Ship form design method and system for high-performance large-load river-sea direct ship
By combining the NSGA-III algorithm with maneuverability and seakeeping prediction models, the contradiction between seakeeping and maneuverability in the design of river-sea direct vessels was resolved. This achieved efficient multi-objective optimization, improved the engineering adaptability and computational efficiency of the design, and met the requirements of high maneuverability in inland waterways and seakeeping in the ocean.
Patent Information
- Application Number
- CN202511992150.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-03-20
AI Technical Summary
Traditional river-sea direct-route vessel designs struggle to balance the conflict between seakeeping and maneuverability in inland waterway and marine environments. This leads to optimization results that are biased towards a single performance index. Furthermore, high-precision hydrodynamic simulations are too time-consuming and cannot support rapid iterative optimization across multiple parameters and operating conditions, resulting in limited engineering adaptability.
A multi-objective optimization algorithm, especially the NSGA-III algorithm, is adopted, combined with a maneuverability prediction model and a seakeeping prediction model. By selecting characteristic parameters that affect the seakeeping and maneuverability of the ship as constraints, multi-objective optimization is carried out to balance the conflict between seakeeping and maneuverability.
It achieves an effective balance between seakeeping and maneuverability in the design of river-sea direct vessels, improves engineering adaptability, shortens the optimization cycle, enhances design efficiency and adaptability, and supports both high maneuverability in inland waterways and seakeeping in the ocean while meeting the requirements.
Smart Images
Figure CN121706261A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of ship design, and in particular to a method and system for designing high-performance, large-capacity river-sea direct vessels. Background Technology
[0002] River-sea direct vessels, as an innovative type of vessel connecting inland waterway and ocean transport, combine the technological advantages of both riverboats and seagoing vessels, providing an efficient and convenient solution for modern shipping. Their core advantage lies in enabling direct transport of goods from inland waterways to the ocean, avoiding multiple transshipments as in traditional transport models, significantly improving transport efficiency and reducing cargo damage risks. In terms of green shipping, river-sea direct vessels generally adopt clean power technologies such as LNG, methanol, or pure electric power, greatly reducing pollutant emissions. At the same time, their innovative designs, such as "bulk and container dual-use," improve cargo capacity utilization, providing shippers with more diversified transport options. As an important carrier linking the Yangtze River Economic Belt and coastal ports, river-sea direct vessels are promoting the optimization of multimodal transport systems and the coordinated development of regional economies.
[0003] The core challenge in designing river-sea direct-route vessels stems from the fundamental differences between inland waterway and marine environments and their contradictory performance requirements. Inland waterways are typically narrow, winding, and have limited depth; for example, some sections of the middle reaches of the Yangtze River have a depth of only about 6 meters. Numerous bridges and small turning radii necessitate vessels with shallow drafts, wide and flat hulls, and small turning diameters for maneuverability. In contrast, the marine environment requires vessels to withstand wind and waves, maintaining stability and safety, typically necessitating deep drafts and slender hull designs to achieve good seakeeping. Coordinating these two drastically different performance requirements within the same vessel type constitutes the fundamental challenge in river-sea direct-route vessel design. Summary of the Invention
[0004] The purpose of this application is to provide a high-performance, large-capacity river-sea direct vessel design method and system that can effectively balance the contradiction between seakeeping and maneuverability, and improve engineering adaptability.
[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a design method for a high-performance, large-capacity river-sea direct vessel, including: Obtain the characteristic parameters corresponding to the parent ship type of the river-sea direct vessel; the characteristic parameters are the ship type parameters that affect the seakeeping and maneuverability of the ship type; The constraint conditions are determined based on the aforementioned feature parameters; Multiple sets of feature parameter schemes are generated based on the constraints; Based on multiple sets of characteristic parameter schemes, a ship geometry model is constructed and numerical simulations are performed to obtain relevant parameter data on river and sea maneuverability and seakeeping performance. Based on the relevant parameter data, the ship type parameters of the optimal river-sea direct vessel type are obtained by using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model.
[0006] In one embodiment, the characteristic parameters include: the ship's waterline length, main dimension ratio, hull form factor, and mid-longitudinal section area.
