A shaftless propeller blade design method based on the Isight platform

Abstract

The present invention discloses a shaftless propeller blade design method based on the Isight platform, comprising the following steps: analyzing the working process of the shaftless propeller to obtain parameters of the shaftless propeller blade having a strong performance correlation with the shaftless propeller; setting the parameters of the shaftless propeller blade as optimization parameters, and setting the thrust and efficiency of the shaftless propeller as optimization targets; collecting sample data; based on a sample database, selecting a Kriging method to establish a relationship between the parameters of the shaftless propeller blade and the thrust and efficiency of the shaftless propeller and establishing an approximate model; selecting a multi-objective particle swarm algorithm as an optimization algorithm for the approximate model; performing optimization calculations to obtain the parameters of the shaftless propeller blade. The shaftless propeller blade design method based on the Isight platform of the present invention can quickly and accurately obtain the optimal parameters of the blade, which can not only reduce the calculation and experimental costs of the shaftless propeller design process, but also significantly reduce the prediction time and improve the design efficiency of the shaftless propeller.

Description

Technical Field

[0001] The present invention relates to the technical field of marine propeller blade design, and in particular to a shaftless propeller blade design method based on the Isight platform. Background Art

[0002] With the gradual development of the shipping industry in recent years, the demand for ship propulsion efficiency and cabin space utilization has increased, necessitating larger shafting transmission components for conventional ship propulsion systems. Traditional ship propulsion systems require shafting systems that penetrate the hull to connect components such as the propeller, which not only consumes a significant amount of cabin space but also increases energy loss and reduces propulsion efficiency. Furthermore, the complex shafting system significantly increases noise and shafting vibration during navigation, reducing the ship's stealth and stability. To address this situation, the concept of shaftless propulsion has begun to emerge. Thanks to the integration of the motor and rotor blades, shaftless propulsion eliminates the traditional cabin propulsion shafting and sealing system, avoiding the complex structural design of pod-type and azimuth-type electric propulsion systems. This significantly improves cabin space utilization and propulsion efficiency.

[0003] As a new type of propulsion system, the shaftless propulsion system combines many of the advantages of podded and ducted propulsion systems. Thanks to the integration of the motor rotor and propeller blades, the shaftless propulsion system eliminates the traditional cabin propulsion shafting and sealing system, avoiding the complex shafting design of traditional propulsion systems. This offers numerous advantages in ship design, propulsion efficiency, vibration and noise reduction, manufacturing, and maintenance.

[0004] The propeller blades are the core component of a shaftless propulsion system. Their rotation converts the engine's rotational power into propulsion. Therefore, the design of the propeller blades has a significant impact on the propulsion performance of a ship, making it a key research area for improving propulsion performance.

[0005] The existing technology has at least the following problems:

[0006] As the core component of the shaftless propeller, the design of the blade directly affects the performance of the shaftless propeller. In the design process of traditional shaftless propeller blades, the geometric parameters of the blades are often changed first, and then numerical simulation is performed using computational fluid dynamics software or testing is performed using a test platform. The thrust and torque and other performance parameters of the shaftless propeller before and after the blade parameters are modified are compared until the blade with the best performance parameters is obtained. When predicting the performance parameters of the shaftless propeller, the traditional design method of the shaftless propeller blade requires the reconstruction of the geometric model of the blade and the processing of new test samples. This not only takes a lot of time and the cost of processing samples, but also has a low degree of automation, a long blade design cycle, and low efficiency. Summary of the Invention

[0007] Based on the above-mentioned deficiencies in the existing technology, the technical problem to be solved by the present invention is to provide a shaftless propeller blade design method based on the Isight platform, which can quickly and accurately obtain the optimal parameters of the blades, which can not only reduce the calculation cost and test cost of the shaftless propeller design process, but also greatly reduce the prediction time and improve the design efficiency of the shaftless propeller.

