Underwater vehicle modeling method and system based on digital twinning

Abstract

The application discloses a kind of underwater vehicle modeling method and system based on digital twinning, it is related to a kind of modeling method applied to underwater vehicle multidisciplinary hyper-realistic numerical simulation;Including underwater vehicle physical entity, detection system, information transmission system, underwater vehicle digital twinning model, twinning model application system, underwater vehicle digital twinning model and underwater vehicle physical entity real-time virtual mapping can carry out multidisciplinary collaborative simulation of hyper-realistic;Twinning model application system can not only provide predictive guidance for underwater vehicle physical entity operation, but also provide a large number of hyper-realistic simulation model databases that can be benchmarked for underwater vehicle each scale model simulation, ensure the accuracy of underwater vehicle each working condition, multidisciplinary numerical simulation, improve the credibility of underwater vehicle multidisciplinary design optimization, shorten development cycle, save design cost.

Description

Technical Field

[0001] This invention belongs to the field of digital twin technology, specifically relating to a modeling method and system for underwater vehicles based on digital twins. Background Technology

[0002] Digital twins utilize mechanistic models, real-time sensor data, operational history data, and expert knowledge to construct real-time, "dynamic" twin models with multi-disciplinary and multi-scale coupling characteristics. They enable precise simulation and mapping of the form and performance of real physical entities in virtual space. The constructed twin model can be used to simulate, analyze, and optimize the real physical entity, assisting it in achieving intelligent and optimized control and operation, and realizing its optimal output performance, thus reflecting the entire lifecycle of the corresponding physical equipment. A digital twin can be viewed as a digital mapping system of one or more important, interdependent equipment systems. Internationally, digital twin technology is considered one of the leading technologies for future defense, and its development in areas such as fault prediction and health management of flight systems like aircraft and launch vehicles is rapid. Scholars from various countries have conducted research on this technology in numerous fields.

[0003] Hydrodynamic performance, propeller-driven propulsion performance, structural mechanics performance, acoustic stealth performance, and motion control capabilities are crucial performance indicators for underwater vehicles. However, due to limitations in experimental conditions, most existing data rely on simulation methods to study underwater vehicles. Many scholars focus solely on individual performance indicators, neglecting the interrelationships between other performance metrics. A few scholars have conducted multidisciplinary and multi-objective studies on underwater vehicles, but these studies often focus on a few disciplines within a specific operating condition. Furthermore, they overlook the fact that the surrounding marine environment changes in real time during underwater vehicle operation. Boundary conditions such as seawater velocity, direction, density, and temperature change with the underwater vehicle's operating conditions, thus failing to reflect the real-time, dynamic, multi-condition operating status of the underwater vehicle. Consequently, it is impossible to monitor the various performance indicators of underwater vehicles in real time, nor to predict and optimize related indicators and parameters under multiple operating conditions. Secondly, there is currently a lack of reliable, credible, and hyper-realistic databases for multi-condition and multi-disciplinary collaborative simulation of underwater vehicles, which limits the development of multi-disciplinary high-fidelity design optimization for underwater vehicles. Summary of the Invention

[0004] To address the aforementioned technical challenges, this invention proposes a digital twin-based underwater vehicle modeling method and system. Through digital twin technology, the physical entity of the underwater vehicle is mapped in real-time to its digital twin model, enabling the multidisciplinary collaborative simulation model to reflect the operational status of the physical entity in real time. The twin database of the twin model application system not only provides guidance for the operation of the physical entity but also offers a highly realistic, benchmarkable multidisciplinary collaborative simulation model database for underwater vehicle design optimization, thereby improving the accuracy and efficiency of underwater vehicle design optimization.

[0005] To achieve the objectives of this invention, a method and system for modeling underwater vehicles based on digital twins are provided. The system comprises a physical entity of the underwater vehicle, a detection system, an information transmission system, a digital twin model of the underwater vehicle, and a twin model application system. The digital twin model of the underwater vehicle includes at least several multidisciplinary digital twin sub-models, such as a hydrodynamic digital twin sub-model, a propeller-driven propulsion digital twin sub-model, a structural performance digital twin sub-model, a noise radiation digital twin sub-model, and a motion performance digital twin sub-model. These sub-models are mapped in real-time between the physical entity of the underwater vehicle and the detection system and the information transmission system. The multiple multidisciplinary digital twin sub-models of the hydrodynamic digital twin sub-model, propeller-driven propulsion digital twin sub-model, structural performance digital twin sub-model, noise radiation digital twin sub-model, and motion performance digital twin sub-model are used in this system. The digital twin sub-model integrated into a multidisciplinary simulation integration and optimization design software platform enables high-fidelity collaborative simulation and multidisciplinary, multi-objective optimization of the underwater vehicle's digital twin model. The simulation process and results of the underwater vehicle's digital twin model, along with real-time physical detection data and historical operational data collected by the detection system during the actual operation of the underwater vehicle's physical entity, constitute the twin database of the twin model application system. This twin database, after data analysis and processing, can provide predictive guidance for the operation of the underwater vehicle's physical entity and offer a large database of comparable hyper-realistic simulation models for simulations of various scale models of the underwater vehicle. This ensures the accuracy of multidisciplinary numerical simulations of the underwater vehicle under various operating conditions and improves the credibility of the underwater vehicle's design optimization. Specifically, the steps include:

[0006] S1. Construct a digital twin model of the underwater vehicle based on the physical characteristics and environmental conditions of the underwater vehicle physical entity;

[0007] S2. Based on the digital twin model of the underwater vehicle established in S1, simulations are performed simultaneously on the underwater vehicle's hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance.

