Underwater bottom sealing concrete pouring flow evolution simulation method and system and storage medium

Abstract

The application discloses a kind of underwater bottom sealing concrete pouring flow state evolution simulation method, system and storage medium, method includes: collection underwater bottom sealing area three-dimensional space data and anti-dispersion concrete initial rheological parameter, builds three-dimensional fluid calculation domain model.Rely on rheological parameter to establish water-concrete two-phase flow evolution model, embedding rheological parameter time-varying correction function that can reflect the underwater loss of anti-dispersion component.Combining conduit layout form and flow control scheme sets dynamic inlet boundary, obtains phase interface, velocity field, concentration field and other flow state data by model numerical operation.Rely on simulation data to extract concrete diffusion, anti-dispersion distribution characteristics, determine pouring blind area and segregation risk area, according to this to prepare conduit flow collaborative optimization control instruction.Provide reliable technical support for improving bottom sealing concrete forming quality.

Description

Technical Field

[0001] This invention belongs to the field of concrete engineering technology, and more specifically, relates to a method, system and storage medium for simulating the flow evolution of underwater bottom sealing concrete pouring. Background Technology

[0002] In infrastructure projects such as bridges and hydraulic structures, underwater sealing of sheet pile cofferdams is a crucial step in deep foundation pit construction. The quality of the sealing concrete directly affects the seepage prevention effect of the cofferdam and the safety of dry operations in the foundation pit, making it an indispensable control link in underwater foundation construction. Currently, most underwater sealing construction in China still relies on an experience-based management model. Key parameters such as tremie pipe layout and concrete pumping flow rate are determined based on the past experience of construction personnel, lacking systematic and quantitative pre-construction simulation methods, thus limiting the overall level of construction management in the industry.

[0003] Underwater casting environments are constantly surrounded by still and flowing water. During the spreading process, concrete is continuously subjected to shearing and erosion by the water flow. The anti-dispersing components added to the mix are easily stripped away and lost, leading to a continuous deterioration of the concrete's rheological properties. This, in turn, causes segregation defects such as slurry-aggregate separation and slurry loss. Most existing conventional numerical simulation techniques set concrete rheological parameters as fixed constants, which cannot reflect the changes in physical properties caused by the loss of anti-dispersing agents. The concrete diffusion morphology obtained from the simulation differs significantly from the actual working conditions on the construction site, making it difficult to use as a basis for construction plan preparation.

[0004] In actual engineering projects, the cofferdams are densely packed with pile foundations and steel casings. The original rock surface at the bottom of the pit is undulating, and the concrete flow channels are chaotic and varied. During multi-pipe coordinated pouring operations, it is difficult to unify the rhythm of concrete level rise and fall at each discharge port. Manual dynamic adjustment of the pumping flow of each pipe has significant lag, often resulting in insufficient concrete filling in low-lying areas, creating blind spots in the pouring process and posing a risk of water seepage to the cofferdam. Many of these defects can only be discovered through physical inspection after pouring is completed, leading to cumbersome and costly repair procedures.

[0005] With the increasing number of large-scale deep-water infrastructure projects each year, the requirements for the density and seepage prevention performance of the bottom sealing structure are constantly rising. The traditional post-construction remedial management model can no longer meet the needs of construction development. The industry urgently needs new simulation technologies to achieve pre-judgment of pouring defects and pre-optimization of pouring parameters, thereby avoiding engineering problems such as segregation and material shortage from the source, reducing construction rework costs, and ensuring the construction quality and long-term structural safety of underwater foundation projects. Summary of the Invention

[0006] This invention aims to overcome industry challenges such as reliance on construction experience for underwater sealing and the inability of traditional numerical simulations to reflect the deterioration of concrete performance caused by the loss of anti-dispersion components. It relies on a dynamic rheological correction two-phase flow model to simulate the underwater flow law of concrete, identify blind spots and segregation risks in advance, and optimize the multi-pipe collaborative pumping strategy to improve the forming quality of underwater sealing concrete from the source.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a method for simulating the flow evolution of underwater bottom sealing concrete pouring, comprising: S1. Obtain the three-dimensional spatial data of the underwater sealing area to be poured and the initial rheological parameters of the high-flowability anti-dispersion concrete, and construct a three-dimensional fluid computational domain model of the underwater sealing area to be poured. S2. Based on the initial rheological parameters, a water-concrete two-phase flow evolution model considering anti-dispersion characteristics is constructed; the two-phase flow evolution model introduces a time-varying correction function for the rheological parameters that characterizes the underwater decay effect of the anti-dispersion components; S3. Based on the duct layout scheme and flow control strategy, dynamic inlet boundary conditions are set for the three-dimensional fluid computational domain model, and the two-phase flow evolution model is used for numerical solution to obtain the flow evolution data of the high-flowability anti-dispersion concrete underwater; the flow evolution data includes phase interface distribution, velocity field and concentration field; S4. Based on the flow evolution data, extract the concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics, identify the pouring blind zone and segregation risk zone, and generate duct collaborative control optimization instructions for adjusting the flow control strategy accordingly.

[0008] Furthermore, in S1, constructing a three-dimensional fluid computational domain model of the underwater sealing area to be poured specifically includes: Acquire 3D point cloud topographic data of the bottom of the underwater foundation pit, as well as structural design data of the steel sheet pile cofferdam and the internal pile foundation steel casing. Perform multi-source data fusion and surface reconstruction to generate a 3D geometric solid model containing undulating rock surface, cofferdam inner wall and dense steel casing boundary. The fluid domain is extracted from the three-dimensional geometric entity model, and spatial discretization is performed using an unstructured mesh. The mesh is locally refined in the preset area of ​​the duct outlet, the boundary layer around the steel casing, and the area where the terrain gradient change at the bottom of the pit is greater than a preset threshold, to generate a three-dimensional fluid computation mesh. The initial yield stress and initial plastic viscosity of the high-flowability anti-dispersion concrete were obtained by rheometer testing. A rheological parameter mapping relationship was established in combination with the dosage of anti-dispersion agent. The initial yield stress and initial plastic viscosity were assigned to the initial concrete phase unit in the three-dimensional fluid computational grid. The initial water phase unit in the three-dimensional fluid computational grid was set as a static water body filling the underwater sealing area to be poured, thus completing the construction of the three-dimensional fluid computational domain model.

