Method and device for predicting peak ice load in the process of ice breaking of an underwater moving body

Abstract

This invention discloses a method and apparatus for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body. The method includes: selecting sea ice thickness and the surfacing speed of the underwater moving body as the main operating condition variables, obtaining the peak ice load during the surfacing and ice-breaking process of the underwater moving body under different operating conditions, organizing the peak ice load and its corresponding operating condition variables to form a sample dataset; based on the sample dataset, constructing a dimensionless sample dataset including dimensionless ice thickness, dimensionless speed, and dimensionless peak ice load based on Buckingham's Π theorem, and introducing first-order terms, second-order terms, and interaction terms in the logarithmic space to establish a generalized logarithmic regression model, solving the coefficients of the generalized logarithmic regression model to obtain a generalized logarithmic regression prediction model; and based on the generalized logarithmic regression prediction model, rapidly obtaining the predicted value of the peak ice load during the surfacing and ice-breaking process of the underwater moving body under given sea ice thickness and surfacing speed conditions.

Description

Technical Field

[0001] This invention relates to the field of computational fluid dynamics, and in particular to a method and apparatus for predicting peak ice loads during the surfacing and ice-breaking process of an underwater moving body. Background Technology

[0002] With the continuous advancement of polar navigation, resource development, and offshore engineering activities, the demand for ultra-large UUVs to perform vertical surfacing and icebreaking missions in sea ice-covered waters is increasing. During the surfacing and icebreaking process, the ice load on ultra-large UUVs is affected by factors such as sea ice thickness, surfacing speed, and hull characteristic dimensions, exhibiting significant transient, nonlinear, and multi-parameter coupling characteristics. Especially under thick ice and high-speed surfacing conditions, the peak ice load increases significantly, directly affecting the structural safety assessment and strength design of key components of ultra-large UUVs. Existing research mainly relies on high-precision numerical simulation methods to obtain the ice load during the surfacing and icebreaking process. Although these methods can reflect the ice layer failure evolution and hull stress characteristics well, they suffer from high computational costs and difficulty in meeting the needs of rapid engineering assessment. At the same time, most existing rapid ice load prediction methods are geared towards ships or ice-covered structures, with relatively few studies specifically for the vertical surfacing and icebreaking scenario of ultra-large UUVs. There is still a lack of a rapid prediction method for peak ice load with clear physical meaning that can characterize the coupled influence of sea ice thickness and surfacing speed. Therefore, there is an urgent need to propose a rapid prediction method for the peak ice load of ultra-large UUVs surfacing and breaking ice in layered ice environments, so as to quickly obtain the peak ice load of ultra-large UUVs surfacing and breaking ice under given sea ice thickness and surfacing speed. Summary of the Invention

[0003] Based on this, it is necessary to address the above problems by proposing a method and device for predicting the peak ice load during the surfacing and ice-breaking process of underwater moving bodies. Based on numerical simulation samples or experimental measurements, a dimensionless parameter system characterizing the surfacing and ice-breaking process of underwater moving bodies is constructed using Buckingham's Π theorem. A peak ice load prediction model is established using a generalized logarithmic regression method, thereby quickly obtaining the peak ice load during the surfacing and ice-breaking process of underwater moving bodies under the corresponding working conditions, given sea ice thickness and surfacing speed.

[0004] In a first aspect, the present invention discloses a method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body, the method comprising the following steps: Step S1: Select sea ice thickness and the buoyancy of underwater moving objects As the main operating condition variable, numerical simulation calculations or experimental measurements are carried out on the surfacing and ice-breaking process of underwater moving bodies under different operating conditions, and the peak ice load is extracted from the simulation calculation results or experimental measurement results. Summarize the peak ice load and the corresponding sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress This forms a sample dataset; Step S2: Based on Buckingham's π theorem, select the maximum diameter of the hull. Water density and sea ice yield stress As the basic quantity, the peak ice load Sea ice thickness Ascent speed The dimensionless ice thickness was obtained by combining the basic quantities with the dimensionless ice thickness. Dimensionless velocity and dimensionless peak ice load The expression; substituting the sample dataset into the dimensionless ice thickness The dimensionless velocity and the dimensionless peak ice load In the expression, the dimensionless parameters under each working condition are calculated to form a dimensionless sample dataset; Step S3: Measure the dimensionless peak ice load As the dependent variable, the dimensionless ice thickness Dimensionless velocity Establish a dimensionless peak ice load as the independent variable. With dimensionless ice thickness Dimensionless velocity The functional relationship between them, and the dimensionless ice thickness in the functional relationship. Dimensionless velocity and dimensionless peak ice load Logarithmic transformation is performed, introducing linear, quadratic, and interaction terms into the logarithmic space to establish a generalized logarithmic regression model; based on the dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved to obtain the generalized logarithmic regression prediction model; Step S4: Predict the peak ice load during the ice-breaking process of the underwater moving body ascent based on the generalized logarithmic regression prediction model.

