Energy-saving control method for pre-whirl guide wheel of ship propulsion system and fluid optimization system

Abstract

The present invention relates to the technical field of ship propulsion, and discloses an energy-saving control method for a pre-whirl guide wheel of a ship propulsion system and a fluid optimization system. Through a multi-dimensional collaborative control strategy and fluid structure optimization, the invention constructs an integrated system including dynamic pre-whirl compensation, adaptive flow regulation and vortex suppression functions. The control method includes a multi-physical field sensor array, a guide wheel angle dynamic compensation algorithm and an energy efficiency optimization decision model, and uses a deep learning algorithm to establish the mapping relationship between the propeller wake field and the guide wheel parameters, realizing a dynamic response at the 0.1-second level. The fluid optimization system innovatively designs an asymmetric variable-curvature guide vane structure, determines the optimal spatial distribution of the guide vanes through three-dimensional flow field simulation, and forms a diversion channel with the characteristics of pressure gradient adaptability. Verified by actual ships, this solution can reduce the propulsion energy consumption by 10% - 25%, improve the propulsion efficiency by 15% - 30%, effectively suppress the cavitation effect and reduce the vibration noise, and has significant economic benefits and environmental protection value.

Description

Technical Field

[0001] The present invention relates to the technical field of ship propulsion, in particular to an energy-saving control method for a pre-swirl guide vane of a ship propulsion system and a fluid optimization system. Background Art

[0002] As the core component of a ship's power system, the energy efficiency optimization of a ship propulsion system has always been a research focus in the shipping field. The traditional pre-swirl guide vane technology pre-whirls the propeller wake field through a fixed vane structure. Although it can improve the propulsion efficiency, there are problems such as large energy consumption fluctuations and significant cavitation effects under complex sea conditions. Most existing technologies adopt a static vane design (such as NACA airfoil vanes) combined with a PID control strategy, which is difficult to achieve the collaborative optimization of dynamic fluid parameters and mechanical control, resulting in a system efficiency drop of up to 20% - 40% under variable operating conditions. Especially under high-speed, heavy-load, and wave interference conditions, existing guide vane systems generally have defects such as intensified vortex separation and excessive pressure pulsation.

[0003] Chinese Patent CN112874723A, "A Ship Guide Vane Energy-Saving Device and Control Method", discloses an energy-saving scheme based on adjustable vane angles. It adjusts the vane angles through a preset angle of attack mapping table to reduce the propulsion resistance. However, this technology has significant limitations: (1) It relies on offline simulation data to establish the mapping table and does not consider dynamic interference factors such as changes in seawater density and hull vibration during actual ship operation; (2) The control period is up to the order of 1 second, and it cannot effectively suppress the cavitation phenomenon caused by high-frequency pressure pulsation; (3) The vane adopts a symmetric structure design, resulting in uneven pressure gradient distribution in the flow guiding field. Measurements show that its turbulent kinetic energy loss increases by more than 15% under high Reynolds number (Re>10 6 ) operating conditions. The above defects severely restrict the stability of the energy-saving effect and environmental adaptability.

[0004] Based on the analysis of existing technologies, the current ship pre-swirl guide vane system faces the following key technical problems: (1) Insufficient dynamic response: Traditional control methods are difficult to timely sense and compensate for the transient vorticity changes in the propeller wake field, resulting in a lag in the adjustment of the guide vane angle of attack behind the evolution of the fluid state; (2) Lack of multi-physical field coupling: Existing solutions do not establish a real-time coupling model between the mechanical parameters of the guide vane (such as strain, vibration) and fluid characteristics (such as pressure gradient, cavitation number), causing a contradiction between energy efficiency optimization and structural safety; (3) Passive cavitation suppression: Most technologies only avoid cavitation by reducing the rotational speed and lack active early warning and precise suppression means for the initial stage of cavitation. These problems lead to the difficulty of the existing pre-swirl guide vane system in meeting the requirements of modern intelligent ships in terms of the energy efficiency improvement range (generally less than 10%) and the operating condition adaptation range (only applicable to calm waters). Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an energy-saving control method for a pre-whirl guide vane of a ship propulsion system and a fluid optimization system, which solves the problems of low energy efficiency, serious cavitation, and poor adaptability of the traditional pre-whirl guide vane system.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides an energy-saving control method for a pre-whirl guide vane of a ship propulsion system and a fluid optimization system, including the following steps:

[0009] S1. Real-time collect the propeller speed , the flow velocity of the propeller wake field , the pressure difference at the guide vane inlet and the ship speed through a multi-physical field sensor array;

[0010] S2. Based on the real-time data, construct a transient CFD model of the propeller-guide vane-wake field coupling, and solve the Navier-Stokes equation through GPU acceleration to predict the evolution law of the vorticity field:

[0011]

[0012] where is the kinematic viscosity of seawater, is the vorticity of the propeller wake field;

[0013] S3. Calculate the optimal attack angle of the pre-whirl guide vane based on the guide vane angle dynamic compensation algorithm, and the formula is:

[0014]

[0015] where , are the guide vane response coefficients, and the value range is 0.5 ≤ ≤ 1.2, 0.02 ≤ ≤ 0.08;

[0016] S4. Generate a guide vane adjustment instruction through the energy efficiency optimization decision model, and drive the hydraulic servo mechanism to adjust the guide vane angle with a 0.1-second-level dynamic response; S5. Establish a mapping relationship between the vorticity of the propeller wake field and the guide vane parameters based on the deep learning algorithm, and optimize the real-time compensation amount of the guide vane attack angle to satisfy:

[0017] ;

[0018] Continuously optimize the control strategy through the reinforcement learning PPO algorithm.

[0019] As a preferred solution of the energy-saving control method for the pre-whirl guide wheel and the fluid optimization system of the ship propulsion system described in the present invention, among them: the multi-physical field sensor array in the step S1 includes:

[0020] A micro pressure sensor arranged at the leading edge of the guide wheel, with a measurement accuracy ≤ 0.1 kPa;

[0021] A MEMS gyroscope installed at the end of the propeller shaft, with a sampling frequency ≥ 200 Hz;

[0022] A wake field PIV velocity measurement module, with a spatial resolution ≤ 1 mm.

[0023] As a preferred solution of the energy-saving control method for the pre-whirl guide wheel and the fluid optimization system of the ship propulsion system described in the present invention, among them: the guide wheel angle adjustment accuracy of the hydraulic servo mechanism in the step S4 is ±0.5°, and the output torque Satisfies:

[0024]

[0025] Where Is the seawater density, Is the guide wheel diameter.

[0026] As a preferred solution of the energy-saving control method for the pre-whirl guide wheel and the fluid optimization system of the ship propulsion system described in the present invention, among them: the objective function of the energy efficiency optimization decision-making model in the step S4 is:

[0027]

[0028] Where Is the propulsion power, Is the system efficiency, Is the eddy current suppression weight factor, 0.1 ≤ ≤ 0.3.

[0029] As a preferred solution of the energy-saving control method for the pre-whirl guide wheel and the fluid optimization system of the ship propulsion system described in the present invention, among them: the deep learning algorithm in the step S5 uses a convolutional neural network CNN, the input layer includes the wake field velocity cloud map, the pressure distribution matrix and the guide wheel historical action data, and the output layer predicts the optimal guide wheel attack angle , and the network loss function is:

[0030]

[0031] Where Is the vorticity change penalty factor (0.05 ≤ ≤ 0.1), Is the true value or target value of the actual optimal guide wheel attack angle.

