RSM-GA based self-righting structure optimization method for inflatable rescue boat

Abstract

The application discloses an RSM-GA-based inflatable rescue boat self-righting structure optimization method, belongs to the technical field of ship engineering and optimization design, and comprises the following steps: S1, a three-dimensional model of the inflatable rescue boat is established by using computer-aided design software, and static stability analysis is performed by using simulation software; S2, a second-order polynomial regression model of the self-righting capability of the inflatable rescue boat and design parameters is established by using a response surface method, the interaction of each parameter is quantified, and an optimization target is determined; and S3, a genetic algorithm is combined to perform multi-objective optimization on the second-order polynomial model, while an engineering constraint condition is met, and optimal design parameters of the self-righting structure are obtained; the RSM-GA-based inflatable rescue boat self-righting structure optimization method can effectively reduce sailing resistance, improve sailing efficiency, and significantly improve the self-righting capability and sailing performance of the rescue boat while ensuring the stability and safety of the rescue boat.

Description

Technical Field

[0001] This invention relates to the field of marine engineering and optimization design technology, and in particular to an optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA. Background Technology

[0002] Against the backdrop of global climate change, extreme weather events are occurring frequently, and floods pose a serious threat to human life and property. In water rescue missions, rescue boats are core equipment, and their performance directly determines rescue efficiency and safety. However, traditional rescue boats have many shortcomings in complex water environments, especially in terms of self-righting ability and navigation stability. In the event of capsizing, rescue boats need to have the ability to quickly regain a right buoyancy to ensure the safety of personnel and continue the rescue mission. However, the design of existing rescue boats often struggles to achieve a balance between self-righting performance and navigation drag, especially under varying loads and sea conditions.

[0003] Existing research largely focuses on theoretical analysis and preliminary simulations, lacking systematic studies on multi-objective optimization under actual working conditions. For example, the optimal design of a self-righting structure not only requires increasing the restoring arm but also considering the increase in navigation resistance to ensure the maneuverability and response speed of the rescue vessel in complex waters. However, traditional methods often struggle to find a balance between these two aspects, leading to limitations in practical applications. In recent years, with the development of computing technology, advanced optimization techniques such as Response Surface Methodology (RSM) and Genetic Algorithm (GA) have been widely used in engineering design. RSM can effectively construct mathematical models between design parameters and performance indicators, quantifying the interactions between parameters and providing a theoretical basis for the optimization of complex systems. Genetic Algorithms, with their powerful global search capabilities and efficient optimization performance, are widely used in multi-objective optimization problems. However, there are no reports of combining these two methods for the optimal design of a rescue vessel's self-righting structure.

[0004] Therefore, this invention proposes an optimization design method for the self-righting structure of rescue boats based on response surface methodology and genetic algorithm. The aim is to significantly improve the self-righting performance and navigation stability of rescue boats through multi-objective optimization design, while reducing navigation resistance, so as to meet the needs of rescue missions in complex waters. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides an optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA, comprising the following steps:

[0007] S1. Use computer-aided design software to build a three-dimensional model of the inflatable rescue boat and use simulation software to perform static stability analysis.

[0008] S2. Using the response surface methodology (RSM), a second-order polynomial regression model of the self-righting capability of the inflatable rescue boat and its design parameters is established to quantify the interaction of each parameter and determine the optimization objective.

[0009] S3. Combine the genetic algorithm (GA) to perform multi-objective optimization on the second-order polynomial model, while satisfying the engineering constraints, to obtain the optimal design parameters of the self-aligning structure.

[0010] Preferably, the specific steps of S1 are as follows:

[0011] S11. Taking the rescue boat with rear airbag as the research object, simplify the self-righting structure, define the floating body size parameters and position parameters relative to the hull, and use computer-aided software to model the floating body of the self-righting structure to generate a three-dimensional model file that conforms to a common format, and establish a three-dimensional model of the inflatable rescue boat and the self-righting structure.

[0012] S12. Import the three-dimensional model into the static stability analysis module of the ship simulation software to perform static stability analysis on the rescue boat and obtain the recovery arm data under different tilt angles and different design parameters.

