A design method of low-resistance inland river ship type based on CFD optimization

Abstract

The application relates to a design method of a low-resistance inland river ship type based on CFD optimization, which comprises the following steps: establishing an inland river ship type parameterization model, taking ship length, ship width, draft, square coefficient and bow body, midship body and stern body line type parameters as design variables; constructing a multi-objective optimization model with the minimum total resistance and wave resistance as the target, and setting the draft, stability and cargo carrying capacity as constraint conditions; performing flow field simulation calculation on the ship type under multiple speed conditions by using a computational fluid dynamics method to obtain resistance performance data; iteratively updating the design variables by combining a multi-objective optimization algorithm, and obtaining optimal ship type parameters through a closed-loop coupling optimization process; and finally outputting an inland river ship type with excellent low-resistance performance. The application also provides a corresponding design system, which comprises a parameterization modeling module, a CFD simulation module, an optimization calculation module and a result output module. The application can significantly reduce ship resistance, improve design efficiency and energy-saving performance, and has good engineering application value.

Description

Technical Field

[0001] This application relates to the field of ship hydrodynamic performance optimization and computational fluid dynamics applications, and in particular to a design method for low-drag inland waterway vessels based on CFD optimization. Background Technology

[0002] With the rapid development of inland waterway shipping and the increasing requirements for energy conservation and emission reduction, reducing ship navigation resistance and improving energy efficiency have become important research directions in the field of ship design. Ship resistance directly affects fuel consumption and operating costs. In particular, for inland waterway transport vessels that operate on fixed routes for a long time, the quality of their ship type has a significant impact on economic and environmental performance.

[0003] In existing technologies, inland waterway vessel design mainly relies on empirical formulas, model tests, and experience-based adjustments by designers. While these methods can meet basic design requirements to some extent, they suffer from long optimization cycles, high testing costs, and a high degree of reliance on experience in the design results. Furthermore, traditional design methods often optimize for a single design condition, making it difficult to consider comprehensive performance under different speeds and loads, resulting in unstable resistance performance of the vessel during actual operation.

[0004] In recent years, with the development of computational fluid dynamics (CFD) technology, its application in ship hydrodynamic performance analysis has gradually increased. Numerical simulation can predict the flow field around the hull, thereby evaluating the ship's resistance performance. However, in current applications, CFD is mostly used for single analysis or auxiliary verification, and has not yet been deeply integrated with systematic optimization design methods. It lacks automated and closed-loop optimization mechanisms. In addition, the application of multi-objective optimization methods in ship design is still insufficient, and its ability to coordinate the optimization of multiple indicators such as total resistance, wave-making resistance, and propulsion efficiency is limited.

[0005] Therefore, in view of the shortcomings of the existing technology, the inventors believe that the existing inland waterway vessel design methods have problems such as low optimization efficiency, strong reliance on experience and difficulty in achieving multi-objective comprehensive optimization. There is an urgent need for an efficient design method that can combine CFD simulation and optimization algorithms to achieve a systematic improvement in the resistance performance of inland waterway vessels. Summary of the Invention

[0006] To address the problems of low optimization efficiency, strong reliance on experience, and difficulty in achieving multi-objective integrated optimization in existing inland waterway vessel design methods, this application provides a design method for low-drag inland waterway vessels based on CFD optimization.

[0007] The design method for low-drag inland waterway vessels based on CFD optimization provided in this application adopts the following technical solution: Firstly, a low-drag inland waterway vessel design method based on CFD optimization includes the following steps: S1. Establish a parametric model of inland waterway vessel type, and set the length, beam, draft, block coefficient, and bow, midship and stern line parameters as design variables; S2. Set the optimization objective function and constraints, where the optimization objective includes at least minimizing total resistance and minimizing wave-making resistance, and the constraints include draft limits, stability requirements, and cargo capacity requirements. S3. Generate an initial ship type sample based on the parametric model; S4. Using computational fluid dynamics (CFD) methods, flow field simulation calculations were performed on the ship type samples under multiple speed conditions to obtain the resistance performance data of each ship type sample. S5. Input the resistance performance data into a multi-objective optimization algorithm to iteratively update the design variables and obtain the optimized ship hull parameters. S6. Reconstruct the ship type based on the optimized ship type parameters and repeat steps S4 and S5 until the convergence condition is met. S7. Output a low-drag inland waterway vessel type that meets the optimization objective.

