Design parameter optimization method, device, equipment and storage medium

Abstract

The application discloses a design parameter optimization method and device, equipment and a storage medium, and comprises the following steps: determining a fluid-solid assembly model and a flow field model corresponding to a dredging pump according to current design parameters of the dredging pump and working conditions corresponding to the dredging pump; establishing a fluid-solid assembly coupling model corresponding to the dredging pump according to the fluid-solid assembly model and the flow field model, and determining current stress parameters and current deformation variable parameters of the dredging pump according to the fluid-solid assembly coupling model; if the current stress parameters are less than preset limit stress, the current design parameters are taken as target design parameters; if the current stress parameters are greater than or equal to the preset limit stress, an optimization function is determined according to the current stress parameters and the current deformation variable parameters, the current design parameters are optimized according to the optimization function, next design parameters are obtained, the next design parameters are taken as the current design parameters, and the step of determining the fluid-solid assembly model and the flow field model corresponding to the dredging pump according to the current design parameters of the dredging pump and the working conditions corresponding to the dredging pump is returned to.

Description

Technical Field

[0001] This invention relates to the field of dredging engineering, and more particularly to methods, apparatus, equipment and storage media for optimizing design parameters. Background Technology

[0002] Dredging pumps are indispensable key transport equipment in dredging projects, playing a crucial role in waterway cleaning, channel maintenance, and ecological environment protection. In actual dredging operations, the pump body structure withstands complex and transient pressure loads from fluids such as mud. Excessive stress on the pump body or excessive deformation can compromise the safety of dredging operations.

[0003] Currently, the design process of dredging pumps typically references water pump design methods, employing static ballast models or steady-state flow models to approximate the pressure of the dredging slurry. However, when conducting strength analysis and optimization of the designed dredging pumps, the pump body structure mainly relies on trial and error by experienced designers.

[0004] However, compared to water pumps, dredging pumps have a more complex structure and require higher accuracy in strength calculations. Therefore, referencing water pump design methods and relying on human experience to design the structure of dredging pumps, and then optimizing the design parameters, can lead to deviations in the strength analysis of dredging pumps. This can affect the design accuracy of dredging pumps, resulting in insufficient strength under high loads or extreme operating conditions, and posing potential safety hazards. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for optimizing design parameters. It enables the simulation of the fluid-solid assembly model of the dredging pump based directly on the working conditions of the dredging pump. It fully considers the dynamic characteristics of fluid dynamic load and accurately simulates the instantaneous pressure changes and flow characteristics experienced by the pump body during operation.

[0006] According to one aspect of the present invention, a method for optimizing design parameters is provided, the method comprising:

[0007] Based on the current design parameters of the dredging pump and its corresponding operating conditions, determine the fluid-solid assembly model and flow field model corresponding to the dredging pump.

[0008] Based on the fluid-solid assembly model and the flow field model, a fluid-solid assembly coupling model corresponding to the dredging pump is established, and the current stress parameters and current deformation parameters of the dredging pump are determined based on the fluid-solid assembly coupling model.

[0009] If the current stress parameter is less than the preset limit stress, then the current design parameter will be used as the target design parameter for the dredging pump.

[0010] If the current stress parameter is greater than or equal to the preset limit stress, then the optimization function is determined based on the current stress parameter and the current deformation parameter. The current design parameters are then optimized based on the optimization function to obtain the next design parameter. The next design parameter is then used as the current design parameter. The process then returns to the previous step of determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters of the dredging pump and the corresponding working conditions of the dredging pump.

[0011] The design parameter optimization method provided in this embodiment of the invention determines the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump; establishes a fluid-solid assembly coupling model corresponding to the dredging pump based on the fluid-solid assembly model and flow field model, and determines the current stress parameters and current deformation parameters of the dredging pump based on the fluid-solid assembly coupling model; determines whether the current stress parameters are less than a preset limit stress; if so, the current design parameters are used as the target design parameters of the dredging pump; if not, an optimization function is determined based on the current stress parameters and current deformation parameters, and the current design parameters are optimized based on the optimization function to obtain the next design parameters, which are then used as the current design parameters. The process then returns to the previous step of determining the fluid-solid assembly coupling model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump. In the above technical solution, on the one hand, a coupled assembly model of the fluid domain and structural domain of the dredging pump is established. Furthermore, based on the fluid-structure interaction model and flow field model, an integrated coupled model of the fluid domain and structural domain of the dredging pump during operation was simulated. This model can simulate the degree of influence of fluid acting on the dredging pump, solving the problem that current dredging pump designs only use hydrostatic pressure models or steady-state flow models to approximate the equivalent mud pressure without considering the impact of dynamic loads on the pump structure. This achieves full consideration of the dynamic characteristics of fluid dynamic loads and accurately simulates the instantaneous pressure changes and flow characteristics experienced by the pump body during operation. On the other hand, when determining the current stress parameters, the model determines whether the current design parameters need optimization based on the current stress parameters and the ultimate stress. This solves the problem that current strength analysis of dredging pump design has deviations, which may lead to excessive safety margins in the dredging pump body, increasing manufacturing or operating costs, or insufficient strength of the pump body under extreme high-load conditions, leading to safety accidents. This model achieves direct comparison between the current stress parameters and the ultimate stress, and selects the processing of the current design parameters based on the comparison results. This ensures that the dredging pump designed according to the target design parameters will not have excessive safety margins or insufficient strength under extreme high-load conditions. Furthermore, by using the current stress parameters and current deformation parameters to obtain the optimization function and optimizing the current design parameters, the optimization of the current design parameters is achieved based on the stress and deformation changes of the dredging pump, thereby improving the optimization efficiency.

