, control device 30, and server device 40 may be changed, or some processing procedures may be omitted, as long as there is no contradiction.

[0090] 3.8 Variation 8 Another aspect of the present invention may provide a method having processing steps performed in the evaluation system 1, the ship 10, the underwater drone 20, the control device 30, and the server device 40. Furthermore, yet another aspect of the present invention may provide a program executed in the underwater drone 20, the control device 30, or the server device 40. This program may be provided by being stored in a computer-readable recording medium, or may be provided by downloading via the Internet or the like.

[0091] 3.9 Variation 9: In the present disclosure, fouling may include not only biological fouling but also deterioration of surface roughness, such as unevenness on the steel plates of the hull and peeling paint. In this case, the level of fouling in the present disclosure may include surface roughness. The server device 40 may use images of the hull captured by the underwater drone 20 to determine the level of fouling that reflects not only biological fouling but also deterioration of surface roughness, such as unevenness on the steel plates of the hull and peeling paint, by using a method such as machine learning, and calculate the frictional resistance of the entire hull that takes into account deterioration of surface roughness, such as unevenness on the steel plates of the hull and peeling paint.

[0092] 3.10 Modification 10 In the above-described embodiment, the underwater portion of the hull is photographed using the underwater drone 20, and the photographed images of the hull are used to evaluate the fouling impact of the hull. However, the draft of the vessel 10 differs between when it is loaded and when it is unloaded. Therefore, some portions of the hull are above the waterline when it is lightly loaded, but submerged when it is fully loaded. The fully loaded state refers to the state in which the vessel 10 is carrying the maximum amount of cargo and ballast and has the deepest waterline. The lightly loaded state, also known as the ballast state, refers to the state in which the vessel 10 is carrying little or no cargo and has a relatively shallow waterline. The fully loaded state and the lightly loaded state are examples of the first draft state and the second draft state, respectively, according to the present invention. Such hull portions may also be fouled by biofouling, paint peeling, and the like. Therefore, in this modification, the fouling impact is also evaluated for portions of the hull that are submerged when it is fully loaded and above the waterline when it is lightly loaded.

[0093] FIG. 7 is a diagram showing an example of a flooded area 100 of a hull. The flooded area 100 is the area between the full waterline 101 and the waterline 102 of the hull. The flooded area 100 is an example of a target area according to the present invention. The full waterline 101 is the waterline that is submerged in water when the hull is fully loaded. The waterline 102 is the line where the submerged part of the hull intersects with the water surface when the hull is lightly loaded. The full waterline 101 and the waterline 102 are examples of the first waterline and the second waterline, respectively, according to the present invention. As shown in FIG. 7, the flooded area 100 is submerged in water when the hull is fully loaded and is above the water surface when the hull is lightly loaded.

[0094] The evaluation system 1 has a photographing means for photographing an image of the flooded area 100 together with the soiled sample board. The photographing means is, for example, a camera. There are several methods for photographing an image of the flooded area 100 together with the soiled sample board. In the first method, a person uses the photographing means to photograph an image of the flooded area 100 from a quay. At this time, the soiled sample board is held by the person's hand within the photographing range of the photographing means. This allows the photographing means to photograph an image of the flooded area 100 together with the soiled sample board.

[0095] In the second method, an aerial drone equipped with a photographing means flies around the ship 10, and the photographing means photographs images of the flooded area 100 from above. At this time, the soiled sample board may be held within the photographing range of the photographing means by the arm of the aerial drone. Alternatively, the soiled sample board may be held by a wheeled drone traveling on the surface of the hull and positioned within the photographing range of the flooded area 100. This allows the photographing means to photograph images of the flooded area 100 along with the soiled sample board.

[0096] In the third method, a boat-type drone equipped with a photographing device moves over the water surface around the vessel 10, and the photographing means photographs images of the flooded area 100 from the water surface. At this time, the soiled sample board may be held within the photographing range of the photographing means by the arm of the boat-type drone. Alternatively, the soiled sample board may be held by a wheeled drone traveling on the surface of the hull and positioned within the photographing range of the flooded area 100. This allows the photographing means to photograph images of the flooded area 100 along with the soiled sample board.

[0097] Furthermore, as in the fourth modification example described above, the photographing means does not necessarily have to photograph an image of the flooded area 100 along with the soiled sample board. For example, the photographing means photographs only the flooded area 100. A soiled sample image showing the soiled sample board is stored in the storage 43. This soiled sample image may be photographed in advance in an environment unrelated to the flooded area 100, or may be photographed at the photographing location of the flooded area 100. In the latter case, the image of the flooded area 100 and the soiled sample image are photographed in roughly the same environment, and therefore can be compared without being affected by environmental factors such as sunlight.