[0008] By adopting the above technical solutions, parametric modeling, CFD simulation analysis, and multi-objective optimization algorithms can be organically combined to construct a closed-loop design process of "modeling-simulation-optimization-re-simulation," realizing the transformation of inland waterway vessel morphology from experience-driven to data-driven and automated optimization. Through unified parametric control of parameters such as length, beam, draft, and bow, midships, and stern hull forms, morphology generation and adjustment become more efficient and flexible. Flow field simulation calculations under multiple speed conditions enable comprehensive acquisition of resistance performance data for vessels under different operating states, improving the accuracy and reliability of performance evaluation. By introducing multi-objective optimization algorithms, under the premise of meeting constraints such as draft, stability, and cargo capacity, synergistic optimization of total resistance and wave-making resistance is achieved, improving the overall hydrodynamic performance of the vessel morphology. Simultaneously, an iterative update mechanism automatically searches for the optimal parameter combination, improving optimization efficiency and reducing manual intervention. The resulting low-resistance vessel morphology can effectively reduce navigation resistance and fuel consumption, enhancing the economy and energy efficiency of inland waterway vessels.

[0009] Optionally, the parameterized model can parametrically express the bow entry angle, midship section distribution, and stern contraction curve of the hull through curve control points or mathematical functions.

[0010] By adopting the above technical solutions, the key hull lines can be accurately parametrically described and continuously adjusted through control points or functions, improving modeling flexibility and geometric expression accuracy, facilitating linkage updates with optimization algorithms, and enhancing hull optimization efficiency and design reliability.

[0011] Optionally, the CFD simulation uses a three-dimensional viscous flow model based on the Reynolds-averaged Navier-Stokes equations, and combines the free surface capture method to solve the flow field around the hull.

[0012] By adopting the above technical solutions, the accuracy of numerical simulation of three-dimensional viscous flow and free surface effects around the hull is improved, and resistance and wave characteristics are accurately predicted, thereby providing reliable flow field data support for hull optimization and enhancing the scientific nature and stability of the optimization results.

[0013] Optionally, the free surface is simulated using the VOF method, and the turbulence model is either the k-ε model or the k-ω SST model.

[0014] By adopting the above technical solutions, the accuracy of capturing free liquid surfaces and gas-liquid two-phase interfaces is improved, and the ability to simulate turbulent structures is enhanced, which more accurately reflects the wave-making and flow field change characteristics of the ship and improves the reliability of resistance prediction.

[0015] Optionally, the multi-objective optimization algorithm may employ one or more combinations of genetic algorithms, NSGA-II algorithms, or particle swarm optimization algorithms.

[0016] By adopting the above technical solutions, the system possesses strong global search capabilities and multi-objective collaborative optimization capabilities, which can effectively avoid local optima, improve the efficiency of ship type parameter optimization and the stability of optimization results, thereby enhancing the overall design quality.

[0017] Optionally, the multi-speed operating conditions include at least the low-speed operating condition, the design speed operating condition, and the full-load speed operating condition; The optimization process involves the coordinated optimization of local hull structures, including the bow shape, stern shape, and bottom surface transition structure. The convergence condition is that the rate of change of the objective function is lower than a preset threshold or the number of iterations reaches a set upper limit.

[0018] By adopting the above technical solutions, the ship hull optimization can cover multiple operating conditions such as low speed, design speed, and full load, improving the applicability and engineering robustness of the optimization results. At the same time, the bow hull, stern hull, and bottom curved transition structure are adjusted in a coordinated manner, which can comprehensively reduce wave-making resistance and local flow separation. By setting convergence conditions, the iteration process can be effectively controlled, invalid calculations can be avoided, optimization efficiency and computational stability can be improved, and a balanced improvement in the overall performance of the ship hull can be achieved.