[0009] Furthermore, in S2, a water-concrete two-phase flow evolution model considering anti-dispersion properties is constructed, specifically including: The fluid volume method (VOF) was used to establish the mixed-phase continuity equation and momentum conservation equation for water-concrete two-phase flow. In the viscous stress tensor of the momentum conservation equation, the Herschel-Bulkley non-Newtonian fluid constitutive model was introduced to characterize the shear thinning and yielding characteristics of the high-flowability anti-dispersion concrete. A concentration convection-diffusion equation for the anti-dispersion component is established to track the component loss process at the water-concrete interface. The concentration convection-diffusion equation includes an interface elution source term, which is determined by the difference between the local water flow shear stress at the interface and the critical scour shear stress of the anti-dispersion component. This term is used to characterize the spalling effect of the anti-dispersion component caused by water flow shear scour. Based on the local concentration field of the anti-dispersion component obtained by solving the concentration convection-diffusion equation, the time-varying correction function of the rheological parameters is constructed. The time-varying correction function of the rheological parameters sets the yield stress, consistency coefficient and flow index in the Herschel-Bulkley non-Newtonian fluid constitutive model as dynamic variables of the local concentration field. When the local concentration is lower than the preset critical concentration for anti-dispersion failure, the flow phase change mechanism is triggered, and the fluid properties of the corresponding grid element are degenerated from non-Newtonian fluid to Newtonian fluid to simulate the segregation and dispersion process of concrete.

[0010] Furthermore, the method for constructing the time-varying correction function for the S2 rheological parameters is as follows: Define the relative concentration of the anti-dispersing component. ,in This represents the concentration of the anti-dispersion component within the spatial grid cell at the current moment. The initial concentration of the anti-dispersing component; The yield stress in the Herschel-Bulkley non-Newtonian fluid constitutive model Consistency coefficient and liquidity index Constructed respectively with respect to the relative concentration The attenuation correction function is specifically expressed as follows: Corrected yield stress: Corrected consistency coefficient: Corrected liquidity index: In the formula, , and These are the initial yield stress, initial consistency coefficient, and initial flow index of high-flowability, anti-dispersion concrete, respectively. , and All are material sensitivity coefficients calibrated through underwater anti-dispersion erosion tests, and , , .

[0011] Furthermore, the specific execution logic of the triggering flow phase transition mechanism is as follows: Within each computation time step, determine the relative concentration of each grid cell. Is it less than the preset critical concentration threshold for anti-dispersion failure? ; like Then, the decay correction function is used to update the grid cell in real time. , and To solve the momentum conservation equation while maintaining the non-Newtonian fluid properties; like If the mesh element is found to have segregated and dispersed, a forced phase transition is triggered, and the fluid properties of the mesh element are degraded to those of a Newtonian fluid, causing its yield stress to be lowered. Flow Index and the consistency coefficient Equivalently replaced by the constant plastic viscosity of a Newtonian fluid. To characterize the aggregate settling and slurry loss state after complete elution of the anti-dispersing components.

[0012] Furthermore, in step S3, dynamic inlet boundary conditions are set for the three-dimensional fluid computational domain model based on the conduit layout scheme and flow control strategy, specifically including: The bottom outlet of multiple conduits is set as the mass flow inlet boundary and a total constant mass flow is set. The initial mass flow of each conduit is allocated based on the ratio of its Thiessen polygon control area to the total pouring area. A feedback adjustment mechanism based on the phase interface height is introduced in the numerical solution process: within each calculation time step, the real-time height of the concrete phase interface directly below each duct is obtained by extracting the elevation of the intersection point of the concrete volume fraction isosurface and the central axis of each duct. Calculate the phase interface height difference below adjacent ducts. When the phase interface height difference exceeds a preset cooperative height difference threshold, trigger dynamic inlet boundary condition update. The dynamic inlet boundary condition update adopts a closed-loop feedback control method based on liquid level height deviation. The flow compensation amount is calculated according to the deviation value between the real-time height of each conduit phase interface and the average height of all conduit phase interfaces. The initial flow of each conduit after superimposing the compensation amount is normalized, and the total constant mass flow rate is redistributed according to the normalization ratio.

[0013] Furthermore, in step S4, the characteristics of concrete diffusion morphology and anti-dispersion performance distribution are extracted to identify pouring blind zones and segregation risk zones, specifically including: Pouring blind zone identification: Extract the set of grids with concrete phase volume fraction lower than the preset filling threshold on the bottom boundary of the computational domain, and combine them with the three-dimensional topographic undulation data at the bottom of the foundation pit to mark the connected regions located in the topographic depressions and with a phase volume fraction lower than the filling threshold as pouring blind zones. Segregation risk zone identification: Extract regions within the computational domain where the velocity gradient is greater than a preset shear threshold, calculate the ratio of the corrected plastic viscosity to the initial plastic viscosity within this region, and mark the corresponding region as a segregation risk zone when the ratio is lower than a preset anti-dispersion failure threshold.