[0008] S3. The detection system collects real-time physical detection data, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, as well as the environmental conditions described in S1, of the underwater vehicle physical entity during actual operation. The environmental conditions of the underwater vehicle physical entity are transmitted to the underwater vehicle digital twin model in real time through the information transmission system by establishing a communication protocol between the system and the model. After processing, the real-time physical detection data is compared in real time with the simulation results corresponding to the underwater vehicle digital twin model in S2 to correct the model.

[0009] S4. Integrate the underwater vehicle digital twin sub-model that was corrected in real time in S3 into the multidisciplinary simulation integration and optimization design software platform. Build the underwater vehicle digital twin model that is real-time mapped and corrected to the physical entity of the underwater vehicle through the detection system and the information transmission system, and perform high-fidelity multidisciplinary collaborative simulation.

[0010] S5. Based on the process and results of high-fidelity multidisciplinary collaborative simulation of the underwater vehicle digital twin model described in S4, and the real-time physical detection data and historical operation data of the underwater vehicle physical entity collected in real time by the detection system during actual operation, a twin database of the twin model application system is constructed.

[0011] S6. Perform intelligent data analysis and processing on the data in the twin database of the twin model application system established in S5 to provide predictive guidance for the actual operation and management of the underwater vehicle physical entity.

[0012] S7. Based on the data in the twin database of the twin model application system described in S5, build high-fidelity multidisciplinary collaborative simulation models of different scales according to actual needs, including but not limited to underwater vehicle hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance.

[0013] S8. The multi-disciplinary collaborative simulation optimization of the high-fidelity models of underwater vehicles with different scales, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, as described in S7, shall be carried out according to the target requirements.

[0014] The physical characteristics of the underwater vehicle physical entity described in S1 include at least the geometric structural parameters, mass attributes, electrical control system structure, material properties, and the connections between its components. Each component includes at least the underwater vehicle hull, propeller propulsion system, and control system. The geometric structural parameters, mass attributes, electrical control system structure, material properties, and the connections between its components are obtained from the underwater vehicle physical entity's CAD drawings, electrical control diagrams, and material database. The geometric structural parameters include at least the reference area and propeller diameter of the underwater vehicle physical entity. The mass attributes include at least the mass, center of mass, and moment of inertia of each component and the underwater vehicle physical entity as a whole.

[0015] The environmental conditions of the underwater vehicle physical entity described in S1 are measured in real time by sensors in the detection system, including at least the flow velocity of the water around the underwater vehicle physical entity relative to the underwater vehicle physical entity, the flow velocity of the water entering the propeller, the flow direction of the water, the temperature of the water, the density of the water, the water pressure, the depth of the underwater vehicle physical entity, the rotational speed of the underwater vehicle physical entity's propeller, and the speed, angular velocity, acceleration, and angular acceleration of the underwater vehicle physical entity during movement.

[0016] The method and steps for constructing a digital twin model of an underwater vehicle as described in S1 are as follows:

[0017] S11. Use digital 3D software, including but not limited to Catia and SolidWorks, to create a 3D digital model that is consistent with the physical entity of the underwater vehicle.

[0018] S12. Based on the three-dimensional digital model established in S11, establish models including but not limited to the following: using finite element software ANSYS to establish a hydrodynamic computational fluid dynamics model that includes at least the underwater vehicle hull and the propeller propulsion system; a computational fluid dynamics model that includes at least the propeller propulsion system; a structural response model that reflects the structural performance of at least the underwater vehicle hull and the propeller propulsion system; using acoustic simulation software LMS virtual.Lab and Actran to establish a noise radiation model considering fluid-structure-acoustic coupling that includes at least the underwater vehicle hull and the propeller propulsion system; and using dynamic system modeling and simulation tools including but not limited to MATLAB Simulink to establish a control system model that is consistent with the physical entity of the underwater vehicle and can simulate the motion control performance of the physical entity of the underwater vehicle.

[0019] S13. The real-time environmental conditions of the underwater vehicle physical entity described in S1 are loaded as boundary conditions into the hydrodynamic computational fluid dynamics model established in S12, including but not limited to the following: a computational fluid dynamics model that includes at least the underwater vehicle hull and the propeller propulsion system; a computational fluid dynamics model that includes at least the propeller propulsion system; a structural response model that reflects at least the structural performance of the underwater vehicle hull and the propeller propulsion system; a noise radiation model that considers fluid-structure-acoustic coupling and includes at least the underwater vehicle hull and the propeller propulsion system; and a control system model that is consistent with the underwater vehicle physical entity and can simulate the motion control performance of the underwater vehicle physical entity. This completes the construction of the hydrodynamic digital twin model, the propeller propulsion digital twin model, the structural performance digital twin model, the noise radiation digital twin model, and the motion performance digital twin model.