[0012] According to another aspect of the present invention, an apparatus for optimizing design parameters is provided, the apparatus comprising:

[0013] The modeling module is used to determine the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump.

[0014] The determination module is used to establish a fluid-solid assembly coupling model corresponding to the dredging pump based on the fluid-solid assembly model and the flow field model, and to determine the current stress parameters and current deformation parameters of the dredging pump based on the fluid-solid assembly coupling model.

[0015] The judgment module is used to determine the target design parameters of the dredging pump if the current stress parameter is less than the preset limit stress; if the current stress parameter is greater than or equal to the preset limit stress, it determines the optimization function based on the current stress parameter and the current deformation parameter, optimizes the current design parameters according to the optimization function, obtains the next design parameter, and uses the next design parameter as the current design parameter. Then, it returns to the execution of the steps of determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters of the dredging pump and the corresponding working conditions of the dredging pump.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory that is communicatively connected to at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform a method for optimizing design parameters according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute a method for optimizing design parameters of any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a method for optimizing design parameters according to any embodiment of the present invention.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0024] Figure 1 A flowchart illustrating a method for optimizing design parameters provided in an embodiment of the present invention;

[0025] Figure 2 A flowchart illustrating a method for optimizing another design parameter provided in an embodiment of the present invention;

[0026] Figure 3 The fluid domain model corresponding to the dredging pump provided in the embodiments of the present invention;

[0027] Figure 4 The structural domain model corresponding to the dredging pump provided in the embodiments of the present invention;

[0028] Figure 5 This invention provides dynamic pressure distribution characteristic information for an embodiment of the invention.

[0029] Figure 6 This is another dynamic pressure distribution feature provided in the embodiments of the present invention;

[0030] Figure 7 A schematic diagram of the structure of the design parameter optimization device provided in the embodiments of the present invention;

[0031] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "current," "next," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Figure 1 This is a flowchart illustrating a design parameter optimization method provided by an embodiment of the present invention. This embodiment is applicable to the design of structural parameters for dredging pumps. The method can be executed by a design parameter optimization device, which can be implemented in hardware and / or software and can be configured in an electronic device. In this embodiment, the electronic device can be a computer or a terminal device. Figure 1 As shown, the method includes:

[0035] S101. Based on the current design parameters of the dredging pump and the corresponding working conditions of the dredging pump, determine the fluid-solid assembly model and flow field model corresponding to the dredging pump.

[0036] The current design parameters refer to the current design parameters of the dredging pump. For example, these parameters may include the material thickness, curvature, and geometric dimensions of the dredging pump. The operating conditions refer to the conditions of the working area where the designed dredging pump may be applied. For example, what type of soil it will be used on, or whether the dredging pump will be used for subsea engineering or general river channel reconstruction. The fluid-structure integration model is the overall model of the fluid and structural domains of the dredging pump. The flow field model is a model used to describe the fluid flow state within the dredging pump under its operating conditions.

[0037] Specifically, based on the current design parameters of the dredging pump, a structural domain model of the dredging pump can be established first. Then, the corresponding operating conditions of the dredging pump, such as the properties of the fluid itself during operation, can be added. That is, based on the operating conditions, the rheological model of the fluid can be determined, allowing for the establishment of an integrated model of the dredging pump's fluid and structural domains. When establishing the model, to ensure a comprehensive establishment of the dredging pump's fluid domain model, it is advisable to include the strength verification-related structures of the dredging pump within the model. These include components such as the impeller, front liner, rear liner, and suction inlet anti-wear ring.

[0038] Specifically, when determining the rheological model corresponding to a fluid based on its operating conditions, the flow field model can also be determined based on the fluid's inherent properties and the fluid-solid assembly model. For example, if a dredging pump is used to widen a river channel, the characteristics of the slurry at the channel can be determined, such as its density, viscosity, velocity, and flow rate. Based on these slurry characteristics, a rheological model can be established. Subsequently, based on the rheological model and the structural domain model of the dredging pump, the flow field model can be determined.

[0039] In this embodiment, an integrated model of the fluid domain and structural domain of the dredging pump is established, taking into account the potential relationship between the internal fluid domain structure of the dredging pump, which varies due to the design structure, and the structure of the dredging pump itself. Furthermore, a flow field model is simulated based on the operating conditions of the dredging pump. When modeling the dredging pump, the dynamic characteristics of the fluid dynamic load are fully considered, providing an accurate model foundation for subsequently simulating the pressure and flow characteristics experienced by the pump body during operation.

[0040] S102. Based on the fluid-solid assembly model and the flow field model, establish the fluid-solid assembly coupling model corresponding to the dredging pump, and determine the current stress parameters and current deformation parameters of the dredging pump based on the fluid-solid assembly coupling model.

[0041] The fluid-structure interaction model is based on the coupling effect between the fluid and the dredging pump's structure. The current stress parameter is the stress value that causes the dredging pump's stress to change due to the fluid's action. The current deformation parameter reflects the deformation of the dredging pump caused by the fluid's action under the current design parameters. Optionally, the current stress parameter is the maximum stress value. The current deformation parameter is the deformation corresponding to the maximum stress value.