[0014] Furthermore, in step S4, a duct coordinated control optimization instruction for adjusting the flow control strategy is generated accordingly, specifically including: A fitness function is constructed with the flow rate adjustment of each duct as the decision variable and the minimization of the variance of the flatness of the top surface of the sealing concrete and the minimization of the volume of the segregation risk zone as the dual optimization objectives. The fitness function is iteratively solved using the particle swarm optimization algorithm (PSO). Under the constraints of the total pouring volume conservation and the upper limit of the pumping capacity of each duct, the optimal flow distribution ratio of each duct in the next time step is output as the duct collaborative control optimization instruction.

[0015] As a second aspect of the present invention, an underwater bottom sealing concrete pouring flow evolution simulation system is also provided, comprising: The three-dimensional computational domain modeling unit is used to acquire the three-dimensional spatial data of the underwater sealing area to be poured and the initial rheological parameters of the high-flowability anti-dispersion concrete, and to construct the three-dimensional fluid computational domain model of the underwater sealing area to be poured. A two-phase flow model construction unit is used to construct a water-concrete two-phase flow evolution model considering anti-dispersion characteristics based on the initial rheological parameters; the two-phase flow evolution model introduces a time-varying correction function of the rheological parameters characterizing the underwater decay effect of the anti-dispersion components; The flow regime numerical solution unit is used to set dynamic inlet boundary conditions for the three-dimensional fluid computational domain model according to the duct layout scheme and flow control strategy, and to perform numerical solution using the two-phase flow evolution model to obtain the flow regime evolution data of the high-flowability anti-dispersion concrete underwater; the flow regime evolution data includes phase interface distribution, velocity field and concentration field; The risk identification and optimization unit is used to extract concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics based on the flow evolution data, identify pouring blind zones and segregation risk zones, and generate duct collaborative control optimization instructions for adjusting the flow control strategy.

[0016] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor as described in any one of the claims, a method for simulating the flow evolution of underwater bottom sealing concrete pouring.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The underwater bottom sealing concrete pouring flow evolution simulation method of the present invention integrates the three-dimensional point cloud topography of the foundation pit bottom, the cofferdam and steel casing structure data to complete geometric reconstruction, uses unstructured mesh to divide the computational domain and densifies the mesh at the duct outlet, around the steel casing and in areas of abrupt topographic gradient changes, and then uses the initial rheological parameters obtained from rheological testing to complete the material property assignment of the mesh cells, establishing a three-dimensional fluid computational domain model. The entire modeling process completely restores the complex spatial boundaries of the construction site, abandons the simplification of foundation pit topography and retaining components in conventional modeling, and ensures that the model geometry is consistent with the actual engineering, which can constrain the flow range of concrete, eliminate the calculation errors caused by geometric simplification, and provide a computational carrier that fits the site environment for subsequent two-phase flow numerical calculations. From the modeling level, it narrows the gap between simulation results and actual pouring conditions, ensuring that the basic conditions for subsequent flow field calculations have engineering reference value.

[0018] 2. The underwater bottom sealing concrete pouring flow evolution simulation method of the present invention establishes the two-phase flow basic governing equations using the VOF method, introduces the Herschel-Bulkley constitutive model to describe the rheological properties of concrete, adds a component diffusion equation with an interface elution source term, establishes a rheological parameter decay function based on the relative concentration of anti-dispersion components, and sets a concentration critical threshold to trigger the fluid phase transition rule. This approach abandons the traditional simulation mode of setting fixed rheological parameters, dynamically adjusts various rheological indices of concrete according to the agent loss caused by water flow scouring, and simulates the change law of concrete from non-Newtonian fluid to Newtonian fluid after anti-dispersion failure by relying on the phase transition mechanism. It accurately reflects the physical changes of concrete in the underwater environment due to the gradual segregation of anti-dispersion components, allowing the numerical model to quantitatively represent the performance degradation process of concrete after water scouring.

[0019] 3. The underwater bottom sealing concrete pouring flow evolution simulation method of the present invention allocates the initial pouring flow rate according to the control area of ​​the duct, and dynamically adjusts the inlet flow rate by constructing a closed-loop feedback mechanism based on the real-time liquid level difference below the duct. After solving the model, it identifies pouring blind zones and segregation risk zones based on volume fraction and velocity gradient data. With flatness and risk zone volume as optimization objectives, it uses a particle swarm optimization algorithm to output optimized duct flow rate commands. The dynamic boundary setting fits the operational logic of multi-duct collaborative pumping on site, and the flow rate is corrected in real time to meet the overall pouring quality conservation requirements. It relies on simulation results to identify potential construction defects in advance. The optimized flow rate scheme can be directly used to guide on-site pouring control. Parameter optimization is completed in the construction preparation stage to reduce quality defects such as insufficient filling and aggregate segregation during the actual pouring stage. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for simulating the flow evolution of underwater bottom sealing concrete pouring, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a typical underwater bottom sealing concrete multi-pipe collaborative pouring construction scenario according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 Please refer to Figure 1 This embodiment 1 provides a method for simulating the flow evolution of underwater bottom sealing concrete pouring, including: S1. Obtain the three-dimensional spatial data of the underwater sealing area to be poured and the initial rheological parameters of the high-flowability anti-dispersion concrete, and construct a three-dimensional fluid computational domain model of the underwater sealing area to be poured. S2. Based on the initial rheological parameters, a water-concrete two-phase flow evolution model considering anti-dispersion characteristics is constructed; the two-phase flow evolution model introduces a time-varying correction function for the rheological parameters that characterizes the underwater decay effect of the anti-dispersion components; S3. Based on the duct layout scheme and flow control strategy, dynamic inlet boundary conditions are set for the three-dimensional fluid computational domain model, and the two-phase flow evolution model is used for numerical solution to obtain the flow evolution data of the high-flowability anti-dispersion concrete underwater; the flow evolution data includes phase interface distribution, velocity field and concentration field; S4. Based on the flow evolution data, extract the concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics, identify the pouring blind zone and segregation risk zone, and generate duct collaborative control optimization instructions for adjusting the flow control strategy accordingly.