[0020] The detection system described in S3 collects real-time physical detection data of the underwater vehicle's physical entity during actual operation, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance. This data includes at least the underwater vehicle's physical entity's drag and drag coefficient, the propeller's advance coefficient, thrust coefficient, torque coefficient, and open-water efficiency; the pulsating pressure and strain of the underwater vehicle's physical entity's hull and propeller propulsion system structure; the sound pressure spectrum relationship and total sound pressure level of the underwater vehicle's physical entity's noise detection points; and the underwater vehicle's physical entity's pitch, roll, yaw, buoyancy, straight-line, oblique-line, and hovering motion attitudes.

[0021] The processing of real-time physical detection data described in S3 includes at least filtering, noise reduction, and fast Fourier transform.

[0022] The method for correcting the underwater vehicle digital twin model described in S3 includes, but is not limited to: comparing the simulation results of the drag coefficient of the hydrodynamic digital twin model with the drag coefficient results detected by the physical entity of the underwater vehicle in real time, and modifying the boundary conditions and model parameters of the hydrodynamic digital twin model in real time according to the result deviation to keep the results consistent. The drag coefficient calculation formula is as follows:

[0023]

[0024] Among them, C x Let X be the drag coefficient, q be the dynamic pressure, ρ be the density of the water, v be the velocity of the water relative to the physical entity of the underwater vehicle, and S be the reference area of ​​the physical entity of the underwater vehicle.

[0025] The simulation results of the propeller advance coefficient, propeller thrust coefficient, propeller torque coefficient, and propeller open-water efficiency of the propeller-driven propulsion digital twin sub-model are compared in real time with the propeller output advance coefficient, propeller output thrust coefficient, propeller output torque coefficient, and propeller output open-water efficiency detected by the physical entity of the underwater vehicle. Based on the deviations, the boundary conditions and model parameters of the propeller-driven propulsion digital twin sub-model are modified in real time to ensure consistency. The calculation formulas for the propeller advance coefficient, propeller thrust coefficient, propeller torque coefficient, and propeller open-water efficiency are as follows:

[0026]

[0027] Where V is the flow velocity of the water entering the propeller, n is the propeller rotational speed, D is the propeller diameter, J is the propeller advance coefficient, and K... T K is the propeller thrust coefficient. Q Let T be the propeller torque coefficient, Q be the propeller thrust, and η be the propeller open-water efficiency.

[0028] The simulation results of the structural stress and deformation of the digital twin model of the structural performance are compared in real time with the results of the pulsating pressure and strain of the underwater vehicle hull and the propeller propulsion system structure detected by the physical entity of the underwater vehicle. The boundary conditions and model parameters of the digital twin model of the structural performance are modified in real time according to the result deviation to keep the results consistent.

[0029] The simulation results of the sound pressure spectrum relationship and total sound pressure level of the noise radiation digital twin model, which considers fluid-structure-acoustic coupling and includes mechanical noise, propeller noise, and hydrodynamic noise, are compared in real time with the sound pressure spectrum relationship and total sound pressure level of the noise detection points detected by the physical entity of the underwater vehicle. The boundary conditions and model parameters of the noise radiation digital twin model are modified in real time according to the deviation of the results to keep the results consistent.

[0030] The formula for calculating the total sound pressure level is:

[0031]

[0032] Among them, SPL1, SPL2, SPL n These represent the sound pressure levels at different frequencies.

[0033] The simulation results of the underwater vehicle's motion attitude obtained from the digital twin sub-model of motion performance are compared with the motion attitude of the physical entity of the underwater vehicle in real time. The boundary conditions and model parameters of the digital twin sub-model of motion performance are modified in real time according to the result deviation to keep the results consistent.

[0034] The multidisciplinary simulation integration and optimization design software platform described in S4 includes, but is not limited to, Isight, Optimus, and DADOS. The integrated underwater vehicle digital twin sub-models enable real-time data transmission and exchange. The underwater vehicle digital twin model and the underwater vehicle physical entity are mapped in real time through the environmental conditions and real-time physical detection data to correct the underwater vehicle digital twin model and improve the establishment of the underwater vehicle digital twin model.

[0035] The intelligent data analysis and processing described in S6 includes, but is not limited to, artificial neural networks, Bayesian networks, decision trees, and random forests; the provision of predictive guidance for the actual operation and management of the underwater vehicle physical entity includes at least the following: providing navigation schemes for the underwater vehicle physical entity to reduce drag, reduce noise, and increase propulsion efficiency; predicting weak points in the underwater vehicle physical entity's hull and propeller propulsion system structure that may deform and fail; and providing control strategies that can improve the maneuverability of the underwater vehicle physical entity.