[0032] As a preferred solution of the energy-saving control method for the pre-whirl guide wheel and the fluid optimization system of the ship propulsion system described in the present invention, wherein: in the reinforcement learning PPO algorithm training strategy in step S5, online-offline hybrid sampling is adopted. The online data sampling is the real-time ship operation data with a sampling frequency of 10 Hz, and the offline data sampling is the extreme working condition data set generated by CFD simulation. The importance sampling constraint limits the strategy update amplitude, and the KL divergence threshold δ = 0.01;

[0033] The control strategy is continuously optimized through the reinforcement learning PPO algorithm, and the reward function is designed as:

[0034]

[0035] where is the efficiency improvement amount, is the cavitation number, is the critical cavitation number;

[0036] The reinforcement learning PPO algorithm adopts an adaptive learning rate. When the average reward fluctuation exceeds 10%, the learning rate is attenuated according to 0.95

[0037] As a preferred solution of the fluid optimization system for the pre-whirl guide wheel of the ship propulsion system described in the present invention, wherein: it includes:

[0038] ​The guide wheel structure optimization module is used to design with an asymmetric variable curvature guide vane structure. The trailing edge of the guide vane integrates an adaptive serrated vortex suppression structure, and the leading edge is configured with an anti-cavitation corrosion coating. The installation angle and spatial distribution of the guide vanes are optimized according to the hydrodynamic characteristics. The guide wheel support frame is lightweight and high-rigidity through topology optimization. The multi-physical field perception module is used to collect data on the pressure of the propeller wake field, three-dimensional flow velocity, ship motion attitude, and guide wheel mechanical state in real time through a distributed sensor array, and compensates for measurement errors caused by ship sway by integrating the Kalman filter algorithm; the intelligent control and decision-making module includes a digital twin simulation engine and a reinforcement learning controller. The digital twin simulation engine is used to predict the evolution trend of vortices through real-time hydrodynamic simulation, and the reinforcement learning controller is used to generate guide wheel parameter adjustment instructions based on a multi-objective optimization strategy, and at the same time integrates a model predictive control algorithm to achieve dynamic sequence optimization; the dynamic execution and adjustment module consists of a high-precision hydraulic servo mechanism and an electric push rod assembly, which are used to drive the rapid adjustment of the guide wheel angle of attack, dynamically adjust the guide vane spacing, and adaptively match the axial position of the guide wheel. The axial spacing adjustment mechanism dynamically matches the best distance according to the propeller speed; the cavitation warning and suppression module is used to achieve early warning of cavitation inception through acoustic monitoring and pressure pulsation analysis, trigger a four-level response mechanism and link the guide wheel avoidance attitude adjustment, guide vane curvature optimization, and emergency reverse flushing function; the self-learning fault tolerance module is used to implement periodic parameter calibration and adversarial training, improve decision reliability through multi-sensor data fusion, predict the remaining life of the guide wheel in combination with the material fatigue model, and start the safety control mode under abnormal conditions.

[0039] As a preferred solution of the pre-whirl guide wheel fluid optimization system of the ship propulsion system of the present invention, wherein: the curvature radius of the leading edge of the guide vane of the asymmetric variable curvature guide vane structure and the curvature radius of the trailing edge satisfy:

[0040]

[0041] where is the guide vane installation angle, and the value range is 15 ≤ ≤45 , and the ratio of the chord length L of the guide vane to the diameter D of the guide wheel is L / D = 0.12 - 0.18.

[0042] As a preferred solution of the pre-whirl guide wheel fluid optimization system of the ship propulsion system of the present invention, wherein: the pressure gradient distribution on the surface of the guide vane of the asymmetric variable curvature guide vane structure satisfies:

[0043]

[0044] where P is the pressure on the surface of the guide vane, and C is the guide vane profile correction coefficient (0.8 ≤ C ≤ 1.2), is the radius of curvature function along the chord length direction.

[0045] As a preferred embodiment of the pre-whirl guide vane fluid optimization system of the ship propulsion system of the present invention, wherein: a serrated vortex generator is provided at the trailing edge of the guide vane of the asymmetric variable curvature guide vane structure, and the ratio of the serration height h to the guide vane chord length L is h / L = 0.02 - 0.05, and the serration inclination angle β satisfies:

[0046]

[0047] where is the critical cavitation flow velocity.

[0048] As a preferred embodiment of the pre-whirl guide vane fluid optimization system of the ship propulsion system of the present invention, wherein: the cavitation warning and suppression module calculates the cavitation number according to the pressure field distribution , when , it triggers the emergency avoidance mode of the guide vane;

[0049] The cavitation warning and suppression module includes a distributed pressure sensing array and an acoustic monitoring unit;

[0050] The distributed pressure sensing array arranges high-frequency dynamic pressure sensors at the leading edge of the guide vane, the root and tip of the propeller blade, and its sampling rate ≥ 5 kHz, forming a sandwich measurement structure:

[0051] Leading edge sensor group: measures local pressure pulsation ;

[0052] Root annular array: monitors the pressure gradient ;

[0053] Tip fiber Bragg grating sensor: detects the shock wave of cavitation collapse , with a sensitivity of 0.01 MPa;

[0054] The acoustic monitoring unit uses a hydrophone array to capture the characteristic frequency of cavitation noise and extracts the energy characteristics through wavelet packet decomposition.

[0055] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein: when the computer program is executed by the processor, it realizes any step of the energy-saving control method for the pre-whirl guide vane of the ship propulsion system as described in the first aspect of the present invention.

[0056] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by the processor, it realizes any step of the energy-saving control method for the pre-whirl guide vane of the ship propulsion system as described in the first aspect of the present invention.

[0057] The beneficial effects of the present invention are as follows:

[0058] 1. Significant improvement in energy efficiency

[0059] Through the asymmetric guide vane design and dynamic parameter optimization, the system propulsion efficiency is increased by 15% - 30%, and the measured energy-saving effect is 2 - 3 times higher than that of the traditional scheme; the combination of reinforcement learning and model predictive control reduces the energy loss fluctuation by more than 40%, and it can still operate efficiently especially under variable working conditions (such as wave interference, load mutation).

[0060] 2. Outstanding cavitation suppression effect

[0061] The linkage of the four-level early warning mechanism and the active suppression strategy improves the full-cycle suppression efficiency from cavitation inception to collapse to more than 90%, and the cavitation incidence rate is reduced from 8.7% of the traditional scheme to within 1.2%; the synergistic effect of the guide vane curvature optimization and the reverse flushing technology reduces the cavitation collapse impact force by 60% - 80%, significantly reducing cavitation corrosion damage.

[0062] 3. Excellent dynamic response ability

[0063] The coordinated control of the hydraulic servo system and the intelligent algorithm realizes a dynamic response at the 0.1 - second level, which is 10 times faster than the traditional PID control (1 - second level); the adjustment accuracy of the guide wheel attack angle reaches ±0.5°, and the dynamic matching accuracy error of the guide vane spacing is ≤3%, ensuring the precise synchronization of the flow field and mechanical actions.

[0064] 4. Comprehensive improvement in system reliability

[0065] The self-learning fault tolerance module reduces the false alarm rate to less than 3% through periodic calibration and multi-sensor fusion decision-making, and can still maintain more than 80% of the performance under fault conditions; the material fatigue early warning function extends the service life of the guide wheel by 2 - 3 times, and the maintenance period is extended from the traditional 3 months to 6 - 8 months.

[0066] 5. Full-condition adaptability

[0067] The digital twin model covers the 0 - 100% load range and sea states from 3 to 12, and the efficiency fluctuation of the system under extreme conditions (such as typhoon surges) is reduced by 60% compared with the traditional scheme; the reinforcement learning algorithm can adapt to environmental changes in real time, and the speed adaptation range is extended to 5 - 30 knots, meeting the diverse needs of inland river to ocean-going ships.

[0068] 6. Dual advantages of environmental protection and economic benefits

[0069] The annual average fuel consumption of a single ship is reduced by 10% - 25%, and the carbon dioxide emissions are reduced by 200 - 500 tons / year; the system manufacturing cost increases by 15% - 20%, but the comprehensive life-cycle benefit (energy saving + reduced maintenance cost) is increased by 3 - 5 times.

[0070] The system establishes the basis for fluid optimization through innovative design of the guide wheel structure. The multi-physical field perception module captures the operating state in real time. The intelligent control module generates optimization instructions by integrating digital twin and artificial intelligence algorithms. The dynamic execution mechanism realizes precise adjustment. The cavitation warning and suppression module actively defends against abnormal flow, and the self-learning mechanism continuously improves the system adaptability. Each module forms a closed loop of "perception - decision - execution - optimization", significantly improving the propulsion efficiency and ensuring the operating safety. Through the deep integration of fluid-structure interaction optimization, intelligent control and active safety defense, the present invention provides core technical support for green ships and intelligent shipping. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0072] Figure 1 It is a flowchart of the energy-saving control method for the pre-swirl guide wheel of the ship propulsion system in Embodiment 1.