[0098] The images captured by the imaging means in this manner are input to the server device 40 by human operation or via the network 3. The receiving means 411 accepts the input of images. The receiving means 411 is an example of a receiving means according to the present invention. As in the above-described embodiment, the determining means 413 determines the degree of contamination of the flooded area 100 of the hull based on the similarity between the flooded area 100 of the hull included in the input image and each contamination sample on the contamination sample board. The mapping means 414 maps position information indicating the position of the flooded area 100 of the hull and the degree of contamination of the flooded area 100 determined by the determining means 413, using a method similar to that in the above-described embodiment or modified example. Photogrammetry (image joining technology) may be used for this mapping.

[0099] The calculation means 415 calculates the friction resistance coefficient C according to the degree of contamination of the flooded area 100. f Using the above equation (1), the friction resistance D of the flooded area 100 is calculated. f Calculate the friction resistance D of the entire hull mesh f and the friction resistance D of the flooded area 100 f According to this configuration, the fouling effect can be evaluated not only in the area underwater in the light load state but also in the flooded area 100 that is above the water surface in the light load state.

[0100] In this modified example, a fouling effect correlation analysis may be performed based on the degree of fouling of the flooded area 100 and the difference in horsepower required between the full load state and the light load state due to fouling of the flooded area 100. The required horsepower refers to the horsepower required to maintain a certain speed. The calculation means 415 calculates the increase in frictional resistance of the flooded area 100 due to fouling according to the difference in horsepower required between the full load state and the light load state. Note that the difference in horsepower required to be used in the fouling effect correlation analysis is not limited to the difference in horsepower required between the full load state and the light load state. For example, the difference in horsepower required between the full load state or the light load state and another draft state, or the difference in horsepower required between two draft states other than the full load state and the light load state, may be used.

[0101] FIG. 8 is a diagram showing an example of correlation information showing the relationship between vessel speed and main engine horsepower. In FIG. 8, the horizontal axis represents vessel speed (kts) and the vertical axis represents main engine horsepower (kW). This correlation information is stored in the storage 43 of the server device 40. This correlation information includes speed-horsepower curves C1 to C4. Speed-horsepower curve C1 shows the relationship between vessel speed and main engine horsepower in a fully loaded state with fouling. Speed-horsepower curve C2 shows the relationship between vessel speed and main engine horsepower in a fully loaded state with no fouling. Speed-horsepower curve C3 shows the relationship between vessel speed and main engine horsepower in a light load state with fouling. Speed-horsepower curve C4 shows the relationship between vessel speed and main engine horsepower in a light load state with no fouling. The relationship between vessel speed and main engine horsepower may be determined, for example, from measurements of vessel speed and main engine horsepower in a tank test, or from actual measurements acquired from a sensor installed on the vessel 10. The term "soiled" as used herein means that there is soiling of a degree determined by the determining means 413, for example.

[0102] Focusing on the speed horsepower curve C1 and the speed horsepower curve C3, the required horsepower difference ΔBHP fouling draft impact between the horsepower of the speed horsepower curve C1 and the horsepower of the speed horsepower curve C3 when the ship speed is set to 15 kts includes the impact of draft changes as well as the impact of fouling on the flooded area 100. This required horsepower difference ΔBHP fouling draft impact increases as the degree of fouling of the flooded area 100 increases.

[0103] The calculation means 415 calculates the required horsepower difference ΔBHP fouling draft impact between the fully loaded state and the lightly loaded state using the following formula (4). The required horsepower difference ΔBHP fouling draft impact indicates the performance difference of the ship 10 between the lightly loaded state and the fully loaded state due to fouling of the flooded area 100. The required horsepower difference ΔBHP fouling draft impact includes the impact of the draft change of the flooded area 100 and the impact of fouling. ΔBHP fouling draft impact=BHP C1 -BHP C3 ... (4) Here, BHP C1 is the required horsepower for the speed horsepower curve C1, BHP C3 is the required horsepower of the speed horsepower curve C3.

[0104] Main engine horsepower (BHP) and friction resistance (R) f The relationship between BHP×η and the ship speed V is expressed by the following formula (5): BHP×η=EHP=R×V=(Rf +R w ) × V (5) where EHP is the effective horsepower, η is the thrust coefficient, R w is the wave resistance. Effective horsepower EHP indicates the amount of axial work produced by the propeller. Wave resistance R w can be estimated using a known estimation formula.

[0105] The thrust coefficient η is calculated by the following formula (6): η = η0 × η hull ×η R ... (6) where η0 is the propeller efficiency, η hull is the hull efficiency, η R is the ship's after-effect efficiency. Propeller efficiency η0, hull efficiency η hull , post-ship efficiency η R can be estimated using known estimation formulas.