[0036] The construction of high-fidelity models at different scales as described in S7 includes, but is not limited to, multidisciplinary collaborative simulation models of the underwater vehicle's hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance. The different scales may be based on, but are not limited to, geometric similarity criteria, motion similarity criteria, and dynamic similarity criteria.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention provides a digital twin-based underwater vehicle modeling method and system. It proposes utilizing digital twin technology to acquire real-time sensor data of the underwater vehicle's physical entity. The underwater vehicle's digital twin model then loads, updates, and corrects various boundary conditions based on this acquired sensor data. Through real-time synchronous interaction and feedback between the physical entity and the underwater vehicle's digital twin virtual model, the underwater vehicle's digital twin model achieves high fidelity and real-time synchronization, more realistically reflecting the multi-condition working state of the underwater vehicle's physical entity. The simulation process and results of the underwater vehicle's digital twin model, along with real-time physical detection data and historical operational data of the underwater vehicle's physical entity during actual operation, constitute the twin database of the twin model application system. This database provides guidance for the operational control of the underwater vehicle's physical entity and ensures navigation prediction; it also provides a large database of comparable hyper-realistic simulation models for underwater vehicle simulations at various scales. This simulation model database ensures the accuracy of numerical simulations of underwater vehicles under various conditions and across multiple disciplines, improves the credibility and efficiency of underwater vehicle design optimization, enhances economic benefits, and contributes to the upgrading of the digital industry. Attached Figure Description

[0039] Figure 1 A flowchart for establishing and refining a digital twin model of an underwater vehicle;

[0040] Figure 2 A flowchart for the creation, correction, and application of digital twin models of underwater vehicles; Detailed Implementation

[0041] The present invention will now be described with reference to the accompanying drawings.

[0042] like Figure 1 and Figure 2As shown, the underwater vehicle modeling method and system based on digital twins consists of an underwater vehicle physical entity, a detection system, an information transmission system, an underwater vehicle digital twin model, and a twin model application system. The underwater vehicle digital twin model includes at least several multidisciplinary digital twin sub-models, such as a hydrodynamic digital twin sub-model, a propeller-driven propulsion digital twin sub-model, a structural performance digital twin sub-model, a noise radiation digital twin sub-model, and a motion performance digital twin sub-model. These sub-models are mapped in real-time between the detection system and the underwater vehicle physical entity. The various multidisciplinary digital twin sub-models (hydrodynamic, propeller-driven propulsion, structural performance, noise radiation, and motion performance) are also mapped to the physical entity in real-time. The integrated multidisciplinary simulation integration and optimization design software platform enables high-fidelity co-simulation of the underwater vehicle's digital twin model and multi-disciplinary, multi-objective optimization. The simulation process and results of the underwater vehicle's digital twin model, along with real-time physical detection data and historical operational data collected by the detection system during the actual operation of the underwater vehicle's physical entity, constitute the twin database of the twin model application system. This twin database, after data analysis and processing, can provide predictive guidance for the operation of the underwater vehicle's physical entity and provide a large database of comparable hyper-realistic simulation models for simulations of various scale models of the underwater vehicle, ensuring the accuracy of multidisciplinary numerical simulations under various operating conditions and improving the credibility of the underwater vehicle's design optimization. Specifically, the steps include:

[0043] S1. Construct a digital twin model of the underwater vehicle based on the physical characteristics and environmental conditions of the underwater vehicle physical entity;

[0044] S2. Based on the digital twin model of the underwater vehicle established in S1, simulations are performed simultaneously on the underwater vehicle's hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance.

[0045] S3. The detection system collects real-time physical detection data, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, as well as the environmental conditions described in S1, of the underwater vehicle physical entity during actual operation. The environmental conditions of the underwater vehicle physical entity are transmitted to the underwater vehicle digital twin model in real time through the information transmission system by establishing a communication protocol between the system and the model. After processing, the real-time physical detection data is compared in real time with the simulation results corresponding to the underwater vehicle digital twin model in S2 to correct the model.

[0046] S4. Integrate the underwater vehicle digital twin sub-model that was corrected in real time in S3 into the multidisciplinary simulation integration and optimization design software platform. Build the underwater vehicle digital twin model that is real-time mapped and corrected to the physical entity of the underwater vehicle through the detection system and the information transmission system, and perform high-fidelity multidisciplinary collaborative simulation.

[0047] S5. Based on the process and results of high-fidelity multidisciplinary collaborative simulation of the underwater vehicle digital twin model described in S4, and the real-time physical detection data and historical operation data of the underwater vehicle physical entity collected in real time by the detection system during actual operation, a twin database of the twin model application system is constructed.

[0048] S6. Perform intelligent data analysis and processing on the data in the twin database of the twin model application system established in S5 to provide predictive guidance for the actual operation and management of the underwater vehicle physical entity.

[0049] S7. Based on the data in the twin database of the twin model application system described in S5, build high-fidelity multidisciplinary collaborative simulation models of different scales according to actual needs, including but not limited to underwater vehicle hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance.

[0050] S8. The multi-disciplinary collaborative simulation optimization of the high-fidelity models of underwater vehicles with different scales, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, as described in S7, shall be carried out according to the target requirements.

