A method and system for predicting the speed-power of a large bulk carrier

Abstract

The application provides a large bulk carrier speed power prediction method and system, first, the trial data of a large bulk carrier and a ship model and the resistance data of the ship model measured in the test are obtained, and the main dimension parameter data of the ship model is obtained, then the CFD superposition method is used for calculation to establish a stable calculation strategy, then the StarCCM+ software is used for CFD simulation of the ship model according to the stable calculation strategy, the frictional resistance and the viscous pressure resistance of the ship model are obtained, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the ship are calculated respectively by using the frictional resistance formula, and the first shape factor of the ship model is calculated, finally, the viscous resistance coefficient of the ship is calculated according to the frictional resistance coefficient of the ship and the first shape factor, the speed power of the ship is calculated according to the viscous resistance coefficient of the ship, the second frictional resistance coefficient of the ship model and the total resistance coefficient of the ship model in the test data, the prediction speed is effectively improved, the modeling cost is reduced, and the prediction precision is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of ship forecasting technology, specifically to a method and system for forecasting the speed and power of large bulk carriers. Background Technology

[0002] The shipping industry is vital to the global economy, but its development has also led to a gradual increase in greenhouse gas emissions. Against the backdrop of current global efforts to conserve energy and reduce emissions, the shipping industry is undergoing a significant transformation. Speed ​​and power prediction technology, as a core technology in ship performance research, has long been a focus of attention for organizations such as the International Towing Tank Conference (ITTC), the International Maritime Organization (IMO), and the industry. Accurate calculation of the shape factor plays a crucial role in predicting the speed and power of actual ships. Currently, there are two main methods for predicting the speed and power of large, fat ships: one based on model testing, and the other based on numerical calculation.

[0003] Currently, ship model testing using model-based methods involves conducting tests in a towing tank using a scaled-down ship model. The tests are diverse and repeatable. Ship resistance prediction still primarily relies on model-based resistance testing. Common extrapolation methods include the two-dimensional method (ITTC, 1957) and the three-dimensional method / shape factor method (ITTC, 1978), both of which are currently in use. For large, bulky ships, the three-dimensional method is typically used to predict the actual ship resistance, and the ITTC recommends a similar extrapolation strategy. However, the time-consuming and labor-intensive nature of model-based resistance testing, particularly in model fabrication and testing, makes it impractical and uneconomical in the design phase. Furthermore, because the test conditions of the model are not entirely similar to those of the actual ship, the conversion methods used cannot account for all factors affecting ship performance, thus limiting the limitations of model-based testing.

[0004] With the rapid development of computational fluid dynamics, the industry is paying increasing attention to the role of numerical computation (CFD) in ship performance research. CFD simulations are inexpensive, can be used for real-ship simulations, and have significant advantages in plotting detailed flow fields. They can also simulate complex phenomena that are difficult to simulate in tank tests. However, using CFD directly to calculate the resistance of real ships has the problems of large computational load and low accuracy. Further in-depth research is needed to determine whether CFD can be used exclusively to calculate and predict the resistance performance of real ships. Summary of the Invention

[0005] To address the problems of time-consuming, labor-intensive, long-cycle, impractical, uneconomical, limited, computationally intensive, and low-accuracy ship resistance prediction methods, this invention provides a method for predicting the speed and power of large bulk carriers. By employing a combination of ship model experiments and CFD numerical calculations for actual ship speed and power prediction, this method effectively improves prediction speed, reduces modeling costs, and significantly enhances prediction accuracy, achieving wave-making-free prediction. This invention also relates to a system for predicting the speed and power of large bulk carriers.

[0006] The technical solution of the present invention is as follows:

[0007] A method for predicting the speed and power of large bulk carriers, characterized by comprising the following steps:

[0008] Data acquisition steps: Obtain the principal dimension parameter data of the actual large bulk carrier and the test data of the ship model obtained from the experiment; and obtain the principal dimension parameter data of the ship model based on the principal dimension parameter data of the actual ship.

[0009] Stability calculation strategy acquisition steps: The flow domain within a certain range of the ship model is divided into grids using an unstructured grid, and prism layers are created on the hull and rudder surfaces of the ship model. Based on the thickness of the prism layers, wall functions are used to refine the prism layers and the divided grids. Based on the refined grids and according to the growth rate and number of prism layers, multiple grid schemes are obtained. The hydrostatic resistance under each grid scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data in the ship model test data is compared. The grid scheme with the smallest resistance error is obtained as the stability calculation strategy.

[0010] The shape factor calculation steps are as follows: Based on the stable calculation strategy, the CFD simulation of the ship model is performed using Star CCM+ software to obtain the frictional resistance and viscous pressure resistance of the ship model at different time steps. Then, the first frictional resistance coefficient and viscous pressure resistance coefficient of the ship model are obtained. Based on the principal dimension parameter data of the actual ship and the ship model and the ship model test data, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship are calculated using the ITTC frictional resistance formula. The first shape factor of the ship model is calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient and the viscous pressure resistance coefficient.

[0011] Steps for calculating the speed and power of a real ship: Calculate the viscous drag coefficient of the real ship based on the friction drag coefficient and the first shape factor. Calculate the total drag coefficient of the real ship based on the viscous drag coefficient of the real ship, the second friction drag coefficient of the ship model, and the total drag coefficient of the ship model from the ship model test data. Calculate the total drag and speed power of the real ship based on the total drag coefficient of the real ship.

[0012] Preferably, in the shape factor calculation step, the second shape factor of the ship model is also calculated based on the first frictional resistance coefficient and the viscous pressure resistance coefficient.

