A digital twin method for ship propulsion shafting

Abstract

The present application belongs to the technical field of ship propulsion plant design methods, and in particular relates to a digital twin method for ship propulsion shafting. The method comprises the following steps: establishing a geometric model of the propulsion shafting system; constructing a 1D prediction model of the shafting system; analyzing the dynamics of the shafting system; constructing a digital twin of the propulsion shafting system; building a connection between the digital twin and the entity; iterating the 1D prediction model of the digital twin; based on the 1D prediction model, the present application builds a visual three-dimensional geometric model, improves the overall digital twin level of the ship propulsion shafting system, enhances the visibility of the propulsion shafting system, and improves the life analysis, fault warning, and performance calculation capabilities of the digital twin of the propulsion shafting system; based on the real-time update of data from the data acquisition system and the comparison of historical operating data, a solution is provided for the update and iteration of the 1D prediction model of the ship propulsion shafting system.

Description

Technical Field

[0001] The present application belongs to the technical field of ship power plant design methods, and in particular relates to a digital twin method for ship propulsion shaft systems. Background Art

[0002] The propulsion shafting system, also known as the transmission shafting system, comprises drive shafts, connecting devices, support systems, and sealing devices, and is a crucial component of a ship's propulsion system. With the continuous advancement of ship design concepts, propulsion shafting systems are becoming increasingly complex, with higher application requirements. Furthermore, the shafting system's operating conditions significantly impact the reliability and maneuverability of the propulsion system. To ensure the design and application of propulsion shafting systems and minimize the likelihood of operational failures, shafting life analysis, fault warnings, and performance calculations are being conducted. Digital twinning methods are becoming increasingly mature, often integrating disciplines such as dynamics, thermodynamics, material mechanics, fluid mechanics, elasticity, and vibration dynamics to construct one-dimensional mathematical models. Traditional digital twinning methods typically focus on establishing mathematical models of the corresponding entities, or on the creation and verification of various types of models, with limited attention paid to model visualization. Entire propulsion shafting systems, on the other hand, only generate digital twins of individual components and lack visualization capabilities on mobile devices. Consequently, monitoring and predicting propulsion shafting conditions lacks comprehensiveness, integrativeness, and visibility. Summary of the Invention

[0003] The purpose of this application is to address the problem that the overall digital twin of the propulsion shaft system lacks comprehensiveness, integration and visibility. Taking into account the overall operating conditions, a mobile-based digital twin method for the propulsion shaft system is proposed to improve the reliability and visibility of the digital twin model.

[0004] To achieve the above objectives, this application adopts the following technical solutions.

[0005] A propulsion shaft system digital twin method of the present invention comprises the following steps:

[0006] Step 1: Establish the geometric model of the propulsion shaft system

[0007] Specifically, a virtual geometric model is established based on the physical propulsion shaft system object, in which the motor is used as the input end of the propulsion shaft system to provide propulsion power. The required data mainly include input power, speed, frequency, torque, external dimensions, etc.; the data required for the propulsion shaft system mainly include the overall layout of the shaft system, the inner and outer diameters of each shaft segment, the material properties of each shaft system component, the model and position of each bearing, the model and size of each connection part, etc.; the data required for the load end mainly include external dimensions, mass, output power, torque, etc.

[0008] Step 2: Build a 1D prediction model for the shaft system

[0009] Specifically, based on the propulsion shafting geometric model established in step 1, a propulsion shafting 1D prediction model is established. The calculation model includes:

[0010] Torque power calculation model: N = K * D 5 *ρ*n 2

[0011] P=N*n / 9550

[0012] Where N is the shafting output torque, K is the propeller torque coefficient, D is the propeller diameter, ρ is the water density, n is the shafting speed, and P is the shafting output power;

[0013] Step 3: Shafting Dynamics Analysis

[0014] Specifically, based on the propulsion shafting geometry model established in step 1, the finite element method is used to establish a vibration and alignment calculation model based on the rotor dynamics of the propeller shafting, including:

[0015] Total shaft weight:

[0016] Where G is the total weight of the shaft system, ρ i is the mass density of each shaft segment, D i is the outer diameter of each shaft segment, d i is the inner diameter of each shaft segment, l i is the length of each axis, G e is the total mass of other equipment in the propulsion shaft system, i is the number of shaft segments, and I is the total number of shaft segments;

[0017] Vibration calculation model of rotor dynamics equation:

[0018] Where [M] is the propulsion shaft system mass matrix, [C] is the propulsion shaft system damping matrix, [K] is the propulsion shaft system stiffness matrix, x is the propulsion shaft system displacement, F is the propulsion shaft system force, and t is the propulsion shaft system operation time;

[0019] Finite element method is used to establish the calibration calculation model:

[0020] [K]a 2n×1 =P 2n×1

[0021] Where a 2n×1 is the node displacement vector, P 2n×1 is the system load vector;

[0022] Step 4: Build a digital twin of the propulsion shaft system

[0023] Specifically, based on the geometric model in step one, and based on the requirements of visualization, interactivity, and feedback, the digital twin model of the propulsion shaft system is imported into the selected 3D modeling development engine; based on the computational analysis models in steps two and three, a suitable programming language is selected as the scripting language to write the relevant processing logic;

[0024] Step 5: Build the connection between digital twins and entities

[0025] Based on the digital twin in step 4, establish an information interaction and real-time update system between the virtual and physical propulsion shaft system.

[0026] Specifically, it means selecting the information data that needs to be collected, including: real-time shaft speed, shaft output torque, shaft output power, shaft vibration frequency, shaft vibration amplitude, shaft rotation time, bending stress of each shaft section of the shaft, and reaction force of each support bearing of the shaft.

[0027] Import the collected information data into the human-computer interaction software and link it with the parameters in the propulsion shaft system digital twin on the mobile terminal. Use the programming language to mobilize the corresponding calculation software to solve the calculation and analysis models in steps two and three.

[0028] Step 6: Digital Twin 1D Prediction Model Iteration

[0029] Specifically, based on the solution results in step 5, the rule model is constructed, including:

[0030] Create boundary conditions:

[0031] a. The shaft speed is less than the critical speed, that is, n <n cr ;

[0032] b. The bending stress σ at each shaft end of the shaft system is not greater than the maximum allowable stress [σ], that is, σ<[σ];

[0033] c. The difference in reaction force between the front and rear bearings of the large gear does not exceed 20% of the total weight of the span shaft section and the large gear, that is, ΔN<0.2G C ;

[0034] Based on the experimental data after connecting to the entity, when the experimental error is greater than 2.5%, data calibration is performed, the entity data is re-measured and fed back to the digital twin, the static characteristic indicators of the sensor instrument are determined, the systematic error value is eliminated, and the similarity between the digital twin and the entity is improved;

[0035] Further refinements to the aforementioned propulsion shaft system digital twin method include utilizing a suitable 3D engine to achieve comprehensive observation and visualization of the propulsion shaft system digital twin model through animation and free-viewpoint rotation, displaying corresponding calculation and analysis results at corresponding locations, and enabling processing of the results. Processing methods include storage, export, drawing, and simulation calculations.

[0036] Its beneficial effects are:

[0037] 1) Based on 3D modeling software and 1D prediction models, the calculation results are displayed in a visual form, which enhances the efficiency of human-computer data interaction, makes the expression of calculation results more consistent with the human brain's understanding, simplifies complex data, and makes important data intuitive and easy to understand;

[0038] 2) The construction of digital twins based on entities can achieve simulation experiments with smaller errors, reduce design problems, improve design efficiency, and realize the propulsion shaft system digital twin to perform life analysis, fault warning and performance calculation on the entity.

[0039] 3) Based on the accumulation of real data, the digital twin model can be updated and iterated, eliminating the errors caused by the data acquisition system of the digital twin, improving the accuracy of the digital twin, and enhancing the fault prediction capability of the propulsion shaft system digital twin. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart for the implementation of the shafting digital twin approach is provided;

[0041] Figure 2 Build the client software framework for digital twins. DETAILED DESCRIPTION

[0042] The present application is described in detail below with reference to specific embodiments.