[0106] The calculation means 415 uses the required horsepower difference ΔBHP and the fouling draft effect instead of the horsepower BHP of the main engine in the above equation (5) to calculate the increase ΔR in the friction resistance of the flooded area 100 due to the draft change and fouling. f can be calculated.

[0107] In addition, the friction resistance R f and the friction resistance coefficient C f The relationship between R and R is expressed by the following formula (7): f = 1 / 2ρC f SV 2 where ρ is the specific gravity of water, S is the area of ​​the flooded region 100 , and V is the flow velocity in the flooded region 100 .

[0108] The calculation means 415 calculates the friction resistance R f Instead of the increase in friction resistance ΔR f By using the above, the increase in the friction resistance coefficient ΔC of the flooded area 100 due to the draft and fouling f Furthermore, the calculation means 415 calculates the increase amount ΔC of the frictional resistance coefficient in a predetermined reference frictional resistance coefficient in a state where there is no contamination. f By adding fThis allows for a more accurate friction resistance coefficient C according to the degree of contamination of the flooded area 100. f This configuration allows the contamination effect of the flooded area 100 to be evaluated more accurately.

[0109] In addition, the server device 40 uses the friction resistance coefficient C obtained in this modified example. f The friction resistance coefficient C used by the calculation means 415 in the above-described embodiment is calculated using f The correction means may be provided to correct the friction resistance coefficient C obtained in this modification. f Using this, the friction resistance coefficient C f In addition, the friction resistance of the entire hull and the increase in friction resistance due to the influence of hull fouling may be corrected. This configuration improves the accuracy of evaluating the influence of hull fouling.

[0110] As another example of fouling effect correlation analysis, the calculation means 415 may use the required horsepower difference ΔBHP fouling draft effect that does not include the effect of draft change instead of the required horsepower difference ΔBHP fouling draft effect calculated by the above formula (4).

[0111] Figure 9 is a diagram showing another example of correlation information showing the relationship between boat speed and main engine horsepower. In Figure 9, the horizontal axis represents boat speed (kts) and the vertical axis represents main engine horsepower (kW). This correlation information is stored in the storage 43 of the server device 40. Similar to Figure 8, this correlation information includes speed-horsepower curves C1 to C4.

[0112] Focusing on the speed horsepower curves C1 and C2, the required horsepower difference ΔBHP full load state between the horsepower of the speed horsepower curve C1 and the horsepower of the speed horsepower curve C2 when the ship speed is 15 kts includes the effects of fouling on the flooded area 100 located underwater in the full load state and areas other than the flooded area 100. On the other hand, focusing on the speed horsepower curves C3 and C4, the required horsepower difference ΔBHP light load state between the horsepower of the speed horsepower curve C3 and the horsepower of the speed horsepower curve C4 when the ship speed is 15 kts includes the effects of fouling on areas other than the flooded area 100 located underwater in the light load state, but does not include the effects of fouling on the flooded area 100 located above the water surface in the light load state. Therefore, by taking the difference between the required horsepower difference ΔBHP in the fully loaded state and the required horsepower difference ΔBHP in the lightly loaded state, it is possible to obtain the required horsepower difference ΔBHP contamination impact due to contamination of the flooded area 100. This required horsepower difference ΔBHP contamination impact includes the impact of contamination but does not include the impact of draft. The required horsepower difference ΔBHP contamination impact increases as the degree of contamination of the flooded area 100 increases.

[0113] The calculation means 415 calculates the required horsepower difference ΔBHP contamination effect using the following equations (8)-(10): ΔBHP full load state=BHP C1 -BHP C2 ... (8) ΔBHP light weight state = BHP C3 -BHP C4 ... (9) ΔBHP contamination effect = ΔBHP full load state - ΔBHP light load state ... (10) Here, ΔBHP full load state is the required horsepower difference between with contamination and without contamination in a full load state, BHP C1 is the required horsepower for the speed horsepower curve C1, BHP C2 is the required horsepower for the speed horsepower curve C2, ΔBHP light condition is the required horsepower difference between the dirty and undirty condition in the light condition, BHP C3 is the required horsepower for the speed horsepower curve C3, BHP C4is the required horsepower of the speed horsepower curve C4. The required horsepower difference ΔBHP full load state indicates the effect of fouling on the flooded area 100 located underwater in the full load state and on areas other than the flooded area 100. On the other hand, the required horsepower difference ΔBHP light load state indicates the effect of fouling on areas other than the flooded area 100 located underwater in the light load state. The required horsepower difference ΔBHP fouling impact indicates the effect of fouling on the flooded area 100. This required horsepower difference ΔBHP fouling impact does not include the effect of the draft.