[0051] The physical characteristics of the underwater vehicle physical entity described in S1 include at least the geometric structural parameters, mass attributes, electrical control system structure, material properties, and the connections between its components. Each component includes at least the underwater vehicle hull, propeller propulsion system, and control system. The geometric structural parameters, mass attributes, electrical control system structure, material properties, and the connections between its components are obtained from the underwater vehicle physical entity's CAD drawings, electrical control diagrams, and material database. The geometric structural parameters include at least the reference area and propeller diameter of the underwater vehicle physical entity. The mass attributes include at least the mass, center of mass, and moment of inertia of each component and the underwater vehicle physical entity as a whole.

[0052] The environmental conditions of the underwater vehicle physical entity described in S1 are measured in real time by sensors in the detection system, including at least the flow velocity of the water around the underwater vehicle physical entity relative to the underwater vehicle physical entity, the flow velocity of the water entering the propeller, the flow direction of the water, the temperature of the water, the density of the water, the water pressure, the depth of the underwater vehicle physical entity, the rotational speed of the underwater vehicle physical entity's propeller, and the speed, angular velocity, acceleration, and angular acceleration of the underwater vehicle physical entity during movement.

[0053] The method and steps for constructing a digital twin model of an underwater vehicle as described in S1 are as follows:

[0054] S11. Use digital 3D software, including but not limited to Catia and SolidWorks, to create a 3D digital model that is consistent with the physical entity of the underwater vehicle.

[0055] S12. Based on the three-dimensional digital model established in S11, establish models including but not limited to the following: using finite element software ANSYS to establish a hydrodynamic computational fluid dynamics model that includes at least the underwater vehicle hull and the propeller propulsion system; a computational fluid dynamics model that includes at least the propeller propulsion system; a structural response model that reflects the structural performance of at least the underwater vehicle hull and the propeller propulsion system; using acoustic simulation software LMS virtual.Lab and Actran to establish a noise radiation model considering fluid-structure-acoustic coupling that includes at least the underwater vehicle hull and the propeller propulsion system; and using dynamic system modeling and simulation tools including but not limited to MATLAB Simulink to establish a control system model that is consistent with the physical entity of the underwater vehicle and can simulate the motion control performance of the physical entity of the underwater vehicle.

[0056] S13. The real-time environmental conditions of the underwater vehicle physical entity described in S1 are loaded as boundary conditions into the hydrodynamic computational fluid dynamics model established in S12, including but not limited to the following: a computational fluid dynamics model that includes at least the underwater vehicle hull and the propeller propulsion system; a computational fluid dynamics model that includes at least the propeller propulsion system; a structural response model that reflects at least the structural performance of the underwater vehicle hull and the propeller propulsion system; a noise radiation model that considers fluid-structure-acoustic coupling and includes at least the underwater vehicle hull and the propeller propulsion system; and a control system model that is consistent with the underwater vehicle physical entity and can simulate the motion control performance of the underwater vehicle physical entity. This completes the construction of the hydrodynamic digital twin model, the propeller propulsion digital twin model, the structural performance digital twin model, the noise radiation digital twin model, and the motion performance digital twin model.

[0057] The detection system described in S3 collects real-time physical detection data of the underwater vehicle's physical entity during actual operation, including but not limited to hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance. This data includes at least the underwater vehicle's physical entity's drag and drag coefficient, the propeller's advance coefficient, thrust coefficient, torque coefficient, and open-water efficiency; the pulsating pressure and strain of the underwater vehicle's physical entity's hull and propeller propulsion system structure; the sound pressure spectrum relationship and total sound pressure level of the underwater vehicle's physical entity's noise detection points; and the underwater vehicle's physical entity's pitch, roll, yaw, buoyancy, straight-line, oblique-line, and hovering motion attitudes.

[0058] The processing of real-time physical detection data described in S3 includes at least filtering, noise reduction, and fast Fourier transform.

[0059] The method for correcting the underwater vehicle digital twin model described in S3 includes, but is not limited to: comparing the simulation results of the drag coefficient of the hydrodynamic digital twin model with the drag coefficient results detected by the physical entity of the underwater vehicle in real time, and modifying the boundary conditions and model parameters of the hydrodynamic digital twin model in real time according to the result deviation to keep the results consistent. The drag coefficient calculation formula is as follows:

[0060]

[0061] Among them, C x Let X be the drag coefficient, q be the dynamic pressure, ρ be the density of the water, v be the velocity of the water relative to the physical entity of the underwater vehicle, and S be the reference area of ​​the physical entity of the underwater vehicle.

[0062] The simulation results of the propeller advance coefficient, propeller thrust coefficient, propeller torque coefficient, and propeller open-water efficiency of the propeller-driven propulsion digital twin sub-model are compared in real time with the propeller output advance coefficient, propeller output thrust coefficient, propeller output torque coefficient, and propeller output open-water efficiency detected by the physical entity of the underwater vehicle. Based on the deviations, the boundary conditions and model parameters of the propeller-driven propulsion digital twin sub-model are modified in real time to ensure consistency. The calculation formulas for the propeller advance coefficient, propeller thrust coefficient, propeller torque coefficient, and propeller open-water efficiency are as follows:

[0063]

[0064] Where V is the flow velocity of the water entering the propeller, n is the propeller rotational speed, D is the propeller diameter, J is the propeller advance coefficient, and K... T K is the propeller thrust coefficient. Q Let T be the propeller torque coefficient, Q be the propeller thrust, and η be the propeller open-water efficiency.