[0007] In one embodiment, the waterline length of the vessel includes the design waterline length and the waterline surface coefficient; the design waterline length is the horizontal distance between the hull surface and the waterline when fully loaded.
[0008] In one embodiment, the main dimension ratios include length-to-beam ratio, beam-to-draft ratio, depth-to-draft ratio, length-to-depth ratio, and length-to-draft ratio.
[0009] In one embodiment, the hull form coefficients include a block coefficient, a roll damping coefficient at midship level, a waterline coefficient, and a prismatic coefficient.
[0010] In one embodiment, the longitudinal section area includes the area at the tail end of the longitudinal section and the area at the head end of the longitudinal section.
[0011] In one embodiment, the constraint is an environmental constraint; the environmental constraint includes: channel depth, waves, and wind speed.
[0012] In one embodiment, based on the relevant parameter data, a multi-objective optimization algorithm is used to optimize the hull parameters of the optimal river-sea direct-route vessel type using a maneuverability prediction model and a seakeeping prediction model. Specifically, this includes: The relevant parameter data are input into the handling performance prediction model and the seakeeping performance prediction model respectively to obtain the seakeeping parameters and handling parameters; The fitness is calculated based on the constraints according to the wavekeeping parameters and maneuverability parameters. Based on the fitness, a multi-objective optimization algorithm is used for iterative optimization to obtain the ship type parameters of the optimal river-sea direct vessel type.
[0013] In one embodiment, the multi-objective optimization algorithm is the NSGA-III algorithm.
[0014] Secondly, this application provides a high-performance, large-capacity river-sea direct-route vessel design system, including: The acquisition module is used to acquire the characteristic parameters corresponding to the parent ship type of the river-sea direct vessel; the characteristic parameters are ship type parameters that affect the seakeeping and maneuverability of the ship type. A constraint determination module is used to determine constraint conditions based on the feature parameters; The generation module is used to generate multiple sets of feature parameter schemes based on the constraints. The construction and simulation module is used to construct a ship geometry model based on multiple sets of characteristic parameter schemes and perform numerical simulations to obtain relevant parameter data on river and sea maneuverability and seakeeping. The optimization module is used to optimize the ship type parameters of the optimal river-sea direct vessel type based on the relevant parameter data using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model.
[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a high-performance, large-capacity river-sea direct vessel hull design method and system. It directly selects hull parameters that affect the seakeeping and maneuverability of the hull as characteristic parameters, and determines the constraints based on the characteristic parameters. Finally, it uses a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model to optimize the hull, effectively balancing the conflict between seakeeping and maneuverability, thereby improving the engineering adaptability of the final hull design parameters. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is an application environment diagram of a high-performance, large-capacity river-sea direct vessel hull design method according to an embodiment of this application. Figure 2 A flowchart illustrating a high-performance, large-capacity river-sea direct vessel design method provided in an embodiment of this application; Figure 3 A schematic diagram of the calculation process of a hull performance prediction model for a high-performance, large-capacity river-sea direct vessel hull design method provided in an embodiment of this application; Figure 4 A schematic diagram of the iterative optimization model of the hull parameters for a high-performance, large-capacity river-sea direct vessel hull design method provided in an embodiment of this application; Figure 5 A functional module diagram of a high-performance, large-capacity river-sea direct vessel hull design system provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0019] This application addresses the core challenge of synergistically optimizing maneuverability and seakeeping for river-sea direct vessels in complex navigation areas (inland waterways and oceans), specifically manifested in the following issues: Multi-objective conflict problem: Traditional ship design methods have difficulty balancing the contradiction between seakeeping and maneuverability, resulting in optimization results that are biased towards a single performance index.
[0020] Computational efficiency bottleneck: High-precision hydrodynamic simulation takes too long and cannot support rapid iterative optimization of multiple parameters and multiple working conditions, thus restricting the exploration of design space.
[0021] Engineering adaptability limitations: Conventional optimization algorithms are prone to getting stuck in local optima and cannot dynamically adapt to the different performance weight requirements of inland waterway and ocean navigation segments.