[0005] Secondly, the present invention also discloses a peak ice load prediction device for the surfacing and ice-breaking process of an underwater moving body, the device comprising: The sample data construction module is used to select sea ice thickness. and the buoyancy of underwater moving objects As the main operating condition variable, numerical simulation calculations or experimental measurements are carried out on the process of underwater moving bodies surfacing and breaking ice under different operating conditions, and the peak ice load is extracted from the simulation calculation results or experimental measurement results. Summarize the peak ice load and the corresponding sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress This forms a sample dataset; A dimensionless sample data construction module is used to select the maximum diameter of the hull based on Buckingham's π theorem. Water density and sea ice yield stress As the basic quantity, the peak ice load Sea ice thickness Ascent speed The dimensionless ice thickness was obtained by combining the basic quantities with the dimensionless ice thickness. Dimensionless velocity and dimensionless peak ice load The expression; substituting the sample dataset into the dimensionless ice thickness The dimensionless velocity and the dimensionless peak ice load In the expression, the dimensionless parameters under each working condition are calculated to form a dimensionless sample dataset; The generalized logarithmic regression prediction model modeling module is used to model the dimensionless peak ice load. As the dependent variable, the dimensionless ice thickness Dimensionless velocity Establish a dimensionless peak ice load as the independent variable. With dimensionless ice thickness Dimensionless velocity The functional relationship between them, and the dimensionless ice thickness in the functional relationship. Dimensionless velocity and dimensionless peak ice load Logarithmic transformation is performed, introducing linear, quadratic, and interaction terms into the logarithmic space to establish a generalized logarithmic regression model; based on the dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved using the least squares method to obtain the generalized logarithmic regression prediction model. A peak ice load prediction module is used to predict the peak ice load during the ice-breaking process of an underwater moving body surfacing, based on the generalized logarithmic regression prediction model.

[0006] Thirdly, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described in the first aspect.

[0007] Fourthly, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described in the first aspect.

[0008] By implementing the above embodiments, the present invention has the following beneficial effects: 1. This invention constructs a dimensionless parameter system applicable to the surfacing and icebreaking process of underwater moving bodies in layered ice environments based on Buckingham's Π theorem. It incorporates parameters that play a major role in peak ice load, such as sea ice thickness, surfacing velocity, hull characteristic dimensions, water density, and sea ice yield stress, into a unified dimensionless expression, thus locking in the core controlling factors of icebreaking and conforming to the physical mechanism. Since peak ice load is the dependent variable to be examined, sea ice thickness and surfacing velocity have a significant impact on peak ice load. Dimensionless parameters for ice thickness, dimensionless velocity, and dimensionless peak ice load are constructed, and the physical meaning of the parameters is clear, facilitating mechanism analysis.

[0009] 2. This invention employs a generalized logarithmic regression model to model the icebreaking load, adapting to the power-law nonlinear evolution characteristics of icebreaking load with ice thickness and ascent velocity. Through logarithmic transformation, the complex nonlinear relationship is converted into a linear regression form that is easy to solve. By introducing a first-order term into the logarithmic space, the influence of sea ice thickness and ascent velocity on peak ice load is characterized separately. By introducing a second-order term, the marginal gain attenuation / enhancement effects of sea ice thickness and ascent velocity can be characterized. By introducing an interaction term, the coupling synergistic effect between sea ice thickness and ascent velocity can be characterized. In summary, the generalized logarithmic regression model of this invention better reflects real icebreaking mechanics, improving prediction accuracy and precision. Attached Figure Description