[0019] Optionally, the tail profile optimization includes setting a wake straightening structure or guide fins to improve the wake distribution.

[0020] By adopting the above technical solutions, setting a wake straightening structure or guide fin at the stern of the hull can effectively improve the flow field separation and vortex phenomenon at the stern, reduce the intensity and energy loss of the wake vortex, reduce wave-making resistance and additional resistance at the stern, and improve propulsion efficiency and navigation stability. At the same time, optimizing the wake distribution makes the fluid transition smoother and improves the overall hydrodynamic performance and energy-saving effect of the hull.

[0021] Optionally, the convergence condition is that the rate of change of the objective function is lower than a preset threshold or the number of iterations reaches a set upper limit.

[0022] By adopting the above technical solutions, the optimization iteration process can be effectively controlled, invalid or over-calculation can be prevented, and computational efficiency can be improved. At the same time, it can be ensured that the optimization results converge when the rate of change of the objective function meets the set threshold, thus ensuring the stability and reliability of ship design parameters and improving optimization accuracy and engineering feasibility.

[0023] Secondly, a low-drag inland waterway vessel design system based on CFD optimization includes: A parametric modeling module, which is used to establish a parametric model of inland waterway vessel type; It includes a CFD simulation module, which is used to perform multi-condition flow field calculations on the ship type and output resistance data; It includes an optimization calculation module, which is used to iteratively optimize the ship hull parameters based on a multi-objective optimization algorithm; It includes a data interaction module, which is used to realize data transmission between various modules; It includes a result output module, which is used to output the optimized low-drag hull form and its performance parameters.

[0024] By adopting the above technical solutions, an intelligent ship hull optimization design system integrating parametric modeling, CFD flow field simulation, multi-objective optimization calculation, and result output was constructed, realizing modular and integrated collaborative operation of the ship design process. The parametric modeling module is used to quickly generate adjustable hull geometry models, improving modeling efficiency and flexibility. The CFD simulation module is used to perform flow field calculations on different ship types under multiple operating conditions to obtain reliable resistance performance data. The optimization calculation module iteratively optimizes design variables based on multi-objective optimization algorithms to achieve continuous improvement of ship performance. The data interaction module ensures real-time data transmission and collaborative computation between functional modules. The result output module outputs the optimized low-resistance ship hull and its key performance indicators, realizing the transformation of ship design from traditional experience-driven to digital, automated, and intelligent optimization-driven, improving design efficiency, optimization accuracy, and engineering application value.

[0025] Optionally, the optimization calculation module is configured to achieve closed-loop coupled iteration between CFD simulation and optimization algorithm; The parametric modeling module supports the automatic generation and updating of hull lines by adjusting control points or curve functions; The system also includes a visualization module for displaying the 3D model of the ship and the flow field distribution results; The system can output the resistance reduction ratio and energy saving assessment results.

[0026] By adopting the above technical solutions, the optimization calculation module achieves closed-loop coupling iteration of CFD simulation and optimization algorithm, improving the automation and continuous optimization capabilities of hull form optimization; the parametric modeling module realizes rapid generation and updating of hull lines through control points or curve functions, improving modeling efficiency; the visualization module intuitively displays the three-dimensional hull form and flow field distribution results, enhancing analysis capabilities; and it can output the drag reduction ratio and energy saving assessment results, providing a quantitative basis for hull form performance evaluation.