[0013] In the data acquisition step, sea trial data of a large bulk carrier is also acquired. The sea trial data includes speed, power, and seawater temperature. The main dimensional parameter data includes ship length and beam, draft, wetted surface area, displacement volume, and block coefficient. The shape factor calculation step uses uncertainty analysis to analyze the uncertainty of the ship model resistance data in the ship model test data to obtain the uncertainty of the ship model resistance data. Based on the uncertainty result of the ship model resistance data, the third shape factor of the ship model is calculated using the Prohaska method or multiple empirical formulas for shape factors. Then, based on the actual ship sea trial data, the accuracy of the first shape factor, second shape factor, and third shape factor in predicting the actual ship speed and power is determined. When it is determined that the accuracy of the first shape factor and the second shape factor are both higher than the accuracy of the third shape factor, and the accuracy of the first shape factor is higher than the accuracy of the second shape factor, the subsequent actual ship speed and power calculation step uses the first shape factor for calculation.

[0014] Preferably, in the step of obtaining the stability calculation strategy, the ship model is located within a certain range, including a certain distance from the bow, stern, hull symmetry plane, and free surface.

[0015] Preferably, in the step of obtaining the stable calculation strategy, after dividing the flow domain within a certain range of the ship model into a grid using an unstructured grid, the computational domain is also discretized to establish a CFD model, wherein the CFD model includes the computational domain, grid division, boundary conditions, and turbulence model.

[0016] Preferably, in the shape factor calculation step, the correction factor method and the grid convergence index method are also used to evaluate the uncertainty of the CFD model numerical simulation, determine the grid spacing and time step, and compare the CFD model numerical simulation results of the ship model free surface with the experimental results according to the stable calculation strategy and time step to evaluate the effectiveness of the CFD model. According to the stable calculation strategy, Star CCM+ software is used to perform ship model CFD superimposed simulation based on the CFD model.

[0017] A large bulk carrier speed and power prediction system, characterized in that it comprises a data acquisition module, a stability calculation strategy acquisition module, a shape factor calculation module, and a real ship speed and power calculation module connected in sequence.

[0018] The data acquisition module acquires the principal dimension parameter data of the actual large bulk carrier and the test data of the ship model obtained from the experiment. Based on the principal dimension parameter data of the actual ship, the principal dimension parameter data of the ship model is obtained.

[0019] The stability calculation strategy acquisition module uses an unstructured mesh to divide the watershed within a certain range of the ship model, and creates prism layers on the hull and rudder surfaces of the ship model. Based on the thickness of the prism layers, a wall function is used to refine the prism layers and the divided mesh. Based on the refined mesh and according to the growth rate and number of prism layers, multiple mesh schemes are obtained. The hydrostatic resistance under each mesh scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data in the ship model test data is compared. The mesh scheme with the smallest resistance error is obtained as the stability calculation strategy.

[0020] The shape factor calculation module uses a stable calculation strategy and Star CCM+ software to perform CFD simulation of the ship model, obtaining the frictional resistance and viscous pressure resistance of the ship model at different time steps. Then, the first frictional resistance coefficient and viscous pressure resistance coefficient of the ship model are obtained. Based on the principal dimension parameter data of the actual ship and the ship model, as well as the ship model test data, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship are calculated using the ITTC frictional resistance formula. The first shape factor of the ship model is calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient, and the viscous pressure resistance coefficient.

[0021] The actual ship speed and power calculation module calculates the actual ship viscous resistance coefficient based on the actual ship's frictional resistance coefficient and the first shape factor. It also calculates the actual ship's total resistance coefficient based on the actual ship's viscous resistance coefficient, the ship model's second frictional resistance coefficient, and the ship model's total resistance coefficient from the ship model test data. Finally, it calculates the actual ship's total resistance and actual ship speed and power based on the actual ship's total resistance coefficient.

[0022] Preferably, the shape factor calculation module further calculates the second shape factor of the ship model based on the first frictional resistance coefficient and the viscous pressure resistance coefficient;

[0023] The data acquisition module also acquires sea trial data of a large bulk carrier, including speed, power, and seawater temperature. The main dimensional parameters include length, beam, draft, wetted surface area, displacement volume, and block coefficient. The shape factor calculation module uses uncertainty analysis to analyze the uncertainty of the model resistance data in the model test data, obtaining the uncertainty of the model resistance data. Based on the uncertainty result of the model resistance data, the Prohaska method or multiple empirical formulas for shape factors are used to calculate the third shape factor of the model. Then, based on the sea trial data, the accuracy of the first, second, and third shape factors in predicting the speed and power of the actual ship is judged. When it is determined that the accuracy of the first and second shape factors is higher than that of the third shape factor, and the accuracy of the first shape factor is higher than that of the second shape factor, the subsequent actual ship speed and power calculation module uses the first shape factor for calculation.

[0024] Preferably, in the stability calculation strategy acquisition module, the ship model is located at a certain distance from the bow, stern, hull symmetry plane, and free surface.

[0025] Preferably, in the stable calculation strategy acquisition module, after dividing the flow domain within a certain range of the ship model into a grid using an unstructured grid, the computational domain is also discretized to establish a CFD model. The CFD model includes the computational domain, grid division, boundary conditions, and turbulence model.