[0008] In order to achieve the above-mentioned purpose, the present invention adopts the following technical measures:

[0009] A shaftless propeller blade design method based on the Isight platform includes the following steps:

[0010] Step 1: Analyze the working process of the shaftless propeller to obtain the parameters of the shaftless propeller blade that are highly correlated with the performance of the shaftless propeller;

[0011] Step 2: Set the parameters of the shaftless propeller blades as optimization parameters, and set the thrust and efficiency of the shaftless propeller as optimization targets;

[0012] Step 3: Collect sample data and set up the experimental design component in the Isight platform. At the same time, determine the value range and reference value of the parameters of the shaftless propeller blades, and set the thrust and efficiency of the shaftless propeller as the output response parameters;

[0013] Step 4: Based on the sample database, the Kriging method is selected to establish the relationship between the parameters of the shaftless propeller blades and the thrust and efficiency of the shaftless propeller, and an approximate model is established. The error analysis model provided by the Isight platform is used to evaluate the goodness of fit of the approximate model and verify the rationality of the established approximate model.

[0014] Step 5: Select the multi-objective particle swarm optimization algorithm as the optimization algorithm for the approximate model;

[0015] Step 6: After the optimization algorithm of the approximate model is completed, an optimization calculation is performed to obtain the parameters of the shaftless propeller blades when the thrust and efficiency of the shaftless propeller are optimal.

[0016] Preferably, in step 1, the parameters of the shaftless propeller blades include the blade disk ratio, the blade pitch ratio, and the advance coefficient.

[0017] Furthermore, in step 3, the disk ratio of the shaftless propeller blades, the pitch ratio of the blades and the advance coefficient and the corresponding performance parameters of the maximum thrust and torque of the shaftless propeller are established into a database, and then the number of samples is determined based on the range of the pitch ratio of the shaftless propeller blades, the disk ratio of the blades and the advance coefficient.

[0018] Preferably, in step 2, the optimal Latin hypercube method is used for experimental design.

[0019] Furthermore, in step 4, the Kriging method is used to construct an approximate model, and the pitch ratio, disk ratio, and advance coefficient of the shaftless propeller blades are selected as input variables, and the thrust and efficiency of the shaftless propeller are selected as output variables.

[0020] From the above, the shaftless propeller blade design method based on the Isight platform of the present invention collects the performance parameters of the shaftless propeller when it is working, analyzes the working mechanism of the shaftless propeller, and obtains factors that are highly correlated with the performance of the shaftless propeller. The parameters of the disk ratio of the blades of the shaftless propeller, the pitch ratio of the blades, the advance coefficient, and the speed of the shaftless propeller are established into a database together with the thrust and efficiency results of the shaftless propeller corresponding thereto. Based on the Isight platform, an approximate model is constructed and verified, and a suitable optimization algorithm is selected for optimization calculation to obtain the thrust and efficiency of the shaftless propeller, the pitch ratio of the blades of the shaftless propeller, the disk ratio of the blades, and the advance coefficient parameters when the thrust and efficiency of the shaftless propeller are optimal.

[0021] The proposed method for designing shaftless propeller blades based on the Isight platform can quickly and accurately determine optimal blade parameters. This not only reduces the computational and experimental costs of the shaftless propeller design process, but also significantly shortens prediction time, improving shaftless propeller design efficiency. This method not only provides new insights for the future optimization of shaftless propeller blades but also promotes the application of the Isight platform in research areas such as marine propeller blade design. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0023] Figure 1 is a flow chart of a design method for a shaftless propeller blade based on the Isight platform of the present invention;

[0024] Figure 2 is the R of the goodness of fit evaluation of the approximate model in the embodiment of the present invention 2 value (coefficient of determination) and RMS value (mean square error);

[0025] Figure 3 It is a setting diagram of parameters such as population size, number of subgroups and evolutionary generations in the multi-objective particle swarm algorithm in an embodiment of the present invention;

[0026] Figure 4 1 is an optimization result diagram of the design method of the shaftless propeller blade based on the Isight platform in the present invention;

[0027] Figure 5 This is a comparison chart of the efficiency of the new blade obtained by the design method of the shaftless propeller blade based on the Isight platform in the embodiment of the invention and the original blade;

[0028] Figure 6 This is a comparison chart of the thrust coefficients of the new blades obtained by the design method of the shaftless propeller blade based on the Isight platform in the embodiment of the invention and the original blades. DETAILED DESCRIPTION

[0029] like Figures 1-6 As shown, a shaftless propeller blade design method based on the Isight platform of the present invention includes the following steps:

[0030] Step 1: Analyze the working process of the shaftless propeller and identify factors that are highly correlated with the performance of the shaftless propeller. The analysis identifies the blade disc ratio, blade pitch ratio, and advance coefficient that are highly correlated with the performance of the shaftless propeller.