[0042] Specifically, a fluid-structure interaction (FSI) model corresponding to the dredging pump can be established based on the flow field model and the fluid-structure interaction (FSI) model. For example, the FSI model corresponding to the dredging pump can be determined based on the interaction relationships between these components and the influence of fluid action on the dredging pump structure. Furthermore, the coupling stress of the relevant structures for strength verification can be considered, taking into account the flow details within the impeller main channel and clearances.

[0043] After establishing the fluid-structure interaction model, the working intensity of the dredging pump corresponding to the current design parameters can be simulated based on the fluid interaction model and the flow field model. The stress distribution of the dredging pump under the working intensity can be simulated based on the working intensity. Based on the stress distribution, the current stress parameters and current deformation parameters of the dredging pump under the action of the fluid can be determined.

[0044] In this embodiment, based on the fluid-structure integration model and the flow field model, the integrated coupling model of the fluid domain and the structural domain of the dredging pump during operation is simulated. This model can simulate the degree of influence of fluid acting on the dredging pump, solving the problem that in the current design of dredging pumps, only the hydrostatic pressure model or steady-state flow model is used to approximate the equivalent mud pressure without considering the influence of dynamic loads on the pump body structure. This model fully considers the dynamic characteristics of fluid dynamic loads and accurately simulates the instantaneous pressure changes and flow characteristics experienced by the pump body during operation.

[0045] S103. Determine whether the current stress parameter is less than the preset limit stress; if yes, proceed to S104; if no, proceed to S105.

[0046] Among them, the ultimate stress is a parameter related to the strength limit stress of the dredging pump. For example, the yield strength and fatigue limit of the pump body material.

[0047] Specifically, determine whether the current stress parameter is less than the ultimate stress. If it is less, it means that the current design parameter meets the design specifications, therefore, step S104 can be executed. If it is greater than or equal to, it means that the current design parameter does not meet the design specifications, therefore, the current design parameter can be optimized based on step S105 until the design parameter meets the design specifications.

[0048] S104. Use the current design parameters as the target design parameters for the dredging pump.

[0049] The target design parameters are the final design parameters determined in the structural parameter design of this dredging pump.

[0050] Specifically, if the current design parameters meet the ultimate stress, then the current design parameters can be determined as the target design parameters for the dredging pump. At this point, the structural parameter design of the dredging pump is complete, and the structural parameters of the dredging pump can be based on the target design parameters.

[0051] S105. Determine the optimization function based on the current stress parameters and current deformation parameters, optimize the current design parameters according to the optimization function, obtain the next design parameters, and use the next design parameters as the current design parameters; return to execute S101.

[0052] The optimization function is used to optimize the current design parameters. The next design parameter is the optimized design parameter.

[0053] Specifically, by using the gradient changes of the current stress parameters and the current deformation parameters as a guide, it can be determined whether the change in the current design parameters will lead to a larger change in the current stress parameters or a larger change in the current deformation parameters, thereby determining the optimization of the current design parameters.

[0054] For example, the stress gradient can be determined based on the current stress parameters, and the deformation gradient can be determined based on the current deformation parameters. Based on the stress gradient and deformation gradient, a first sub-optimization function can be determined. Furthermore, a second sub-optimization function is obtained by weighted summing of the current stress parameters and current deformation parameters. The next design parameters can be obtained by optimizing the current design parameters using the first and second sub-optimization functions.

[0055] When the next design parameter is obtained, the number of optimizations can be recorded, and the next design parameter can be used as the current design parameter. Then, S101 can be executed again until the current design parameter is less than the ultimate stress.

[0056] In this embodiment, when determining the current stress parameters, the need for optimization of the current design parameters is determined based on the comparison between the current stress parameters and the ultimate stress. This addresses the problem of deviations in the current design strength analysis of dredging pumps, which could lead to excessive safety margins in the pump body, increasing manufacturing or operating costs, or insufficient strength under extreme high-load conditions, resulting in safety accidents. This approach directly compares the current stress parameters with the ultimate stress, and selects appropriate processing methods for the current design parameters based on the comparison results. This ensures that the dredging pump designed according to the target design parameters will not have excessive safety margins or insufficient strength under extreme high-load conditions. Furthermore, by obtaining an optimization function using the current stress parameters and current deformation parameters, and optimizing the current design parameters, targeted optimization of the current design parameters is achieved based on the stress and deformation changes of the dredging pump, thereby improving optimization efficiency.