[0065] The simulation results of the structural stress and deformation of the digital twin model of the structural performance are compared in real time with the results of the pulsating pressure and strain of the underwater vehicle hull and the propeller propulsion system structure detected by the physical entity of the underwater vehicle. The boundary conditions and model parameters of the digital twin model of the structural performance are modified in real time according to the result deviation to keep the results consistent.

[0066] The simulation results of the sound pressure spectrum relationship and total sound pressure level of the noise radiation digital twin model, which considers fluid-structure-acoustic coupling and includes mechanical noise, propeller noise, and hydrodynamic noise, are compared in real time with the sound pressure spectrum relationship and total sound pressure level of the noise detection points detected by the physical entity of the underwater vehicle. The boundary conditions and model parameters of the noise radiation digital twin model are modified in real time according to the deviation of the results to keep the results consistent.

[0067] The formula for calculating the total sound pressure level is:

[0068]

[0069] Among them, SPL1, SPL2, SPL n These represent the sound pressure levels at different frequencies.

[0070] The simulation results of the underwater vehicle's motion attitude obtained from the digital twin sub-model of motion performance are compared with the motion attitude of the physical entity of the underwater vehicle in real time. The boundary conditions and model parameters of the digital twin sub-model of motion performance are modified in real time according to the result deviation to keep the results consistent.

[0071] The multidisciplinary simulation integration and optimization design software platform described in S4 includes, but is not limited to, Isight, Optimus, and DADOS. The integrated underwater vehicle digital twin sub-models enable real-time data transmission and exchange. The underwater vehicle digital twin model and the underwater vehicle physical entity are mapped in real time through the environmental conditions and real-time physical detection data to correct the underwater vehicle digital twin model and improve the establishment of the underwater vehicle digital twin model.

[0072] The intelligent data analysis and processing described in S6 includes, but is not limited to, artificial neural networks, Bayesian networks, decision trees, and random forests; the provision of predictive guidance for the actual operation and management of the underwater vehicle physical entity includes at least the following: providing navigation schemes for the underwater vehicle physical entity to reduce drag, reduce noise, and increase propulsion efficiency; predicting weak points in the underwater vehicle physical entity's hull and propeller propulsion system structure that may deform and fail; and providing control strategies that can improve the maneuverability of the underwater vehicle physical entity.

[0073] The construction of high-fidelity models at different scales as described in S7 includes, but is not limited to, multidisciplinary collaborative simulation models of the underwater vehicle's hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance. The different scales may be based on, but are not limited to, geometric similarity criteria, motion similarity criteria, and dynamic similarity criteria.

[0074] The multi-disciplinary collaborative simulation models described in S8 for underwater vehicles, including but not limited to those for hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, which are built at different scales as described in S7, are optimized through multi-disciplinary and multi-objective collaborative simulation based on the target requirements, including but not limited to the following schemes:

[0075] Preferably, the underwater vehicle is optimized by taking drag reduction, noise reduction, improved propeller propulsion efficiency and maneuverability as the objective function, wet surface area and shell structure strength as constraints, and parameters of the bow, midship and stern sections as design variables. The optimization is carried out by using optimal Latin hypercube sampling, constructing a radial basis function neural network approximation model, and using a second-generation non-dominated genetic algorithm for multi-condition, multi-disciplinary and multi-objective collaborative simulation.

[0076] Preferably, the underwater vehicle is optimized using Latin hypercube sampling, a second-order response surface approximation model, and a BP neural network algorithm optimized by a genetic algorithm. The objective functions are drag reduction, noise reduction, and improved propeller propulsion efficiency. The constraints are the bow volume of the underwater vehicle and the strength of the propeller blades. The design variables are the serrated trailing edge structural parameters and the leading edge nodal structural parameters of the propeller blades.

