CN-122021475-A - Simulation control method for roughness of additive construction river model

CN122021475ACN 122021475 ACN122021475 ACN 122021475ACN-122021475-A

Abstract

The invention discloses a simulation control method for the roughness of an additive-built river model, and relates to the technical field of hydraulic engineering. The method comprises the steps of obtaining actual measurement data of a prototype river course roughness and a geometric scale, determining a target Manning roughness coefficient of each roughness control partition of a model bed surface, reversely determining a 3D printing process parameter combination corresponding to each partition based on the target Manning roughness coefficient, determining an interlayer rotation angle sequence for inhibiting surface roughness anisotropy, generating multi-direction cross printing paths for enabling deposition strips of each printing layer to be overlapped in different directions according to the printing process parameter combination and the interlayer rotation angle sequence, generating control instructions according to the paths, driving additive manufacturing equipment to execute layer-by-layer printing of each partition bed surface, and assembling formed roughened bed surface plates into a water tank. The invention realizes the reverse analytic design of parameters and the control of resistance isotropy.

Inventors

HUANG JUNKAI
WEI XIANGLONG
LI HAO
GAO TIANZE
Tai Zhuoran
DING JIANFENG

Assignees

河海大学

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1.A similar simulation control method for the roughness of an additionally-constructed river model is characterized by comprising the following steps: obtaining actual measurement data of the roughness of a prototype river channel of a river reach to be simulated and a geometric scale of a river model; determining target Manning roughness coefficients of a plurality of roughness control areas in the river model bed surface according to actual measurement data of the roughness of the prototype river channel and the geometric scale; Determining a 3D printing process parameter combination corresponding to each roughness control zone based on the target Manning roughness coefficient; determining an interlayer rotation angle sequence for suppressing surface roughness anisotropy; Generating a multi-direction cross printing path for enabling deposition strips of each printing layer to be overlapped in a cross mode in different directions according to the 3D printing process parameter combination and the interlayer rotation angle sequence; Generating a control instruction according to the multi-direction cross printing path, controlling the material adding manufacturing equipment to execute the material adding construction of the bed surface of each roughness control zone, and obtaining a printed and molded rough bed surface plate; Assembling the roughened bed panel block into a test water tank.
2. The method of claim 1, wherein determining the target manning roughness coefficients for the plurality of roughness control zones in the river model deck based on the prototype river course roughness measured data and the geometric scale comprises: Based on the Froude similarity criterion, performing scale transformation on actual measurement data of the roughness of the prototype river channel by using a geometric scale to obtain a converted roughness coefficient; dividing a river model bed surface into a plurality of roughness control zones according to river terrain features and water flow boundary conditions; and mapping the converted roughness coefficient to a corresponding roughness control partition to obtain the target Manning roughness coefficient of each roughness control partition.
3. The method of claim 1, wherein determining a combination of 3D printing process parameters corresponding to each roughness control zone based on a target manning roughness coefficient comprises: Obtaining a pre-constructed three-parameter coupling roughness prediction model, wherein the three-parameter coupling roughness prediction model is a response surface polynomial taking nozzle diameter, printing layer height and path distance as independent variables and predicting Manning roughness coefficients as dependent variables; And (3) carrying out inverse parameter solving on the target Manning roughness coefficient by using a three-parameter coupling roughness prediction model to obtain a 3D printing process parameter combination, wherein the 3D printing process parameter combination comprises a corresponding nozzle diameter, a printing layer height and a path distance.
4. A method according to claim 3, wherein the inverse parameter solution to the target manning roughness coefficient using a three-parameter coupling roughness prediction model to obtain a 3D printing process parameter combination comprises: in a preset parameter value range, determining a parameter solution of which the absolute value of the difference between a predicted Man Ning Caolv coefficient output by the three-parameter coupling roughness prediction model and a target Manning roughness coefficient is smaller than or equal to a preset roughness deviation threshold value as a feasible solution set; And in the feasible solution sets, screening by taking the maximized path distance as an optimization target, and taking the screened corresponding parameters as the nozzle diameter, the printing layer height and the path distance in the 3D printing process parameter combination respectively.
5. The method of claim 1, wherein determining the sequence of interlayer rotation angles for suppressing surface roughness anisotropy comprises: Acquiring a pre-constructed monolayer directivity roughness function and a pre-set total printing layer number; constructing a multilayer superposition directional roughness function based on the single-layer directional roughness function and the total printing layer number; And solving to obtain an interlayer rotation angle sequence by taking the extremely poor of the minimized multilayer superposition directional roughness function in all water flow direction angles as an optimization target.
6. The method according to claim 5, wherein the sequence of interlayer rotation angles is obtained by solving for an optimization objective that minimizes the range of the multilayer superimposed directional roughness function over all water flow direction angles by: Decomposing the single-layer directional roughness function into a Fourier series; Generating an equidistant angle distribution sequence according to the total printing layer number so as to enable the superimposed harmonic wave transmission factors of each order to return to 0; And taking the equidistant angular distribution sequence as an interlayer rotation angle sequence.
7. The method of claim 6, further comprising, after the equidistant angular distribution pattern is obtained and before the step of defining the equidistant angular distribution pattern as an interlayer rotation angle pattern: acquiring preset interlayer attenuation weights; calculating a residual fundamental frequency harmonic transfer factor based on the interlayer attenuation weight and the equidistant angle distribution sequence; When the absolute value of the residual fundamental frequency harmonic transfer factor is larger than or equal to a preset residual threshold value, applying a fine adjustment amount to the printing direction angle corresponding to the topmost printing layer, so that the real part and the imaginary part of the fine-adjusted residual fundamental frequency harmonic transfer factor are both equal to 0, and obtaining a correction angle sequence; The corrected angle sequence is used as the interlayer rotation angle sequence.
8. The method of claim 1, wherein assembling the roughened bed panel into the test flume comprises: Acquiring a preset space dimension specification and a preset joint filling material; According to the space dimension specification, arranging a plurality of roughened bed panel blocks along the water flow direction, and enabling the joint direction of adjacent roughened bed panel blocks to be perpendicular to the water flow direction; filling gaps among the roughened bed surface plates by using a caulking material; the space dimension specification comprises length, width and height, wherein the height is not lower than a preset thickness threshold, and the caulking material comprises glass cement, concrete slurry, epoxy mortar or butyl rubber.
9. The method according to claim 1, characterized by controlling the additive manufacturing apparatus to perform bed surface additive building of each roughness control zone in accordance with the multidirectional cross-print path generation control instructions, in particular by: in each printing layer, carrying out region segmentation according to the space boundary of each roughness control partition to obtain a corresponding partition boundary polygon; cutting the multi-direction cross printing paths corresponding to the roughness control subareas into corresponding subarea boundary polygons; Generating non-extrusion travel tracks for articulation between adjacent roughness control zones to prevent the additive manufacturing apparatus from leaving unwanted deposits in transitions across the zones; And uniformly encoding the path coordinates of each layer and a switching instruction generated based on the combination of the 3D printing process parameters of each partition into a control file for driving the additive manufacturing equipment to execute bed surface additive construction.
10. The method of claim 1, wherein the horizontal movement speed of the printheads of the additive manufacturing apparatus is dynamically determined according to a kinematic equation during bed surface additive building that controls the additive manufacturing apparatus to perform each roughness control zone.

