A cross-scale simulation prediction system for film formation of oil droplet groups on heat exchange surface

Abstract

The application provides a heat exchange surface oil droplet group adhesion film forming cross-scale simulation prediction system and relates to the technical field of multiphase flow simulation. The system comprises the following steps: firstly, a calculation domain model is constructed, and a VOF model is established to initialize a flow field; secondly, a DPM model is used to track the trajectory of discrete oil droplets and obtain collision data; thirdly, a multi-state wall collision condition judgment module extracts a characteristic thickness or a volume fraction according to whether there is a continuous oil phase at the collision position; fourthly, a DPM-to-VOF state conversion trigger module terminates trajectory tracking and generates a phase change instruction when the data meets the standard; fifthly, a source term dynamic coupling module converts the mass and velocity of removed particles into equivalent source terms and adds them to the VOF equation; and finally, a VOF interface evolution analysis module outputs the continuous oil film morphology. The application takes into account the mass oil droplet transport efficiency and the continuous oil film tracking accuracy, and realizes high-fidelity cross-scale simulation prediction.

Description

Technical Field

[0001] This invention relates to the field of multiphase flow simulation technology, and in particular to a cross-scale simulation and prediction system for the adhesion and film formation of oil droplets on a heat exchange surface. Background Technology

[0002] Plate heat exchangers are widely used in the waste heat recovery and treatment of produced water in oil fields. Wastewater carries a large number of dispersed, tiny oil droplets. After entering the heat exchanger, the fluid is subjected to multiple physical effects, including narrow flow channels, velocity shear, and abrupt changes in the corrugated structure. These tiny oil droplets frequently collide with and adhere to the heat exchanger walls. The adhered oil droplets accumulate and coalesce on the walls, eventually growing into a continuously distributed oil film. This wall-attached oil film not only alters the hydrodynamic boundary of the near-wall region but also significantly increases the thermal resistance of the heat exchange system. This type of interfacial fouling deposition leads to a severe degradation of the thermal-hydraulic performance of the heat exchange equipment. To explore anti-fouling mechanisms, computational fluid dynamics multiphase flow numerical simulation has become a routine method for guiding the optimal design of industrial equipment.

[0003] Existing technologies face a technical bottleneck in analyzing the growth and evolution mechanism of continuous oil films, where computational efficiency and interface tracking accuracy cannot be simultaneously achieved. Currently, mainstream multiphase flow numerical simulation methods mainly include volumetric tracking methods and discrete phase transport models. Volumetric tracking methods can accurately analyze the continuous interface evolution processes, such as the spreading and merging of oil films on the wall. However, when the flow field contains a massive number of dispersed oil droplets, volumetric tracking methods must distinguish the physical interface of each tiny droplet at the grid scale. As the grid density increases exponentially, interface tracking of massive particles leads to a complete loss of computational resources. Discrete phase transport models employ the Eulerian-Lagrangian computational framework, exhibiting extremely high computational efficiency when handling high-frequency, small-scale oil droplet transport. However, discrete phase transport models are limited by the fundamental physical assumptions of point particles and cannot analyze the morphological flattening of individual oil droplets after adhering to the wall, the coalescence of tiny droplets, and the interphase interface evolution phenomena in the formation of large-scale continuous oil films. The Chinese Journal of Engineering Thermophysics, 2022, 43(2): 410-418, also confirmed that a single mathematical model of multiphase flow cannot overcome the algorithmic barrier of converting discrete particles into continuous liquid films.

[0004] Current underlying theories of fluid dynamics simulation are beginning to explore cross-scale numerical concepts that couple discrete particle tracking with continuous phase interface capture. However, the conservation laws for the mass and momentum conversion source terms of the aforementioned coupling mechanism are extremely complex to construct and have not yet been applied in simulations of complex flow fields in heat exchangers or related to oilfield wastewater treatment. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the purpose of this invention is to provide a cross-scale simulation prediction system for oil droplet swarm adhesion and film formation on heat exchange surfaces. This invention solves the problem in the prior art of cross-scale prediction that cannot simultaneously balance the computational efficiency of massive oil droplet transport and the accuracy of tracking the evolution of continuous oil film interfaces.