[0026] Preferably, in the shape factor calculation module, the uncertainty of the CFD model numerical simulation is evaluated by the correction factor method and the grid convergence index method, the grid spacing and time step are determined, and the CFD model numerical simulation results of the ship model free surface are compared with the experimental results according to the stable calculation strategy and time step to evaluate the effectiveness of the CFD model numerical simulation. According to the stable calculation strategy, the Star CCM+ software is used to perform CFD superimposed simulation of the ship model based on the CFD model.

[0027] The beneficial effects of this invention are as follows:

[0028] This invention provides a method for predicting the speed and power of large bulk carriers. First, it acquires the principal scale parameters of the actual large bulk carrier and experimental data from a ship model. Based on the principal scale parameters of the actual carrier, it obtains the principal scale parameters of the ship model. Then, it uses an unstructured mesh to divide the flow area within a certain range of the ship model into grids. Prismatic layers are created on the hull and rudder surfaces of the ship model. Based on the thickness of the prismatic layers and using wall functions, the prismatic layers and the divided mesh are refined. Based on the refined mesh and according to the growth rate and number of prismatic layers, multiple mesh schemes are obtained. The hydrostatic resistance under each mesh scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data from the experimental data is compared. The mesh scheme with the smallest resistance error is used as the stability calculation strategy, laying the foundation for subsequent CFD numerical simulation of the ship model. Then, based on the stability calculation strategy and using Star... The CCM+ software was used to perform CFD simulation of the ship model, obtaining the frictional resistance and viscous-pressure resistance of the ship model at different time steps. This yielded the first frictional resistance coefficient and the viscous-pressure resistance coefficient of the ship model. Based on the principal dimension parameters of the actual ship and the ship model, as well as the ship model test data, and using the ITTC frictional resistance formula, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship were calculated respectively. The first shape factor of the ship model was calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient, and the viscous-pressure resistance coefficient. Finally, the viscous resistance coefficient of the actual ship was calculated based on the frictional resistance coefficient and the first shape factor. The total resistance coefficient of the actual ship was calculated based on the viscous resistance coefficient of the actual ship, the second frictional resistance coefficient of the ship model, and the total resistance coefficient of the ship model from the ship model test data. Finally, the total resistance and the speed and power of the actual ship were calculated based on the total resistance coefficient. This invention combines the advantages of model tests as a benchmark with the precise calculation capabilities and wave-free operation of CFD methods. By establishing a method for predicting the resistance of actual ships based on CFD analysis of shape factors and combined with ship model test results, the expected goal is achieved. This method can effectively improve the speed of prediction, reduce modeling costs, and greatly improve the accuracy of prediction. It is a practical, feasible, and cost-effective solution.

[0029] This invention also relates to a speed and power prediction system for large bulk carriers. This system corresponds to the aforementioned speed and power prediction method for large bulk carriers and can be understood as a system that implements the aforementioned speed and power prediction method for large bulk carriers. It includes a data acquisition module, a stability calculation strategy acquisition module, a shape factor calculation module, and a real ship speed and power calculation module connected in sequence. Each module works in concert with the others and uses a combination of ship model experiments and CFD numerical calculations to predict the speed and power of the real ship. It is suitable for large ships, especially large bulk carriers, effectively improving the prediction speed, reducing modeling costs, and greatly improving the prediction accuracy, achieving wave-making-free prediction. Attached Figure Description

[0030] Figure 1This is a flowchart of the method for predicting the speed and power of large bulk carriers according to the present invention.

[0031] Figure 2 This is a schematic diagram of the computational domain mesh for simulating the free surface of the ship's hull according to the present invention.

[0032] Figure 3 This is a schematic diagram of the computational domain mesh for the ship hull stacking simulation of the present invention.

[0033] Figure 4 This is a schematic diagram of the actual ship speed-output power curve of the present invention. Detailed Implementation

[0034] The present invention will now be described with reference to the accompanying drawings.

[0035] This invention relates to a method for predicting the speed and power of large bulk carriers, the flowchart of which is shown below. Figure 1 As shown, the steps are as follows:

[0036] I. Data Acquisition Steps: Acquire the principal dimension parameters of the actual large bulk carrier and the experimental data of the ship model. Based on the principal dimension parameters of the actual ship, obtain the principal dimension parameters of the ship model.

[0037] Specifically, the main dimensional parameters of the large bulk carrier are first obtained. The scale ratio is determined based on the main dimensional parameters of the actual ship. Then, the main dimensional parameters of the ship model are obtained, as well as the test data of the ship model obtained from the experiment. The main dimensional parameters mainly include the ship length and beam, draft, wetted surface area of ​​the hull, displacement volume and block coefficient, etc. The test data of the ship model includes the speed, the total resistance coefficient of the ship model and the resistance data of the ship model obtained from multiple repeated resistance tests. The resistance test process and error sources are clarified.

[0038] Furthermore, this data acquisition step can also acquire actual sea trial data of large bulk carriers. The sea trial data includes speed, power and seawater temperature. Based on the seawater temperature, the dynamic viscosity coefficient of the fluid at a certain temperature is obtained. Based on the ratio of the dynamic viscosity coefficient μ of the fluid at a certain temperature to its density ρ, the kinematic viscosity coefficient of the fluid is calculated.

[0039] II. Stability Calculation Strategy Acquisition Steps: The flow region within a certain range of the ship model is divided into grids using an unstructured grid. Prismary layers are created on the hull and rudder surfaces of the ship model. Based on the thickness of the prism layers, wall functions are used to refine the prism layers and the divided grids. Based on the refined grids and according to the growth rate and number of prism layers, multiple grid schemes are obtained. The hydrostatic resistance under each grid scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data in the ship model test data is compared. The grid scheme with the smallest resistance error is obtained as the stability calculation strategy.