[0023] Please refer to Figure 2 This embodiment 1 further elaborates on the above steps.

[0024] (1) Three-dimensional computational domain modeling The actual underwater sealing construction site is constrained by the natural pit bottom topography, cofferdam retaining structure, and dense pile foundation steel casing, resulting in a complex spatial structure. Previous modeling often simplified the boundary structures, easily leading to discrepancies between the simulation space and the actual site conditions. Therefore, before constructing the three-dimensional fluid computational domain model of the underwater sealing area to be poured, it is necessary to obtain the three-dimensional spatial data of the area and the initial rheological parameters of the high-flowability, anti-dispersion concrete. In the underwater sealing area to be poured, a multibeam echo sounder is used to obtain the three-dimensional point cloud topographic data of the pit bottom. Simultaneously, CAD structural design drawings of the steel sheet pile cofferdam and the internal pile foundation steel casing are extracted, obtaining their spatial coordinates, diameter, and depth information, among other structural design data.

[0025] Subsequently, the point cloud data and structural design data were unified into the same coordinate system, multi-source data fusion was performed, and a non-uniform rational B-spline surface reconstruction algorithm was used to process the gaps and overlaps between the data, generating a closed three-dimensional geometric solid model that includes real terrain undulations, the inner wall of the cofferdam, and the boundary of the dense steel casing.

[0026] Next, based on the generated 3D geometric solid model, fluid domain extraction was performed using preprocessing software, defining the internal space of the sheet pile cofferdam as the computational domain. An unstructured tetrahedral mesh was used to spatially discretize the computational domain to accommodate the requirements of complex geometric boundaries. To ensure computational accuracy in key areas, local mesh refinement was performed in the preset area at the duct outlet, the boundary layer around the steel casing, and areas where the topographic gradient at the pit bottom changed beyond a preset threshold, generating a 3D fluid computational mesh.

[0027] Finally, the initial yield stress and initial plastic viscosity of the high-flowability anti-dispersion concrete were obtained by rheometer testing. A rheological parameter mapping relationship was established in combination with the dosage of anti-dispersion agent. The initial yield stress and initial plastic viscosity were assigned to the initial concrete phase unit in the three-dimensional fluid computational grid. The initial water phase unit in the three-dimensional fluid computational grid was set as a static water body filling the underwater sealing area to be poured, thus completing the construction of the three-dimensional fluid computational domain model.

[0028] (2) Construction of two-phase flow model In this embodiment, a water-concrete two-phase flow evolution model considering anti-dispersion properties is constructed based on the acquired initial rheological parameters. The two-phase flow evolution model introduces a time-varying correction function for the rheological parameters, characterizing the underwater attenuation effect of the anti-dispersion components. The specific implementation process of constructing this model and correction function includes four core stages: establishing the basic equations of two-phase flow, component concentration tracking and source term derivation, derivation of the dynamic correction formula for rheological parameters, and equivalent degradation of the flow phase transition mechanism.

[0029] First, the fundamental equations for two-phase flow are established. The Volume of Fluid (VOF) method is used to establish the mixed-phase continuity equation and momentum conservation equation for the water-concrete two-phase flow to capture the evolution of the free surface and phase interface during underwater casting. To characterize the shear thinning and yielding properties of high-fluidity, anti-dispersion concrete, a Herschel-Bulkley non-Newtonian fluid constitutive model is introduced into the viscous stress tensor of the momentum conservation equation. This constitutive model is based on the initial yield stress... Initial consistency coefficient and initial flow index These three initial rheological parameters define the initial rheological state of concrete before it is subjected to water flow.

[0030] Secondly, the derivation process of the anti-dispersibility component concentration tracking and the interface elution source term was performed. A concentration convection-diffusion equation for the anti-dispersibility component was established to track the component loss process at the water-concrete interface. To quantitatively characterize the anti-dispersibility component spalling effect caused by water flow shear scouring, an interface elution source term was included in the concentration convection-diffusion equation. The interface elution source item The derivation logic is as follows: the exfoliation rate of the anti-dispersion component is directly proportional to the portion of the destructive force exerted by the water flow that exceeds the material's resistance. Therefore, the interface elution source term... Local water flow shear stress at the phase interface Critical scour shear stress of the anti-dispersion component The difference is determined, and its derivation formula is as follows: In the formula, This is the interface elution rate coefficient, used to characterize the component loss rate under unit excess shear stress; The threshold cutoff function, in its physical sense, means that only local shear stress in water flow is considered as a threshold cutoff function. Greater than the critical scour shear stress The spalling effect only occurs when the source term is zero; otherwise, the source term is zero. By solving this equation, the concentration distribution of the anti-dispersion component in each spatial grid cell within the computational domain at different times can be obtained.

[0031] Next, based on the obtained concentration field, the time-varying correction function for the rheological parameters is constructed and derived. The relative concentration of the anti-dispersion component is defined. Its calculation formula is ,in, This represents the concentration of the anti-dispersion component within the spatial grid cell at the current moment. The initial concentration of the anti-dispersing component, relative concentration As a dimensionless attenuation indicator, it is used to measure the retention degree of the anti-dispersion component within the current grid cell. Subsequently, the rheological parameters in the Herschel-Bulkley non-Newtonian fluid constitutive model are constructed as a function of the relative concentration. The decay correction function.