[0010] To more clearly illustrate the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] in: Figure 1 This is a flowchart of a method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to a specific embodiment of the present invention. Figure 2This is a contour map showing the variation of dimensionless peak ice load with dimensionless ice thickness and dimensionless velocity for 20 samples in a specific embodiment of the present invention. Figure 3 This is a figure showing the dimensionless peak ice load fitting surface and sample point distribution of 20 samples in a specific embodiment of the present invention. Figure 4 This is a graph showing the logarithmic relationship between dimensionless peak ice load and dimensionless ice thickness and dimensionless velocity for 20 samples in a specific embodiment of the present invention. Figure 5 This is a comparison chart of the predicted peak ice load values ​​and finite element simulation results for 20 samples in a specific embodiment of the present invention; Figure 6 This is a structural block diagram of a peak ice load prediction device for the surfacing and ice-breaking process of an underwater moving body according to a specific embodiment of the present invention. Figure 7 This is a structural block diagram of a computer device according to a specific embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] refer to Figure 1 This invention discloses a method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body. The method includes the following steps: Step S1: Establish the sample dataset Select sea ice thickness and the buoyancy of underwater moving objects As the main operating condition variable, numerical simulation calculations or experimental measurements are carried out on the process of underwater moving bodies surfacing and breaking ice under different operating conditions, and the peak ice load is extracted from the simulation calculation results or experimental measurement results. Sorting out peak ice load and the corresponding sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress Operating parameters are used to form a sample dataset.

[0014] Specifically, in one embodiment, based on the requirements of ultra-large UUV polar operations, the sea ice thickness... The ascent speeds are set to 0.2 m, 0.4 m, 0.6 m, 0.8 m, and 1.0 m. Setting the speeds to 0.5 m / s, 1.0 m / s, 1.5 m / s, and 2.0 m / s, the speeds were compared for different sea ice thicknesses. and ascent speed By combining these elements, 20 sets of icebreaking operation scenarios for ultra-large UUVs were constructed, among which the largest diameter of the hull... Water density and sea ice yield stress For quantitative parameters under operating conditions, the maximum diameter of the hull is... Take 2.75m, water density Take 998 kg / m 3 Sea ice yield stress Take 2.1 × 10 6 Pa.

[0015] For each operating condition, numerical simulation calculations of the icebreaking process of the ultra-large UUV are conducted separately. In one specific embodiment, the numerical simulation calculation includes the following steps: Step S11: Construct a three-dimensional geometric model of the underwater moving body and the ice layer, mesh the three-dimensional geometric model of the underwater moving body and the ice layer to obtain the surfacing and icebreaking calculation model; import the surfacing and icebreaking calculation model into explicit dynamic / explicit fluid-structure interaction analysis software, define the Lagrange solid element algorithm for the underwater moving body, define the arbitrary Lagrange-Eulerian ALE fluid element algorithm for the water and air domains, select the ice constitutive model or equivalent solid model for the ice layer, and set the state equations and physical parameters for the air and water domains. Step S12: Locally refine the mesh along the expected surfacing trajectory of the underwater moving body and the area where the underwater moving body may come into contact with the ice layer; delineate and constrain multiple feature regions based on the mesh topology, including the node set at the bottom of the water area, the unit surface segment set in the air domain and the periphery of the water area, and the contact surface segment set corresponding to the outer surface of the underwater moving body and the bottom surface of the ice layer; define the corresponding solid unit set in the water area and the air domain and its surrounding area to describe the continuous transition of fluid volume and boundary; Step S13: In the explicit dynamic / explicit fluid-structure interaction analysis software, establish the coupling relationship between the fluid domain and the solid domain; the fluid domain includes the air domain and the water domain; the solid domain includes the underwater moving body and the ice layer; select the coupling algorithm for the interaction between ALE fluid elements and Lagrangian solid elements; apply corresponding velocity constraints or non-reflective boundary conditions to the bottom and sidewalls of the water domain and the outer boundary of the air domain, apply initial buoyancy conditions to the underwater moving body, and apply gravity and constraint conditions to the ice layer to ensure that all materials are at rest or in a predetermined equilibrium state at the initial moment of calculation; establish the contact constraints between the ice layer and the underwater moving body according to the buoyancy path of the underwater moving body and the expected contact area, and set the contact stiffness and contact damping parameters; Step S14: Set the simulation termination time and time step control conditions, and enable ALE mesh smoothing and remapping functions to ensure that the fluid mesh remains normal during the underwater motion body's ascent, collision, and ice breaking process; start the solution process and output the time history data of the pressure field on the outer surface of the underwater motion body, the contact force between the ice layer and the hull of the underwater motion body, and the fluid-structure interaction force according to the pre-set output control card, while generating animation frame files for visualization; perform partitioned integration on the normal pressure and tangential shear stress of the surface elements of the underwater motion body hull, and subtract the hydrodynamic background components under the corresponding working conditions to obtain the ice load time history of the entire process of the underwater motion body's ascent and ice breaking; Step S15: Extract the peak contact force from the ice load time history as the peak ice load under the corresponding working condition. .