Description

Simulation control method for roughness of additive construction river model Technical Field The invention relates to the technical field of hydraulic engineering, in particular to a simulation control method for the roughness of an additive building river model. Background The river model is used as an important physical tool for researching river channel renovation, shipping engineering and hydrodynamic characteristics, and can realize the consistency reproduction of water flow resistance and a prototype. In order to simulate the flow state evolution and the hydraulic boundary layer characteristics of a natural river channel, the distribution construction of the roughness of the surface of a model bed determines the accuracy of similar reproduction of water flow dynamics and flow state deduction. Aiming at the construction of the roughness of the river model bed surface, the traditional physical roughening mode mainly comprises the steps of manually paving block structures with different sizes on the model bed surface, arranging strip-shaped elements or covering films and other materials. In recent years, the development of additive manufacturing technology has enabled the extrusion molding of concrete beds with surface relief using 3D printing technology. In a conventional additive building scenario, the process typically presets a single nozzle diameter as a roughness control variable, and stacks and forms layer-by-layer materials using uniform parallel straight fill paths. However, the existing additive building roughening method has the problems of single roughness control dimension and directional deviation of water flow resistance of the forming surface in the aspects of process parameter matching and fluid isotropy control. Disclosure of Invention The invention aims to provide a similar simulation control method for the roughness of an additive building river model, which aims to solve the problems in the prior art. According to the technical scheme, the simulation control method for the roughness of the additive construction river model comprises the following steps: obtaining actual measurement data of the roughness of a prototype river channel of a river reach to be simulated and a geometric scale of a river model; determining target Manning roughness coefficients of a plurality of roughness control areas in the river model bed surface according to actual measurement data of the roughness of the prototype river channel and the geometric scale; Determining a 3D printing process parameter combination corresponding to each roughness control zone based on the target Manning roughness coefficient; determining an interlayer rotation angle sequence for suppressing surface roughness anisotropy; Generating a multi-direction cross printing path for enabling deposition strips of each printing layer to be overlapped in a cross mode in different directions according to the 3D printing process parameter combination and the interlayer rotation angle sequence; Generating a control instruction according to the multi-direction cross printing path, controlling the material adding manufacturing equipment to execute the material adding construction of the bed surface of each roughness control zone, and obtaining a printed and molded rough bed surface plate; Assembling the roughened bed panel block into a test water tank. The invention has the beneficial effects that the reverse analytic design of parameters and the control of resistance isotropy are realized. The related art effects will be described in detail below in connection with specific embodiments. Drawings FIG. 1 is a flow chart of a method for simulating and controlling the roughness of an additive-built river model according to an embodiment of the present application. FIG. 2 is a flow chart of determining target Manning roughness coefficients for a plurality of roughness control zones in a river model bed surface based on actual measurement data and geometric scales of a prototype river channel according to an embodiment of the present application. FIG. 3 is a flow chart of a combination of 3D printing process parameters based on a target Manning roughness coefficient and determining corresponding roughness control zones, according to an embodiment of the present application. FIG. 4 is a flowchart of a method for performing an inverse parameter solution to a target Manning roughness coefficient using a three-parameter coupling roughness prediction model and obtaining a 3D printing process parameter combination according to an embodiment of the present application. Fig. 5 is a flowchart of determining an interlayer rotation angle sequence for suppressing surface roughness anisotropy according to an embodiment of the present application. Detailed Description In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with