[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0023] The high-performance, large-capacity river-sea direct vessel design method provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send the characteristic parameters corresponding to the parent hull type of the river-sea direct vessel to server 102. Server 102 receives the characteristic parameters corresponding to the parent hull type of the river-sea direct vessel, determines constraints based on the characteristic parameters, generates multiple sets of characteristic parameter schemes based on the constraints, constructs a hull geometry model based on the multiple sets of characteristic parameter schemes, and performs numerical simulations to obtain relevant parameter data regarding the maneuverability and seakeeping of the river-sea direct vessel. Based on the relevant parameter data, and using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model, it optimizes the hull parameters of the optimal river-sea direct vessel hull type. Server 102 can feed back the obtained optimal hull parameters of the river-sea direct vessel hull type to terminal 101. In addition, in some embodiments, the design of high-performance, large-capacity river-sea direct vessels can also be implemented by the server 102 or the terminal 101. For example, the terminal 101 can directly design the high-performance, large-capacity river-sea direct vessel based on the characteristic parameters corresponding to the parent vessel type of the river-sea direct vessel to be processed, or the server 102 can design the high-performance, large-capacity river-sea direct vessel from the data storage system.
[0024] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.
[0025] In one exemplary embodiment, such as Figure 2 As shown, a high-performance, large-capacity river-sea direct vessel design method is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the following steps are included.
[0026] Step 201: Obtain the characteristic parameters corresponding to the parent ship type of the river-sea direct vessel; the characteristic parameters are the ship type parameters that affect the seakeeping and maneuverability of the ship type.
[0027] Step 202: Determine the constraints based on the feature parameters.
[0028] Step 203: Generate multiple sets of feature parameter schemes based on the constraints.
[0029] Step 204: Construct a ship geometry model based on multiple sets of characteristic parameter schemes and perform numerical simulations to obtain relevant parameter data on river and sea maneuverability and seakeeping.
[0030] Step 205: Based on the relevant parameter data, optimize using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model to obtain the optimal river-sea direct vessel parameters.
[0031] By implementing the above steps, the ship hull parameters that affect seakeeping and maneuverability are directly selected as characteristic parameters, and the constraints are determined by the characteristic parameters. Finally, a multi-objective optimization algorithm is used to optimize the ship based on the maneuverability prediction model and the seakeeping prediction model, effectively balancing the conflict between seakeeping and maneuverability, thereby improving the engineering adaptability of the final designed ship hull parameters.
[0032] In one exemplary embodiment, the characteristic parameters include: the ship's waterline length, main dimension ratio, hull form factor, and mid-longitudinal section area.
[0033] The waterline length of the vessel includes the design waterline length and the waterline coefficient; the design waterline length is the horizontal distance between the hull surface and the waterline when fully loaded. The principal dimensional ratios include the length-to-beam ratio, the beam-to-draft ratio, the depth-to-draft ratio, the length-to-depth ratio, and the length-to-draft ratio. The hull form coefficients include the block coefficient, the roll damping coefficient at midship level, the waterline coefficient, and the rhombus coefficient. The midship longitudinal section area includes the stern area and the bow area of the midship longitudinal section.
[0034] In practical applications, the constraints are environmental constraints; these environmental constraints include: channel depth, waves, and wind speed. Determining the constraints based on these characteristic parameters specifically includes: The required ship geometry conditions for design include ship length, beam, principal dimensional parameters, as well as ship geometry coefficients such as block coefficient and rhombus coefficient, and the corresponding environmental constraints such as channel depth, waves, and wind speed.
[0035] In practical applications, the hull parameters of the optimal river-sea direct-route vessel type are obtained by optimizing the relevant parameter data based on the maneuverability prediction model and the seakeeping prediction model using a multi-objective optimization algorithm. Specifically, this includes: inputting the relevant parameter data into the maneuverability prediction model and the seakeeping prediction model respectively to obtain seakeeping parameters and maneuverability parameters; calculating fitness based on the constraints according to the seakeeping parameters and maneuverability parameters; and iteratively optimizing the hull parameters of the optimal river-sea direct-route vessel type using a multi-objective optimization algorithm based on the fitness.
[0036] In one exemplary embodiment, the multi-objective optimization algorithm is the NSGA-III algorithm.