[0016] The detailed process of the above numerical simulation calculation can be found in the patent disclosure with application number 202610171598.3 and title: Calculation method of ice load for surfacing and breaking ice of ultra-large vehicles in layered ice environment.

[0017] In another embodiment, experimental measurements can be conducted for each operating condition, and the peak ice load can be obtained through physical model experiments. .

[0018] Step S2: Establish dimensionless sample data Selecting peak ice load Sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress As the main control variable affecting the peak ice load of ultra-large UUVs during surfacing and icebreaking, based on Buckingham's π theorem, the maximum diameter of the hull is selected. Water density and sea ice yield stress As the basic quantity, the peak ice load Sea ice thickness Ascent speed After combining the above three basic quantities with dimensionless processing, dimensionless ice thickness is obtained. Dimensionless velocity and dimensionless peak ice load The expression; substituting the sample dataset obtained in step S1 into the dimensionless ice thickness. Dimensionless velocity and dimensionless peak ice load In the expression, the dimensionless parameters under each working condition are calculated to form a dimensionless sample dataset.

[0019] In this step, Buckingham's Π theorem states: if a physical process involves n There are 1 physical variable, and the physical variable contains 1 physical variable. k The original functional relationship is represented by a fundamental dimension. f ( x 1, x 2, … , x n )=0 can be simplified to nk Functional relationships between independent dimensionless parameters: This transforms the relationship between peak ice load and dimensional variables into a relationship between dimensionless parameters.

[0020] In this invention, peak ice load Sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress Among the six physical variables, there are three fundamental dimensions: mass, length, and time. Therefore, we construct three independent dimensionless parameters and their expressions.

[0021] Since peak ice load is the dependent variable to be investigated, sea ice thickness and uplift velocity have a significant impact on peak ice load. Therefore, it is preferable to construct a dimensionless ice thickness... Dimensionless velocity and dimensionless peak ice load The expression.

[0022] In this invention, the preferred dimensionless ice thickness is... Dimensionless velocity and dimensionless peak ice load The expression is as follows: .

[0023] In the above formula, the dimensionless ice thickness Characterizing the relative scale of sea ice and the hull, this parameter directly determines the geometric characteristics of the contact area and the failure mode of sea ice transitioning from local fracturing to overall bending; dimensionless velocity. The Cauchy number, which measures the ratio of inertial force to material strength, is used to quantitatively describe the dynamic correction of static breakage mechanisms by kinetic energy impact during high-speed ascent; dimensionless peak ice load. Characterizes the dimensionless amplification of peak ice load relative to sea ice yield strength and hull cross-sectional dimensions.

[0024] The present invention performs the above-mentioned dimensionless processing on sea ice thickness, rising speed and peak ice load respectively, which not only reflects the physical mechanism of actual working conditions, but also completely decouples the three dimensionless parameters, which is beneficial to subsequent analysis of the influence of ice thickness, speed and their coupling on peak ice load.

[0025] Substitute the sample dataset into the expression for the dimensionless parameter, calculate the dimensionless parameter for each working condition, and form a dimensionless sample dataset.

[0026] Step S3: Establish a generalized logarithmic regression model Dimensionless peak ice load As the dependent variable, the dimensionless ice thickness Dimensionless velocity Establish a dimensionless peak ice load as the independent variable. With dimensionless ice thickness Dimensionless velocity Functional relationship between them: For dimensionless ice thickness in functional relations Dimensionless velocity and dimensionless peak ice load Logarithmic transformation is performed, introducing linear, quadratic, and interaction terms into the logarithmic space to establish a generalized logarithmic regression model. Based on a dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved to obtain the generalized logarithmic regression prediction model.