[0040] Star CCM+ software was used for CFD calculations to study numerical calculation strategies, analyze the impact of different calculation strategies (such as mesh generation and convergence studies) on resistance results, and explore effective numerical calculation methods. Specifically, for the numerical simulation of ship model resistance tests, a numerical simulation mesh scheme with a free surface was selected. First, a half-computation domain was created, and an unstructured hexahedral mesh was used to mesh the flow domain within a certain range of the ship model (i.e., the bow, stern, hull symmetry plane, and free surface). The computation domain was then discretized to establish a CFD model, which included the computation domain, computational mesh generation, boundary conditions, and turbulence model. Next, prism layers were created on the hull and rudder surfaces of the ship model to generate near-wall meshes. A wall function was used, and the thickness y+ of the prism layers was set to keep the y+ value above 30. While ensuring that the y+ value remained unchanged, the prism layers and parts of the mesh at a certain distance from the bow, stern, hull symmetry plane, and free surface were refined, i.e., the mesh was further refined. Figure 2 The mesh in the area represented by the dark black lines is refined. Based on this refined mesh, the growth rate and number of prism layers are varied to obtain multiple different mesh schemes (different calculation strategies, i.e., different total number of meshes). The hydrostatic resistance of the ship model under each mesh scheme is calculated. Then, by comparing the hydrostatic resistance with the ship model's resistance data, the mesh scheme with the smallest resistance error (i.e., higher accuracy) is determined and used as the stable calculation strategy. This stable calculation strategy is subsequently used for CFD stacked model simulation of the ship model (without a free surface).

[0041] The computational domain boundaries (i.e., the outermost boundaries of the computational domain, including the inlet boundary, outlet boundary, lateral boundaries, symmetry plane, upper boundary, and lower boundary) are placed far enough away from the ship to avoid their influence on the numerical solution. The inlet boundary is 1.5 Lpp from the bow, the outlet boundary is 2.5 Lpp from the stern, the lateral boundary is 2 Lpp from the symmetry plane, the upper boundary is 1 Lpp from the waterline, and the lower boundary is 2 Lpp from the waterline. Boundary conditions are set as follows: the computational domain inlet is a velocity inlet, the outlet is a pressure outlet, and the upper, lower, and lateral boundaries are all velocity inlets, with the lateral boundaries designated as symmetry planes. The velocity inlet boundary conditions are used to simulate the inlet, upper, and lower boundaries to simulate the deep water and infinite air assumptions. For free surface computational simulations of the ship model, the hull surface is a no-slip wall boundary condition with a smooth surface. For stacked model simulations, the upper boundary of the computational domain is also defined as a symmetry plane to reflect the wetted hull surface. The generated computational domain mesh is shown below. Figure 3As shown. Furthermore, the mesh is refined to capture the Kelvin wake. Since the flow around the hull involves complex separation and wake phenomena, and turbulence models play a crucial role in accurately predicting wake and flow separation, the shear stress transport SSTk-ω turbulence model is adopted. This model combines the advantages of the k-ω and k-ε turbulence models, resulting in more accurate wall treatment.

[0042] Furthermore, to assess the uncertainty of the CFD model numerical simulation and determine sufficient grid spacing and time step, a verification study with a free surface was conducted using the (V&V) verification method. Specifically, the correction factor method and the grid convergence index method were used to assess the uncertainty of the CFD model numerical simulation, determine the grid spacing and time step, and, based on the stable calculation strategy and time step, compare the CFD model numerical simulation results with experimental results on the ship model with the free surface to evaluate the effectiveness of the CFD model.

[0043] Specifically, the validation study aimed to demonstrate the capability of the proposed model and software for specific computations. Numerical uncertainties in spatial and temporal discretization were estimated using the grid convergence index (GCI) method based on Richardson extrapolation. The correction factor (CF) method and the grid convergence index (GCI) method are available at the International Towing Tank Conference (ITTC) (2017).

[0044] The apparent order p of this method a Calculate according to the following formula:

[0045]

[0046]

[0047] s = sign(ε 32 / ε 21 (3)

[0048] In the above formula, r 21 and r 32 It is a refinement factor. It is the refinement factor r 21 The apparent order p a Power of 1 It is the refinement factor r 32 The apparent order p a For studies of the spatial convergence of 3D models, the power of this is relevant. For the study of time convergence, r 21= Δt1 / Δt2, where N is the number of different grids (N1 is the number of fine grids, N2 is the number of medium grids, and N3 is the number of coarse grids), Δt is the different time step (Δt1, Δt2, and Δt3 can be 0.01, 0.02, 0.04, or 0.08 respectively, and ε exists. 32 =φ3-φ2, ε 21 =φ2-φ1, φ k It is an important variable, corresponding to the first frictional resistance coefficient C of the k-th grid. F Or the total drag coefficient C of the ship model T ,For example C corresponding to the first grid F Or C T , C corresponding to the second grid F Or C T , C corresponding to the third grid F Or C T .

[0049] extrapolated value Calculate according to the following formula:

[0050]

[0051] Approximate relative error and extrapolation relative error Calculate according to the following formulas respectively:

[0052]

[0053]

[0054] Fine-grid convergence index Calculate according to the following formula:

[0055]

[0056]

[0057] In the above formula, Indicates the extrapolated value. This represents an approximate relative error. Indicates the relative error of extrapolation. p represents the convergence exponent of the fine mesh, and p represents the apparent order. a , Represents the refinement factor r 21 The apparent order is raised to the power of p.