[0057] The design parameter optimization method provided in this invention determines the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump. Based on the fluid-solid assembly model and flow field model, a fluid-solid assembly coupling model corresponding to the dredging pump is established, and the current stress parameters and current deformation parameters of the dredging pump are determined based on the fluid-solid assembly coupling model. It is then determined whether the current stress parameters are less than a preset limit stress. If so, the current design parameters are used as the target design parameters of the dredging pump. If not, an optimization function is determined based on the current stress parameters and current deformation parameters. The current design parameters are optimized according to the optimization function to obtain the next design parameters, which are then used as the current design parameters. The process then returns to the previous steps of determining the fluid-solid assembly coupling model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump. In the above technical solution, on the one hand, the establishment of a coupled assembly model of the fluid domain and structural domain of the dredging pump considers the possible relationship between the internal fluid domain structure of the dredging pump, which changes due to the design structure, and the structure of the dredging pump itself. Furthermore, based on the fluid-structure interaction model and flow field model, an integrated coupled model of the fluid domain and structural domain of the dredging pump during operation was simulated. This model can simulate the degree of influence of fluid acting on the dredging pump, solving the problem that current dredging pump designs only use hydrostatic pressure models or steady-state flow models to approximate the equivalent mud pressure without considering the impact of dynamic loads on the pump structure. This achieves full consideration of the dynamic characteristics of fluid dynamic loads and accurately simulates the instantaneous pressure changes and flow characteristics experienced by the pump body during operation. On the other hand, when determining the current stress parameters, the model determines whether the current design parameters need optimization based on the current stress parameters and the ultimate stress. This solves the problem that current strength analysis of dredging pump design has deviations, which may lead to excessive safety margins in the dredging pump body, increasing manufacturing or operating costs, or insufficient strength of the pump body under extreme high-load conditions, leading to safety accidents. This model achieves direct comparison between the current stress parameters and the ultimate stress, and selects the processing of the current design parameters based on the comparison results. This ensures that the dredging pump designed according to the target design parameters will not have excessive safety margins or insufficient strength under extreme high-load conditions. Furthermore, by using the current stress parameters and current deformation parameters to obtain the optimization function and optimizing the current design parameters, the optimization of the current design parameters is achieved based on the stress and deformation changes of the dredging pump, thereby improving the optimization efficiency.

[0058] Figure 2This is a flowchart illustrating another design parameter optimization method provided by an embodiment of the present invention. Based on the above embodiments and other examples, this embodiment provides a detailed explanation of the steps of "determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump," "determining the current stress parameters and current deformation parameters of the dredging pump based on the fluid-solid assembly coupling model," and "determining the optimization function based on the current stress parameters and current deformation parameters." Figure 2 As shown, the method includes:

[0059] S201. Determine the fluid characteristics corresponding to the fluid inside the dredging pump based on the working conditions, and determine the fluid-solid assembly model corresponding to the dredging pump based on the current design parameters and fluid characteristics.

[0060] Specifically, based on the inherent structure and strength verification of the dredging pump itself, a structural domain architecture for the fluid-structure interaction (FSI) model can be established first. Then, based on current design parameters and operating conditions, the structural domain architecture can be numerically defined and expanded. Furthermore, the fluid characteristics within the dredging pump can be determined using the operating conditions. A rheological model can be obtained based on these characteristics and added to the structural domain architecture, thereby perfecting the fluid domain model corresponding to the dredging pump. Finally, the FSI model for the dredging pump can be obtained.

[0061] For example, Figure 3 The fluid domain model corresponding to the dredging pump provided in the embodiments of the present invention. Figure 4 This is a structural domain model corresponding to the dredging pump provided in an embodiment of the present invention. Figure 3 The fluid domain model includes impeller water body 10 and pump casing water body 20. Figure 4 The structural domain model includes the rear liner 30, rear end cover 40, pump casing 50, impeller anti-wear ring 60, front end cover 70, and front liner 80. Figure 3 The fluid domain model shown is similar to Figure 4 By combining the structural domain models shown, the fluid-solid assembly model corresponding to the dredging pump can be obtained.

[0062] In this embodiment, the strength verification-related structures such as the impeller, front liner, rear liner, and suction inlet anti-wear ring are considered in the fluid-solid assembly model design corresponding to the dredging pump. This provides a model basis for considering the coupling stress of these components and the flow details in the impeller main channel and gaps, as well as the mutual influence between these components.

[0063] S202. Determine the flow field model based on fluid characteristics and fluid-solid assembly model.

[0064] Specifically, based on the operating conditions, the fluid characteristics acting on the dredging pump during operation can be determined, i.e., a rheological model can be established. For example, if the fluid is slurry, the density, viscosity, velocity, and flow rate of the slurry can be determined. Then, based on the rheological model and using the Large Eddy Simulation (LES) method and other filtering models, the flow field model can be determined.

[0065] S203. Based on the fluid-solid assembly model and the flow field model, establish the fluid-solid assembly coupling model corresponding to the dredging pump.

[0066] Specifically, the fluid-solid assembly model is transformed to obtain the corresponding finite element model. The connection relationship between the components of the fluid-solid assembly model is established, and the coupling relationship of the internal structure of the dredging pump and the flow field model are used to determine the fluid-solid assembly coupling model.

[0067] For example, firstly, the established fluid-structure interaction (FSI) model is converted into a finite element model, and the components are meshed and their properties are set. The meshing criteria and property settings can be adjusted appropriately according to the current design parameters and operating conditions. For example, during meshing, in order to better simulate the flow field and structural field inside the dredging pump and avoid the influence of complex geometric features and flow characteristics on the simulation results, the mesh can be locally refined at the tongue and fillet positions, thereby improving the mesh quality and fineness and better capturing the changes in fluid flow and structural stress.

[0068] Subsequently, based on the coupling relationships within the dredging pump's internal structure, the connection relationships between the components of the finite element model were established. Furthermore, the flow field model was combined with the established fluid-structure interaction model in a specific coupling manner, considering the interaction between fluid and solid, such as the pressure and shear forces exerted by the fluid on the solid, and the influence of solid deformation on the flow field. This resulted in the fluid-structure interaction coupling model of the dredging pump. For example, a dynamic-static coupling surface was set between the impeller water and the pump casing water, and a static coupling surface was set between the pump casing water and the structural domain. The advantage of this setup is that, on the static coupling surface, the fluid pressure distribution will directly act on the pump structure, enabling bidirectional fluid-structure interaction analysis.