[0027] Specifically, the expression for the generalized logarithmic regression model is: In the above formula, For the intercept term, The coefficients are those of a first-order regression. The regression coefficients are quadratic terms. For the interaction term coefficient.

[0028] This invention employs a generalized logarithmic regression model to model the icebreaking load, adapting to the power-law nonlinear evolution characteristics of icebreaking load with ice thickness and ascent speed. Through logarithmic transformation, the complex nonlinear relationship is converted into a linear regression form that is easy to solve. By introducing a first-order term into the logarithmic space, the influence of sea ice thickness and ascent speed on peak ice load is characterized. By introducing a second-order term, the marginal gain attenuation / enhancement effects of sea ice thickness and ascent speed can be characterized. By introducing an interaction term, the coupling synergistic effect of sea ice thickness and ascent speed can be characterized. In summary, the generalized logarithmic regression model of this invention better reflects real icebreaking mechanics, improving prediction accuracy and precision.

[0029] Preferably, in the process of solving the coefficients of the generalized logarithmic regression model based on a dimensionless sample dataset to obtain the generalized logarithmic regression prediction model, the least squares method is used to solve the coefficients of the generalized logarithmic regression model. The equation after logarithmic transformation is still a linear regression framework. Solving the coefficients using the least squares method is fast, has no local optima, and is highly stable.

[0030] Preferably, after obtaining the generalized logarithmic regression prediction model, the process also includes evaluating and verifying the effectiveness of the generalized logarithmic regression prediction model to ensure the effectiveness of the prediction model.

[0031] In one specific embodiment, the predicted value of peak ice load using the prediction model can be evaluated against the sample dataset using one or more of the following: root mean square error (RMSE), mean absolute error (MAE), normalized root mean square error (NRMSE), and coefficient of determination (R²).

[0032] Step S4: Predict peak ice load Given the pre-set maximum hull diameter, water density, and sea ice yield stress, input the sea ice thickness and buoyancy speed. Based on the generalized logarithmic regression prediction model obtained in step 3, the predicted peak ice load during the buoyancy and ice-breaking process of the underwater moving body under the corresponding working conditions can be calculated.

[0033] In one specific embodiment, to facilitate the calculation of peak ice load, the peak ice load during the surfacing and icebreaking process of an underwater moving body is predicted based on a generalized logarithmic regression prediction model, including the following steps: Step S41: Perform exponential reduction on the generalized logarithmic regression prediction model to transform the regression relationship in the logarithmic space into a dimensionless explicit prediction relationship for peak ice load, thus obtaining the dimensionless peak ice load prediction model.

[0034] In one specific embodiment, the expression for the dimensionless peak ice load prediction model is: Step S42: Measure the dimensionless ice thickness Dimensionless velocity and dimensionless peak ice load Substituting the expression into the dimensionless peak ice load prediction model of step S41, we obtain the explicit prediction formula for the peak ice load.

[0035] In one specific embodiment, the formula for the dimensionless parameter is as follows: .

[0036] Substituting the formulas for the dimensionless parameters into the expression of the dimensionless peak ice load prediction model, we obtain the explicit prediction formula for the peak ice load as follows: In the above formula, This represents the predicted peak ice load.

[0037] Step S43: Predict the peak ice load during the surfacing and ice-breaking process of an underwater moving body using an explicit prediction formula for peak ice load.

[0038] The following are specific examples. Based on the requirements of ultra-large UUV polar operations, sea ice thickness The ascent speeds are set to 0.2 m, 0.4 m, 0.6 m, 0.8 m, and 1.0 m. Setting the speeds to 0.5 m / s, 1.0 m / s, 1.5 m / s, and 2.0 m / s, the speeds were compared for different sea ice thicknesses. and ascent speed By combining these elements, 20 sets of icebreaking operation scenarios for ultra-large UUVs were constructed, among which the largest diameter of the hull... Water density and sea ice yield stress For quantitative parameters under operating conditions, the maximum diameter of the hull is... Take 2.75m, water density Take 998 kg / m 3 Sea ice yield stress Take 2.1 × 10 6 Pa.

[0039] For each working condition, numerical simulation calculations were carried out on the floating and icebreaking process of the ultra-large UUV. The simulation results of each working condition variable and the corresponding peak ice load are shown in Table 1.

[0040] Table 1 Comparison of peak ice loads in numerical simulation and model prediction The regression coefficients of the generalized logarithmic regression model calculated using the 20 samples in Table 1 are shown in Table 2.