[0058] That is, to conduct research based on the total drag coefficient C of the ship model. TThe calculation of spatial discretization error parameters and time discretization error parameters can yield the spatial discretization C. T The numerical uncertainty and time uncertainty.

[0059] Verification process: To verify the effectiveness of the simulation model, the optimal grid scheme (i.e., stable calculation strategy) and time step of the verification process were used to compare the CFD model numerical simulation results of the ship model free surface simulation with the experimental results. It was found that the relative deviation between the two was small, which shows that the CFD model numerical simulation results and the ship model experimental results are in good agreement, indicating that the CFD model simulation calculation and the ship model experiment have achieved good consistency.

[0060] III. Shape Factor Calculation Steps: Based on the stable calculation strategy and using StarCCM+ software, a CFD simulation of the ship model is performed to obtain the frictional resistance and viscous pressure resistance of the ship model at different time steps. Then, the first frictional resistance coefficient and viscous pressure resistance coefficient of the ship model are obtained. Based on the principal dimension parameter data of the actual ship and the ship model, as well as the ship model test data, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship are calculated using the ITTC frictional resistance formula. The first shape factor of the ship model is calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient, and the viscous pressure resistance coefficient. Finally, the second shape factor of the ship model is calculated based on the first frictional resistance coefficient and the viscous pressure resistance coefficient.

[0061] Specifically, based on a stable calculation strategy and using StarCCM+ software for CFD simulation of the ship model, the frictional resistance C of the ship model at different time steps was obtained. F Viscosity resistance C PV and the sum of the two, viscous resistance C V Therefore, the first frictional resistance coefficient C of the ship model can be obtained respectively. fm_cfd Viscosity and pressure resistance coefficient C pvm_cfd and viscous drag coefficient C vm_cfd Then, based on the principal dimension parameters of the actual ship and the ship model, as well as the ship model test data, and using the ITTC friction resistance formula (ITTC-57), the second friction resistance coefficient C of the ship model was calculated. fm_ittc The coefficient of frictional resistance of a real ship C fs_ittc Calculate according to the following formula:

[0062]

[0063] Re = Lv / υ (10)

[0064] In the above formula, Re is the Reynolds number, L is the ship length, v is the speed, and υ is the fluid kinematic viscosity coefficient, which is the ratio of the fluid's dynamic viscosity coefficient μ to its density ρ at a certain temperature.

[0065] Then, based on the first frictional resistance coefficient C fm_cfd Second frictional resistance coefficient C fm_ittc The first shape factor of the ship model is calculated using the viscous drag coefficient, and the second shape factor is calculated based on the first friction drag coefficient and the viscous drag coefficient. According to Hughes' hypothesis (1954), assuming that the viscous drag is equal to the total drag of the stacked model simulation, the first shape factor 1+k and the second shape factor are calculated according to the following formula:

[0066]

[0067] have

[0068] or

[0069] In the above formula, C fm_cfd C is the coefficient of friction resistance. fm_ittc C is the second frictional resistance coefficient. pvm_cfd The first shape factor 1+k value can be obtained from equation (12) for different speeds, and the second shape factor value can be obtained from equation (13) for different speeds.

[0070] Then, uncertainty analysis is used to analyze the uncertainty of the ship model resistance data in the ship model test data to obtain the uncertainty of the ship model resistance data. Based on the uncertainty results of the ship model resistance data, the third shape factor of the ship model is calculated using the Prohaska method or multiple empirical formulas for shape factors. Then, based on the actual ship trial data, the accuracy of the first shape factor, the second shape factor and the third shape factor in predicting the actual ship speed and power is judged respectively. When it is determined that the accuracy of the first shape factor and the second shape factor are both higher than the accuracy of the third shape factor, and the accuracy of the first shape factor is higher than the accuracy of the second shape factor, the subsequent actual ship speed and power calculation steps are performed using the first shape factor.

[0071] Specifically, uncertainty analysis is first used to analyze the resistance data to obtain the uncertainty of the ship model resistance data at the nominal temperature (15℃) to assess the uncertainty of the ship model test. This verifies the accuracy of the ship model resistance data obtained from the test, eliminates the influence of test errors, and lays the foundation for calculating a more accurate shape factor based on the ship model resistance data. Then, based on the uncertainty results of the ship model resistance data, the third shape factor of the ship model is calculated using the Prohaska method or multiple empirical formulas for shape factors.

[0072] Based on the uncertainty results of the ship model resistance data, the Prohaska method was used to generate a linear relationship graph, and then the third shape factor 1+k of the ship model was fitted and calculated according to the following formula:

[0073]

[0074] In the above formula, C tm C is the total drag coefficient of the ship model. fm Let C be the frictional resistance coefficient of the ship model, Fr be the Froude number, and y be the slope. tm / C fm With Fr 4 / C fm If a linear relationship is plotted, the intercept of the line is the value of the third shape factor 1+k.

[0075] Alternatively, the second shape factor of the ship model can be calculated directly using multiple empirical formulas for shape factors, according to the following formula:

[0076] 1) Gross and Watanabe's empirical formula for shape factor:

[0077]

[0078] In the above formula, L is the ship's length, B is the ship's beam, T is the average draft, and C... B It is the square coefficient.

[0079] 2) Marintek's empirical formula for shape factor:

[0080]

[0081]

[0082] In the above formula, C B It is the square coefficient, L WL It is the design waterline length, T FP T AP These represent the draft at the bow and stern of the ship, respectively.