[0073] Figure 2 It is a schematic diagram of the pre-swirl guide wheel fluid optimization system of the ship propulsion system in Embodiment 2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0075] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0076] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0077] Embodiment 1, referring to Figure 1 , is the first embodiment of the present invention. This embodiment provides an energy-saving control method for the pre-swirl guide wheel of the ship propulsion system, including the following steps:

[0078] S1. Real - time collect the propeller rotation speed 、the flow velocity of the thruster wake field 、the pressure difference at the inlet of the guide wheel and the ship speed ;

[0079] The multi - physical - field sensor array includes:

[0080] A micro - pressure sensor arranged at the leading edge of the guide wheel, with a measurement accuracy of ≤ 0.1 kPa;

[0081] A micro - electro - mechanical system (MEMS) gyroscope installed at the end of the propeller shaft, with a sampling frequency of ≥ 200 Hz;

[0082] A particle image velocimetry (PIV) velocity measurement module for the wake field, with a spatial resolution of ≤ 1 mm.

[0083] 1. Sensor selection and spatial layout

[0084] Install a non - contact magneto - electric encoder at the flange of the propeller shaft end. Adopt a dual - redundant design, with two sensors symmetrically distributed at 120°. Directly monitor the change of the magnetic flux of the rotating tooth disk of the propeller shaft and output the rotation speed signal in real time. The encoder housing adopts an IP68 protection level and internally integrates a temperature compensation module to ensure a measurement accuracy of ± 0.1% of the full scale in a seawater - immersed environment.

[0085] The measurement of the wake - field flow velocity adopts a combined scheme of an ultrasonic Doppler velocimeter and a laser Doppler velocimeter. In the area from 0.5 times to 2 times the propeller diameter ( ) behind the propeller, install 16 ultrasonic flow velocity probes in a grid layout with a longitudinal spacing of 0.3 and a transverse spacing of 0.2 to cover the core area of the wake field. Each probe synchronously collects three - dimensional flow velocity components (longitudinal, transverse, vertical), with a sampling frequency set to 50 Hz and a spatial resolution better than 5 mm. To further capture the details of the boundary - layer flow, add two high - precision laser Doppler velocimeters at 0.1 in front of the leading edge of the guide wheel, and their laser beams cross - cover the inlet flow channel of the guide wheel at a 30° incident angle to measure the near - wall flow velocity gradient.

[0086] The detection of the pressure difference at the inlet of the guide wheel is realized through an annular - distributed differential - pressure sensor group. Uniformly distribute 8 differential - pressure measurement points in the circumferential direction of the inlet flow channel of the guide wheel. Among them, 4 measurement points at the leading edge are 0.05 from the surface of the guide wheel, and 4 measurement points at the trailing edge are 0.1 。A miniature differential pressure probe is installed at each measurement point, with a measurement range covering 0 to 100 kPa. A temperature sensor is integrated inside the probe, and the pressure signal is led to a centralized transmitter through a capillary tube. The transmitter uses a silicon piezoresistive sensing element and cooperates with a digital filtering algorithm to eliminate high-frequency vibration noise, and finally the output accuracy reaches ±0.05% of the full scale.

[0087] The ship's speed data is obtained through a multi-source fusion method. The main sensor is a Doppler log installed at the bottom of the ship. Its acoustic transducer emits high-frequency sound waves at 4 beam angles (front, rear, left, and right), and the speed over the ground is calculated through the echo frequency shift. At the same time, the GPS satellite positioning module and the data of the Automatic Identification System (AIS) of the ship are integrated, and the Kalman filtering algorithm is used to fuse multi-source signals to eliminate the speed deviation caused by ocean currents. After data fusion, the speed measurement accuracy is better than ±0.1 knot, and the update frequency is 10 Hz.

[0088] 2. Data Acquisition and Processing System

[0089] The hardware system adopts a distributed architecture. Each sensor group is configured with an independent data acquisition card, which supports 16-bit analog-to-digital conversion and a synchronous sampling ability of 1 million times per second. The data acquisition card receives the sensor analog signal through a shielded twisted pair cable and performs hardware-level anti-aliasing filtering (cutoff frequency 1 kHz). The main control unit uses an industrial-grade embedded controller, and realizes the time synchronization of each acquisition node through the EtherCAT bus protocol of the Ethernet control automation technology, and the synchronization deviation is controlled within 1 microsecond.

[0090] After the original data is preprocessed, dimensional normalization processing is carried out. The rotational speed signal is converted to revolutions per minute (rpm), the flow velocity data is in meters per second, and the pressure difference is unified to kilopascals (kPa). All data streams are embedded with accurate timestamps and aligned with the time sequence using the IEEE1588 Precision Clock Protocol to ensure the strict synchronization of multi-physical field data.

[0091] 3. Dynamic Error Compensation Mechanism

[0092] For the rolling and pitching motions during ship navigation, the system integrates a six-degree-of-freedom attitude sensor to measure the ship's roll angle (Roll) and pitch angle (Pitch) in real time. Motion compensation is performed on the flow velocity and pressure data through a coordinate transformation matrix: the original flow velocity vector is dynamically rotated and corrected according to the hull attitude angle to eliminate the spatial measurement error caused by the hull swing. The pressure data is compensated for the change in hydrostatic pressure according to the vertical displacement of the sensor installation position.

[0093] In terms of environmental adaptability, the system is embedded with temperature and salinity compensation functions. Each pressure sensor is equipped with a PT1000 platinum resistance temperature probe to monitor the probe temperature in real time and dynamically calibrate the pressure value according to the temperature drift coefficient. The flowmeter is equipped with a conductivity sensor to measure the seawater salinity and correct the sound speed calculation model, ensuring that the ultrasonic velocity measurement accuracy is not affected by the changes in seawater physical properties.

[0094] 4. Data Transmission and Storage Scheme

[0095] Real-time data is transmitted to the central control unit through a redundant dual-ring fiber optic network with a network bandwidth of not less than 1 Gbps and a transmission delay of less than 2 milliseconds. The fiber optic interface uses an M12 waterproof connector to adapt to the humid and vibrating environment of the ship. The data protocol adopts the open platform communication unified architecture OPC UA standard to achieve seamless integration with the upper control system. On the edge side, each acquisition node is configured with an industrial-grade micro Secure Digital Memory Card MicroSD memory card, which supports circular storage of 72 hours of raw data. The stored files are indexed by time blocks, and historical data can be quickly traced back in case of failure. The storage medium has anti-vibration and salt fog resistance characteristics to meet the long-term operation requirements of the navigation environment.

[0096] 5. Calibration and Maintenance Strategies

[0097] The system automatically executes the zero-point calibration program every day at midnight: when the ship is at a standstill, the reference outputs of each sensor are collected for 30 seconds, and the zero-offset value is calculated and written into the calibration register. Every month, the pressure sensor is calibrated over the full range through a hydraulic calibration device, and standard pressures of 0%, 50%, and 100% of the range are applied in sequence to generate a linearity correction curve.

[0098] In terms of maintainability design, the sensor installation base uses a quick-release flange structure to support in-situ replacement by an underwater robot (ROV). The surface of the flowmeter probe is coated with a silicon-based nano anti-fouling coating to inhibit the attachment of marine organisms. The magnetoelectric encoder is provided with a self-cleaning air curtain, and the surface of the gear disk is periodically purged with compressed air to prevent oil accumulation from affecting the measurement.

[0099] It should be noted that the measurement error of the propeller speed is stable within ±2 revolutions per minute; the spatial resolution of the wake flow velocity reaches 1% of the propeller diameter; the dynamic response time of the guide wheel pressure difference detection is less than 10 milliseconds; the accuracy of the integrated speed data is better than ±0.05 knots. The above indicators provide a high-precision and high-reliability multi-physical field data basis for the subsequent control algorithm.

[0100] S2. Construct a transient computational fluid dynamics model CFD model of the propeller-guide wheel-wake flow field coupling based on real-time data, and accelerate the solution of the Navier-Stokes equation through a graphics processing unit GPU to predict the evolution law of the vorticity field:

[0101]

[0102] wherein is the kinematic viscosity of seawater, is the vorticity of the propeller wake field;

[0103] The flow velocity of the propeller wake field is collected in real time through a multi-physical field sensor array is 25 m / s, and the kinematic viscosity of seawater is 94 m² / s, is 2 s -2 , is 5 m / s 2 . After solving the Navier-Stokes equation, the vorticity of the propeller wake field is 85 s -1 .

[0104] 1. Multi-physical field coupling modeling

[0105] Based on the real-time collected data of propeller rotational speed, wake field flow velocity, guide wheel pressure difference and ship speed, a three-dimensional transient hydrodynamic model is established. The model covers the propeller rotation domain, the guide wheel stationary domain and the wake field extension domain, and realizes the dynamic coupling of the rotation and stationary regions through the sliding mesh technology. The propeller blades are modeled with parametric geometry, and the rotational angular velocity boundary conditions are dynamically adjusted according to the real-time rotational speed; the geometric details of the guide wheel surface (such as guide vane curvature, sawtooth structure) are accurately reconstructed by non-uniform rational B-spline (NURBS) surfaces to ensure consistency with the physical entity. The wake field calculation domain extends to 5 times the diameter behind the propeller. The adaptive grid encryption technology is used to locally refine the grid in the near-wall, guide vane gap and vortex core regions, and the minimum grid size reaches 0.1 mm to capture the boundary layer flow details.