Claims

1. A method and system for modeling underwater vehicles based on digital twins, characterized in that, It consists of an underwater vehicle physical entity, a detection system, an information transmission system, an underwater vehicle digital twin model, and a twin model application system. The underwater vehicle digital twin model includes multiple multidisciplinary digital twin sub-models such as a hydrodynamic digital twin sub-model, a propeller-driven propulsion digital twin sub-model, a structural performance digital twin sub-model, a noise radiation digital twin sub-model, and a motion performance digital twin sub-model. These sub-models are mapped in real-time between the detection system and the underwater vehicle physical entity through the detection system and the information transmission system. The integration of subject-specific digital twin sub-models into a multi-disciplinary simulation integration and optimization design software platform enables high-fidelity collaborative simulation and multi-disciplinary, multi-objective optimization of the underwater vehicle's digital twin model. The simulation process and results of the underwater vehicle's digital twin model, along with real-time physical detection data and historical operational data collected by the detection system during the actual operation of the underwater vehicle's physical entity, constitute the twin database of the twin model application system. This twin database, after data analysis and processing, can provide predictive guidance for the operation of the underwater vehicle's physical entity and provide a large database of comparable hyper-realistic simulation models for underwater vehicle model simulations at various scales. Specifically, the steps include: S1. Construct a digital twin model of the underwater vehicle based on the physical characteristics and environmental conditions of the underwater vehicle physical entity; S2. Based on the digital twin model of the underwater vehicle established in S1, the hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance are simulated simultaneously. S3. The detection system collects real-time physical detection data on the hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance of the underwater vehicle physical entity during actual operation, as well as the environmental conditions described in S1. The environmental conditions of the underwater vehicle physical entity are transmitted to the underwater vehicle digital twin model in real time through the information transmission system by establishing a communication protocol between the system and the model. After processing, the real-time physical detection data is compared in real time with the simulation results corresponding to the underwater vehicle digital twin model in S2 to correct the model. S4. Integrate the underwater vehicle digital twin sub-model that was corrected in real time in S3 into the multidisciplinary simulation integration and optimization design software platform, build the underwater vehicle digital twin model that is corrected in real time with the physical entity of the underwater vehicle, and perform high-fidelity multidisciplinary collaborative simulation. S5. Based on the process and results of high-fidelity multidisciplinary collaborative simulation of the underwater vehicle digital twin model described in S4, and the real-time physical detection data and historical operation data of the underwater vehicle physical entity collected in real time by the detection system during actual operation, a twin database of the twin model application system is constructed. S6. Perform intelligent data analysis and processing on the data in the twin database of the twin model application system established in S5 to provide predictive guidance for the actual operation and management of the underwater vehicle physical entity. S7. Based on the data in the twin database of the twin model application system described in S5, build high-fidelity multidisciplinary collaborative simulation models of different scales according to actual needs, including the hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance of the underwater vehicle. S8. The multi-disciplinary collaborative simulation optimization of the high-fidelity multi-scale models of the underwater vehicle, including hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance, built in different proportions as described in S7, is carried out according to the target requirements.

2. The underwater vehicle modeling method and system based on digital twins according to claim 1, characterized in that, The physical characteristics of the underwater vehicle physical entity described in S1 include the geometric structural parameters, mass attributes, electrical control system structure, material properties, and the connections between its components. The components include the underwater vehicle hull, propeller propulsion system, and control system. The geometric structural parameters, mass attributes, electrical control system structure, material properties, and the connections between its components are obtained from the CAD drawings, electrical control diagrams, and material database of the underwater vehicle physical entity. The geometric structural parameters include the reference area and propeller diameter of the underwater vehicle physical entity. The mass attributes include the mass, center of mass, and moment of inertia of each component and the underwater vehicle physical entity as a whole.

3. The underwater vehicle modeling method and system based on digital twins according to claim 1, characterized in that, The environmental conditions of the underwater vehicle physical entity described in S1 are measured in real time by sensors in the detection system, including the water flow velocity relative to the underwater vehicle physical entity, the water flow velocity entering the propeller, the water flow direction, the water temperature, the water density, the water pressure, the depth of the underwater vehicle physical entity, the propeller speed of the underwater vehicle physical entity, and the speed, angular velocity, acceleration, and angular acceleration of the underwater vehicle physical entity during movement.

4. The underwater vehicle modeling method and system based on digital twins according to claim 2, characterized in that, The method and steps for constructing a digital twin model of an underwater vehicle as described in S1 are as follows: S11. Establish a three-dimensional digital model consistent with the physical entity of the underwater vehicle using the digital 3D software Catia; S12. Based on the three-dimensional digital model established in S11, a hydrodynamic computational fluid dynamics model including the underwater vehicle hull and the propeller propulsion system, a computational fluid dynamics model of the propeller propulsion system including the propeller propulsion system, and a structural response model reflecting the structural performance of the underwater vehicle hull and the propeller propulsion system are established using the finite element software ANSYS. A noise radiation model considering fluid-structure-acoustic coupling is established using the acoustic simulation software LMS virtual.Lab. A control system model consistent with the physical entity of the underwater vehicle and capable of simulating the motion control performance of the physical entity of the underwater vehicle is established using the dynamic system modeling and simulation tool MATLAB Simulink. S13. The real-time environmental conditions of the underwater vehicle physical entity described in S1 are loaded as boundary conditions into the hydrodynamic computational fluid dynamics model, the propeller propulsion computational fluid dynamics model, the structural response model reflecting the structural performance of the underwater vehicle hull and the propeller propulsion system, the noise radiation model considering fluid-structure-acoustic coupling, and the control system model consistent with the underwater vehicle physical entity that can simulate the motion control performance of the underwater vehicle physical entity, which are established in S12. This completes the construction of the hydrodynamic digital twin model, the propeller propulsion digital twin model, the structural performance digital twin model, the noise radiation digital twin model, and the motion performance digital twin model.