[0013] S13. Output the static stability analysis results.

[0014] Preferably, the dimensional parameters in S11 include the airbag width D and the airbag cross-sectional radius R, and the positional parameters include the float installation height H and the distance from the float to the stern L.

[0015] Preferably, the specific steps of S2 are as follows:

[0016] S21. Based on the static stability analysis results, a quadratic polynomial regression model is established using the response surface methodology between the restoring arm GZ and the heel angle θ, airbag width D, airbag cross-sectional radius R, float height H, and float distance L from the stern.

[0017] S22. Draw a response surface plot of the influence of each design parameter on the recovery arm data. Use the response surface plot and variance analysis to evaluate the influence trend of each design variable on the recovery arm of the inflatable rescue boat, determine the key optimization variables, and set the optimization target.

[0018] Preferably, the quadratic polynomial regression model in S21 takes the following form:

[0019]

[0020] Where β0 is the constant term, and β1, β2, β3, β4, and β5 are the linear regression coefficients of each individual variable. 11 β 22 β 33 β 44 β 55 β is the regression coefficient of the quadratic term. 12 β 13 β 14 β 15 β 23 β 24 β 25 β 34 β 35 β 45 X represents the regression coefficient of the interaction term. i Let X1 be the heel angle, X2 be the airbag width, X3 be the airbag cross-sectional radius, X4 be the float height, X5 be the distance of the float from the stern, and ε be the error term. For variance;

[0021] To enhance the fitting ability to the effect of the tilt angle θ, additional sine terms sin(θ) and sin(θ) are introduced into the above regression model. 2 (θ), the regression equation can be expressed as:

[0022]

[0023] Where, β 21 and β 22 These are the regression coefficients of the sine term.

[0024] Preferably, the optimization objective set in S22 is to minimize the integral value of the static stability curve in the range of 90° to 180°, and the restoring arm is always greater than 0 in the full tilt angle range.

[0025] Preferably, the specific steps of S3 are as follows:

[0026] S31. Set genetic parameters, define population size, crossover probability, mutation probability, and maximum number of iterations;

[0027] S32. Perform individual coding. Discretize the design parameters of the self-righting structure, such as airbag width D, airbag cross-sectional radius R, floating body height H, and floating body distance from the stern L, and use real number coding to represent the individual. Each individual contains 4 key design variables.

[0028] S33. Set the fitness function, using the optimization objective of the recovery lever arm in S22 as the fitness function, and calculate the individual fitness value;

[0029] S34. Perform genetic algorithm operations to optimize the fitness function and determine the optimal design parameters of the self-correcting structure.

[0030] Preferably, in S33, the fitness function minimizes the integral value of the static stability curve over the range of θ = 90° to 180°:

[0031]

[0032] Where θ is the tilt angle.

[0033] Preferably, the specific operation of the genetic algorithm in S34 is as follows:

[0034] S341. Initialize the population, generate an initial population that satisfies the engineering constraints, and calculate the fitness value of each individual according to the fitness function.

[0035] S342. Perform a selection operation based on fitness values, selecting parent individuals from the current population for reproduction;

[0036] S343. Perform crossover operations on the selected parent individuals to produce new offspring individuals, thereby increasing the diversity of the population and accelerating the convergence speed.

[0037] S344. Perform mutation operations on offspring individuals according to the set mutation probability to increase the search aperture and avoid local optima;

[0038] S345. Perform iterative updates, repeatedly executing selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness function converges.

[0039] S346. Output the optimal solution and obtain the individual with the best fitness value. Use the self-righting structure design parameters corresponding to this individual as the final optimization result to ensure the improvement of the self-righting capability of the inflatable rescue boat and optimize the stability of the route.

[0040] Therefore, the present invention employs the above-mentioned optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA, which has the following beneficial effects:

[0041] (1) By combining response surface methodology with genetic algorithm, the self-righting performance of inflatable rescue boat can be significantly improved under complex conditions of multi-parameter coupling, while effectively reducing navigation resistance and improving rescue efficiency. Moreover, the introduction of genetic algorithm ensures the globality of the optimization process and avoids the design limitations caused by local optimal solutions in traditional methods.