[0083] After calculating the third shape factor, the accuracy of the first, second, and third shape factors in predicting the actual ship speed and power was judged based on the actual ship trial data. It was determined that the accuracy of the first and second shape factors was close and higher than that of the third shape factor. Furthermore, the accuracy of the first shape factor was 1% higher than that of the second shape factor. Therefore, the more accurate first shape factor was used for subsequent calculations to make the final calculated actual ship speed and power more accurate.

[0084] IV. Steps for calculating the speed and power of the actual ship: Calculate the viscous resistance coefficient of the actual ship based on the frictional resistance coefficient and the first shape factor. Calculate the total resistance coefficient of the actual ship based on the viscous resistance coefficient of the actual ship, the second frictional resistance coefficient of the ship model, and the total resistance coefficient of the ship model from the ship model test data. Calculate the total resistance and speed power of the actual ship based on the total resistance coefficient of the actual ship.

[0085] Specifically, based on the actual ship friction resistance coefficient C fs_ittc The viscous drag coefficient C of the actual ship was calculated using the first shape factor 1+k. vs_ittc That is, according to formula C fs_ittc (1+k)=C vs_ittc The actual ship viscous drag coefficient C was obtained. vs_ittc Finally, based on the actual ship's viscous drag coefficient C vs_ittc The second frictional resistance coefficient C of the ship model fm_ittc The total resistance coefficient C of the actual ship is calculated from the total resistance coefficient of the ship model. TS According to the 19th ITTC meeting (1990), the actual ship's total resistance coefficient C TS Calculate according to the following formula:

[0086] C TS = (1+k)C fs_ittc +(C TM -C fm_ittc )+ΔC F +C A +C AAS (18)

[0087] That is, C TS =C vs_ittc +(C TM -C fm_ittc )+ΔC F +C A +C AAS (19)

[0088] In the above formula, C vs_ittc C represents the viscous drag coefficient of a real ship. TM C is the total drag coefficient of the ship model. fm_ittc Let ΔC be the second frictional resistance coefficient of the ship model. F C is the roughness subsidy coefficient. A = (5.68 - 0.6logRe) × 10 -3 C AAS ΔC is the air drag coefficient. F The roughness subsidy coefficient is calculated as follows:

[0089]

[0090] Among them, L wl The actual ship's waterline length is k h This refers to the surface roughness of the hull. When no actual measurement data is available, a suggested value of 150 μm can be used. However, for modern coatings, different values ​​must be considered.

[0091] Finally, the total resistance of the actual ship and the corresponding actual ship speed and power are calculated based on the total resistance coefficient of the actual ship. This is the actual ship speed and power result obtained by extrapolation based on the combination of model test and numerical calculation method. The predicted actual ship speed and power result is compared with the actual ship trial data result, thus verifying that this method greatly improves the accuracy of the prediction.

[0092] Implementation Results: A case study was conducted on a 206,000-ton bulk carrier with known sea trial results, demonstrating the effectiveness of the implementation. Figure 4 As shown in the actual ship speed-output power curve, the results of the actual ship power prediction using the combined model test and numerical calculation method of this invention are consistent with the actual ship sea trial results and are within the allowable error range, thus demonstrating that the combined model test and numerical calculation method is practical and feasible.

[0093] This invention also relates to a speed and power prediction system for large bulk carriers. This system corresponds to the aforementioned speed and power prediction method for large bulk carriers and can be understood as a system that implements the aforementioned method. The system includes a data acquisition module, a stability calculation strategy acquisition module, a shape factor calculation module, and a real-ship speed and power calculation module connected in sequence. Specifically,

[0094] The data acquisition module acquires the principal dimension parameter data of the actual large bulk carrier and the test data of the ship model obtained from the experiment. Based on the principal dimension parameter data of the actual ship, the principal dimension parameter data of the ship model is obtained.

[0095] The stability calculation strategy acquisition module uses an unstructured mesh to divide the watershed within a certain range of the ship model, and creates prism layers on the hull and rudder surfaces of the ship model. Based on the thickness of the prism layers, a wall function is used to refine the prism layers and the divided mesh. Based on the refined mesh and according to the growth rate and number of prism layers, multiple mesh schemes are obtained. The hydrostatic resistance under each mesh scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data in the ship model test data is compared. The mesh scheme with the smallest resistance error is obtained as the stability calculation strategy.

[0096] The shape factor calculation module uses a stable calculation strategy and Star CCM+ software to perform CFD simulation of the ship model, obtaining the frictional resistance and viscous pressure resistance of the ship model at different time steps. Then, the first frictional resistance coefficient and viscous pressure resistance coefficient of the ship model are obtained. Based on the principal dimension parameter data of the actual ship and the ship model, as well as the ship model test data, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship are calculated using the ITTC frictional resistance formula. The first shape factor of the ship model is calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient, and the viscous pressure resistance coefficient.

[0097] The actual ship speed and power calculation module calculates the actual ship viscous resistance coefficient based on the actual ship's frictional resistance coefficient and the first shape factor. It also calculates the actual ship's total resistance coefficient based on the actual ship's viscous resistance coefficient, the ship model's second frictional resistance coefficient, and the ship model's total resistance coefficient from the ship model test data. Finally, it calculates the actual ship's total resistance and actual ship speed and power based on the actual ship's total resistance coefficient.

[0098] Preferably, in the shape factor calculation module, the second shape factor of the ship model is also calculated based on the first frictional resistance coefficient and the viscous pressure resistance coefficient.