[0043] This application is a propulsion shafting digital twin method, which is mainly used in the technical design and design application stages of ship shafting. In order to improve the level of propulsion shafting digital twins, enhance the visibility of propulsion shafting, improve the level of propulsion shafting design and application, and improve the life analysis, fault warning, and performance calculation capabilities of propulsion shafting digital twins, in the propulsion shafting digital twin design method, based on the 1D prediction model, a visual 3D geometric model is built, and certain technical means are used to connect it with the entity, perform real-time updates of state parameters, and realize the overall design application of the propulsion shafting digital twin.

[0044] The basic principle of the digital twin method of ship propulsion shafting in this application is as follows Figure 1 As shown, the basic steps include:

[0045] Step 1: Establish a geometric model of the propulsion shaft system. Specifically, a virtual geometric model is established based on the physical propulsion shaft system object. The motor is used as the input end of the propulsion shaft system to provide propulsion power. The required data mainly includes input power, speed, frequency, torque, and external dimensions. The data required for the propulsion shaft system mainly includes the overall layout of the shaft system, the inner and outer diameters of each shaft segment, the material properties of each shaft system component, the model and position of each bearing, and the model and size of each connection part. The data required for the load end mainly includes external dimensions, mass, output power, torque, etc.

[0046] Step 2: Construct a 1D prediction model for the shaft system. Specifically, based on the geometric model of the propulsion shaft system established in step 1, a 1D prediction model for the propulsion shaft system is constructed. The calculation model includes:

[0047] Torque power calculation model: N = K * D 5 *ρ*n 2 and P = N*n / 9550;

[0048] Where N is the shafting output torque, K is the propeller torque coefficient, D is the propeller diameter, ρ is the water density, n is the shafting speed, and P is the shafting output power;

[0049] Step 3: Shafting dynamics analysis. Specifically, based on the propulsion shafting geometry model established in step 1, the finite element method is used to establish a vibration and alignment calculation model based on the rotor dynamics of the propeller shafting, including:

[0050] Where G is the total weight of the shaft system, ρ i is the mass density of each shaft segment, D i is the outer diameter of each shaft segment, d i is the inner diameter of each shaft segment, l i is the length of each axis, G e is the total mass of other equipment in the propulsion shaft system, i is the shaft segment number, and I is the total number of shaft segments;

[0051] Vibration calculation model of rotor dynamics equation:

[0052] Where [M] is the propulsion shaft system mass matrix, [C] is the propulsion shaft system damping matrix, [K] is the propulsion shaft system stiffness matrix, x is the propulsion shaft system displacement, F is the propulsion shaft system force, and t is the propulsion shaft system operation time;

[0053] Using the finite element method, the calibration calculation model is established: [K]a 2n×1 =P 2n×1 ;

[0054] Where a 2n×1 is the node displacement vector, P2n×1 is the system load vector;

[0055] Step 4: Build a digital twin of the propulsion shaft system.

[0056] Specifically, based on the geometric model in step one, and based on the requirements of visualization, interactivity, and feedback, the digital twin model of the propulsion shaft system is imported into the selected 3D modeling development engine; based on the computational analysis models in steps two and three, a suitable programming language is selected as the scripting language to write the relevant processing logic;

[0057] Step 5: Build the connection between the digital twin and the entity.

[0058] Based on the digital twin in step 4, establish an information interaction and real-time update system between the virtual and physical propulsion shaft system. Specifically, select the information data to be collected, including:

[0059] a. Real-time shaft speed;

[0060] b. Shaft output torque;

[0061] c. Shaft output power;

[0062] d. Shaft vibration frequency;

[0063] e. Shaft system vibration amplitude;

[0064] f. Shaft rotation time;

[0065] g. Bending stress of each shaft segment of the shaft system;

[0066] h. The reaction force of each supporting bearing of the shaft system;

[0067] Import the collected information data into the human-computer interaction software and link it with the parameters in the propulsion shaft system digital twin on the mobile terminal. Use the programming language to mobilize the corresponding calculation software to solve the calculation and analysis models in steps two and three.

[0068] Step 6: Iterate the digital twin 1D prediction model.