[0031] Step 2: The pitch ratio of the shaftless propeller blades, the disk ratio of the blades, and the advance coefficient are set as optimization parameters, and the thrust and efficiency of the shaftless propeller are set as optimization targets. Considering that the full factorial experimental design will reduce the accuracy of the model when the number of samples is large, the optimal Latin hypercube experimental design method can retain relatively complete information and will not reduce the calculation accuracy of the model. In addition, compared with the orthogonal experimental design, the optimal Latin hypercube method is more flexible and can retain more design parameters at the same time without reducing the number of samples. Therefore, the present invention uses the optimal Latin hypercube method for experimental design.

[0032] Step 3: Collect sample data. Sample points can usually be obtained through experimental design, numerical simulation, physical experiments, empirical databases and other methods. The geometric parameters of the disk ratio of the shaftless propeller blades, the pitch ratio of the blades, the advance coefficient, the operating parameters of the speed and the corresponding performance parameters of the maximum thrust and torque of the shaftless propeller are established into a database. Then, according to the range of the pitch ratio of the shaftless propeller blades, the disk ratio of the blades, and the advance coefficient in the experimental design of the present invention, the number of samples is determined. After selecting the appropriate experimental design method and sample number, the experimental design component can be set in the Isight platform, and the value range and reference value of the pitch ratio of the shaftless propeller blades, the disk ratio of the blades, and the advance coefficient can be determined at the same time, and the thrust and efficiency of the shaftless propeller are set as output response parameters. The range of the shaftless propeller geometric parameters and operating condition parameters used to establish the database is:

[0033] Blade parameters: The blade area ratio ranges from 0.6 to 1.1, with an interval of 0.1;

[0034] The pitch ratio of the blades ranges from 0.6 to 1.3, with an interval of 0.1;

[0035] Working condition parameters: the speed coefficient range is 0.1 to 0.9, with an interval of 0.1;

[0036] After selecting the appropriate experimental design method and sample size, the experimental design components can be set up in the Isight platform. The pitch ratio and disk ratio of the shaftless propeller blades, the range and reference value of the advance coefficient, and the thrust and efficiency of the shaftless propeller can be set as output response parameters.

[0037] Step 4: Based on the sample database, select a suitable algorithm to establish the relationship between the pitch ratio of the shaftless propeller blades and the disk ratio of the blades, the speed coefficient and the thrust and efficiency of the shaftless propeller and establish an approximate model. The Isight platform provides a variety of commonly used algorithm models, such as response surface method (RSM), neural network (RBF / EBF), cross polynomial model (Chebyshev), Kriging method (Kriging), etc. The present invention belongs to nonlinear problems. The Kriging method (Kriging) has a better fitting effect on problems with higher nonlinearity, and its effectiveness is not affected by random errors. Therefore, the present invention uses the Kriging method to construct an approximate model, selects the pitch ratio of the shaftless propeller blades and the disk ratio of the blades, and the speed coefficient as input variables, and the thrust and efficiency of the shaftless propeller as output variables. After the approximate model is established, the R2 value (determination coefficient) and RMS value (mean square error) are used to evaluate the goodness of fit of the approximate model to verify the rationality of the established approximate model.

[0038] In this embodiment of the invention, an approximate model is constructed using the Kriging method. The pitch ratio and blade surface ratio of the shaftless propeller blades, as well as the advance coefficient, are selected as input variables, and the thrust and efficiency of the shaftless propeller are selected as output variables. Table 1 shows the polynomial coefficients of the approximate model, where x01 is the blade pitch ratio, x02 is the blade surface ratio, x03 is the shaftless propeller speed, and x04 is the advance coefficient.

[0039] Table 1 Polynomial coefficients of the approximate model

[0040]

[0041] After the approximate model is established, the error analysis model provided by the Isight platform is used to evaluate the approximate model's fit and verify the rationality of the established approximate model. Figure 2It can be seen that the R2 value and RMS value are 0.99732 and 0.99555, 0.01197 and 0.01833 respectively. The former value is close to 1 and the latter value is close to 0. The test sample points are all close to the actual results, indicating that the calculated value is slightly different from the actual value, the accuracy of the approximate model is high, the approximate model has a good fit with the actual sample points, and the approximate model is reliable.