[0037] The high-performance, large-capacity river-sea direct vessel design method provided in this application includes the following aspects: The objective function is determined based on the objectives of hull form optimization. In this application, the objective function is primarily selected to relate to the hull's seakeeping and maneuverability. In hull form optimization, the objective function is a mathematical expression used to evaluate the hull's seakeeping and maneuverability.
[0038] Variables are selected based on the objective function. The selection of variables and characteristic parameters primarily focuses on hull-type parameters that influence seakeeping and maneuverability. These include key parameters such as waterline length, main dimension ratio, block coefficient, and mid-longitudinal section area. Parameters used to evaluate the quality of seakeeping and maneuverability include the ship's maneuverability index and roll period.
[0039] Define clear constraints. When optimizing hull parameters, ensure that other performance indicators, such as still water resistance, are met while optimizing seakeeping and maneuverability. Define the range of parameter variation to ensure that hull parameter variations are within a certain range.
[0040] In this application, the selected optimization parameter model is a multi-objective optimization algorithm for river-sea direct vessels based on NSGA-III and a hydrodynamic-kinetic coupling model. The core of the algorithm model combines the multi-objective optimization capabilities of NSGA-III with the accurate simulation of the hydrodynamic-kinetic coupling model to achieve synergistic optimization of the "maneuverability-seakeeping" of river-sea direct vessels. Through the optimization of the algorithm model, the optimal hull parameters that satisfy both the maneuverability and seakeeping requirements of river-sea direct vessels are obtained.
[0041] In another exemplary embodiment, a specific processing procedure for the design of a high-performance, large-capacity river-sea direct vessel is provided, such as... Figure 3 As shown, it includes the following steps: Select the parent ship type of the river-sea direct vessel and measure and obtain the initial value vi of the characteristic parameter Pi corresponding to the parent ship.
[0042] The constraints are defined as the range of variation of the characteristic parameter Pi relative to the initial value vi of the parent ship during the design process.
[0043] Generate N sets of characteristic parameter schemes within the range of constraint changes.
[0044] Based on the characteristic scheme parameters, a ship geometry model was constructed, and relevant parameter data on the maneuverability and seakeeping of the river-sea direct vessel were obtained using simulation software.
[0045] Input data acquisition: Key parameters of the hull type, such as waterline length, main dimension ratio, hull form factor, and mid-longitudinal section area, along with relevant parameters concerning the maneuverability and seakeeping of river-sea direct vessels, are used as inputs to the intelligent optimization model of the entire hull type parameters. Iterative optimization is performed based on the key and relevant parameters, specifically including: The data were preprocessed using zero-mean standardization.
[0046] The preprocessed data is input into the intelligent optimization model of the ship's parameters; after optimization, the ship's parameters that achieve the best maneuverability and seakeeping performance can be obtained, such as the waterline length, main dimension ratio, hull form factor, mid-longitudinal section area and other key parameters.
[0047] Maneuverability and seakeeping prediction models were established. These models will subsequently be used to measure the impact of ship hull parameters on maneuverability and seakeeping. The maneuverability prediction model takes key and relevant parameters as input and maneuverability assessment parameters as output, while the seakeeping prediction model takes key and relevant parameters as input and seakeeping assessment parameters as output.
[0048] A multi-objective optimization algorithm of NSGA-III was constructed to obtain the optimal hull parameters that satisfy the maneuverability and seakeeping of river-sea direct vessels. The hull optimization design of river-sea direct vessels was carried out and the design results were obtained.
[0049] The waterline length parameters of the hull type involved in the above scheme include: the design waterline length and the waterline coefficient. The design waterline length refers to the horizontal distance between the hull surface and the waterline when fully loaded, and is one of the main dimensions of the ship.
[0050] The main dimensional ratio parameters of the ship type involved in the above scheme include: length-to-beam ratio (length between perpendiculars to beam); beam-to-draft ratio (beam to draft); depth-to-draft ratio (depth to draft); length-to-depth ratio (length to depth); and length-to-draft ratio (length to draft).
[0051] The ship type parameters involved in the above scheme include: block coefficient, roll damping coefficient at midship level, waterline coefficient, and prismatic coefficient.
[0052] The longitudinal section area parameters of the ship type involved in the above scheme include: the stern area of the longitudinal section and the bow area of the longitudinal section.