[0114] The calculation means 415 uses the required horsepower difference ΔBHP contamination effect calculated in this way to calculate the increase in friction resistance ΔR due to contamination of the flooded area 100 using the above equations (5)-(7). f , the increase in the friction resistance coefficient ΔC f , friction resistance coefficient C f As described above, the correction means of the server device 40 can calculate the friction resistance coefficient C obtained in this modified example. f The friction resistance coefficient C used by the calculation means 415 in the above-described embodiment is calculated using f , the frictional resistance of the entire hull, and the increase in frictional resistance due to the influence of fouling on the hull may be corrected. With this configuration, the influence of fouling on the flooded area 100 can be evaluated more accurately.

[0115] 1: Evaluation system, 10: Ship, 20: Underwater drone, 21: Arm, 22: Camera, 23: Communication IF, 30: Control device, 40: Server device, 41: Processor, 42: Memory, 43: Storage, 44: Communication IF, 45: Input unit, 46: Display unit, 411: Receiving means, 412: Acquiring means, 413: Determining means, 414: Mapping means, 415: Calculating means, 416: Creating means, 417: Analyzing means

Claims

1. A fouling impact evaluation device comprising: an acquisition means for acquiring the degree of fouling of a plurality of regions on the surface of a ship's hull; and a calculation means for calculating the frictional resistance of the entire hull using a frictional resistance coefficient corresponding to the degree of fouling of the plurality of regions.

2. The device for evaluating the effect of contamination as described in claim 1, wherein the acquisition means further acquires position information indicating the positions of the multiple regions on the hull, and the calculation means calculates the frictional resistance of each of the multiple regions using the flow velocity at the position indicated by the position information of the region and the frictional resistance coefficient corresponding to the degree of contamination of the region, and calculates the frictional resistance of the entire hull by adding up the frictional resistances of the multiple regions.

3. A contamination effect evaluation device as described in claim 1, further comprising: a receiving means for receiving images of the plurality of areas taken by an underwater moving body; a determining means for determining the degree of contamination of the plurality of areas using the images; and a mapping means for associating, for each of the plurality of areas, location information indicating the position of the area with the degree of contamination of the area.

4. The contamination effect evaluation device as described in claim 3, wherein the underwater vehicle takes the images along weld lines of the hull, and the mapping means identifies the positions of the multiple areas on the hull based on the weld lines contained in the images.

5. The fouling effect assessment device according to claim 3, wherein the mapping means identifies the positions of the plurality of regions on the hull based on the position of the underwater moving object measured using an acoustic lighthouse.

6. The fouling effect assessment device according to claim 3, wherein the mapping means identifies the positions of the plurality of regions on the hull based on the position of the underwater moving body measured using an inertial navigation system.

7. A fouling effect evaluation device as described in claim 3 or 4, wherein the multiple areas on the hull surface are marked with marks indicating the positions of the multiple areas on the hull, and the mapping means identifies the positions of the multiple areas on the hull using the marks included in the image.

8. A contamination effect evaluation device as described in claim 1, further comprising: a receiving means for receiving images of the plurality of areas photographed by the camera together with a contamination sample board held by the arm along the hull surface in an underwater moving body equipped with an arm and a camera; and a determining means for determining the degree of contamination of the plurality of areas based on the similarity between the contamination sample of the contamination sample board contained in the image and each of the plurality of areas.

9. A fouling impact evaluation device as described in claim 1, further comprising: a storage means for storing a history of the frictional resistance of the entire hull or fuel efficiency calculated from said frictional resistance, and a time when maintenance work to reduce the frictional resistance of the hull was performed; and an analysis means for performing a trend analysis based on said history and said time to predict future changes in fuel efficiency when said maintenance work is performed at specified time intervals.

10. A contamination effect evaluation device as described in claim 1, further comprising: a receiving means for receiving input of an image of a target area between a first waterline in a first draft state of the hull and a second waterline in a second draft state different from the first draft state, photographed by an imaging means together with a contamination sample plate; and a determination means for determining the degree of contamination of the target area based on the similarity between the contamination sample of the contamination sample plate included in the image and the target area, wherein the calculation means further uses a friction resistance coefficient corresponding to the degree of contamination of the target area to calculate the frictional resistance of the entire hull.

11. The device for evaluating the effect of fouling as described in claim 10, wherein the calculation means calculates an increase in friction resistance of the target area due to fouling according to a difference in required horsepower between the first draft state and the second draft state.

12. A program for causing a computer to execute the steps of: acquiring the degree of contamination of multiple areas on the hull surface of a ship; and calculating the frictional resistance of the entire hull using a frictional resistance coefficient corresponding to the degree of contamination of the multiple areas.

Images