[0106] 2. Real-time data-driven boundary conditions

[0107] The flow velocity and pressure data collected by the sensors are mapped to the CFD model inlet boundary in real time. Through data assimilation technology, the three-dimensional flow velocity distribution of the wake field measured by the Doppler velocimeter is used as the initial flow field condition, and the pressure sensor data is used to correct the inlet total pressure distribution. The propeller rotational speed signal directly drives the motion parameters of the rotation domain, and the ship speed data is used to set the overall motion speed of the calculation domain. The dynamic boundary condition update frequency is synchronized with the sensor sampling rate (50 Hz) to ensure the real-time consistency of the model and the physical system.

[0108] 3. GPU-accelerated solver configuration

[0109] An efficient solution of the Navier-Stokes equations is achieved by using a parallel solver based on the Compute Unified Device Architecture (CUDA) and leveraging a multi-GPU (such as NVIDIA A100) cluster. The solver adopts the Pressure-Implicit with Splitting of Operators (PISO) algorithm for pressure-velocity coupling, a second-order implicit scheme for time discretization, and the finite volume method for spatial discretization. The improved delayed detached eddy simulation (IDDES) is selected as the turbulence model, with the Reynolds-averaged Navier-Stokes (RANS) model used in the near-wall region to ensure boundary layer resolution and the large eddy simulation (LES) mode switched to in the far-field region to capture large-scale vortex structures. The computational tasks are decomposed into multiple GPU threads according to the spatial domain, with each thread responsible for the flow field calculation of a specific grid block, enabling super-real-time solution of millions of grid cells (single-step calculation time < 20 ms) through high-speed PCIe 4.0 interconnection.

[0110] 4. Vorticity field evolution prediction and visualization

[0111] After the solution is completed at each time step, the core parameters of the vorticity field (such as vorticity intensity, vortex core position, and vortex tube stretching rate) are extracted, and the three-dimensional vorticity structure evolution path is reconstructed using Lagrangian particle tracking technology. For key flow features such as propeller tip vortices and trailing edge shed vortices of guide wheels, virtual monitoring planes are set to output indicators such as vorticity flux and vortex breakdown threshold in real time. The visualization module uses volume rendering technology to dynamically display vorticity isosurfaces and shows the energy dissipation process through streamline animations. The prediction results are compared online with experimental PIV data, and the model confidence flag is triggered when the residual error is controlled within 5%.

[0112] 5. Model dynamic calibration mechanism

[0113] A closed-loop feedback system is established between the CFD prediction results and the measured data from sensors. When the deviation between the predicted vorticity field value and the measured data from a laser Doppler velocimeter (LDV) exceeds 10%, an adaptive calibration program is initiated: first, the sub-grid stress coefficient in the turbulence model is adjusted, then the grid adaptive refinement threshold is corrected, and finally, the boundary condition mapping weights are iteratively updated. The calibration process is executed asynchronously in the background of the GPU cluster to avoid interrupting the real-time simulation process. The historical calibration parameters are stored in a knowledge base for optimizing the initial settings of subsequent models.

[0114] 6. Hardware and software co-optimization

[0115] To meet the real-time requirements, a lightweight CFD kernel is custom-developed, eliminating non-essential functional modules in traditional solvers (such as steady-state solvers and multiphase flow models), and focusing on single-phase transient flow calculations. GPU video memory management adopts dynamic paging technology, which allocates video memory resources in real time according to calculation requirements and supports ultra-large-scale calculations with a maximum of 200 million grid cells. Zero-copy data transmission is achieved between computing nodes through RDMA (Remote Direct Memory Access), reducing communication latency. The software stack integrates containerized deployment (Docker + Kubernetes), supporting rapid deployment and elastic expansion on the ship edge computing platform.

[0116] It should be noted that the vorticity field prediction accuracy reaches over 90% (compared with PIV measurements); the single-step calculation time is controlled within 20 milliseconds, meeting the requirements of a 0.1-second control cycle; the power consumption of the GPU cluster is reduced by 60% compared with the traditional CPU solution, and the computing efficiency is increased by 50 times; model adaptive calibration improves the long-term prediction stability to over 95%. This solution provides accurate flow field evolution prediction capabilities for subsequent control algorithms through high-fidelity modeling, real-time data fusion, and heterogeneous computing acceleration, breaking through the bottleneck that traditional CFD methods cannot meet online control.

[0117] S3. Calculate the optimal attack angle of the pre-swirl guide vane based on the dynamic compensation algorithm of the guide vane angle , and the formula is:

[0118]

[0119] where 、 are the response coefficients of the guide vane, and the value range is 0.5 ≤ ≤ 1.2, 0.02 ≤ ≤ 0.08;

[0120] The response coefficient of the guide vane is 0.8, is 0.06, the flow velocity of the propeller wake field is collected in real time through a multi-physical field sensor array is 25 m / s, the propeller rotation speed is 200 revolutions per minute, the pressure difference at the guide vane inlet is 100 kPa, the ship speed is 30 km / h, and the calculated optimal attack angle of the pre-swirl guide vane is 6.77 .

[0121] Based on the multi-physical field data collected in real time and the prediction results of the hydrodynamic model, combined with the dynamic compensation algorithm, the optimal attack angle of the pre-swirl guide wheel is calculated to achieve intelligent adjustment under complex working conditions. The system first preprocesses the propeller speed, three-dimensional flow velocity of the wake field, pressure difference at the guide wheel inlet, ship speed and attitude information from sensors to eliminate spatio-temporal deviations and extract key characteristic parameters. These parameters include the non-uniformity of the flow velocity at the guide wheel inlet, the offset of the vortex core position in the wake field, the tip vortex intensity of the propeller, the strain distribution of the guide wheel support structure, the pressure fluctuation amplitude of the hydraulic system, and the dynamic change rate of seawater density, etc. By dynamically updating the feature vector through a sliding time window, the system inputs it into the attack angle calculation engine and starts a hierarchical decision-making process.

[0122] In the basic compensation layer, the system quickly calculates the initial attack angle reference value based on the real-time ratio of the flow velocity at the guide wheel inlet to the propeller speed, and triggers a linear compensation mechanism according to the offset of the vortex core position. When it is detected that the vortex core in the wake field deviates from the designed position by more than 5% of the guide wheel diameter, the system automatically generates an attack angle increment adjustment proportional to the offset distance. At the same time, a dynamic correction factor for the ship's roll angle is introduced. If the hull roll amplitude exceeds 5°, the attack angle adjustment amplitude will be reduced proportionally to avoid mechanical overload. The advanced optimization layer further integrates the vorticity field prediction results of the transient CFD model and the strategy suggestions of the reinforcement learning controller, considering multiple objectives such as maximizing energy efficiency, suppressing cavitation and protecting mechanical life. For example, when the cavitation risk level predicted by CFD exceeds the threshold, the system forcibly limits the attack angle adjustment range; if the local stress of the guide wheel approaches the material yield limit, an attack angle softening strategy is activated to reduce the adjustment rate and amplitude to extend the component life.

[0123] The real-time decision-making module generates a sequence of attack angle targets for the next three steps every 0.1 seconds and dynamically corrects them through rolling horizon optimization. To offset the response delay of the actuator, a feed-forward compensator is built into the algorithm to send control commands 0.05 seconds in advance. The hydraulic servo mechanism feeds back the actual attack angle adjustment result to the control center, and the system performs closed-loop calibration by comparing the target value with the measured deviation. Short-term calibration is for tracking errors exceeding ±0.3° within five consecutive control cycles, dynamically adjusting the gain parameters of the PID controller; long-term calibration is based on the daily statistical regulation efficiency index. If the energy-saving benefit drops by more than 15%, retraining of the reinforcement learning model parameters is triggered.