[0041] Table 2 Regression coefficients of the generalized logarithmic regression model The peak ice load was predicted using the prediction model for the 20 samples in Table 1, and the predicted values ​​are shown in Table 1. As can be seen from Table 1, in the 20 sample conditions, the predicted peak ice load values ​​obtained by the prediction model are generally close to the corresponding numerical calculation results. Except for the condition with a sea ice thickness of 0.6 m and an ascent speed of 1.5 m / s, where the relative error is 12.83%, the relative errors for the other conditions are all less than 10%. The root mean square error (RMSE) is 4.90 × 10³ kN, the mean absolute error (MAE) is 3.36 × 10³ kN, and the coefficient of determination (R²) is 0.995. The closer R² is to 1, the higher the model's fitting accuracy, indicating that within the sample condition range, the prediction model can well reflect the overall pattern of peak ice load variation with sea ice thickness and ascent speed.

[0042] To demonstrate the interpolation calculation capability of the prediction model of this invention for working conditions that were not used in the sample construction, three additional interpolation working conditions within the range of the sample working conditions and not used for model construction were selected for numerical calculation, and the results are shown in Table 3.

[0043] Table 3 Comparison of predicted peak ice load values ​​and numerical calculation results under interpolation conditions As shown in Table 3, the relative errors between the predicted peak ice load values ​​and the numerical calculation results under the three interpolation conditions are 1.26%, 2.61%, and 6.09%, respectively, all less than 10%. Therefore, under the pre-set conditions of the maximum hull diameter, water density, and sea ice yield stress, and within the operating range of this embodiment where the sea ice thickness is 0.2 m to 1.0 m and the buoyancy speed is 0.5 m / s to 2.0 m / s, the sea ice thickness and buoyancy speed can be directly input, and the predicted peak ice load value of the ultra-large UUV's buoyancy and icebreaking process can be quickly obtained using the prediction model.

[0044] Combination Figures 2 to 5 The modeling process and prediction results for this specific case are explained. For example... Figure 2 As shown, within the operating range of this case, the dimensionless peak ice load generally increases with the increase of dimensionless ice thickness and dimensionless velocity; in the region with a large combination of dimensionless ice thickness and dimensionless velocity, the contour lines bend, indicating that the effects of sea ice thickness and uplift velocity on the peak ice load are not simply additive. Combined with... Figure 3 and Figure 4 As can be seen, changes in both sea ice thickness and uplift velocity have an interactive effect on peak ice load. From Figure 5 As can be seen, the predicted peak ice load is quite close to the numerical calculation result.

[0045] refer to Figure 6 The present invention also discloses a peak ice load prediction device for the surfacing and ice-breaking process of an underwater moving body, comprising: The sample data construction module is used to select sea ice thickness. and the buoyancy of underwater moving objects As the main operating condition variable, numerical simulation calculations or experimental measurements are carried out on the process of underwater moving bodies surfacing and breaking ice under different operating conditions, and the peak ice load is extracted from the simulation calculation results or experimental measurement results. Summarize the peak ice load and the corresponding sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress This forms a sample dataset; A dimensionless sample data construction module is used to select the maximum diameter of the hull based on Buckingham's π theorem. Water density and sea ice yield stress As the basic quantity, the peak ice load Sea ice thickness Ascent speed The dimensionless ice thickness was obtained by combining the basic quantities with the dimensionless ice thickness. Dimensionless velocity and dimensionless peak ice load The expression; substituting the sample dataset into the dimensionless ice thickness The dimensionless velocity and the dimensionless peak ice load In the expression, the dimensionless parameters under each working condition are calculated to form a dimensionless sample dataset; The generalized logarithmic regression prediction model modeling module is used to model the dimensionless peak ice load. As the dependent variable, the dimensionless ice thickness Dimensionless velocity Establish a dimensionless peak ice load as the independent variable. With dimensionless ice thickness Dimensionless velocity The functional relationship between them, and the dimensionless ice thickness in the functional relationship. Dimensionless velocity and dimensionless peak ice load Logarithmic transformation is performed, introducing linear, quadratic, and interaction terms into the logarithmic space to establish a generalized logarithmic regression model; based on the dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved using the least squares method to obtain the generalized logarithmic regression prediction model. A peak ice load prediction module is used to predict the peak ice load during the ice-breaking process of an underwater moving body surfacing, based on the generalized logarithmic regression prediction model.