[0032] For the corrected yield stress and the corrected consistency coefficient The derivation is based on the following: the loss of anti-dispersing components leads to the destruction of the flocculation structure inside the cement paste and the weakening of the steric hindrance effect between particles, which macroscopically manifests as a nonlinear power-law decay of yield stress and consistency. Therefore, the relationship is constructed as follows: as well as For the corrected liquidity index The derivation is based on the following: the flow index reflects the degree of shear thinning of a fluid, and the characteristic parameter of shear thinning is defined as... As the anti-dispersing components are lost, the non-Newtonian properties of concrete gradually weaken, and the shear thinning characteristic parameter should decrease with concentration. Therefore, the relationship is constructed as follows: After rearranging, we get: In the three relationships mentioned above, , and These are the initial yield stress, initial consistency coefficient, and initial flow index of the aforementioned high-flowability, anti-dispersion concrete, respectively. The yield stress material sensitivity coefficient, This refers to the consistency coefficient and material sensitivity coefficient. This is the material sensitivity coefficient for flow index. , and All of these are constants calibrated through underwater anti-dispersion erosion tests, and satisfy the following conditions: , , Through the above derivation, the yield stress Consistency coefficient and liquidity index It is set as a dynamic variable of the local concentration field.

[0033] Finally, within each computation time step, the logic for determining the flow phase transition mechanism and deriving the equivalent degradation is executed. A critical concentration threshold for anti-dispersion failure is set. Determine the relative concentration of each grid cell. Is it less than the threshold? .like If the concrete still possesses anti-dispersion ability, the aforementioned attenuation correction function is used to update the mesh element in real time. , and To solve the momentum conservation equation while maintaining the non-Newtonian fluid properties, if... If the condition is met, the mesh cell is determined to have segregated and dispersed, forcibly triggering the flow phase transition mechanism and degrading the fluid properties of the mesh cell to Newtonian fluid.

[0034] In the degeneracy derivation, since Newtonian fluids do not possess yield stress and have no shear thinning properties, the yield stress of this mesh element is set to... Flow Index At this point, the original Herschel-Bulkley constitutive equations (in the formula) For shear stress, The shear rate (for shear rate) mathematically degenerates strictly into... According to Newton's law of internal friction of fluids (in the formula) (This refers to the Newtonian fluid dynamic viscosity). By comparison, the degraded consistency coefficient can be seen to be... In terms of both dimensions and physical meaning, it is strictly equivalent to the dynamic viscosity of a Newtonian fluid. Therefore, the consistency coefficient... Equivalently replaced by the constant plastic viscosity of a Newtonian fluid. Among them, constant plastic viscosity This is used to characterize the residual fluid viscosity under aggregate settling and slurry loss conditions after complete elution of the anti-dispersing components. Through this equivalent degradation derivation, the entire segregation and dispersion process of concrete degrading from a non-Newtonian fluid to a Newtonian fluid is simulated.

[0035] (3) Numerical solution of flow regime When multiple conduits are used simultaneously for underwater concrete pouring, the initial material supply distribution is often determined by the experience of the construction personnel, which can easily lead to unreasonable flow distribution. Therefore, it is necessary to set dynamic inlet boundary conditions for the three-dimensional fluid computational domain model based on the conduit layout scheme and flow control strategy. The specific process for setting dynamic inlet boundary conditions is as follows: First, the bottom outlets of the multiple conduits are set as the mass flow inlet boundary, and the total constant mass flow rate of the underwater casting system is set as follows: The initial flow rate distribution for each conduit is set based on the ratio of its affected area to the total pouring area. Initial mass flow rate of root canal The calculation formula is: In the formula, For the first The area affected by the Thiessen polygon on the bottom sealing plane of the root canal. The total pouring area is the sum of the areas affected by all the conduits. In the numerical solution process, a feedback adjustment mechanism based on the phase interface height is introduced: within each fluid calculation time step, the VOF isosurface with a concrete volume fraction of 0.5 is extracted and compared with the first... The elevation of the intersection point of the central axis of the duct is used to obtain the real-time height of the concrete phase interface directly below the duct. ; Next, the height difference of the phase interface below two adjacent conduits is calculated. When the phase interface height difference Exceeding the preset collaborative height difference threshold When the dynamic inlet boundary condition is updated, a proportional adjustment algorithm with mass conservation constraints is used to redistribute the inlet mass flow rate of each duct. The specific formula for the proportional adjustment algorithm with mass conservation constraints is as follows: In the formula, and The first and second are respectively the adjusted and unadjusted numbers. Inlet mass flow rate of root canals, Adjust the gain coefficient to the preset flow rate ratio. For all The arithmetic mean of the height of the phase interface directly below the root vessels. Total number of catheters For the duct index variable; the formula is passed through the molecule The difference term increases the flow rate in the low-level conduit and decreases the flow rate in the high-level conduit, and the summation and normalization operation of the denominator ensures that the sum of the flow rates of all conduits after adjustment is strictly equal to the total constant mass flow rate. .

[0036] Subsequently, the two-phase flow evolution model was used for numerical solution to obtain the flow evolution data of the high-flowability anti-dispersion concrete underwater; the flow evolution data includes phase interface distribution, velocity field and concentration field.

[0037] In other preferred embodiments, the specific process of numerically solving using a two-phase flow evolution model is as follows: During the solver configuration and initialization phase, a set of governing equations is established, including continuity equations, momentum equations, fluid volume transport equations, and convection-diffusion equations for anti-dispersion component concentrations. A pressure-velocity coupled algorithm is used for transient solutions. To ensure the stability and convergence of the multiphysics calculation, an adaptive time step strategy and Courant number constraint are set to ensure that the physical field variables can be smoothly iterated and updated within each calculation time step.