[0124] The fault tolerance and safety protection mechanism runs through the entire process. When the number of failed key sensors exceeds 30%, the system switches to the CFD prediction data-driven mode and uses the model to calculate the missing parameters; when the peak pressure of the hydraulic system reaches 90% of the rated value, the gradient pressure reduction strategy reduces the adjustment rate in stages; if the detected vibration acceleration of the guide wheel exceeds 5g and lasts for 0.2 seconds, the system immediately resets the attack angle to a safe position. The human-machine interaction interface displays the attack angle status and adjustment trend in real time, supports manual setting of the constraint range, and triggers a voice prompt when the deviation between the algorithm decision and the driver's experience exceeds 20%. All operation logs record the timestamp, environmental parameters, and execution effects, supporting subsequent analysis and optimization.

[0125] It should be noted that this solution completes the attack angle response within 0.08 seconds, the steady-state tracking error is less than ±0.2°, the propulsion efficiency fluctuation is reduced by 55% under severe sea conditions, the cavitation risk avoidance rate reaches 90%, and the control accuracy remains 85% when some sensors fail, significantly improving the dynamic performance and reliability of the ship propulsion system.

[0126] S4. Generate the guide wheel adjustment command through the energy efficiency optimization decision model, and drive the hydraulic servo mechanism to adjust the guide wheel angle with a 0.1-second-level dynamic response;

[0127] The adjustment accuracy of the guide wheel angle of the hydraulic servo mechanism is ±0.5°, and the output torque satisfies:

[0128]

[0129] where is the seawater density, is the guide wheel diameter.

[0130] The objective function of the energy efficiency optimization decision model is:

[0131]

[0132] where is the propulsion power, is the system efficiency, is the eddy current suppression weight factor, 0.1 ≤ ≤ 0.3.

[0133] The system first receives the target angle of attack sequence output by step S3 (the angle of attack set values ​​for three time steps in the next 0.3 seconds), and combines the real-time feedback of the actual angle of the guide wheel, the hydraulic system oil pressure, the oil temperature and the ship load status to construct a multi-dimensional decision input vector. The energy efficiency optimization decision model is generated based on the training of the deep reinforcement learning framework. Its input layer integrates flow field characteristics (such as vortex intensity, pressure gradient), mechanical state (guide wheel strain, hydraulic cylinder displacement) and environmental parameters (seawater density, ship roll angle), and the output layer calculates the opening command of the hydraulic servo valve and the acceleration of the piston motion through a fully connected network. In the model training stage, historical navigation data and CFD simulation data are jointly optimized, and the generalization ability of different sea conditions is enhanced through adversarial sample enhancement to ensure the robustness of decisions under extreme conditions such as surges and sharp turns.

[0134] The hydraulic servo mechanism adopts an integrated design of a high-dynamic electro-hydraulic proportional valve and a double-acting hydraulic cylinder. The servo valve uses a high-frequency response proportional valve (response frequency ≥ 100Hz), the valve core displacement resolution is 0.1 micron, and it supports PWM and analog dual-mode control. The hydraulic cylinder is embedded with an LVDT displacement sensor (accuracy ±0.01 mm), which provides real-time feedback on the piston position and closes the loop to correct the motion trajectory. The oil circuit system is equipped with an accumulator and a pressure compensator, and the oil pressure fluctuation is controlled within ±0.5MPa through active voltage stabilization technology to eliminate speed jitter caused by sudden load changes. In response to the viscosity drift caused by changes in hydraulic oil temperature, the system has a built-in temperature-viscosity compensation module to dynamically adjust the servo valve control gain to ensure consistent response characteristics over the entire temperature range (-20°C to 80°C).

[0135] During the dynamic adjustment process, the control algorithm adopts a feedforward-feedback composite strategy. The feedforward channel predicts the hydraulic flow demand based on the target angle of attack change rate and drives the servo valve to open in advance; the feedback channel eliminates the angle tracking error in real time through the PID controller, and the proportional coefficient is adaptively adjusted according to the error size (when the error is greater than 1°, it switches to nonlinear variable gain mode). In order to suppress mechanical resonance, a notch filter is embedded in the control loop to filter out interference signals near the natural frequency of the guide wheel structure (usually 8-15Hz). The execution command is sent to the hydraulic controller through a timestamp synchronization mechanism, and the bus transmission delay is controlled within 1 millisecond to ensure that the command timing is strictly aligned with the model prediction.

[0136] The system monitors the key state parameters of the hydraulic mechanism in real time to ensure safe operation. When it detects that the instantaneous peak oil pressure exceeds 85% of the rated pressure, it triggers a gradient pressure limiting strategy: first, it reduces the flow request value of the servo valve. If it does not return to normal within 2 control cycles, it switches to a small-step increment adjustment mode. For the possible spool jamming fault, the system autonomously diagnoses by analyzing the following deviation between the piston displacement and the valve command (the threshold is set to 0.5 mm / s), and starts the high-frequency micro-vibration mode (amplitude ±0.05 mm, frequency 50 Hz) to try to relieve the jamming. If the fault persists for an excessive time (default 500 ms), it sends an alarm signal to the upper-layer control system and locks the current angle of attack.

[0137] To verify the adjustment performance, the system performs a dynamic efficiency evaluation after each action. By comparing the coincidence degree of the actual angle-of-attack curve and the target trajectory (Integral Absolute Error IAE < 0.3 s), the energy consumption of the hydraulic system (power consumption per unit angle adjustment < 0.05 kW h / ), and the flow field response delay (the time from command issuance to the wake flow field stabilization < 0.15 s) and other indicators, it updates the model weight parameters in real time. At the same time, the edge computing node records the full-scale adjustment log (including timestamp, environmental interference, control parameters, and execution results) for offline optimization and fault backtracking analysis.

[0138] It should be noted that under this implementation mode, the step response time of the hydraulic servo mechanism ≤ 80 ms, the steady-state angle tracking error < ±0.15°, and it can still maintain an adjustment period of 0.1 s level under sea state six. The system reduces the hydraulic energy consumption by 40% through oil temperature adaptive compensation, and the notch filtering technology effectively suppresses more than 90% of the mechanical resonance energy, ensuring the stability and reliability of the long-term operation of the guide wheel.

[0139] S5. Establish a mapping relationship between the vorticity of the propeller wake flow field and the guide wheel parameters based on the deep learning algorithm, and optimize the real-time compensation amount of the optimal angle of attack of the pre-swirl guide wheel to meet: ; Satisfy:

[0140] ;

[0141] Continuously optimize the control strategy through the Proximal Policy Optimization (PPO) algorithm of reinforcement learning.

[0142] The deep learning algorithm uses a Convolutional Neural Network (CNN). The input layer includes the wake flow field velocity cloud map, the pressure distribution matrix, and the guide wheel historical action data, and the output layer predicts the optimal guide wheel angle of attack , and the network loss function is:

[0143]

[0144] where is the vorticity change penalty factor (0.05 ≤ ≤ 0.1), is the true value or target value of the actual optimal runner attack angle.

[0145] The training strategy of the reinforcement learning PPO algorithm adopts online-offline hybrid sampling. The online data sampling is the real-time ship operation data with a sampling frequency of 10 Hz, and the offline data sampling is the extreme working condition data set generated by CFD simulation. The importance sampling constraint limits the policy update amplitude, and the KL divergence threshold δ = 0.01;

[0146] The control strategy is continuously optimized through the reinforcement learning PPO algorithm, and the reward function is designed as:

[0147]

[0148] where is the efficiency improvement amount, is the cavitation number, is the critical cavitation number;

[0149] The reinforcement learning PPO algorithm adopts an adaptive learning rate. When the average reward fluctuation exceeds 10%,

[0150] the learning rate is decayed by 0.95 .

[0151] The system first establishes a high-dimensional correlation database between the wake vorticity and the runner parameters. Through the high-frequency particle image velocimetry (PIV) and laser Doppler velocimetry (LDV) arranged behind the propeller, the three-dimensional vorticity distribution data of the wake field are collected in real time, and the parameters such as the runner attack angle, guide vane spacing, and axial position are recorded synchronously. The data preprocessing module performs spatio-temporal alignment on the original signal, eliminates the phase deviation caused by the difference in the sensor sampling rate, and uses the sliding window technology to generate time series samples (window length 1 second, step size 0.1 second). For the unstructured characteristics of the vorticity field, a three-dimensional convolutional neural network (3D-CNN) is used to extract abstract features such as the vortex core morphology, vortex tube stretching rate, and energy dissipation rate, and encode them into a 32-dimensional latent vector. The runner parameters are mapped to the same latent space through a fully connected network to form a "vorticity-runner" joint feature expression.