[0053] The maneuverability assessment parameter for river-sea direct vessels involved in the above scheme is the turning index P. The index P is a good maneuverability criterion, appropriately reflecting how easily a vessel can change course. The larger the P value, the better the vessel's turning ability, and the easier it is for the vessel to change course. Specifically: .
[0054] in, .
[0055] .
[0056] The initial velocity of rotation, The length is denoted by T. T is the ratio of the inertial moment coefficient to the damping moment coefficient. A larger T value indicates a larger inertial moment and a smaller damping moment during ship motion. K is the ratio of the rudder turning moment coefficient to the damping moment coefficient. A larger K value indicates a larger turning moment generated by the rudder and a smaller damping moment. The K and T indices obtained from Z-shaped maneuvering tests with different rudder angles are different, but both have almost the same rate of change. Therefore, the turning index P is relatively stable, which is an important advantage. as well as For the dimensionless form of K and T, used to evaluate ship maneuverability.
[0057] The seakeeping parameters for the river-sea direct vessels mentioned above are: roll angle and pitch angle.
[0058] like Figure 4 As shown, the multi-objective optimization algorithm flow of NSGA-III involved in the above scheme is as follows: 1. Input data acquisition: The key parameters of the ship type, such as waterline length, main dimension ratio, ship type coefficient, mid-longitudinal section area, and relevant parameters related to the maneuverability and seakeeping of river-sea direct vessels, are used as inputs for the entire model.
[0059] 2. Data preprocessing was performed using zero-mean standardization.
[0060] 3. Input the preprocessed data into the model.
[0061] 4. Fitness calculation: For each ship type, fitness is calculated by analyzing seakeeping and maneuverability parameters and combining them with constraints. If the constraints are violated, the fitness will decrease.
[0062] The specific calculation steps are as follows: First, the motion response is calculated using the prediction model in step 3. Then, fitness parameters related to seakeeping and handling are calculated using the following formula.
[0063] (1) Calculation of wave resistance adaptability: Weighted average calculation of wavekeeping adaptability: .
[0064] (2) Calculation of handling fitness: Weighted composite calculation of controllability fitness: .
[0065] In the formula To respectively determine seakeeping performance and handling adaptability, These are the weighting coefficients. A higher value indicates that the performance indicator is more important in the overall evaluation. A smaller value indicates that the indicator is relatively minor and has a smaller impact on the overall fitness. and Roll and pitch angles are parameters used to assess a ship's seakeeping performance.
[0066] 5. Data grouping and optimal solution selection: The NSGA-III is used to quickly divide the ship type parameter data into multiple levels according to fitness, and retain the optimal solution of each level.
[0067] 6. Data Iteration and Optimization: By selecting operators to retain individuals with good applicability, the selected individuals are iterated and optimized to generate new datasets.
[0068] The data optimization process employs an elite retention strategy, and the specific selection process is as follows: (1) Fitness ranking.
[0069] (2) Identification and selection of elite individuals.
[0070] (3) Genetic manipulation is used to generate a new population and replace individuals with poor fitness in the population with elite individuals.
[0071] 7. Iteration Termination: Repeat steps 4-6 until the maximum number of iterations is reached or convergence is achieved.
[0072] Ship performance optimization is a core aspect of ship design, involving two key indicators: maneuverability and seakeeping. This application proposes a multi-objective optimization algorithm for river-sea direct vessels based on NSGA-III and a hydrodynamic-kinetic coupling model. By establishing the relationship between ship geometric features and performance indicators, it achieves efficient and accurate hull form optimization. This process integrates maneuverability index parameter analysis and seakeeping parameter analysis, forming a complete intelligent hull form design method. Furthermore, this method can quickly obtain different hull form schemes, expanding the design space to a certain extent and enabling more design options. In addition, the established maneuverability performance prediction model and seakeeping prediction model can be used for rapid prediction of seakeeping and maneuverability of other design schemes, helping to improve design efficiency during preliminary design. The hull form design scheme for river-sea direct vessels designed using the method of this application improves the maneuverability of river-sea direct vessels in inland waterways while also considering their seakeeping performance in marine environments, thus improving the operational efficiency and safety of river-sea direct vessels. The hydrodynamic-kinetic coupling model is a traditional CFD calculation method used to calculate the relationship between hull form parameters and ship physical performance.