[0069] Specifically, based on the solution results in step 5, the rule model is constructed, including: establishing boundary conditions:

[0070] a. The shaft speed is less than the critical speed, that is, n <n cr ;

[0071] b. The bending stress σ at each shaft end of the shaft system is not greater than the maximum allowable stress [σ], that is, σ<[σ];

[0072] c. The difference in reaction force between the front and rear bearings of the large gear does not exceed 20% of the total weight of the span shaft section and the large gear, that is, ΔN<0.2G C;

[0073] Based on the experimental data after connecting to the entity, when the experimental error is greater than 2.5%, data calibration is performed, the entity data is re-measured and fed back to the digital twin, the static characteristic indicators of the sensor instrument are determined, the systematic error value is eliminated, and the similarity between the digital twin and the entity is improved;

[0074] Utilizing a suitable 3D engine, animation and free-viewing rotation enable comprehensive observation and visualization of the propulsion shaft system digital twin model, displaying corresponding calculation and analysis results at the corresponding location, and allowing for processing of the results. Processing methods include storage, export, drawing, and simulation calculations.

[0075] The following describes the research method of this invention using a propulsion shafting system and verifies the feasibility of the digital twin method for this propulsion shafting system. The example is based on a propulsion shafting test bench. The shafting system consists of two intermediate shafts, a stern shaft, a thrust bearing, two intermediate bearings, two stern bearings, a load hydraulic cylinder, a large mass disk, several flanges, and couplings.

[0076] First, a geometric model of the propulsion shaft system was established based on the propulsion shaft system design assembly drawing. To improve calculation accuracy and the visibility of the digital twin, the propulsion shaft system rig was modeled as a whole, including the flanges and couplings on the rig. Based on the geometric model of the propulsion shaft system, a mesh was then generated.

[0077] Select appropriate software as the digital twin development engine, import the propulsion shaft system geometry model into the software, and select a suitable programming language as the scripting language to write the processing logic related to the propulsion shaft system statics and dynamics analysis. The digital twin software is developed with the pop-up management module as the core to implement the respective logic. Data required for transmission between components is exchanged through the common data module. The corresponding data obtained by the data acquisition system is imported into the common data module and assigned to the relevant units for real-time data interaction and update.

[0078] Analyze the static and dynamic calculation results, with the goal of ensuring that the calculated output torque and power have an error of less than 2.5% compared to the actual output torque and power. Perform data calibration and add boundary conditions:

[0079]

[0080] Where n0 is the actual speed of the propulsion shaft system, Δn is the difference between the speed of the digital twin and the actual speed, N0 is the actual output torque of the propulsion shaft system, ΔN is the difference between the output torque of the digital twin and the actual torque, and σ is the bending stress at each shaft end.

[0081] Table 1 Comparison between actual torque power and predicted torque power

[0082] Serial number Actual speed (r / min) Actual power (kW) Predicted power (kW) error 1 100 0.23 0.24 4.3％ 2 200 1.99 1.93 3.0％ 3 500 30.6 30.1 1.6％ 4 1000 244.5 240.83 1.5％

[0083] Combined with Table 1, this method is used to generate digital twins of the propulsion shaft system. The error between the digital twin and the propulsion shaft system entity is small, and the static and dynamic analysis data are valid. Overall, when the shaft system speed, torque, and bending moment stress are within the specified range and the data acquisition system is normal, the digital twin obtained by this method has good accuracy and high visualization, achieving the expected goal. The final digital twin client software architecture is as follows: Figure 2 shown.

[0084] Due to computer hardware limitations, the digital twin dynamics calculation accuracy of the digital twin method used in this embodiment is limited. This serves only to verify the effectiveness of the proposed method. For different objects, digital twin personnel can calibrate target data based on actual conditions and then iterate the calculation model to obtain more accurate propulsion shafting dynamics analysis results.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the scope of protection of the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the essence and scope of the technical solutions of the present application.