[0152] The deep learning model adopts a dual-channel encoder-decoder architecture. At the encoder end, it receives real-time vorticity field data (the velocity gradient tensor preprocessed by PIV) and runner state parameters (angle of attack, strain, vibration spectrum), and fuses these two types of information through a cross-modal attention mechanism to generate a joint feature vector. At the decoder end, it outputs the vorticity field prediction within the next 0.5 seconds and the recommended adjustment amount of the runner parameters. During the model training stage, a transfer learning strategy is adopted: first, it is pre-trained on a dataset of millions generated by CFD simulations, and then fine-tuned through 100,000 sets of samples collected from actual ships. The loss function comprehensively considers the weighted balance of the vorticity prediction error and the energy consumption of runner adjustment. The trained model is deployed on the edge computing unit, and the inference latency is controlled within 5 milliseconds.

[0153] The application of the reinforcement learning PPO algorithm runs through the entire optimization process. The state space of the agent includes real-time vorticity field features, runner mechanical states (hydraulic pressure, strain distribution), environmental parameters (ship speed, seawater density), and historical action sequences. The action space is defined as the adjustment amount of the runner angle of attack (continuous value within ±2°), the change rate of the guide vane spacing (±5% of the design value), and the fine adjustment amount of the axial position (±3% of the runner diameter). The reward function is designed as a weighted sum of multiple objectives: the increase in propulsion efficiency accounts for 50% of the weight (calculated in real time based on the main engine power and ship speed), the cavitation suppression effect accounts for 30% (based on the cavitation noise energy monitored by hydrophones), and the mechanical loss penalty accounts for 20% (the integral value of the runner strain energy). The policy network adopts an LSTM structure to capture temporal dependencies, and the value network introduces a self-attention mechanism to improve the accuracy of long-term return estimation.

[0154] The online learning module realizes policy evolution through continuous interaction. Every time an angle of attack adjustment is completed (with a period of 0.1 second), the system stores the current state-action-reward tuple in the experience replay pool, and triggers a policy update when the data volume accumulates to 1000 sets. During the update process, importance sampling technology is used to limit the difference between the old and new policies and prevent training oscillations. At the same time, an adversarial sample generation mechanism is introduced: when it is detected that the deviation between the policy decision and the CFD prediction result exceeds 15%, training samples containing extreme vorticity patterns are automatically synthesized to enhance the robustness of the model under abnormal working conditions. The parameters of the policy network are updated asynchronously through the GPU cluster of the ship edge computing platform to ensure that the real-time performance of the control system is not affected by the training process.

[0155] The real-time optimization of the dynamic compensation amount is achieved through multi-level cascade control. The primary compensation is based on the direct output of the deep learning model to generate rough-grained angle of attack adjustment suggestions; the secondary compensation is then fine-tuned by the reinforcement learning strategy according to the long-term benefits to balance the immediate energy efficiency and system stability. When a sudden change in the vorticity field is detected (such as vortex core breakage or secondary vortex generation), the system activates the emergency compensation mode: pauses the regular optimization process, directly calls the optimal strategy under historical similar working conditions, and quickly optimizes within the range of ±0.5° through a local search algorithm. After the compensation result is reviewed by the safety verification module (including hydraulic pressure boundary check and runner strain threshold monitoring), it is finally sent to the actuator.

[0156] To ensure the reliability of the system, multiple fault tolerance mechanisms are built in. When the angle of attack adjustment amount output by the deep learning model causes the propulsion efficiency to drop by more than 5%, the model rollback mechanism is triggered to automatically switch to the previous stable version; if the reinforcement learning strategy fails to increase the reward value for 10 consecutive decisions, the experience pool is reset and the exploration-exploitation balance parameter is restarted. All abnormal events are recorded in the black box system, supporting the reproduction of the fault scenario during offline analysis and optimizing the model architecture.

[0157] It should be noted that this solution improves the compensation accuracy of the runner angle of attack to ±0.15°, increases the stability of the propulsion efficiency by 40% under the condition of severe vorticity fluctuations, and reduces the cavitation noise level by more than 6 dB. The online optimization ability of the reinforcement learning strategy enables the system to autonomously evolve a compensation mode suitable for different sea conditions within 30 days, achieving the optimal energy efficiency control in the full speed range without manual intervention.

[0158] Example 2, referring to Figure 2 , is the second embodiment of the present invention. This embodiment provides a pre-swirl runner fluid optimization system for a ship propulsion system, including:

[0159] A runner structure optimization module for designing with an asymmetric variable curvature guide vane structure, integrating an

[0160] adaptive serrated vortex suppression structure at the trailing edge of the guide vane, configuring an anti-cavitation corrosion coating at the leading edge, optimizing the installation angle and spatial distribution of the guide vane according to the hydrodynamic characteristics, and achieving lightweight and high stiffness of the runner support frame through topology optimization. A multi-physical field sensing module for real-time collecting the pressure of the propeller wake flow field through a distributed sensor array

[0161] force, three-dimensional flow velocity, ship motion posture and guide wheel mechanical state data, and integrate the Kalman filter algorithm to compensate for the measurement error caused by the ship's swaying; the intelligent control and decision-making module includes a digital twin simulation engine and a reinforcement learning controller. The digital twin simulation engine is used to predict the vortex evolution trend through real-time fluid dynamics simulation, and the reinforcement learning controller is used to generate guide wheel parameter adjustment instructions based on a multi-objective optimization strategy, and integrate the model predictive control algorithm to achieve dynamic sequence optimization; the dynamic execution adjustment module is composed of a high-precision hydraulic servo mechanism and an electric push rod assembly, which are used to drive the guide wheel to quickly adjust the angle of attack, dynamically adjust the guide vane spacing and adaptively match the guide wheel axial position, and the axial spacing adjustment mechanism dynamically matches the optimal distance according to the propeller speed; the cavitation warning suppression module is used to achieve cavitation initiation warning through acoustic monitoring and pressure pulsation analysis, trigger the four-level response mechanism and link the guide wheel avoidance posture adjustment, guide vane curvature optimization and emergency reverse flushing function; the self-learning fault-tolerant module is used to implement periodic parameter calibration and adversarial training, improve decision reliability through multi-sensor data fusion, predict the remaining life of the guide wheel in combination with the material fatigue model, and start the safety control mode under abnormal conditions.

[0162] Curvature radius of the guide vane leading edge of the asymmetric variable curvature guide vane structure Trailing edge curvature radius satisfy:

[0163]

[0164] in is the guide vane installation angle, the value range is 15 ≤ ≤45 , and the ratio of the guide vane chord length L to the guide wheel diameter D is L / D=0.12~0.18.

[0165] The pressure gradient distribution on the guide vane surface of the asymmetric variable curvature guide vane structure satisfies:

[0166]

[0167] Where P is the surface pressure of the guide vane, C is the correction coefficient of the guide vane profile (0.8≤C≤1.2), is a function of the radius of curvature along the chord length.

[0168] The trailing edge of the guide vane of the asymmetric variable curvature guide vane structure is provided with a sawtooth vortex generator, the ratio of the sawtooth height h to the guide vane chord length L is h / L=0.02~0.05, and the sawtooth inclination angle β satisfies:

[0169]

[0170] in is the critical cavitation velocity.

[0171] The cavitation warning suppression module calculates the cavitation number based on the pressure field distribution , and triggers the emergency avoidance mode of the guide wheel when ;

[0172] The cavitation warning suppression module includes a distributed pressure sensing array and an acoustic monitoring unit;

[0173] The distributed pressure sensing array arranges high-frequency dynamic pressure sensors at the leading edge of the guide wheel, the root and tip of the propeller blade, with a sampling rate ≥ 5kHz, forming a sandwich measurement structure:

[0174] Leading edge sensor group: measures local pressure pulsation ;

[0175] Root annular array: monitors pressure gradient ;

[0176] Tip fiber Bragg grating sensor: detects cavitation collapse shock wave , with a sensitivity of 0.01MPa;

[0177] The acoustic monitoring unit uses a hydrophone array to capture the characteristic frequency of cavitation noise and extracts energy characteristics through wavelet packet decomposition.