[0073] The hull optimization design process for river-sea direct vessels is a systematic, multi-objective optimization process aimed at achieving synergistic optimization of maneuverability and seakeeping through intelligent algorithms. First, based on the initial values of the characteristic parameters of the parent vessel, the variation range of each parameter is set. These parameters include key hull parameters such as waterline length, main dimension ratio, hull form factor, and mid-longitudinal section area. The variation range of each parameter must comprehensively consider ship design specifications, hydrodynamic performance requirements, and engineering feasibility. Within the defined constraints, N sets of characteristic parameter schemes are generated to ensure the diversity of design parameters. Subsequently, for each set of characteristic parameter schemes, a hull geometry model is constructed, and numerical simulations of maneuverability and seakeeping performance are performed to obtain key performance parameters such as the turning index and roll angle—that is, relevant parameter data regarding the maneuverability and seakeeping of the river-sea direct vessel.
[0074] After data acquisition, the input data undergoes preprocessing, including zero-mean standardization to eliminate dimensional influences and ensure all parameters are on the same order of magnitude, facilitating subsequent model training and optimization. The preprocessed data will be used as input to establish maneuverability and seakeeping prediction models. Training will then learn the mapping relationship between ship hull parameters and performance indicators. During model validation, it is crucial to ensure that the prediction accuracy meets engineering requirements.
[0075] The multi-objective optimization algorithm based on NSGA-III is the core of this process. It seeks the optimal compromise between the two conflicting objectives of maneuverability and seakeeping. During optimization, the algorithm iteratively generates new combinations of hull parameters and rapidly evaluates their performance using a predictive model, ultimately outputting a set of optimal solutions. Each solution in these sets represents a certain optimal balance between maneuverability and seakeeping, allowing designers to select the most suitable combination of hull parameters based on actual needs. The entire process achieves closed-loop optimization from parametric design and performance prediction to multi-objective optimization, providing an efficient and reliable technical path for the intelligent design of river-sea direct vessels.
[0076] This application presents a multi-objective optimization method for river-sea direct-access vessels based on NSGA-III and a hydrodynamic-kinetic coupling model. Through the deep integration of intelligent algorithms and ship hydrodynamics, it achieves the following technical effects: In terms of performance optimization, this method effectively balances the conflict between seakeeping and maneuverability by continuously optimizing hull parameters, ensuring that the optimized hull simultaneously meets the requirements of high maneuverability in inland waterways and seakeeping in the ocean, thus solving the performance fragmentation problem caused by traditional step-by-step optimization. In terms of computational efficiency, the method combines intelligent algorithm processing models (two performance prediction models) to replace time-consuming CFD simulations, significantly shortening the optimization cycle. Furthermore, the fitness function (emphasizing maneuverability in inland waterways and seakeeping in ocean sections) accelerates algorithm convergence, significantly improving design iteration efficiency. In addition, the maneuverability and seakeeping prediction models constructed by this method can be generalized to other hull designs, supporting rapid performance evaluation and scheme selection, providing a new design solution for intelligent ship design.
[0077] The sequence number of each step in this application does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0078] Based on the same inventive concept, this application also provides a high-performance, large-capacity river-sea direct vessel hull design system for implementing the above-mentioned high-performance, large-capacity river-sea direct vessel hull design method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the high-performance, large-capacity river-sea direct vessel hull design system provided below can be found in the limitations of the high-performance, large-capacity river-sea direct vessel hull design method described above, and will not be repeated here.
[0079] In one exemplary embodiment, such as Figure 5 As shown, a high-performance, large-capacity river-sea direct vessel hull design system is provided, including: The acquisition module is used to acquire the characteristic parameters corresponding to the parent ship type of the river-sea direct vessel; the characteristic parameters are ship type parameters that affect the seakeeping and maneuverability of the ship type.
[0080] The constraint determination module is used to determine the constraint conditions based on the feature parameters.
[0081] The generation module is used to generate multiple sets of feature parameter schemes based on the constraints.
[0082] The construction and simulation module is used to construct a ship geometry model based on multiple sets of characteristic parameter schemes and perform numerical simulations to obtain relevant parameter data on river and sea maneuverability and seakeeping.