[0027] In summary, this application includes at least one of the following beneficial technical effects: 1. By constructing a closed-loop optimization mechanism that combines parametric ship form models with CFD simulation, the ship form design can be transformed from experience-driven to data-driven and automated optimization, thereby improving design efficiency and optimization accuracy; 2. By using a multi-objective optimization algorithm to jointly optimize total resistance and wave-making resistance, and combining it with multi-speed operating condition analysis, the ship type can have better hydrodynamic performance under different operating conditions. 3. By introducing tail guide fins, optimizing the bow shape, and coordinating the adjustment of the bottom surface, the flow field separation and wake distribution are effectively improved, wave-making resistance is reduced, and propulsion efficiency is increased; 4. By using a closed-loop coupling iteration and convergence control mechanism between CFD and optimization algorithms, the computational convergence stability and computational efficiency of the optimization process are improved. 5. Through visualization and energy-saving assessment output modules, the ship's performance can be displayed intuitively and the energy-saving effect can be quantitatively analyzed, thereby enhancing the engineering application value and design decision-making capabilities. Attached Figure Description

[0028] Figure 1 This is a flowchart of a low-drag inland waterway vessel design method based on CFD optimization, according to an embodiment of this application.

[0029] Figure 2 This is a schematic diagram of a low-drag inland waterway vessel design system based on CFD optimization, according to an embodiment of this application. Detailed Implementation

[0030] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0031] Example 1

[0032] like Figure 1As shown in the figure, this embodiment provides a low-drag inland waterway vessel design method based on CFD optimization, which is applicable to the preliminary design optimization stage of conventional inland waterway transport vessels.

[0033] First, a parametric model of inland waterway vessel hull is established, transforming the hull geometry into a set of calculable design variables, including: length L, beam B, draft T, block coefficient Cb, bow entry angle θbow, midship section distribution function fmid(x), and stern contraction curve ftail(x). The bow, midship, and stern are all parametrically expressed using B-spline curves or polynomial functions, allowing for continuous adjustment of the hull geometry through a finite number of parameters. This parametric model transforms hull design from traditional geometric drawing methods to a mathematical model-driven approach, providing a unified variable space for subsequent optimization calculations.

[0034] When constructing the multi-objective optimization model, the optimization objective functions are currently set as follows: f1: minimizing total resistance Rt, f2: minimizing wave-making resistance Rw, and the constraints include: draft T ≤ port and shipping limit value. Stability meets regulatory requirements, and cargo capacity is not lower than design specifications. By establishing a constrained optimization model, the ship design can balance safety and economy. The CFD simulation process uses a three-dimensional viscous flow model based on the Reynolds-averaged Navier-Stokes equations to numerically solve the flow field on the ship hull. The specific steps include: Establish the computational domain and mesh generation (using a hybrid structured and unstructured mesh). The VOF method was used to simulate changes in the free liquid level. The turbulence model adopted is the k-ω SST model; Set multiple speed conditions (low speed, design speed, full load speed); Calculate the pressure distribution, velocity field, and waveform distribution of the ship's hull; Extract total resistance and wave-making resistance data; CFD simulation was used to obtain hydrodynamic performance data corresponding to different ship type parameters.

[0035] Multi-objective optimization and closed-loop iteration are performed using the NSGA-II multi-objective genetic algorithm to iteratively optimize the design variables. The process includes: The process involves initializing the population (initial ship type set), calculating the fitness function using CFD, performing non-dominated sorting and crowding distance calculation, performing selection, crossover and mutation operations, and updating the population. Through a closed-loop iteration of "CFD simulation - optimization calculation - parameter update", the ship type continuously evolves. The iteration stops when the following conditions are met: the rate of change of the objective function is lower than the set threshold or the number of iterations reaches the upper limit; The final output is the optimal combination of ship hull parameters, including: optimal hull line parameters, resistance reduction ratio, and wave-making waveform distribution characteristics. The optimization results show that, compared with the initial ship hull, the total resistance is reduced by about 10% to 18%, and the wave-making resistance is reduced by more than 15%.

[0036] Example 2

[0037] This embodiment further optimizes the drag reduction hull optimization method of the stern guide structure based on Embodiment 1 to improve propulsion efficiency.