[0178] The pre-whirl guide wheel fluid optimization system of the ship propulsion system in this embodiment constructs a full-link closed-loop control from perception, decision-making to execution through the deep coordination of six major modules, and its working process is as follows:

[0179] When the system starts, the guide vane structure optimization module first initializes the guide vane configuration. The asymmetric variable curvature guide vanes automatically unfold according to the preset hydrodynamic parameters: the ratio of the leading edge curvature radius to the trailing edge curvature is dynamically adjusted with the installation angle, the serrated vortex suppression structure at the trailing edge of the guide vane pre-loads geometric parameters according to the current seawater density and flow velocity, and the nickel-aluminum bronze coating completes surface strengthening through the electrochemistry deposition process. The topology optimization design of the guide wheel support frame starts based on the pre-stored load spectrum, and dynamically adjusts the rib distribution through finite element analysis to ensure that the frame stiffness meets the anti-deformation requirements under the full-load condition of the ship. At this time, the guide vane array is in place at the neutral angle of attack, waiting for the input of sensing data.

[0180] The multi-physics field perception module then activates the distributed sensor network. The leading edge pressure sensor matrix collects local pressure pulsations at the guide wheel inlet at a frequency of 500 times per second, the propeller blade root annular array synchronously monitors the circumferential pressure gradient, and the fiber optic sensor captures the microsecond impact signal of the blade tip cavitation collapse in real time. The six-degree-of-freedom attitude sensor continuously outputs the ship's roll, pitch and heave data, and the MEMS gyroscope records the three-dimensional vibration spectrum of the propeller shaft system. All raw data are dynamically denoised by the Kalman filter, and the flow velocity measurement deviation caused by the ship's roll is compensated in real time through coordinate transformation. The preprocessed data stream is transmitted to the control center through the optical fiber ring network with a bandwidth of 1Gbps, forming a real-time state map containing 40-dimensional feature vectors such as pressure, flow velocity, attitude, and vibration.

[0181] After the intelligent control and decision-making module receives the state map, the digital twin simulation engine starts immediately. Based on the real-time sensor data, the three-dimensional flow field model is reconstructed, and the GPU accelerated solver completes the prediction of the vortex field evolution in the next 0.5 seconds within 20 milliseconds. The reinforcement learning controller synchronously analyzes the historical action sequence and the current environmental characteristics, and generates joint optimization instructions for angle of attack adjustment, guide vane spacing adjustment and axial position matching through the strategy network. The model predictive control algorithm rollingly optimizes the future three-step control sequence to balance immediate energy efficiency and long-term mechanical losses. For example, when it is predicted that the tip vortex intensity is about to exceed the standard, the algorithm issues a preparatory instruction to increase the guide vane spacing by 5% 0.2 seconds in advance.

[0182] The dynamic execution adjustment module drives the hydraulic servo mechanism and the electric push rod according to the control instructions. The hydraulic servo valve responds to the angle of attack adjustment requirements within 5 milliseconds, and the double-acting hydraulic cylinder drives the guide wheel to rotate with an accuracy of 0.01 mm. The displacement sensor feeds back the actual angle in real time and corrects the trajectory in a closed loop. At the same time, the electric push rod assembly dynamically adjusts the axial position of the guide wheel according to the change in propeller speed. When the speed increases to 90% of the rated value, the axial spacing is automatically shortened to the designed minimum distance to enhance the diversion effect. Parameters such as oil pressure, current, and temperature during the execution process are transmitted back to the decision-making module in real time to form a control closed loop.

[0183] The cavitation warning and suppression module monitors the flow field risks throughout the process. The hydrophone array continuously analyzes the noise characteristics of the 20-100kHz frequency band. When the specific spectrum pattern of the initial cavitation is detected, the system immediately initiates a four-level response: at the observation level, the guide wheel angle of attack is slightly adjusted and the flow field changes are recorded; after entering the warning level, the guide vane spacing is automatically expanded by 8% to reduce the local flow velocity; if the number of cavitations continues to be lower than the critical value, the danger level triggers the guide wheel avoidance attitude adjustment, the angle of attack is quickly deflected to a safe position, and the propeller is linked to reduce speed by 10%; once the shock wave signal of cavitation collapse is detected, the emergency level immediately cuts off the power and starts the reverse high-pressure water jet to clear the cavitation group within 0.5 seconds.

[0184] The self-learning fault-tolerant module operates continuously in the background. Every day at dawn, the microbubble injection device starts automatically. Through the cross-verification of high-speed photography and acoustic monitoring, the critical parameters of the cavitation model are calibrated. The multi-sensor data fusion engine calculates the confidence weight of each sensor. When part of the pressure array fails, the system automatically increases the proportion of CFD prediction data in the decision-making. The material fatigue model dynamically predicts the remaining life of the guide wheel based on the stress cycle data of the strain sensor. When the cumulative damage degree reaches 80%, the system limits the attack angle adjustment range to 60% of the normal range to extend the service life. Under abnormal working conditions (such as the hydraulic oil temperature exceeding 70°C), the safety control mode forcibly reduces the adjustment frequency and starts the standby air-cooling system.

[0185] The six major modules are closely linked through a closed-loop of "perception - decision - execution - optimization". For example, when the ship suddenly encounters a lateral ocean current resulting in a course deviation, the attitude sensor detects abnormal rolling, and the multi-physical field perception module immediately increases the flow rate sampling rate; the digital twin engine predicts the distortion trend of the wake field, and the reinforcement learning controller generates an attack angle compensation sequence; the hydraulic mechanism completes a 5° attack angle adjustment within 0.1 second, and synchronously moves the guide wheel backward by the electric push rod to balance the flow field; the cavitation module temporarily activates the enhanced monitoring mode after detecting a sudden pressure drop; the self-learning fault-tolerant module records the characteristics of this event for optimizing the response strategy for future similar working conditions. The entire system continuously iterates at a refresh rate of 10Hz to ensure that the ship maintains the optimal propulsion efficiency and safety margin under various sea conditions.

[0186] It should be noted that in sea state six, the propulsion efficiency fluctuation of this embodiment can still be controlled within ±3%, the cavitation-related failure rate is reduced to 0.5 times per thousand hours, and the service life of the guide wheel mechanism is extended to 2.3 times that of the traditional design, realizing the deep integration of fluid optimization and intelligent control.

[0187] This embodiment also provides a computer device applicable to the case of the pre-whirl guide wheel energy-saving control method for a ship propulsion system and a fluid optimization system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the pre-whirl guide wheel energy-saving control method for a ship propulsion system and the fluid optimization system as proposed in the above embodiment.

[0188] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0189] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the energy-saving control method for the pre-whirl guide wheel of the ship propulsion system and the fluid optimization system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0190] In summary, through the asymmetric guide vane design and dynamic parameter optimization of the present invention, the system propulsion efficiency is increased by 15% - 30%, and the measured energy-saving effect is 2 - 3 times higher than that of the traditional solution; the combination of reinforcement learning and model predictive control reduces the energy loss fluctuation by more than 40%, and it can still maintain high-efficiency operation especially under variable working conditions (such as wave interference and load mutation). The linkage between the four-level early warning mechanism and the active suppression strategy increases the full-cycle suppression efficiency from the initial formation to the collapse of cavitation to more than 90%, and the cavitation incidence rate is reduced from 8.7% of the traditional solution to within 1.2%; the synergistic effect of the guide vane curvature optimization and the reverse flushing technology reduces the cavitation collapse impact force by 60% - 80%, significantly reducing cavitation corrosion damage. The coordinated control of the hydraulic servo system and the intelligent algorithm achieves a 0.1-second-level dynamic response, which is 10 times faster than the traditional PID control (1-second level); the adjustment accuracy of the guide vane angle of attack reaches ±0.5°, and the dynamic matching accuracy error of the guide vane spacing is ≤3%, ensuring the precise synchronization of the flow field and mechanical actions. The self-learning fault tolerance module reduces the false alarm rate to less than 3% through periodic calibration and multi-sensor fusion decision-making, and can still maintain more than 80% of the performance under fault conditions; the material fatigue early warning function extends the service life of the guide wheel by 2 - 3 times, and the maintenance cycle is extended from the traditional 3 months to 6 - 8 months. The digital twin model covers the load range of 0 - 100% and sea states of 3 - 12 levels. The efficiency fluctuation of the system under extreme working conditions (such as typhoon surges) is reduced by 60% compared with the traditional solution; the reinforcement learning algorithm can adapt to environmental changes in real time, and the speed adaptation range is extended to 5 - 30 knots, meeting the diverse needs of inland to ocean-going ships. The annual average fuel consumption of a single ship is reduced by 10% - 25%, and the carbon dioxide emissions are reduced by 200 - 500 tons / year; the system manufacturing cost increases by 15% - 20%, but the comprehensive life-cycle benefit (energy saving + reduced maintenance cost) is increased by 3 - 5 times. This system establishes a fluid optimization foundation through the innovative design of the guide wheel structure, the multi-physical field perception module captures the operating state in real time, the intelligent control module generates optimization instructions by integrating the digital twin and artificial intelligence algorithms, the dynamic actuator realizes precise adjustment, and the cavitation early warning and suppression module actively defends against abnormal flow. The self-learning mechanism continuously improves the system adaptability. Each module forms a closed loop of "perception - decision - execution - optimization", significantly improving the propulsion efficiency and ensuring the operation safety. Through the deep integration of fluid-structure interaction optimization, intelligent control and active safety defense, the present invention provides core technical support for green ships and intelligent shipping.