[0038] In the coupled solution of the phase interface distribution and velocity field, the free surface between water and high-flowability anti-dispersion concrete is first tracked using the fluid volume method. By solving the fluid volume transport equation and adopting the interface geometry reconstruction scheme, the concrete volume fraction of each grid cell in the computational domain is updated in real time. The isosurface with a volume fraction reaching a preset threshold is extracted as the water-concrete phase interface, thereby obtaining the phase interface distribution data. Subsequently, the non-Newtonian rheological constitutive model of high-flowability anti-dispersion concrete is introduced into the momentum equation, and the effective viscosity is calculated based on the local rheological parameters of the concrete. Considering the effects of gravity, buoyancy, and interphase surface tension, the momentum equation is iteratively solved using a coupled algorithm to obtain the three-dimensional velocity vector of each grid cell in the computational domain, thereby constructing a global velocity field reflecting the underwater diffusion, tumbling, and flow around obstacles of the concrete.

[0039] In solving the concentration field and dynamically evolving the anti-dispersion properties, the concentration convection-diffusion equation of the anti-dispersion component is solved synchronously within each computation time step. To reflect the scouring effect of water flow on concrete in the underwater casting environment, an interface elution source term is introduced into the concentration equation. This source term quantitatively assesses and subtracts the anti-dispersion component that is stripped off due to water flow shear scouring by calculating the local velocity gradient and water flow shear stress at the phase interface, thereby obtaining the concentration field distribution of the anti-dispersion component. Based on the real-time updated concentration field, the local yield stress and consistency coefficient of the concrete phase mesh element are dynamically corrected using a preset rheological parameter mapping relationship. When a local concentration is detected to be below a critical threshold, a flow phase transition mechanism is triggered to degrade the fluid properties of the region, so as to realistically simulate the degradation of the concrete's anti-dispersion performance and potential segregation.

[0040] In the comprehensive extraction and output stage of flow evolution data, the solver performs spatiotemporal synchronous mapping and serialized storage of the flow field data within the computational domain at the set data output nodes. Fluid volume isosurface data is extracted to generate the three-dimensional spatial morphology of the phase interface distribution; velocity vectors of each grid node are extracted to generate streamline diagrams and vector distributions of the velocity field; and concentration scalars of the anti-dispersion components are extracted to generate spatial distribution cloud maps of the concentration field. The aforementioned phase interface distribution, velocity field, and concentration field data are uniformly encapsulated to form the flow evolution data of high-fluidity anti-dispersion concrete underwater.

[0041] (4) Risk identification and optimization After solving the flow evolution data, based on the acquired flow evolution data including phase interface distribution, velocity field, and concentration field, the concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics are further extracted to identify pouring blind zones and segregation risk zones. Based on this, duct collaborative control optimization instructions for adjusting the flow control strategy are generated. The specific process for generating the duct collaborative control optimization instructions is as follows: First, the extraction process for concrete diffusion morphology and anti-dispersion performance distribution characteristics was performed. For diffusion morphology extraction, a three-dimensional spatial topological analysis was conducted on the phase interface distribution data to calculate the propulsion velocity vector field of the concrete front and the diffusion radius at different azimuth angles. Simultaneously, combined with three-dimensional topographic undulation data of the pit bottom, the rise height and flow angle of the concrete slurry under complex terrain conditions were analyzed, thereby constructing a morphological feature map describing the overall flow trend and coverage of the concrete. For anti-dispersion performance distribution characteristic extraction, the coupled concentration and velocity fields within the computational domain were traversed. For each grid cell, the correlation between its local shear rate and the concentration of the anti-dispersion component was calculated. By comparing the local rheological parameters at the current moment with the standard rheological parameters under the initial mix proportion, the retention rate distribution of concrete cohesion at each spatial location was quantified, forming an anti-dispersion performance distribution characteristic dataset reflecting the spatiotemporal evolution of underwater concrete performance.

[0042] After feature extraction, the identification of pouring blind spots was further carried out. First, all grid cells on the bottom boundary of the computational domain were scanned, and grid sets with concrete phase volume fractions below a preset filling threshold were selected. These grids represent areas that have not yet been effectively filled. Then, the aforementioned low-volume-fraction grid sets were spatially overlaid with the 3D topographic relief data at the bottom of the foundation pit, focusing on identifying the grid subset located in topographic depressions. Based on this, a 3D connected component labeling algorithm was applied to aggregate and determine connected regions located in topographic depressions with a phase volume fraction consistently below the filling threshold. False gaps caused by transient fluid fluctuations were eliminated, and the confirmed unfilled connected regions were officially marked as pouring blind spots, thus achieving automated location of pouring dead zones caused by topography.

[0043] Next, the segregation risk zone is identified. Based on global velocity field data, the velocity gradient tensor modulus of each grid cell in the computational domain is calculated, and high-shear regions with velocity gradients greater than a preset shear threshold are extracted. These regions are typically where water erosion is most intense and where paste-aggregate separation is most likely to occur. For the identified high-shear regions, the plastic viscosity value within the region, corrected for concentration field, is read and compared with the initial plastic viscosity of the concrete to obtain the anti-dispersion performance retention rate. When this ratio is lower than the preset anti-dispersion failure threshold, it indicates that the concrete in this region has experienced significant loss of anti-dispersion components under the shearing action of water flow, resulting in a significant decrease in cohesion. This region is then marked as a segregation risk zone, thereby providing early warning of potential quality defect areas.

[0044] After identifying the pouring blind zone and segregation risk zone, optimization instructions for duct collaborative control are generated to adjust the flow control strategy. First, a multi-objective optimization model is constructed, using the flow adjustment of each duct as the decision variable. The dual optimization objectives are minimizing the variance of the top surface smoothness of the sealing concrete and minimizing the volume of the segregation risk zone, and a comprehensive fitness function is constructed. The variance of the top surface smoothness reflects the uniformity of the pouring surface, while the volume of the segregation risk zone is directly related to the overall quality and safety of the concrete.