[0069] S204. Perform transient flow field analysis on the fluid-solid assembly coupling model and the flow field model to obtain dynamic pressure distribution characteristic information.

[0070] Specifically, Large Eddy Simulation (LES) can be used to simulate the turbulent flow of fluid in a dredging pump. Transient analysis of the flow field can then be performed to extract the dynamic pressure distribution of the fluid acting on the pump body.

[0071] For example, a large eddy simulation method describing water and particles using double Euler techniques can be employed, and a mathematical filtering model can be established to filter out eddies with scales smaller than the filter function from the transient turbulence equations. The filtered eddies can then form the large eddy flow equations. Furthermore, by introducing subgrid-scale stress terms into the large eddy flow equations to represent the filtered-out small eddies, the velocity field, pressure field, and turbulence characteristics of the fluid within the pump can be obtained. Through two-way fluid-structure interaction transient flow field analysis, the dynamic pressure distribution acting on the pump body can be extracted.

[0072] For example, Figure 5 This invention provides dynamic pressure distribution characteristic information for embodiments of the present invention. Figure 5 The diagram shows the dynamic pressure distribution of the dredging pump, with different dynamic pressures indicated by different color zones. The right side shows a legend illustrating the specific pressure values ​​represented by each color zone. In the legend, the pressure increases sequentially from bottom to top. The unit of pressure is Pascals (Pa). Figure 6 This provides another dynamic pressure distribution characteristic information for an embodiment of the present invention. The horizontal axis represents time (s), and the vertical axis represents the pressure pulsation coefficient C. p The figure shows the pressure distribution of the dredging pump under fluid action over time, and the change in its pressure pulsation coefficient.

[0073] S205. Based on the fixed constraints, dynamic pressure distribution characteristics, and flow field model of the dredging pump, determine the stress distribution information of the dredging pump.

[0074] Specifically, pre-selected fixed constraints are determined, and the deformation of the pump structure is solved based on these constraints. Simultaneously, the stress distribution of the pump body is determined using dynamic pressure distribution characteristics, the flow field model, and the deformation information of the pump structure.

[0075] For example, establishing a fixed constraint at the connection between the rear end cover and the bearing housing can accurately characterize the fixed state of the pump body in reality. Simultaneously, the dynamic pressure generated by the transient flow field during pump dredging operation is used as the load condition for strength calculations and applied to the pump body structure to induce solid deformation; that is, the fluid pressure at the boundary serves as the mechanical boundary condition for the solid. Furthermore, by applying corresponding pressure values ​​to the contact surfaces of the pump body—that is, the dynamic pressure distribution characteristics—the actual impact of fluid dynamic loads on the pump body structure can be simulated, ultimately yielding the stress distribution information of the dredging pump body.

[0076] S206. Use finite element analysis to process the stress distribution information to obtain the current stress parameters and current deformation parameters.

[0077] Specifically, by appropriately setting the finite element solution terms for the structural field, stress distribution information can be processed using finite element analysis to obtain the current stress parameters and current deformation parameters. For example, stress distribution information of the dredging pump body under fluid dynamic load can be extracted through finite element analysis, thereby determining the maximum stress value and maximum deformation.

[0078] In this embodiment, by establishing fixed constraints at the connection between the rear end cover and the bearing housing, and by using finite element analysis to process the stress distribution information, the dynamic characteristics of fluid dynamic loads are fully considered. This accurately simulates the instantaneous pressure changes and flow characteristics experienced by the pump body during operation, and provides accurate data for determining whether the current design parameters meet the design requirements based on the current stress parameters. Furthermore, it provides a data and structural basis for optimizing the current design parameters later, using the allowable clearances between the dredging pump structural components and the flow channel requirements as optimization guidelines.

[0079] S207. Determine whether the current stress parameter is less than the preset limit stress; if yes, execute S213; if no, execute S208.

[0080] Specifically, determine whether the current stress parameter is less than the ultimate stress. If it is less, it means that the current design parameter meets the design specifications, so S213 can be executed directly. If it is greater than or equal to, it means that the current design parameter does not meet the design specifications, so the current design parameter can be optimized based on S208 until the design parameter meets the design specifications.

[0081] S208. Obtain the reference stress parameters and reference deformation parameters, and determine the first optimization sub-function based on the reference stress parameters, current stress parameters, reference deformation parameters, and current deformation parameters.

[0082] The first optimization sub-function is used to indicate the dynamic characteristics of stress and deformation. In this embodiment, the first optimization sub-function can enhance the adaptability to different structural regions during the optimization of design parameters, and automatically adjust the optimization weights of each design variable according to the changes in stress and deformation gradients in the structure.

[0083] Specifically, determining the first optimization sub-function includes the following steps:

[0084] (i) Determine the stress gradient based on the reference stress parameters and the current stress parameters, and determine the deformation gradient based on the reference deformation parameters and the current deformation parameters.

[0085] Among them, the reference stress parameters and reference deformation parameters can be obtained based on the better historical stress parameters and historical deformation parameters corresponding to the historical design parameters in the historical design process.

[0086] Specifically, the best historical stress and deformation parameters from the structural parameter database are retrieved as reference stress and deformation parameters. The stress gradient ▽σ(x) can be determined based on the reference stress parameters and the current stress parameters. The deformation gradient ▽ε(x) can be determined based on the reference deformation parameters and the current deformation parameters.