[0046] The peak ice load prediction device for the surfacing and ice-breaking process of underwater moving bodies provided in this embodiment of the invention can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0047] Figure 7 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 7 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a method for predicting peak ice loads during the surfacing and ice-breaking process of an underwater moving body. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement a method for predicting peak ice loads during the surfacing and ice-breaking process of an underwater moving body. Those skilled in the art will understand that... Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does 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 those shown in the figure, or combine certain components, or have different component arrangements.

[0048] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the various steps of the peak ice load prediction method for the above-described underwater moving body surfacing and icebreaking process.

[0049] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps of the peak ice load prediction method for the above-described underwater moving body surfacing and ice-breaking process.

[0050] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0051] 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.

[0052] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body, characterized in that, The method includes the following steps: Step S1: Select sea ice thickness and the buoyancy of underwater moving objects As the main operating condition variable, numerical simulation calculations or experimental measurements are carried out on the process of underwater moving bodies surfacing and breaking ice under different operating conditions, and the peak ice load is extracted from the simulation calculation results or experimental measurement results. Summarize the peak ice load and the corresponding sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress This forms a sample dataset; Step S2: Based on Buckingham's π theorem, select the maximum diameter of the hull. Water density and sea ice yield stress As the basic quantity, the peak ice load Sea ice thickness Ascent speed The dimensionless ice thickness was obtained by combining the basic quantities with the dimensionless ice thickness. Dimensionless velocity and dimensionless peak ice load The expression; substituting the sample dataset into the dimensionless ice thickness The dimensionless velocity and the dimensionless peak ice load In the expression, the dimensionless parameters under each working condition are calculated to form a dimensionless sample dataset; Step S3: Measure the dimensionless peak ice load As the dependent variable, the dimensionless ice thickness Dimensionless velocity Establish a dimensionless peak ice load as the independent variable. With dimensionless ice thickness Dimensionless velocity The functional relationship between them, and the dimensionless ice thickness in the functional relationship. Dimensionless velocity and dimensionless peak ice load Logarithmic transformation is performed, introducing linear, quadratic, and interaction terms into the logarithmic space to establish a generalized logarithmic regression model; based on the dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved to obtain the generalized logarithmic regression prediction model; Step S4: Predict the peak ice load during the ice-breaking process of the underwater moving body ascent based on the generalized logarithmic regression prediction model.

2. The method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to claim 1, characterized in that, The dimensionless ice thickness Dimensionless velocity and dimensionless peak ice load The expression is as follows: 。 3. The method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to claim 1 or 2, characterized in that, The expression for the generalized logarithmic regression model is: In the above formula, For the intercept term, The coefficients are those of a first-order regression. The regression coefficients are quadratic terms. For the interaction term coefficient.

4. The method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to claim 1 or 2, characterized in that, The peak ice load during the surfacing and icebreaking process of an underwater moving body is predicted based on the generalized logarithmic regression prediction model, including the following steps: Step S41: Perform exponential reduction on the generalized logarithmic regression prediction model to obtain the dimensionless peak ice load prediction model; Step S42: Measure the dimensionless ice thickness Dimensionless velocity and dimensionless peak ice load Substituting the expression into the dimensionless peak ice load prediction model, we obtain the explicit prediction formula for the peak ice load. Step S43: Predict the peak ice load during the surfacing and ice-breaking process of the underwater moving body using the explicit prediction formula of the peak ice load.