[0042] (2) The actual engineering constraints were fully considered, and the optimization results have high engineering application value. They can be directly applied to the design and improvement of rescue boats, and the optimization effect can be clearly demonstrated, providing scientific basis and practical guidance for the design of rescue boats.

[0043] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0044] Figure 1 This is a flowchart of an optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA according to the present invention.

[0045] Figure 2 This is a flowchart of step S1 of the self-righting structure optimization method for an inflatable rescue boat based on RSM-GA according to the present invention.

[0046] Figure 3 This invention relates to a three-dimensional side view of an inflatable rescue boat model, which is a self-righting structure optimization method for inflatable rescue boats based on RSM-GA.

[0047] Figure 4 This is a front view of a three-dimensional model of an inflatable rescue boat, which is an optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA according to the present invention.

[0048] Figure 5 This is a top view of a three-dimensional model of an inflatable rescue boat, based on an RSM-GA-based method for optimizing the self-righting structure of such boats.

[0049] Figure 6 This is a flowchart of step S2 of the self-righting structure optimization method for an inflatable rescue boat based on RSM-GA according to the present invention.

[0050] Figure 7 This is a response surface plot showing the interaction between the airbag cross-sectional radius R ratio and the distance L from the stern of the float in the self-righting structure optimization method of an inflatable rescue boat based on RSM-GA according to the present invention.

[0051] Figure 8 This is a flowchart of step S3 of the self-righting structure optimization method for an inflatable rescue boat based on RSM-GA according to the present invention. Detailed Implementation

[0052] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0053] Example

[0054] like Figure 1 As shown, this invention provides an optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA, comprising the following steps:

[0055] like Figure 2 As shown in Figure S1, a three-dimensional model of the inflatable rescue boat is established using computer-aided design software, and static stability analysis is performed using simulation software.

[0056] S11. Taking a rescue boat with a rear-mounted airbag as the research object, the self-righting structure is simplified, and the buoyancy parameters and positional parameters relative to the hull are defined. Computer-aided software is used to model the buoyancy of the self-righting structure, generating a 3D model file conforming to a common format. A 3D model of the inflatable rescue boat and its self-righting structure is established. Dimensional parameters include the airbag width D and the airbag cross-sectional radius R; positional parameters include the buoyancy installation height H and the distance from the buoyancy to the stern L. The constructed 3D model is as follows: Figures 3-5 As shown.

[0057] The main design parameters of the inflatable rescue boat in this embodiment are: length 4.282 meters, width 2.228 meters, and depth 0.675 meters. SolidWorks software is used to model the self-righting floating body and generate a 3D model file in .igs format. Then, the .igs file is imported into the MaxsurfModeler module to complete the establishment of the overall model of the inflatable rescue boat.

[0058] S12. Import the three-dimensional model into the static stability analysis module of the ship simulation software to perform static stability analysis on the rescue boat and obtain the recovery arm data under different tilt angles and different design parameters.

[0059] S13. Output the static stability analysis results.

[0060] like Figure 6 As shown in Figure S2, a second-order polynomial regression model of the self-righting capability of the inflatable rescue boat and the design parameters is established using the response surface methodology (RSM) to quantify the interaction of each parameter and determine the optimization objective.

[0061] S21. Based on the static stability analysis results, a quadratic polynomial regression model was established using the response surface methodology between the restoring arm GZ and the heel angle θ, airbag width D, airbag cross-sectional radius R, float height H, and float distance L from the stern. The heel angle θ, airbag width D, airbag cross-sectional radius R, float height H, and float distance L from the stern were the independent variables, and the restoring arm GZ was the dependent variable.