5. The underwater vehicle modeling method and system based on digital twins according to claim 2, characterized in that, The detection system described in S3 collects real-time physical detection data on the hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance of the underwater vehicle during actual operation. This includes the drag and drag coefficient of the underwater vehicle, the propeller's advance coefficient, thrust coefficient, torque coefficient, and open-water efficiency, the pulsating pressure and strain of the underwater vehicle's hull and propeller propulsion system structure, the sound pressure spectrum relationship and total sound pressure level at the noise detection points of the underwater vehicle, and the underwater vehicle's pitch, roll, yaw, buoyancy, straight-line, oblique-line, and hovering motion attitudes.

6. The underwater vehicle modeling method and system based on digital twins according to claim 1, characterized in that, The processing of real-time physical detection data described in S3 includes methods such as filtering, noise reduction, and fast Fourier transform.

7. The underwater vehicle modeling method and system based on digital twins according to claim 2, characterized in that, The method for correcting the underwater vehicle digital twin model described in S3 is as follows: The simulation results of the drag coefficient of the hydrodynamic digital twin model are compared in real time with the drag coefficient results detected by the physical entity of the underwater vehicle. Based on the deviation in the results, the boundary conditions and model parameters of the hydrodynamic digital twin model are modified in real time to ensure consistency with the results. The drag coefficient calculation formula is as follows: Among them, C x Let X be the drag coefficient, q be the dynamic pressure, ρ be the density of the water, v be the velocity of the water relative to the physical entity of the underwater vehicle, and S be the reference area of ​​the physical entity of the underwater vehicle. The simulation results of the propeller advance coefficient, propeller thrust coefficient, propeller torque coefficient, and propeller open-water efficiency of the propeller-driven propulsion digital twin sub-model are compared in real time with the propeller output advance coefficient, propeller output thrust coefficient, propeller output torque coefficient, and propeller output open-water efficiency detected by the physical entity of the underwater vehicle. Based on the deviations, the boundary conditions and model parameters of the propeller-driven propulsion digital twin sub-model are modified in real time to ensure consistency. The calculation formulas for the propeller advance coefficient, propeller thrust coefficient, propeller torque coefficient, and propeller open-water efficiency are as follows: Where V is the flow velocity of the water entering the propeller, n is the propeller rotational speed, D is the propeller diameter, J is the propeller advance coefficient, and K... T K is the propeller thrust coefficient. Q Where T is the propeller torque coefficient, Q is the propeller thrust, and η is the propeller open-water efficiency. The simulation results of the structural stress and deformation of the digital twin model of the structural performance are compared in real time with the results of the pulsating pressure and strain of the underwater vehicle physical entity's hull and propeller propulsion system structure detected by the underwater vehicle physical entity. The boundary conditions and model parameters of the digital twin model of the structural performance are modified in real time according to the result deviation to keep the results consistent. The simulation results of the sound pressure spectrum relationship and total sound pressure level of the noise radiation digital twin model, which considers fluid-structure-acoustic coupling and includes mechanical noise, propeller noise, and hydrodynamic noise, are compared in real time with the sound pressure spectrum relationship and total sound pressure level of the noise detection points detected by the physical entity of the underwater vehicle. The boundary conditions and model parameters of the noise radiation digital twin model are modified in real time according to the deviation of the results to keep the results consistent. The formula for calculating the total sound pressure level is: Among them, SPL1, SPL2, SPL n The sound pressure level at different frequencies; The simulation results of the underwater vehicle's motion attitude obtained from the digital twin sub-model of motion performance are compared with the motion attitude of the physical entity of the underwater vehicle in real time. The boundary conditions and model parameters of the digital twin sub-model of motion performance are modified in real time according to the result deviation to keep the results consistent.

8. The underwater vehicle modeling method and system based on digital twins according to claim 1, characterized in that, The multidisciplinary simulation integration and optimization design software platform described in S4 includes Isight, Optimus, and DADOS. The integrated underwater vehicle digital twin sub-models achieve real-time data transmission and exchange. The underwater vehicle digital twin model and the underwater vehicle physical entity are mapped in real time through the environmental conditions and real-time physical detection data to correct the underwater vehicle digital twin model and improve the establishment of the underwater vehicle digital twin model.

9. The underwater vehicle modeling method and system based on digital twins according to claim 2, characterized in that, The intelligent data analysis and processing described in S6 includes methods such as artificial neural networks, Bayesian networks, decision trees, and random forests; the provision of predictive guidance for the actual operation and management of the underwater vehicle physical entity includes providing navigation schemes for the underwater vehicle physical entity to reduce drag, reduce noise, and increase propulsion efficiency, predicting weak points in the underwater vehicle physical entity's hull and propeller propulsion system structure that may deform and fail, and providing control strategies that can improve the maneuverability of the underwater vehicle physical entity.

10. The underwater vehicle modeling method and system based on digital twins according to claim 1, characterized in that, The construction of high-fidelity multidisciplinary collaborative simulation models of the underwater vehicle's hydrodynamics, propeller propulsion, structural performance, noise radiation, and motion performance at different scales, as described in S7, can be based on geometric similarity criteria, motion similarity criteria, and dynamic similarity criteria.

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