[0099] In the data acquisition step, sea trial data of a large bulk carrier is also acquired. The shape factor calculation module uses uncertainty analysis to perform uncertainty analysis on the ship model resistance data in the ship model test data to obtain the uncertainty of the ship model resistance data. Based on the uncertainty result of the ship model resistance data, the third shape factor of the ship model is calculated using the Prohaska method or multiple empirical formulas for shape factors. Then, based on the sea trial data, the accuracy of the first shape factor, the second shape factor, and the third shape factor in predicting the actual ship speed and power is judged respectively. When it is determined that the accuracy of the first shape factor and the second shape factor are both higher than the accuracy of the third shape factor, and the accuracy of the first shape factor is higher than the accuracy of the second shape factor, the subsequent actual ship speed and power calculation module uses the first shape factor for calculation.

[0100] The sea trial data includes speed, power, and seawater temperature, while the main dimensional parameters include length and beam, draft, wetted surface area, displacement volume, and block coefficient.

[0101] Preferably, in the stable calculation strategy acquisition module, the ship model is located within a certain range, including a certain distance from the bow, stern, hull symmetry plane, and free surface.

[0102] Preferably, in the stable calculation strategy acquisition module, after dividing the flow domain within a certain range of the ship model into a grid using an unstructured grid, the computational domain is also discretized to establish a CFD model. The CFD model includes the computational domain, grid division, boundary conditions, and turbulence model.

[0103] Preferably, in the shape factor calculation module, the correction factor method and the grid convergence index method are used to evaluate the uncertainty of the CFD model numerical simulation, determine the grid spacing and time step, and compare the CFD model numerical simulation results of the ship model free surface with the experimental results according to the stable calculation strategy and time step to evaluate the effectiveness of the CFD model.

[0104] This invention provides an objective and scientific method and system for predicting the speed and power of large bulk carriers. It uses a combination of model testing and CFD numerical calculation to extrapolate the actual ship resistance of large bulk carriers, and compares and analyzes the extrapolated actual ship speed and power results with the actual ship trial results to verify the practicality and feasibility of this scheme. It effectively improves the prediction speed, reduces modeling costs, and greatly improves the prediction accuracy, achieving wave-free prediction.

[0105] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail with reference to the accompanying drawings and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention. In short, all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention patent.

Claims

1. A method for predicting the speed and power of a large bulk carrier, characterized in that, Includes the following steps: Data acquisition steps: Obtain the principal dimension parameter data of the actual large bulk carrier and the test data of the ship model obtained from the experiment; and obtain the principal dimension parameter data of the ship model based on the principal dimension parameter data of the actual ship. Stability calculation strategy acquisition steps: The flow domain within a certain range of the ship model is divided into grids using an unstructured grid, and prism layers are created on the hull and rudder surfaces of the ship model. Based on the thickness of the prism layers, wall functions are used to refine the prism layers and the divided grids. Based on the refined grids and according to the growth rate and number of prism layers, multiple grid schemes are obtained. The hydrostatic resistance under each grid scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data in the ship model test data is compared. The grid scheme with the smallest resistance error is obtained as the stability calculation strategy. The shape factor calculation steps are as follows: Based on a stable calculation strategy and using Star CCM+ software, a CFD simulation of the ship model is performed to obtain the frictional resistance and viscous pressure resistance of the ship model at different time steps. This yields the first frictional resistance coefficient and the viscous pressure resistance coefficient of the ship model. Based on the principal dimension parameters of the actual ship and the ship model, as well as the ship model test data, and using the ITTC frictional resistance formula, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship are calculated respectively. Finally, the first shape factor of the ship model is calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient, and the viscous pressure resistance coefficient. The first shape factor is calculated according to the following formula: Among them, C fm_cfd C is the coefficient of friction resistance. pvm_cfd C is the viscous pressure resistance coefficient. fm_ittc is the second frictional resistance coefficient, and 1+k is the first shape factor; Steps for calculating the speed and power of a real ship: Calculate the viscous drag coefficient of the real ship based on the friction drag coefficient and the first shape factor. Calculate the total drag coefficient of the real ship based on the viscous drag coefficient of the real ship, the second friction drag coefficient of the ship model, and the total drag coefficient of the ship model from the ship model test data. Calculate the total drag and speed power of the real ship based on the total drag coefficient of the real ship.

2. The method for predicting the speed and power of large bulk carriers according to claim 1, characterized in that, In the shape factor calculation step, the second shape factor of the ship model is also calculated based on the first frictional resistance coefficient and the viscous pressure resistance coefficient. In the data acquisition step, sea trial data of a large bulk carrier is also acquired. The sea trial data includes speed, power, and seawater temperature. The main dimensional parameter data includes ship length and beam, draft, wetted surface area, displacement volume, and block coefficient. The shape factor calculation step uses uncertainty analysis to analyze the uncertainty of the ship model resistance data in the ship model test data to obtain the uncertainty of the ship model resistance data. Based on the uncertainty result of the ship model resistance data, the third shape factor of the ship model is calculated using the Prohaska method or multiple empirical formulas for shape factors. Then, based on the actual ship sea trial data, the accuracy of the first shape factor, second shape factor, and third shape factor in predicting the actual ship speed and power is determined. When it is determined that the accuracy of the first shape factor and the second shape factor are both higher than the accuracy of the third shape factor, and the accuracy of the first shape factor is higher than the accuracy of the second shape factor, the subsequent actual ship speed and power calculation step uses the first shape factor for calculation.