[0062] The quadratic polynomial regression model takes the following form:

[0063]

[0064] Where β0 is the constant term, and β1, β2, β3, β4, and β5 are the linear regression coefficients of each individual variable. 11 β 22 β33 β 44 β 55 β is the regression coefficient of the quadratic term. 12 β 13 β 14 β 15 β 23 β 24 β 25 β 34 β 35 β 45 X represents the regression coefficient of the interaction term. i Let X1 be the heel angle, X2 be the airbag width, X3 be the airbag cross-sectional radius, X4 be the float height, X5 be the distance of the float from the stern, and ε be the error term. To determine the variance, the experimental data were fitted using the least squares method to obtain the regression relationship between the restoring arm GZ and each design parameter.

[0065] To enhance the fitting ability to the effect of the tilt angle θ, additional sine terms sin(θ) and sin(θ) are introduced into the above regression model. 2 (θ), the regression equation can be expressed as:

[0066]

[0067] Where, β 21 and β 22 These are the regression coefficients of the sine term.

[0068] S22. Based on the regression model, plot the response surface diagram of the influence of each design parameter on the restoring arm data, and establish evaluation indicators for assessing the performance of self-righting structures, such as... Figure 7 As shown, the influence trend of each design variable on the righting arm of the inflatable rescue boat is evaluated by response surface plot and combined with analysis of variance (ANOVA). Key optimization variables are identified, and optimization objectives are set. The optimization objective is to minimize the integral value of the static stability curve in the range of 90° to 180°, and to ensure that the righting arm is always greater than 0 in the full range of heel angles, so as to guarantee the self-righting capability of the inflatable rescue boat.

[0069] like Figure 8 As shown, S3 combines a genetic algorithm (GA) to perform multi-objective optimization on the second-order polynomial model, while satisfying engineering constraints, to obtain the optimal design parameters of the self-aligning structure.

[0070] S31. Set genetic parameters, define population size, crossover probability, mutation probability, and maximum number of iterations;

[0071] S32. Perform individual coding. Discretize the design parameters of the self-righting structure, such as airbag width D, airbag cross-sectional radius R, floating body height H, and floating body distance from the stern L, and use real number coding to represent the individual. Each individual contains 4 key design variables.

[0072] S33. Set the fitness function, using the optimization objective of the recovery lever arm in S22 as the fitness function, and calculate the individual fitness value;

[0073] The fitness function minimizes the integral of the static stability curve over the range of θ = 90° to 180°, while ensuring that the restoring lever arm is always greater than 0.

[0074]

[0075] Where θ is the tilt angle.

[0076] S34. Perform genetic algorithm operations to optimize the fitness function and determine the optimal design parameters of the self-correcting structure.

[0077] S341. Initialize the population. Use the Latin hypercube sampling method (LHS) to generate an initial population that satisfies the engineering constraints, and calculate the fitness value of each individual according to the fitness function.

[0078] S342. Based on fitness values, a selection operation is performed using the roulette wheel selection method to select parent individuals from the current population for reproduction.

[0079] S343. Perform crossover on the selected parent individuals using simulated binary crossover (SBX) to produce new offspring individuals, thereby increasing population diversity and accelerating convergence.

[0080] S344. Perform mutation operations on the offspring individuals according to the set mutation probability, using the Gaussian mutation method to increase the search aperture and avoid local optima;

[0081] S345. Perform iterative updates, repeatedly executing selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness function converges.

[0082] S346. Output the optimal solution and obtain the individual with the best fitness value. Use the self-righting structure design parameters corresponding to this individual as the final optimization result to ensure the improvement of the self-righting capability of the inflatable rescue boat and optimize the stability of the route.