[0042] Step 5: After the approximate model is established and verified, the multi-objective particle swarm algorithm is selected as the optimization algorithm of the approximate model. The optimization algorithm is mainly divided into: direct search method and global exploration method. The difference between the global exploration method and the direct search method is that the global exploration method searches for the optimal solution in the entire parameter space, which can effectively avoid obtaining the local optimal solution. Therefore, the present invention uses the global exploration method. Common global exploration methods include: multi-island genetic (MIGA) algorithm, adaptive simulated annealing (ASA) algorithm, particle swarm (PSO) algorithm and automatic optimization expert (Pointer) algorithm. The present invention will select the multi-objective particle swarm algorithm for optimization. The settings of parameters such as population size, number of subgroups and evolutionary generations in the multi-objective particle swarm algorithm are as follows: Figure 3 shown.

[0043] Step 6: After the optimization algorithm of the approximate model is completed, the optimization calculation can be performed to obtain the parameters of the pitch ratio, disk ratio and advance coefficient of the shaftless propeller blade when the thrust and efficiency of the shaftless propeller are optimized. The calculation results are as follows: Figure 4 The optimal parameters are a pitch ratio of 1.55, a disk ratio of 0.95, and an advance coefficient of 0.1, which correspond to a thrust of 34.634 N and a torque of 0.694 N·m.

[0044] Figure 5 and Figure 6 The efficiency and thrust coefficients of the new blades obtained by the design method of the shaftless propeller blades based on the Isight platform in the embodiment of the invention were compared with those of the original blades. The results showed that the efficiency and thrust of the new blades were improved compared with the original blades, thereby proving that the design method of the shaftless propeller blades based on the Isight platform disclosed in the present invention can quickly and accurately obtain the optimal design parameters of the blades.

[0045] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be understood by anyone familiar with the technology within the technical scope disclosed by the present invention should be included in the scope of the present invention.

Claims

1. A shaftless propeller blade design method based on the Isight platform, characterized in that: The following steps are involved: Step 1: Analyze the working process of the shaftless propeller to obtain the parameters of the shaftless propeller blade that are highly correlated with the performance of the shaftless propeller; Step 2: Set the parameters of the shaftless propeller blades as optimization parameters, and set the thrust and efficiency of the shaftless propeller as optimization targets; Step 3: Collect sample data and set up the experimental design component in the Isight platform. At the same time, determine the value range and reference value of the parameters of the shaftless propeller blades, and set the thrust and efficiency of the shaftless propeller as the output response parameters; Step 4: Based on the sample database, the Kriging method is selected to establish the relationship between the parameters of the shaftless propeller blades and the thrust and efficiency of the shaftless propeller, and an approximate model is established. The error analysis model provided by the Isight platform is used to evaluate the goodness of fit of the approximate model and verify the rationality of the established approximate model. Step 5: Select the multi-objective particle swarm optimization algorithm as the optimization algorithm for the approximate model; Step 6: After the optimization algorithm of the approximate model is completed, an optimization calculation is performed to obtain the parameters of the shaftless propeller blades when the thrust and efficiency of the shaftless propeller are optimal.

2. The shaftless propeller blade design method based on the Isight platform according to claim 1 is characterized in that: In step 1, the parameters of the shaftless propeller blade include the blade's disk ratio, the blade's pitch ratio, and the advance coefficient.

3. The shaftless propeller blade design method based on the Isight platform according to claim 2, characterized in that: In step 3, the disk ratio, pitch ratio and advance coefficient of the shaftless propeller blades and their corresponding performance parameters of the maximum thrust and torque of the shaftless propeller are established into a database, and then the number of samples is determined based on the range of the pitch ratio, disk ratio and advance coefficient of the shaftless propeller blades.

4. The shaftless propeller blade design method based on the Isight platform according to claim 1, characterized in that: In step 2, the optimal Latin hypercube method is used for experimental design.

5. The shaftless propeller blade design method based on the Isight platform according to claim 2, characterized in that: In step 4, the Kriging method is used to construct an approximate model, and the pitch ratio, disk ratio, and advance coefficient of the shaftless propeller blades are selected as input variables, and the thrust and efficiency of the shaftless propeller are selected as output variables.

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