[0087] (ii) Determine the first optimization sub-function based on the pre-set allowable stress parameters, stress gradient, pre-set allowable deformation parameters and deformation gradient.

[0088] Among them, the allowable stress parameter is the allowable value of stress. The allowable deformation parameter is the allowable value of deformation.

[0089] Specifically, the first optimization sub-function can be determined according to the following formula:

[0090]

[0091] Where ω(x) is the first optimization subfunction. ▽σ(x) is the stress gradient. ▽ε(x) is the deformation gradient. pcv ε is the allowable stress parameter. pcv δ is the allowable parameter for deformation. θ is the control coefficient for stress and θ is the control coefficient for deformation. These control coefficients are predetermined based on the different material properties of the dredging pump. In this embodiment, the allowable stress parameter and the allowable deformation parameter are used to define the acceptable range of design variables, thereby ensuring that the optimization results meet safety and functional requirements. ▽σ(x)-σ pcv This reflects the degree of deviation between the current stress gradient and the allowable value. ▽ε(x)-ε pcv This reflects the degree of deviation of the current deformation gradient from the allowable value. When ▽σ(x) or ▽ε(x) is close to the allowable value, the first optimization sub-function will tend towards the intermediate value, and the weight adjustment range will be small. When the deviation from the allowable value is large, the first optimization sub-function will change rapidly, thereby increasing the subsequent optimization intensity of the current design parameters.

[0092] S209. Determine the first factor based on the current design parameters and the current stress parameters, and determine the second factor based on the current design parameters and the current deformation parameters.

[0093] The first factor represents the stress variation gradient, and the second factor represents the deformation variation gradient.

[0094] Specifically, the determination of the first factor and the second factor can be carried out according to the following steps:

[0095] Based on the current stress parameters, determine the first sub-factor corresponding to each current design variable; sum all the first sub-factors to obtain the first factor; and based on the current deformation parameters, determine the second sub-factor corresponding to each current design variable; sum all the second sub-factors to obtain the second factor.

[0096] The current design parameters include at least one current design variable. For example, current design variables may include material thickness, curvature, geometric dimensions, etc.

[0097] Specifically, the first factor can be determined using the following formula:

[0098]

[0099] Where A is the first factor. i ={x1, x2, ..., x m}, x i Let σ be the i-th design variable. i (x) represents the current stress parameter corresponding to the i-th design variable. That is, the first sub-factor corresponding to the i-th design variable.

[0100] Furthermore, the second factor can be determined using the following formula:

[0101]

[0102] Where B is the second factor. ε i (x) represents the current shape variable parameter corresponding to the i-th design variable. That is, the second sub-factor corresponding to the i-th design variable.

[0103] S210. Determine the second optimization sub-function based on the pre-set stress weighting factor, the first factor, the pre-set deformation weighting factor, and the second factor.

[0104] The stress weighting factor controls the influence of the stress gradient on the second optimization sub-function. The deformation weighting factor controls the influence of the deformation gradient on the second optimization sub-function. The second optimization sub-function is a weighted sum of the stress change gradient and the deformation change gradient corresponding to the current design parameters.

[0105] Specifically, the second optimization sub-function can be determined according to the following formula:

[0106] F(x) = α*A + β*B;

[0107] Where α is the stress weighting factor and β is the deformation weighting factor. F(x) is the second optimization sub-function.

[0108] It is worth noting that S208 and S209-S210 are parallel steps. That is, S209-S210 can be executed while S208 is executed, or S209-S210 can be executed first and then S208 can be executed, or S208 can be executed first and then S209-S210 can be executed.

[0109] S211. Determine the step size corresponding to the current design parameters, and determine the optimization design parameters based on the step size, the function gradient of the first optimization sub-function, and the second optimization sub-function.

[0110] The step size represents the number of optimization iterations. For example, if the current design parameters have already undergone 3 optimizations, then the step size is 3.

[0111] Specifically, the number of times the current design parameters have been optimized is determined to obtain the step size. Furthermore, the gradient of the second optimization sub-function is calculated, and based on the step size, the gradients of the first and second optimization sub-functions, the optimized design parameters can be obtained.

[0112] For example, the formula for determining the optimized design parameters is:

[0113]

[0114] Where x′ represents the optimized design parameters, and η represents the step size. k These are the current design parameters. ▽F(x) k ω(x) represents the gradient of the second optimization sub-function corresponding to the current design parameters. k Let be the first optimization sub-function corresponding to the current design parameters. In this implementation, to ensure that the yield strength and fatigue limit constraints of the material are met during the optimization process, the stress value σ on any i is... i (x) should satisfy:

[0115] S212. Based on the current design parameters and the optimized design parameters, determine the next design parameters and use the next design parameters as the current design parameters; return to execute S201.

[0116] Specifically, the next design parameters can be determined using the current design parameters and the optimized design parameters. For example, as shown in the following formula:

[0117] x k+1 =x k -x′;

[0118] Where, x k+1 For the next design parameters.

[0119] Furthermore, after obtaining the next design parameter, the next design parameter can be used as the current design parameter, and the process can return to execute S201.

[0120] In this embodiment, the current design parameters are optimized using an optimization function to obtain the next design parameters. This achieves adaptive optimization of the curvature and thickness of the dredging pump structure by using the gradient of stress and deformation index changes as a guide and the allowable clearance between dredging pump structural components and the flow channel requirements as optimization boundaries, thereby improving optimization efficiency.