[0083] The optimization module is used to optimize the ship type parameters of the optimal river-sea direct vessel type based on the relevant parameter data using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model.
[0084] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores design data for high-performance, large-capacity river-sea direct-access vessels. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a design method for a high-performance, large-capacity river-sea direct-access vessel.
[0085] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.
[0086] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.
[0087] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.
[0088] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0089] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.
[0090] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0091] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0093] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for designing the hull form of a high-performance, large-capacity river-sea direct vessel, characterized in that, The design method for high-performance, large-capacity river-sea direct vessels includes: Obtain the characteristic parameters corresponding to the parent ship type of the river-sea direct vessel; the characteristic parameters are the ship type parameters that affect the seakeeping and maneuverability of the ship type; The constraint conditions are determined based on the aforementioned feature parameters; Multiple sets of feature parameter schemes are generated based on the constraints; Based on multiple sets of characteristic parameter schemes, a ship geometry model is constructed and numerical simulations are performed to obtain relevant parameter data on river and sea maneuverability and seakeeping performance. Based on the relevant parameter data, the ship type parameters of the optimal river-sea direct vessel type are obtained by using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model.
2. The high-performance, large-capacity river-sea direct vessel design method according to claim 1, characterized in that, The characteristic parameters include: the ship's waterline length, main dimension ratio, hull form factor, and mid-longitudinal section area.
3. The high-performance, large-capacity river-sea direct vessel design method according to claim 2, characterized in that, The waterline length of the vessel includes the design waterline length and the waterline surface coefficient; the design waterline length is the horizontal distance between the hull surface and the waterline when fully loaded.
4. The high-performance, large-capacity river-sea direct vessel design method according to claim 2, characterized in that, The main dimensional ratios include length-to-beam ratio, beam-to-draft ratio, depth-to-draft ratio, length-to-depth ratio, and length-to-draft ratio.
5. The high-performance, large-capacity river-sea direct vessel design method according to claim 2, characterized in that, The hull form coefficients include the block coefficient, the roll damping coefficient at midship level, the waterline coefficient, and the prism coefficient.
6. The high-performance, large-capacity river-sea direct vessel design method according to claim 2, characterized in that, The longitudinal section area includes the area at the tail end of the longitudinal section and the area at the head end of the longitudinal section.
7. The high-performance, large-capacity river-sea direct vessel design method according to claim 1, characterized in that, The constraints are environmental constraints; the environmental constraints include: channel depth, waves, and wind speed.
8. The high-performance, large-capacity river-sea direct vessel design method according to claim 1, characterized in that, Based on the relevant parameter data, a multi-objective optimization algorithm is used to optimize the hull parameters of the optimal river-sea direct vessel type using a maneuverability prediction model and a seakeeping prediction model. Specifically, these parameters include: The relevant parameter data are input into the handling performance prediction model and the seakeeping performance prediction model respectively to obtain the seakeeping parameters and handling parameters; The fitness is calculated based on the constraints according to the wavekeeping parameters and maneuverability parameters. Based on the fitness, a multi-objective optimization algorithm is used for iterative optimization to obtain the ship type parameters of the optimal river-sea direct vessel type.
9. The high-performance, large-capacity river-sea direct vessel design method according to claim 1, characterized in that, The multi-objective optimization algorithm is the NSGA-III algorithm.
10. A high-performance, large-capacity river-sea direct vessel hull design system, characterized in that, The high-performance, large-capacity river-sea direct vessel design system includes: The acquisition module is used to acquire the characteristic parameters corresponding to the parent ship type of the river-sea direct vessel; the characteristic parameters are ship type parameters that affect the seakeeping and maneuverability of the ship type. A constraint determination module is used to determine constraint conditions based on the feature parameters; The generation module is used to generate multiple sets of feature parameter schemes based on the constraints. The construction and simulation module is used to construct a ship geometry model based on multiple sets of characteristic parameter schemes and perform numerical simulations to obtain relevant parameter data on river and sea maneuverability and seakeeping. The optimization module is used to optimize the ship type parameters of the optimal river-sea direct vessel type based on the relevant parameter data using a multi-objective optimization algorithm based on the maneuverability prediction model and the seakeeping prediction model.