[0045] Finally, a particle swarm optimization algorithm is used to iteratively solve the fitness function. During the solution process, constraints on the total pouring volume conservation and the physical upper limit constraints on the pumping capacity of each duct are applied to prevent overloading or loss of overall control. The algorithm simulates the search behavior of the particle swarm to find the optimal flow distribution scheme in the multidimensional solution space, and finally outputs the optimal flow distribution ratio of each duct in the next time step. This ratio serves as the duct collaborative control optimization instruction, which is sent to the control system to guide the on-site pumping equipment to adjust the discharge flow of each duct in real time, thereby achieving intelligent closed-loop control of the underwater bottom sealing concrete pouring process.

[0046] Example 2 Please refer to Figure 3 This embodiment 2 provides a simulation system for the flow evolution of underwater bottom sealing concrete pouring, including: The three-dimensional computational domain modeling unit is used to acquire the three-dimensional spatial data of the underwater sealing area to be poured and the initial rheological parameters of the high-flowability anti-dispersion concrete, and to construct the three-dimensional fluid computational domain model of the underwater sealing area to be poured. A two-phase flow model construction unit is used to construct a water-concrete two-phase flow evolution model considering anti-dispersion characteristics based on the initial rheological parameters; the two-phase flow evolution model introduces a time-varying correction function of the rheological parameters characterizing the underwater decay effect of the anti-dispersion components; The flow regime numerical solution unit is used to set dynamic inlet boundary conditions for the three-dimensional fluid computational domain model according to the duct layout scheme and flow control strategy, and to perform numerical solution using the two-phase flow evolution model to obtain the flow regime evolution data of the high-flowability anti-dispersion concrete underwater; the flow regime evolution data includes phase interface distribution, velocity field and concentration field; The risk identification and optimization unit is used to extract concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics based on the flow evolution data, identify pouring blind zones and segregation risk zones, and generate duct collaborative control optimization instructions for adjusting the flow control strategy.

[0047] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement any step of a simulation method for the flow evolution of underwater bottom sealing concrete pouring.

[0048] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0049] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0050] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulating the flow evolution of underwater bottom sealing concrete pouring, characterized in that, include: S1. Obtain the three-dimensional spatial data of the underwater sealing area to be poured and the initial rheological parameters of the high-flowability anti-dispersion concrete, and construct a three-dimensional fluid computational domain model of the underwater sealing area to be poured. S2. Based on the initial rheological parameters, a water-concrete two-phase flow evolution model considering anti-dispersion characteristics is constructed; the two-phase flow evolution model introduces a time-varying correction function for the rheological parameters that characterizes the underwater decay effect of the anti-dispersion components; S3. Based on the duct layout scheme and flow control strategy, dynamic inlet boundary conditions are set for the three-dimensional fluid computational domain model, and the two-phase flow evolution model is used for numerical solution to obtain the flow evolution data of the high-flowability anti-dispersion concrete underwater; the flow evolution data includes phase interface distribution, velocity field and concentration field; S4. Based on the flow evolution data, extract the concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics, identify the pouring blind zone and segregation risk zone, and generate duct collaborative control optimization instructions for adjusting the flow control strategy accordingly.

2. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 1, characterized in that, In step S1, constructing a three-dimensional fluid computational domain model of the underwater sealing area to be poured specifically includes: Acquire 3D point cloud topographic data of the bottom of the underwater foundation pit, as well as structural design data of the steel sheet pile cofferdam and the internal pile foundation steel casing. Perform multi-source data fusion and surface reconstruction to generate a 3D geometric solid model containing undulating rock surface, cofferdam inner wall and dense steel casing boundary. The fluid domain is extracted from the three-dimensional geometric entity model, and spatial discretization is performed using an unstructured mesh. The mesh is locally refined in the preset area of ​​the duct outlet, the boundary layer around the steel casing, and the area where the terrain gradient change at the bottom of the pit is greater than a preset threshold, to generate a three-dimensional fluid computation mesh. The initial yield stress and initial plastic viscosity of the high-flowability anti-dispersion concrete were obtained by rheometer testing. A rheological parameter mapping relationship was established in combination with the dosage of anti-dispersion agent. The initial yield stress and initial plastic viscosity were assigned to the initial concrete phase unit in the three-dimensional fluid computational grid. The initial water phase unit in the three-dimensional fluid computational grid was set as a static water body filling the underwater sealing area to be poured, thus completing the construction of the three-dimensional fluid computational domain model.

3. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 1, characterized in that, In S2, a two-phase flow evolution model for water-concrete considering anti-dispersion properties is constructed, specifically including: The fluid volume method (VOF) was used to establish the mixed-phase continuity equation and momentum conservation equation for water-concrete two-phase flow. In the viscous stress tensor of the momentum conservation equation, the Herschel-Bulkley non-Newtonian fluid constitutive model was introduced to characterize the shear thinning and yielding characteristics of the high-flowability anti-dispersion concrete. A concentration convection-diffusion equation for the anti-dispersion component is established to track the component loss process at the water-concrete interface. The concentration convection-diffusion equation includes an interface elution source term, which is determined by the difference between the local water flow shear stress at the interface and the critical scour shear stress of the anti-dispersion component. This term is used to characterize the spalling effect of the anti-dispersion component caused by water flow shear scour. Based on the local concentration field of the anti-dispersion component obtained by solving the concentration convection-diffusion equation, the time-varying correction function of the rheological parameters is constructed. The time-varying correction function of the rheological parameters sets the yield stress, consistency coefficient and flow index in the Herschel-Bulkley non-Newtonian fluid constitutive model as dynamic variables of the local concentration field. When the local concentration is lower than the preset critical concentration for anti-dispersion failure, the flow phase change mechanism is triggered, and the fluid properties of the corresponding grid element are degenerated from non-Newtonian fluid to Newtonian fluid to simulate the segregation and dispersion process of concrete.

4. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 3, characterized in that, The method for constructing the time-varying correction function for the S2 rheological parameters is as follows: Define the relative concentration of the anti-dispersing component. ,in This represents the concentration of the anti-dispersion component within the spatial grid cell at the current moment. The initial concentration of the anti-dispersing component; The yield stress in the Herschel-Bulkley non-Newtonian fluid constitutive model Consistency coefficient and liquidity index Constructed respectively with respect to the relative concentration The attenuation correction function is specifically expressed as follows: Corrected yield stress: ; Corrected consistency coefficient: Corrected liquidity index: In the formula, , and These are the initial yield stress, initial consistency coefficient, and initial flow index of high-flowability, anti-dispersion concrete, respectively. , and All are material sensitivity coefficients calibrated through underwater anti-dispersion erosion tests, and , , .

5. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 3, characterized in that, The specific execution logic of the mechanism for triggering the flow phase transition is as follows: Within each computation time step, determine the relative concentration of each grid cell. Is it less than the preset critical concentration threshold for anti-dispersion failure? ; like Then, the decay correction function is used to update the grid cell in real time. , and To solve the momentum conservation equation while maintaining the non-Newtonian fluid properties; like If the mesh element is found to have segregated and dispersed, a forced phase transition is triggered, and the fluid properties of the mesh element are degraded to those of a Newtonian fluid, causing its yield stress to be lowered. Flow Index and the consistency coefficient Equivalently replaced by the constant plastic viscosity of a Newtonian fluid. To characterize the aggregate settling and slurry loss state after complete elution of the anti-dispersing components.

6. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 1, characterized in that, In step S3, dynamic inlet boundary conditions are set for the three-dimensional fluid computational domain model based on the conduit layout scheme and flow control strategy, specifically including: The bottom outlet of multiple conduits is set as the mass flow inlet boundary and a total constant mass flow is set. The initial mass flow of each conduit is allocated based on the ratio of its Thiessen polygon control area to the total pouring area. A feedback adjustment mechanism based on the phase interface height is introduced in the numerical solution process: within each calculation time step, the real-time height of the concrete phase interface directly below each duct is obtained by extracting the elevation of the intersection point of the concrete volume fraction isosurface and the central axis of each duct. Calculate the phase interface height difference below adjacent ducts. When the phase interface height difference exceeds a preset cooperative height difference threshold, trigger dynamic inlet boundary condition update. The dynamic inlet boundary condition update adopts a closed-loop feedback control method based on liquid level height deviation. The flow compensation amount is calculated according to the deviation value between the real-time height of each conduit phase interface and the average height of all conduit phase interfaces. The initial flow of each conduit after superimposing the compensation amount is normalized, and the total constant mass flow rate is redistributed according to the normalization ratio.

7. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 1, characterized in that, In step S4, the characteristics of concrete diffusion morphology and anti-dispersion performance distribution are extracted to identify pouring blind zones and segregation risk zones, specifically including: Pouring blind zone identification: Extract the set of grids with concrete phase volume fraction lower than the preset filling threshold on the bottom boundary of the computational domain, and combine them with the three-dimensional topographic undulation data at the bottom of the foundation pit to mark the connected regions located in the topographic depressions and with a phase volume fraction lower than the filling threshold as pouring blind zones. Segregation risk zone identification: Extract regions within the computational domain where the velocity gradient is greater than a preset shear threshold, calculate the ratio of the corrected plastic viscosity to the initial plastic viscosity within this region, and mark the corresponding region as a segregation risk zone when the ratio is lower than a preset anti-dispersion failure threshold.

8. The method for simulating the flow evolution of underwater bottom sealing concrete pouring according to claim 1, characterized in that, In step S4, a duct cooperative control optimization instruction for adjusting the flow control strategy is generated accordingly, specifically including: A fitness function is constructed with the flow rate adjustment of each duct as the decision variable and the minimization of the variance of the flatness of the top surface of the sealing concrete and the minimization of the volume of the segregation risk zone as the dual optimization objectives. The fitness function is iteratively solved using the particle swarm optimization algorithm (PSO). Under the constraints of the total pouring volume conservation and the upper limit of the pumping capacity of each duct, the optimal flow distribution ratio of each duct in the next time step is output as the duct collaborative control optimization instruction.

9. A simulation system for the flow evolution of underwater bottom sealing concrete pouring, characterized in that, include: The three-dimensional computational domain modeling unit is used to acquire the three-dimensional spatial data of the underwater sealing area to be poured and the initial rheological parameters of the high-flowability anti-dispersion concrete, and to construct the three-dimensional fluid computational domain model of the underwater sealing area to be poured. A two-phase flow model construction unit is used to construct a water-concrete two-phase flow evolution model considering anti-dispersion characteristics based on the initial rheological parameters; the two-phase flow evolution model introduces a time-varying correction function of the rheological parameters characterizing the underwater decay effect of the anti-dispersion components; The flow regime numerical solution unit is used to set dynamic inlet boundary conditions for the three-dimensional fluid computational domain model according to the duct layout scheme and flow control strategy, and to perform numerical solution using the two-phase flow evolution model to obtain the flow regime evolution data of the high-flowability anti-dispersion concrete underwater; the flow regime evolution data includes phase interface distribution, velocity field and concentration field; The risk identification and optimization unit is used to extract concrete diffusion morphology characteristics and anti-dispersion performance distribution characteristics based on the flow evolution data, identify pouring blind zones and segregation risk zones, and generate duct collaborative control optimization instructions for adjusting the flow control strategy.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-8: a method for simulating the flow evolution of underwater bottom sealing concrete pouring.

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