Claims

1. A propulsion shaft system digital twin method, characterized in that: The steps include: Step 1: Establish a geometric model of the propulsion shaft system. Specifically, a virtual geometric model is established based on the physical propulsion shaft system object, shaft system object data is collected, and the motor is used as the input end of the propulsion shaft system to provide propulsion power. Step 2: Constructing a 1D prediction model for the shaft system. Specifically, based on the geometric model of the propulsion shaft system established in step 1, a 1D prediction model for the propulsion shaft system is constructed. Step 3: Shafting dynamics analysis: Specifically, based on the propulsion shafting geometry model established in Step 1, the finite element method is used to establish a vibration and alignment calculation model based on the rotor dynamics of the propeller shafting. Step 4: Build a digital twin of the propulsion shaft system. Specifically, based on the geometric model from Step 1, import the digital twin of the propulsion shaft system into the selected 3D modeling development engine, ensuring visualization, interactivity, and feedback. Based on the 1D prediction model of the propulsion shaft system from Step 2 and the vibration and alignment calculation model from Step 3, select an appropriate programming language as the scripting language to write the relevant processing logic. Step 5: Build the connection between the digital twin and the entity. Based on the digital twin in step 4, establish an information interaction and real-time update system between the virtual body and the entity of the propulsion shaft system. Specifically, the information data to be collected is selected, imported into the human-computer interaction software, and linked with the parameters in the propulsion shaft system digital twin on the mobile terminal. The corresponding calculation software is then mobilized through the programming language to solve the propulsion shaft system 1D prediction model in step 2 and the vibration and alignment calculation model in step 3. Step 6: Iterate the digital twin 1D prediction model. Specifically, based on the solution results in step 5, build a rule model, including: Establish boundary conditions and, based on the experimental data after connection with the entity, perform data calibration when the experimental error is greater than 2.5%. Remeasure the entity data and feed it back to the digital twin to determine the static characteristic indicators of the sensor instrument, eliminate the system error value, and improve the similarity between the digital twin and the entity.

2. The propulsion shafting digital twin method according to claim 1, characterized in that: The shaft system object data includes: motor input power, speed, frequency, torque, and overall dimensions; overall layout of the propulsion shaft system, inner and outer diameters of each shaft segment, material properties of each shaft system component, model and position of each bearing, model and size of each connecting part; overall dimensions, mass, output power, and torque of the load end part.

3. The propulsion shafting digital twin method according to claim 1, characterized in that: The propulsion shafting 1D prediction model in step 2 includes: Torque power calculation model: and ; Where N is the shafting output torque, K is the propeller torque coefficient, D is the propeller diameter, ρ is the water density, n is the shafting speed, and P is the shafting output power.

4. The propulsion shafting digital twin method according to claim 1, characterized in that: The vibration and alignment calculation model based on the rotor dynamics of the propeller shaft system in step 3 specifically includes: Total shaft weight: ; Where G is the total weight of the shaft system, is the mass density of each shaft segment, D i is the outer diameter of each shaft segment, d i is the inner diameter of each shaft segment, is the length of each axis, G e To propel the total mass of other equipment in the shaft system, is the number of shaft segments, is the total number of shaft segments; Vibration calculation model of rotor dynamics equation: ; Where [M] is the propulsion shaft system mass matrix, [C] is the propulsion shaft system damping matrix, [K] is the propulsion shaft system stiffness matrix, x is the propulsion shaft system displacement, F is the propulsion shaft system force, and t is the propulsion shaft system operation time; Finite element method is used to establish the calibration calculation model: ; Where, is the node displacement vector, is the system load vector.

5. The propulsion shafting digital twin method according to claim 1, characterized in that: The boundary conditions in step 6 specifically refer to: a. The shaft speed is less than the critical speed, that is, n <n cr ; b. The bending stress at each shaft end of the shaft system is not greater than the maximum allowable stress, that is, ; c. The difference in reaction force between the front and rear bearings of the large gear does not exceed 20% of the total weight of the span shaft section and the large gear, that is, ΔN<0.2G C .

6. The propulsion shafting digital twin method according to claim 1, characterized in that: The information data that needs to be collected in step five include: real-time shaft rotation speed, shaft output torque, shaft output power, shaft vibration frequency, shaft vibration amplitude, shaft rotation time, bending stress of each shaft segment of the shaft, and reaction force of each support bearing of the shaft.

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