[0038] I. Tail Structure Optimization Design Based on the tail contraction curve, a wake straightening structure or guide fin is added. The structure is located in the area below the waterline at the stern. The guide fin adopts an adjustable tilt angle structure. Its functions include: guiding the direction of the wake, reducing wake separation, and stabilizing the tail pressure field. II. CFD Wake Analysis The tail flow field was locally refined using CFD, with a focus on analyzing: wake vortex intensity, pressure gradient distribution, and streamline separation point location. The results show that the wake structure becomes significantly more stable and the eddy intensity is significantly reduced after the introduction of the guide fins. III. Optimization Results Compared with ships without aerodynamic structures: stern resistance is reduced by about 12%, propulsion efficiency is increased by about 8%, and ship course stability is significantly improved; IV. Technological Advantages This embodiment achieves a dual-layer optimization mechanism of "overall hull shape optimization + local flow field control" through the collaborative design of local structure and overall optimization, thereby improving the adaptability to complex working conditions.

[0039] Example 3

[0040] The multi-speed robust optimization inland waterway vessel design method in this embodiment focuses on solving the problem of unstable vessel performance at different speeds.

[0041] I. Multi-condition modeling Three typical operating conditions are set: low-speed condition (navigation within port), design speed condition (economic navigation), and full-load speed condition (extreme condition). Each condition is calculated independently using CFD. II. Robust Optimization Objectives Construct the comprehensive objective function: F = w1·Rt_low + w2·Rt_design + w3·Rt_full Multi-condition balanced optimization is achieved through weighting coefficients.

[0042] III. Collaborative Structure Optimization Simultaneously, the following structures are jointly optimized: the bow entry angle, the midships cross section distribution, and the bottom curved transition structure. This approach can reduce local flow separation and improve overall hydrodynamic stability. IV. Optimization Results The optimized hull design performs as follows under three operating conditions: resistance fluctuation is reduced by approximately 25%, hull attitude stability is improved, and resistance under full load condition is reduced by approximately 14%. V. Technical Effects This embodiment achieves unified optimization under multiple operating conditions, enabling the ship type to no longer be limited to a single design point, but to possess engineering robustness.

[0043] Example 4:

[0044] like Figure 2 As shown, this embodiment provides an implementation method for an automated CFD optimization design system based on a system platform.

[0045] The system includes: a parametric modeling module, a CFD simulation module, an optimization calculation module, a data interaction module, a results output module, and a visualization module. The optimization calculation module calls the CFD simulation module through an interface to achieve: parameter input → CFD calculation → drag output → optimization update → recalculation, forming a fully automatic closed loop. The parametric modeling module supports: control point adjustment, curve function reconstruction, and automatic generation of hull shape, eliminating the need for manual remodeling; The visualization module implements: 3D ship shape display, streamline distribution display, and pressure cloud map display. It also outputs: drag reduction ratio and energy saving rate assessment results; The system automates the entire ship design process, reducing the design cycle by approximately 40% to 60%.

[0046] The system achieves closed-loop coupling of CFD and multi-objective optimization, and automatically generates parametric ship types, a unified optimization model for multiple operating conditions, and introduces wake structure + global optimization collaborative design, multi-speed robust optimization mechanism and CFD-driven automatic iterative design system. It can be directly used in the engineering process of ship design institutes for energy-saving ship type modification design and embedded digital twin ship design.

[0047] This application constructs an efficient and automated low-resistance inland waterway vessel design method and system through deep coupling of CFD simulation and multi-objective optimization algorithm, realizing the technological upgrade of vessel design from experience-based design to intelligent optimization design, and has significant engineering application value and energy-saving and emission-reduction effects.