[0083] Therefore, the present invention adopts the above-mentioned optimization method for the self-righting structure of an inflatable rescue boat based on RSM-GA, which can effectively reduce navigation resistance, improve navigation efficiency, and significantly enhance the self-righting ability and navigation performance of the rescue boat while ensuring the stability and safety of the rescue boat.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An RSM-GA-based self-righting structure optimization method for an inflatable rescue boat, characterized by, Includes the following steps: S1. Use computer-aided design software to build a three-dimensional model of the inflatable rescue boat and use simulation software to perform static stability analysis. S11. Taking a rescue boat with a rear-mounted airbag as the research object, the self-righting structure is simplified, the size parameters of the float and its position parameters relative to the hull are defined, and the float of the self-righting structure is modeled using computer-aided software to generate a three-dimensional model file that conforms to a common format. A three-dimensional model of the inflatable rescue boat and the self-righting structure is established. The size parameters include the width D of the airbag and the radius R of the airbag cross section, and the position parameters include the installation height H of the float and the distance L of the float from the stern. S12. Import the three-dimensional model into the static stability analysis module of the ship simulation software to perform static stability analysis on the rescue boat and obtain the recovery arm data under different tilt angles and different design parameters. S13. Output the static stability analysis results; S2. Using the response surface methodology, a second-order polynomial regression model of the self-righting capability of the inflatable rescue boat and its design parameters is established to quantify the interaction of each parameter and determine the optimization objective. S21. Based on the static stability analysis results, a quadratic polynomial regression model is established using the response surface methodology between the restoring arm GZ and the heel angle θ, airbag width D, airbag cross-sectional radius R, float height H, and float distance L from the stern. The quadratic polynomial regression model takes the following form: ； in, For constant terms, , , , , For each individual variable, the linear regression coefficients are... , , , , The regression coefficients are quadratic terms. , , , , , , , , , The regression coefficients for the interaction term, As the independent variable, The tilt angle is... For the width of the airbag, Where is the radius of the airbag cross-section. The height of the floating body. The distance from the buoy to the stern. For the error term, For variance; To enhance the fitting ability to the roll angle The sine term is introduced into the regression model and The regression equation is expressed as: ； wherein and are the regression coefficients of the sine terms, respectively; S22. Draw a response surface plot of the influence of each design parameter on the recovery arm data. Use the response surface plot and analysis of variance to evaluate the influence trend of each design variable on the recovery arm of the inflatable rescue boat, determine the key optimization variables, and set the optimization target. S3. Combine the genetic algorithm to perform multi-objective optimization on the second-order polynomial model, while satisfying the engineering constraints, to obtain the optimal design parameters of the self-aligning structure.

2. The RSM-GA-based self-righting structure optimization method for an inflatable rescue boat according to claim 1, characterized in that: The optimization objective set up in S22 is to minimize the integral value of the static stability curve in the range of The restoring lever is always greater than 0 in the full range of roll angle.

3. The RSM-GA based self-righting structure optimization method for an inflatable rescue boat according to claim 2, characterized in that, The specific steps for S3 are as follows: S31. Set genetic parameters, define population size, crossover probability, mutation probability, and maximum number of iterations; S32. Perform individual coding. Discretize the design parameters of the self-righting structure, such as airbag width D, airbag cross-sectional radius R, floating body height H, and floating body distance from the stern L, and use real number coding to represent the individual. Each individual contains 4 key design variables. S33. Set the fitness function, using the optimization objective of the recovery lever arm in S22 as the fitness function, and calculate the individual fitness value; S34. Perform genetic algorithm operations to optimize the fitness function and determine the optimal design parameters of the self-correcting structure.

4. The RSM-GA-based self-righting structure optimization method for an inflatable rescue boat according to claim 3, characterized in that, In S33, the fitness function is to minimize the static stability curve. Integral values ​​within the range: ； wherein is the angle of inclination.

5. The RSM-GA based self-righting structure optimization method for an inflatable rescue boat according to claim 3, wherein, The specific operation of the genetic algorithm in S34 is as follows: S341. Initialize the population, generate an initial population that satisfies the engineering constraints, and calculate the fitness value of each individual according to the fitness function. S342. Perform a selection operation based on fitness values, selecting parent individuals from the current population for reproduction; S343. Perform crossover operations on the selected parent individuals to produce new offspring individuals; S344. Perform mutation operations on the offspring individuals according to the set mutation probability; S345. Perform iterative updates, repeatedly executing selection, crossover, and mutation operations until the maximum number of iterations is reached or the fitness function converges. S346. Output the optimal solution, obtain the individual with the best fitness value, and take the self-aligning structure design parameters corresponding to the individual as the final optimization result.

Images