5. The method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to claim 1, characterized in that, The numerical simulation calculation includes the following steps: Step S11: Construct a three-dimensional geometric model of the underwater moving body and the ice layer, mesh the three-dimensional geometric model of the underwater moving body and the ice layer to obtain the surfacing and icebreaking calculation model; import the surfacing and icebreaking calculation model into explicit dynamic / explicit fluid-structure interaction analysis software, define the Lagrange solid element algorithm for the underwater moving body, define the arbitrary Lagrange-Eulerian ALE fluid element algorithm for the water and air domains, select the ice constitutive model or equivalent solid model for the ice layer, and set the state equations and physical parameters for the air and water domains. Step S12: Locally refine the mesh along the expected surfacing trajectory of the underwater moving body and the area where the underwater moving body may come into contact with the ice layer; delineate and constrain multiple feature regions based on the mesh topology, including the node set at the bottom of the water area, the unit surface segment set in the air domain and the periphery of the water area, and the contact surface segment set corresponding to the outer surface of the underwater moving body and the bottom surface of the ice layer; define the corresponding solid unit set in the water area and the air domain and its surrounding area to describe the continuous transition of fluid volume and boundary; Step S13: In the explicit dynamic / explicit fluid-structure interaction analysis software, establish the coupling relationship between the fluid domain and the solid domain; the fluid domain includes the air domain and the water domain; the solid domain includes the underwater moving body and the ice layer; select the coupling algorithm for the interaction between ALE fluid elements and Lagrangian solid elements; apply corresponding velocity constraints or non-reflective boundary conditions to the bottom and sidewalls of the water domain and the outer boundary of the air domain, apply initial buoyancy conditions to the underwater moving body, and apply gravity and constraint conditions to the ice layer to ensure that all materials are at rest or in a predetermined equilibrium state at the initial moment of calculation; establish the contact constraints between the ice layer and the underwater moving body according to the buoyancy path of the underwater moving body and the expected contact area, and set the contact stiffness and contact damping parameters; Step S14: Set the simulation termination time and time step control conditions, and enable ALE mesh smoothing and remapping functions to ensure that the fluid mesh remains normal during the underwater motion body's ascent, collision, and ice breaking process; start the solution process and output the time history data of the pressure field on the outer surface of the underwater motion body, the contact force between the ice layer and the hull of the underwater motion body, and the fluid-structure interaction force according to the pre-set output control card, while generating animation frame files for visualization; perform partitioned integration on the normal pressure and tangential shear stress of the surface elements of the underwater motion body hull, and subtract the hydrodynamic background components under the corresponding working conditions to obtain the ice load time history of the entire process of the underwater motion body's ascent and ice breaking; Step S15: Extract the peak contact force from the ice load time history as the peak ice load under the corresponding working condition. F .

6. The method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to claim 1, characterized in that, Based on the dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved to obtain the generalized logarithmic regression prediction model. The least squares method is used to solve the coefficients of the generalized logarithmic regression model.

7. The method for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body according to claim 1, characterized in that, After obtaining the generalized logarithmic regression prediction model, the method further includes: verifying the effectiveness of the generalized logarithmic regression prediction model.

8. A device for predicting peak ice load during the surfacing and ice-breaking process of an underwater moving body, characterized in that, The device includes: The sample data construction module is used to select sea ice thickness. and the buoyancy of underwater moving objects As the main operating condition variable, numerical simulation calculations or experimental measurements are carried out on the process of underwater moving bodies surfacing and breaking ice under different operating conditions, and the peak ice load is extracted from the simulation calculation results or experimental measurement results. Summarize the peak ice load and the corresponding sea ice thickness Ascent speed Maximum diameter of the hull Water density and sea ice yield stress This forms a sample dataset; A dimensionless sample data construction module is used to select the maximum diameter of the hull based on Buckingham's π theorem. Water density and sea ice yield stress As the basic quantity, the peak ice load Sea ice thickness Ascent speed The dimensionless ice thickness was obtained by combining the basic quantities with the dimensionless ice thickness. Dimensionless velocity and dimensionless peak ice load The expression; substituting the sample dataset into the dimensionless ice thickness The dimensionless velocity and the dimensionless peak ice load In the expression, the dimensionless parameters under each working condition are calculated to form a dimensionless sample dataset; The generalized logarithmic regression prediction model modeling module is used to model the dimensionless peak ice load. As the dependent variable, the dimensionless ice thickness Dimensionless velocity Establish a dimensionless peak ice load as the independent variable. With dimensionless ice thickness Dimensionless velocity The functional relationship between them, and the dimensionless ice thickness in the functional relationship. Dimensionless velocity and dimensionless peak ice load Logarithmic transformation is performed, introducing linear, quadratic, and interaction terms into the logarithmic space to establish a generalized logarithmic regression model; based on the dimensionless sample dataset, the coefficients of the generalized logarithmic regression model are solved using the least squares method to obtain the generalized logarithmic regression prediction model. A peak ice load prediction module is used to predict the peak ice load during the ice-breaking process of an underwater moving body surfacing, based on the generalized logarithmic regression prediction model.

9. A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

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