[0121] S213. Use the current design parameters as the target design parameters for the dredging pump.

[0122] Specifically, if the current design parameters meet the ultimate stress, then the current design parameters can be determined as the target design parameters for the dredging pump. At this point, the structural parameter design of the dredging pump is complete, and the structural parameters of the dredging pump can be based on the target design parameters.

[0123] The design parameter optimization method provided in this invention, on the one hand, incorporates the strength verification-related structures such as the impeller, front liner, rear liner, and suction inlet anti-wear ring into the fluid-structure interaction model design corresponding to the dredging pump. This provides a model basis for considering the coupled stress of these components and the mutual influence between them, based on the flow details within the impeller main channel and gaps. On the other hand, by establishing fixed constraints at the connection between the rear end cover and the bearing housing, and processing the stress distribution information using finite element analysis, the dynamic characteristics of fluid dynamic loads are fully considered. This accurately simulates the instantaneous pressure changes and flow characteristics experienced by the pump body during operation, providing accurate data for determining whether the current design parameters meet the design requirements based on the current stress parameters. Furthermore, it provides a data and structural basis for using the allowable clearances between dredging pump structural components and the flow channel requirements as optimization guidelines when optimizing the current design parameters later. Finally, if the current design parameters need to be optimized, the optimization function is used to optimize the current design parameters to obtain the next design parameters. This achieves adaptive optimization of the curvature and thickness of the dredging pump structure by taking the gradient of stress and deformation index changes as the guide and the allowable clearance and flow channel requirements between the dredging pump structural components as the optimization boundary, thereby improving the optimization efficiency.

[0124] Figure 7 A schematic diagram of the structure of the design parameter optimization device provided in an embodiment of the present invention. (See attached diagram.) Figure 7 As shown, the device includes:

[0125] Modeling module 701 is used to determine the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters of the dredging pump and the corresponding working conditions of the dredging pump.

[0126] The determination module 702 is used to establish a fluid-solid assembly coupling model corresponding to the dredging pump based on the fluid-solid assembly model and the flow field model, and to determine the current stress parameters and current deformation parameters of the dredging pump based on the fluid-solid assembly coupling model.

[0127] The judgment module 703 is used to determine the target design parameters of the dredging pump if the current stress parameter is less than the preset limit stress; if the current stress parameter is greater than or equal to the preset limit stress, it determines the optimization function based on the current stress parameter and the current deformation parameter, optimizes the current design parameters according to the optimization function, obtains the next design parameter, and uses the next design parameter as the current design parameter. Then, it returns to the execution of the steps of determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters of the dredging pump and the corresponding working conditions of the dredging pump.

[0128] Optionally, modeling module 701 is specifically used for:

[0129] The fluid characteristics of the fluid inside the dredging pump are determined based on the working conditions, and the fluid-solid assembly model corresponding to the dredging pump is determined based on the current design parameters and fluid characteristics; the flow field model is determined based on the fluid characteristics and fluid-solid assembly model.

[0130] Optionally, module 702 is specifically used for:

[0131] Transient flow field analysis was performed on the fluid-structure interaction model and the flow field model to obtain dynamic pressure distribution characteristics. Based on the fixed constraints of the dredging pump, the dynamic pressure distribution characteristics, and the flow field model, the stress distribution information of the dredging pump was determined. The stress distribution information was processed using finite element analysis to obtain the current stress parameters and current deformation parameters.

[0132] Optionally, the optimization function includes a first optimization sub-function and a second optimization sub-function; the optimization function is determined based on the current stress parameters and the current deformation parameters. The judgment module 703 is specifically used for:

[0133] Obtain reference stress parameters and reference deformation parameters, and determine a first optimization sub-function based on the reference stress parameters, current stress parameters, reference deformation parameters, and current deformation parameters; determine a first factor based on the current design parameters and current stress parameters, and determine a second factor based on the current design parameters and current deformation parameters; determine a second optimization sub-function based on the pre-set stress weighting factor, the first factor, the pre-set deformation weighting factor, and the second factor.

[0134] Optionally, the first optimization sub-function is determined based on the reference stress parameter, the current stress parameter, the reference deformation parameter, and the current deformation parameter. The judgment module 703 is specifically used for:

[0135] The stress gradient is determined based on the reference stress parameters and the current stress parameters, and the deformation gradient is determined based on the reference deformation parameters and the current deformation parameters; the first optimization sub-function is determined based on the pre-set allowable stress parameters, the stress gradient, the pre-set allowable deformation parameters, and the deformation gradient.

[0136] Optionally, the current design parameters include at least one current design variable; a first factor is determined based on the current design parameters and the current stress parameters, and a second factor is determined based on the current design parameters and the current deformation parameters. The judgment module 703 is specifically used for:

[0137] Based on the current stress parameters, determine the first sub-factor corresponding to each current design variable; sum all the first sub-factors to obtain the first factor; and based on the current deformation parameters, determine the second sub-factor corresponding to each current design variable; sum all the second sub-factors to obtain the second factor.

[0138] Optionally, the current design parameters are optimized according to the optimization function to obtain the next design parameters. The judgment module 703 is specifically used for:

[0139] Determine the step size corresponding to the current design parameters, and determine the optimized design parameters based on the step size, the function gradients of the first and second optimized sub-functions; determine the next design parameters based on the current design parameters and the optimized design parameters.