[0048] The implementation principle of the design method for low-resistance inland waterway vessels based on CFD optimization in this application embodiment is as follows: By parametrically expressing the hull geometry, key parameters such as length, beam, draft, and bow, midship, and stern lines are transformed into adjustable design variables. Under the premise of satisfying constraints such as draft, stability, and cargo capacity, a multi-objective optimization model is constructed with the goal of minimizing total resistance and wave-making resistance. Based on this, computational fluid dynamics (CFD) is used to perform three-dimensional flow field numerical simulations of the vessel type under different parameter combinations at various speeds to obtain the corresponding resistance performance data. Then, the design variables are iteratively updated through a multi-objective optimization algorithm, so that the ship morphology parameters continuously approach the optimal solution, and automatic optimization is achieved through a closed-loop coupling mechanism of "simulation-optimization-re-simulation". At the same time, the wake distribution is improved by combining stern morphology optimization and guide structure adjustment, thereby reducing flow separation and energy loss. When the optimization objective function meets the convergence condition, the low-resistance inland waterway ship morphology with the best comprehensive hydrodynamic performance is output, realizing the transformation of ship morphology design from experience-led to data-driven and intelligent optimization, improving design efficiency and optimization accuracy, and effectively reducing ship navigation resistance and energy consumption.

[0049] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A low-resistance inland waterway vessel design method based on CFD optimization, characterized in that, Includes the following steps: S1. Establish a parametric model of inland waterway vessel type, and set the length, beam, draft, block coefficient, and bow, midship and stern line parameters as design variables; S2. Set the optimization objective function and constraints, where the optimization objective includes at least minimizing total resistance and minimizing wave-making resistance, and the constraints include draft limits, stability requirements, and cargo capacity requirements. S3. Generate an initial ship type sample based on the parametric model; S4. Using computational fluid dynamics (CFD) methods, flow field simulation calculations were performed on the ship type samples under multiple speed conditions to obtain the resistance performance data of each ship type sample. S5. Input the resistance performance data into a multi-objective optimization algorithm to iteratively update the design variables and obtain the optimized ship hull parameters. S6. Reconstruct the ship type based on the optimized ship type parameters and repeat steps S4 and S5 until the convergence condition is met. S7. Output a low-drag inland waterway vessel type that meets the optimization objective.

2. The method according to claim 1, characterized in that: The parametric model uses curve control points or mathematical functions to parametrically express the bow entry angle, midship section distribution, and stern contraction curve of the hull.

3. The method according to claim 1, characterized in that: The CFD simulation uses a three-dimensional viscous flow model based on the Reynolds-averaged Navier-Stokes equations, and combines the free surface capture method to solve the flow field around the hull.

4. The method according to claim 3, characterized in that: The free surface is simulated using the VOF method, and the turbulence model is either the k-ε model or the k-ω SST model.

5. The method according to claim 1, characterized in that: The multi-objective optimization algorithm employs one or more combinations of genetic algorithms, NSGA-II algorithms, or particle swarm optimization algorithms.

6. The method according to claim 1, characterized in that: The multiple speed operating conditions include at least the low-speed operating condition, the design speed operating condition, and the full-load speed operating condition; The optimization process involves the coordinated optimization of local hull structures, including the bow shape, stern shape, and bottom surface transition structure. The convergence condition is that the rate of change of the objective function is lower than a preset threshold or the number of iterations reaches a set upper limit.

7. The method according to claim 6, characterized in that: The tail profile optimization includes setting a wake rectification structure or guide fins to improve the wake distribution.

8. The method according to claim 1, characterized in that: The convergence condition is that the rate of change of the objective function is lower than a preset threshold or the number of iterations reaches a set upper limit.

9. A low-drag inland waterway vessel design system based on CFD optimization, characterized in that, include: A parametric modeling module, which is used to establish a parametric model of inland waterway vessel type; It includes a CFD simulation module, which is used to perform multi-condition flow field calculations on the ship type and output resistance data; It includes an optimization calculation module, which is used to iteratively optimize the ship hull parameters based on a multi-objective optimization algorithm; It includes a data interaction module, which is used to realize data transmission between various modules; It includes a result output module, which is used to output the optimized low-drag hull form and its performance parameters.

10. The system according to claim 9, characterized in that: The optimization calculation module is configured to achieve closed-loop coupled iteration between CFD simulation and optimization algorithm; The parametric modeling module supports the automatic generation and updating of hull lines by adjusting control points or curve functions; The system also includes a visualization module for displaying the 3D model of the ship and the flow field distribution results; The system can output the resistance reduction ratio and energy saving assessment results.

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