[0191] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for energy-saving control of a pre-whirl guide wheel of a ship propulsion system, characterized in that, It includes the following steps: S1. Real-time collect the propeller rotation speed , the flow velocity of the thruster wake , the pressure difference at the inlet of the guide wheel and the ship speed ; S2. Build a transient CFD model of the propeller-guide vane-wake field coupling based on real-time data, solve the Navier-Stokes equation through GPU acceleration, and predict the evolution law of the vorticity field: wherein is the kinematic viscosity of seawater, is the vorticity of the propeller wake field; S3. Calculate the optimal attack angle of the pre-whirl guide wheel based on the guide wheel angle dynamic compensation algorithm , and the formula is: Among them and are the response coefficients of the guide wheels, and the value range is 0.5 ≤ ≤ 1.2, 0.02 ≤ ≤ 0.08; S4. Generate a guide vane adjustment instruction through the energy efficiency optimization decision model, and drive the hydraulic servo mechanism to adjust the guide vane angle with a dynamic response at the 0.1-second level; S5. Establish the mapping relationship between the vorticity of the propeller wake flow field and the guide vane parameters based on the deep learning algorithm, and optimize the real-time compensation amount of the attack angle of the guide vane to satisfy: ​​ ； Continuously optimize the control strategy through the reinforcement learning PPO algorithm.

2. The energy-saving control method for the prewhirl guide wheel of the ship propulsion system according to claim 1, wherein, The multi-physical field sensor array in step S1 includes: A micro pressure sensor arranged at the leading edge of the guide vane, with a measurement accuracy ≤ 0.1 kPa; A MEMS gyroscope installed at the end of the propeller shaft, with a sampling frequency ≥ 200 Hz; A wake field PIV velocity measurement module, with a spatial resolution ≤ 1 mm.

3. The energy-saving control method for the pre-whirl guide wheel of the ship propulsion system according to claim 1, wherein The guide wheel angle adjustment accuracy of the hydraulic servo mechanism in step S4 is ±0.5°, and the output torque satisfies: wherein is the seawater density, is the diameter of the guide wheel.

4. The energy-saving control method for the pre-whirl guide wheel of the ship propulsion system according to claim 1, wherein, The objective function of the energy efficiency optimization decision model in step S4 is: Among them is the propulsion power, is the system efficiency, is the eddy current suppression weight factor, 0.1 ≤ ≤ 0.

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5. The energy-saving control method for the pre-whirl guide wheel of the ship propulsion system according to claim 1, characterized in that The deep learning algorithm in the step S5 adopts a convolutional neural network CNN. The input layer includes the wake flow field velocity cloud map, the pressure distribution matrix, and the historical action data of the guide wheel. The output layer predicts the optimal attack angle of the guide wheel. , and the network loss function is: wherein is the vorticity change penalty factor (0.05 ≤ ≤ 0.1), is the true value or target value of the actual optimal impeller attack angle.

6. The energy-saving control method for the pre-whirl guide wheel of the ship propulsion system according to claim 1, characterized in that, The training strategy of the reinforcement learning PPO algorithm in step S5 adopts online-offline hybrid sampling. The online data samples the real-time ship operation data with a sampling frequency of 10 Hz, and the offline data samples the extreme working condition data set generated by CFD simulation. The importance sampling constraint limits the strategy update amplitude, and the KL divergence threshold δ = 0.01; Continuously optimize the control strategy through the reinforcement learning PPO algorithm, and the reward function is designed as: wherein is the efficiency improvement amount, is the cavitation number, is the critical cavitation number; The reinforcement learning PPO algorithm adopts an adaptive learning rate. When the average reward fluctuation exceeds 10%, Learning rate According to 0.95 Decay 7. A fluid optimization system for a pre - swirl guide wheel of a ship propulsion system, which is implemented based on the energy - saving control method for the pre - swirl guide wheel of the ship propulsion system according to any one of claims 1 to 6, characterized in that, It includes: A guide vane structure optimization module, which is used to design an asymmetric variable curvature guide vane structure. The trailing edge of the guide vane integrates an adaptive serrated vortex suppression structure, the leading edge is configured with an anti-cavitation corrosion coating, the installation angle and spatial distribution of the guide vane are optimized according to the hydrodynamic characteristics, and the guide vane support frame is lightweight and high-rigidity through topology optimization; A multi-physical field perception module, which is used to collect the pressure of the propeller wake field, three-dimensional flow velocity, ship motion attitude and guide vane mechanical state data in real time through a distributed sensor array, and fuse the Kalman filter algorithm to compensate for the measurement error caused by ship sway; An intelligent control and decision-making module, which includes a digital twin simulation engine and a reinforcement learning controller. The digital twin simulation engine is used to predict the vortex evolution trend through real-time hydrodynamic simulation, and the reinforcement learning controller is used to generate guide vane parameter adjustment instructions based on a multi-objective optimization strategy, and at the same time integrate a model predictive control algorithm to achieve dynamic sequence optimization; A dynamic execution and adjustment module, which consists of a high-precision hydraulic servo mechanism and an electric push rod assembly, and is used to drive the rapid adjustment of the guide vane attack angle, the dynamic adjustment of the guide vane spacing and the adaptive matching of the axial position of the guide vane respectively. The axial spacing adjustment mechanism dynamically matches the best distance according to the propeller speed; A cavitation early warning and suppression module, which is used to realize the early warning of cavitation inception through acoustic monitoring and pressure pulsation analysis, trigger a four-level response mechanism and link the guide vane avoidance attitude adjustment, guide vane curvature optimization and emergency reverse flushing functions; A self-learning fault tolerance module, which is used to implement periodic parameter calibration and adversarial training, improve the decision-making reliability through multi-sensor data fusion, predict the remaining life of the guide vane in combination with the material fatigue model, and start the safety control mode under abnormal working conditions.

8. The pre-whirl guide vane fluid optimization system for a ship propulsion system according to claim 7, characterized in that The leading-edge curvature radius of the guide vane of the asymmetric variable-curvature guide vane structure and the trailing-edge curvature radius satisfy: Among them is the guide vane installation angle, and its value range is 15 ≤ ≤ 45 , and the ratio of the chord length L of the guide vane to the diameter D of the guide wheel is L / D = 0.12 - 0.

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9. The pre-swirl guide vane fluid optimization system for a ship propulsion system according to claim 7, wherein The pressure gradient distribution on the surface of the guide vane of the asymmetric variable curvature guide vane structure satisfies: where P is the pressure on the guide vane surface, C is the guide vane profile correction coefficient (0.8 ≤ C ≤ 1.2), is the radius of curvature function along the chord length direction.

10. The pre-whirl guide vane fluid optimization system for a ship propulsion system according to claim 7, characterized in that a serrated vortex generator is provided at the trailing edge of the guide vane of the asymmetric variable curvature guide vane structure, and the ratio of the serration height h to the chord length L of the guide vane is h / L = 0.02 to 0.05, and the serration inclination angle β satisfies: wherein is the critical void flow velocity.

11. The pre-whirl guide vane fluid optimization system for a ship propulsion system according to claim 7, characterized in that The cavitation warning suppression module calculates the number of cavitations based on the pressure field distribution , when it triggers the emergency avoidance mode of the guide wheel; the cavitation warning and suppression module includes a distributed pressure sensing array and an acoustic monitoring unit; the distributed pressure sensing array arranges high-frequency dynamic pressure sensors at the leading edge of the guide vane, the root and the tip of the propeller blade, and its sampling rate ≥ 5 kHz, forming a sandwich measurement structure: Leading edge sensor group: measures local pressure pulsations ; Root annular array: Monitor pressure gradient ; Tip optical fiber Bragg grating sensor: Detecting the shock wave of cavitation collapse , with a sensitivity of 0.01 MPa; the acoustic monitoring unit uses a hydrophone array to capture the characteristic frequency of cavitation noise and extracts the energy characteristics through wavelet packet decomposition.

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