[0140] The design parameter optimization device provided in the embodiments of the present invention can execute the design parameter optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0141] Figure 8 This is a schematic diagram of the structure of an electronic device 8 provided in an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0142] like Figure 8As shown, the electronic device 8 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 8. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0143] Multiple components in electronic device 8 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 8 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0144] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for optimizing design parameters.

[0145] In some embodiments, the design parameter optimization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the design parameter optimization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the design parameter optimization method by any other suitable means (e.g., by means of firmware).

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements a method for optimizing design parameters as provided in any embodiment of this invention.

[0153] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0154] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0155] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimizing design parameters, characterized in that, The method includes: Based on the current design parameters of the dredging pump and the corresponding operating conditions of the dredging pump, determine the fluid-solid assembly model and flow field model corresponding to the dredging pump. Based on the fluid-solid assembly model and the flow field model, a fluid-solid assembly coupling model corresponding to the dredging pump is established, and the current stress parameters and current deformation parameters of the dredging pump are determined based on the fluid-solid assembly coupling model. If the current stress parameter is less than the preset limit stress, then the current design parameter is used as the target design parameter of the dredging pump. If the current stress parameter is greater than or equal to the preset limit stress, then an optimization function is determined based on the current stress parameter and the current deformation parameter. The current design parameter is then optimized based on the optimization function to obtain the next design parameter. The next design parameter is then used as the current design parameter. The process then returns to the step of determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameter of the dredging pump and the corresponding working condition of the dredging pump.

2. The method for optimizing design parameters according to claim 1, characterized in that, The step of determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters and operating conditions of the dredging pump includes: Based on the operating conditions, determine the fluid characteristics of the fluid inside the dredging pump, and based on the current design parameters and the fluid characteristics, determine the fluid-solid assembly model corresponding to the dredging pump. The flow field model is determined based on the fluid characteristics and the fluid-solid assembly model.

3. The method for optimizing design parameters according to claim 1, characterized in that, The determination of the current stress parameters and current deformation parameters of the dredging pump based on the fluid-structure interaction model includes: Transient flow field analysis was performed on the fluid-structure interaction model and the flow field model to obtain dynamic pressure distribution characteristic information. Based on the fixed constraints of the dredging pump, the dynamic pressure distribution characteristics, and the flow field model, the stress distribution information of the dredging pump is determined. The stress distribution information is processed using finite element analysis to obtain the current stress parameters and the current deformation parameters.

4. The method for optimizing design parameters according to claim 1, characterized in that, The optimization function includes a first optimization sub-function and a second optimization sub-function; The step of determining the optimization function based on the current stress parameter and the current deformation parameter includes: Obtain reference stress parameters and reference deformation parameters, and determine a first optimization sub-function based on the reference stress parameters, the current stress parameters, the reference deformation parameters, and the current deformation parameters; as well as, A first factor is determined based on the current design parameters and the current stress parameters, and a second factor is determined based on the current design parameters and the current deformation parameters. The second optimization sub-function is determined based on the pre-set stress weighting factor, the first factor, the pre-set deformation weighting factor, and the second factor.

5. The method for optimizing design parameters according to claim 4, characterized in that, The step of determining the first optimization sub-function based on the reference stress parameter, the current stress parameter, the reference deformation parameter, and the current deformation parameter includes: The stress gradient is determined based on the reference stress parameter and the current stress parameter, and the deformation gradient is determined based on the reference deformation parameter and the current deformation parameter. The first optimization sub-function is determined based on the pre-set allowable stress parameters, the stress gradient, the pre-set allowable deformation parameters, and the deformation gradient.

6. The method for optimizing design parameters according to claim 4, characterized in that, The current design parameters include at least one current design variable; The step of determining a first factor based on the current design parameters and the current stress parameters, and determining a second factor based on the current design parameters and the current deformation parameters, includes: Based on the current stress parameters, determine the first sub-factor corresponding to each current design variable; sum all the first sub-factors to obtain the first factor; and, Based on the current deformation parameters, determine the second sub-factor corresponding to each current design variable; sum all the second sub-factors to obtain the second factor.

7. The method for optimizing design parameters according to claim 4, characterized in that, The step of optimizing the current design parameters according to the optimization function to obtain the next design parameters includes: Determine the step size corresponding to the current design parameters, and determine the optimization design parameters based on the step size, the function gradient of the first optimization sub-function, and the second optimization sub-function; Based on the current design parameters and the optimized design parameters, determine the next design parameters.

8. A device for optimizing design parameters, characterized in that, The device includes: The modeling module is used to determine the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters of the dredging pump and the corresponding working conditions of the dredging pump. The determination module is used to establish a fluid-structure interaction model corresponding to the dredging pump based on the fluid-structure interaction model and the flow field model, and to determine the current stress parameters and current deformation parameters of the dredging pump based on the fluid-structure interaction model. The judgment module is used to determine the target design parameters of the dredging pump if the current stress parameter is less than a preset limit stress; if the current stress parameter is greater than or equal to the preset limit stress, it determines an optimization function based on the current stress parameter and the current deformation parameter, optimizes the current design parameters according to the optimization function to obtain the next design parameter, and uses the next design parameter as the current design parameter. Then, it returns to the step of determining the fluid-solid assembly model and flow field model corresponding to the dredging pump based on the current design parameters of the dredging pump and the corresponding working condition of the dredging pump.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for optimizing the design parameters as described in any one of claims 1 to 7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for optimizing the design parameters as described in any one of claims 1 to 7.

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