3. The method for predicting the speed and power of large bulk carriers according to claim 1, characterized in that, In the step of obtaining the stability calculation strategy, the ship model is located within a certain range, including a certain distance from the bow, stern, hull symmetry plane, and free surface.

4. The method for predicting the speed and power of large bulk carriers according to claim 3, characterized in that, In the stable calculation strategy acquisition step, after dividing the flow domain within a certain range of the ship model into a grid using an unstructured grid, the computational domain is also discretized to establish a CFD model. The CFD model includes the computational domain, grid division, boundary conditions, and turbulence model.

5. The method for predicting the speed and power of large bulk carriers according to claim 4, characterized in that, In the shape factor calculation step, the correction factor method and the grid convergence index method are also used to evaluate the uncertainty of the CFD model numerical simulation, determine the grid spacing and time step, and compare the CFD model numerical simulation results of the ship model free surface with the experimental results according to the stable calculation strategy and time step to evaluate the effectiveness of the CFD model. According to the stable calculation strategy, Star CCM+ software is used to perform ship model CFD superimposed simulation based on the CFD model.

6. A speed and power prediction system for large bulk carriers, characterized in that, It includes a data acquisition module, a stability calculation strategy acquisition module, a shape factor calculation module, and a real ship speed and power calculation module, which are connected in sequence. The data acquisition module acquires the principal dimension parameter data of the actual large bulk carrier and the test data of the ship model obtained from the experiment. Based on the principal dimension parameter data of the actual ship, the principal dimension parameter data of the ship model is obtained. The stability calculation strategy acquisition module uses an unstructured mesh to divide the watershed within a certain range of the ship model, and creates prism layers on the hull and rudder surfaces of the ship model. Based on the thickness of the prism layers, a wall function is used to refine the prism layers and the divided mesh. Based on the refined mesh and according to the growth rate and number of prism layers, multiple mesh schemes are obtained. The hydrostatic resistance under each mesh scheme is calculated, and the difference between the hydrostatic resistance and the ship model resistance data in the ship model test data is compared. The mesh scheme with the smallest resistance error is obtained as the stability calculation strategy. The shape factor calculation module, based on a stable calculation strategy and using Star CCM+ software for CFD simulation of the ship model, obtains the frictional resistance and viscous-pressure resistance of the ship model at different time steps. This yields the first frictional resistance coefficient and viscous-pressure resistance coefficient of the ship model. Based on the principal dimension parameters of the actual ship and the ship model, as well as the ship model test data, and using the ITTC frictional resistance formula, the second frictional resistance coefficient of the ship model and the frictional resistance coefficient of the actual ship are calculated respectively. Finally, the first shape factor of the ship model is calculated based on the first frictional resistance coefficient, the second frictional resistance coefficient, and the viscous-pressure resistance coefficient, according to the following formula: Among them, C fm_cfd C is the coefficient of friction resistance. pvm_cfd C is the viscous pressure resistance coefficient. fm_ittc is the second frictional resistance coefficient, and 1+k is the first shape factor; The actual ship speed and power calculation module calculates the actual ship viscous resistance coefficient based on the actual ship's frictional resistance coefficient and the first shape factor. It also calculates the actual ship's total resistance coefficient based on the actual ship's viscous resistance coefficient, the ship model's second frictional resistance coefficient, and the ship model's total resistance coefficient from the ship model test data. Finally, it calculates the actual ship's total resistance and actual ship speed and power based on the actual ship's total resistance coefficient.

7. The large bulk carrier speed and power prediction system according to claim 6, characterized in that, In the shape factor calculation module, the second shape factor of the ship model is also calculated based on the first frictional resistance coefficient and the viscous pressure resistance coefficient. The data acquisition module also acquires sea trial data of a large bulk carrier, including speed, power, and seawater temperature. The main dimensional parameters include length, beam, draft, wetted surface area, displacement volume, and block coefficient. The shape factor calculation module uses uncertainty analysis to analyze the uncertainty of the model resistance data in the model test data, obtaining the uncertainty of the model resistance data. Based on the uncertainty result of the model resistance data, the Prohaska method or multiple empirical formulas for shape factors are used to calculate the third shape factor of the model. Then, based on the sea trial data, the accuracy of the first, second, and third shape factors in predicting the speed and power of the actual ship is judged. When it is determined that the accuracy of the first and second shape factors is higher than that of the third shape factor, and the accuracy of the first shape factor is higher than that of the second shape factor, the subsequent actual ship speed and power calculation module uses the first shape factor for calculation.

8. The large bulk carrier speed and power prediction system according to claim 6, characterized in that, In the stability calculation strategy acquisition module, the ship model is located within a certain range, including a certain distance from the bow, stern, hull symmetry plane, and free surface.

9. The large bulk carrier speed and power prediction system according to claim 8, characterized in that, In the stable calculation strategy acquisition module, after dividing the flow domain within a certain range of the ship model into a grid using an unstructured grid, the computational domain is also discretized to establish a CFD model. The CFD model includes the computational domain, grid division, boundary conditions, and turbulence model.

10. The large bulk carrier speed and power prediction system according to claim 9, characterized in that, In the shape factor calculation module, the uncertainty of the CFD model numerical simulation is evaluated by the correction factor method and the grid convergence index method, the grid spacing and time step are determined, and the CFD model numerical simulation results of the ship model free surface are compared with the experimental results according to the stable calculation strategy and time step to evaluate the effectiveness of the CFD model numerical simulation. According to the stable calculation strategy, the Star CCM+ software is used to perform CFD superimposed simulation of the ship model based on the CFD model.

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