[0006] To achieve the above objectives, the present invention provides the following solution: A cross-scale simulation and prediction system for oil droplet swarm adhesion and film formation on a heat transfer surface includes: The flow field initialization and mesh building module is used to construct the computational domain model of the target heat exchange surface and generate a mesh, and to establish a volumetric fluid volume (VOF) model based on the computational domain model to initialize the physical property parameters of the continuous water phase and discrete oil droplet particles and the flow field boundary conditions to obtain the initial flow field data. The DPM discrete phase trajectory tracking module is used to establish a discrete phase DPM model based on the initial flow field data, and to track the motion trajectory of the discrete oil droplet particles within the computational domain model through the discrete phase DPM model, so as to obtain the collision state data of the discrete oil droplet particles and the target heat exchange surface. The multi-state wall impact condition determination module is used to determine the wall state at the impact point based on the collision state data. When there is no continuous oil phase under the volumetric fluid volume (VOF) model at the impact point, local feature thickness data is obtained; when the continuous oil phase already exists in the grid cell at the impact point, the volume fraction data of the continuous oil phase in the grid cell is obtained. The DPM-to-VOF state transition trigger module is used to terminate the trajectory tracking of the corresponding discrete oil droplet particles in the discrete phase DPM model based on the local feature thickness data and the volume fraction data, and to obtain phase transition command data. The DPM-to-VOF source term dynamic coupling module is used to remove the mass and velocity of the discrete oil droplet particles that trigger the transformation from the discrete phase DPM model based on the phase transition command data, and convert the removed mass and velocity into equivalent mass source terms and equivalent momentum source terms, so as to add them to the control equations of the volumetric fluid VOF model. The VOF oil film interface evolution analysis module is used to solve the governing equations to obtain the continuous oil film morphology and oil phase volume fraction evolution results of the target heat exchange surface.

[0007] The present invention discloses the following technical effects: This invention provides a cross-scale simulation and prediction system for oil droplet aggregation and film formation on heat exchange surfaces. The functional modules of this invention are closely integrated based on the flow of flow field data and command data, forming a cross-scale prediction system with a self-consistent data closed loop and extremely high physical process reproduction. This invention breaks through the underlying algorithm barrier of a single multiphase flow model, achieving cross-scale simulation that accurately analyzes the evolution of continuous interface morphology while ensuring the computational efficiency of discrete phase transport. This invention realizes cross-scale high-fidelity simulation and prediction that balances the spatial transport efficiency of massive oil droplets with the dynamic tracking accuracy of continuous oil films. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of a cross-scale simulation prediction system for oil droplet group adhesion and film formation on a heat exchange surface, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the computational domain of a single-cycle trapezoidal corrugated channel provided in an embodiment of the present invention; Figure 3 A schematic diagram showing the local meshing details and near-wall boundary layer refinement of a meshed computational domain model provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the initial spatial distribution of discrete oil droplet particles at the inlet section provided in an embodiment of the present invention; Figure 5 This is a three-dimensional schematic diagram of the streamlines of the motion trajectory of discrete oil droplet particles inside the corrugated channel provided in an embodiment of the present invention. Figure 6 This is a scatter plot of collision state data on the target heat exchange surface provided in an embodiment of the present invention.

[0010] Figure label: 1-Flow field initialization and mesh construction module, 2-DPM discrete phase trajectory tracking module, 3-Multi-state collision condition determination module, 4-DPM-to-VOF state transformation trigger module, 5-DPM-to-VOF source term dynamic coupling module, 6-VOF oil film interface evolution analysis module. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1 As shown, this invention provides a cross-scale simulation and prediction system for oil droplet swarm adhesion and film formation on a heat exchange surface, comprising: The flow field initialization and mesh building module 1 is used to construct the computational domain model of the target heat exchange surface and divide the mesh, and establish a volumetric fluid volume (VOF) model based on the computational domain model to initialize the physical property parameters of the continuous water phase and discrete oil droplet particles and the flow field boundary conditions to obtain the initial flow field data. The DPM discrete phase trajectory tracking module 2 is used to establish a discrete phase DPM model based on the initial flow field data, and to track the motion trajectory of the discrete oil droplet particles within the computational domain model through the discrete phase DPM model, so as to obtain the collision state data of the discrete oil droplet particles and the target heat exchange surface. The multi-state wall impact condition determination module 3 is used to determine the wall state at the impact point based on the collision state data. When there is no continuous oil phase under the volumetric fluid volume (VOF) model at the impact point, local feature thickness data is obtained; when the continuous oil phase already exists in the grid cell at the impact point, the volume fraction data of the continuous oil phase in the grid cell is obtained. The DPM-to-VOF state transition trigger module 4 is used to terminate the trajectory tracking of the corresponding discrete oil droplet particles in the discrete phase DPM model based on the local feature thickness data and the volume fraction data, and to obtain phase transition command data. The DPM-to-VOF source term dynamic coupling module 5 is used to remove the mass and velocity of the discrete oil droplet particles that trigger the transformation from the discrete phase DPM model based on the phase transition command data, and convert the removed mass and velocity into equivalent mass source terms and equivalent momentum source terms, so as to add them to the control equations of the volumetric fluid VOF model. VOF oil film interface evolution analysis module 6 is used to solve the governing equations to obtain the continuous oil film morphology and oil phase volume fraction evolution results of the target heat exchange surface.

[0014] Furthermore, in a specific embodiment, the flow field initialization and mesh construction module 1 serves as the pre-execution link of the entire cross-scale simulation prediction system. It is mainly used to construct the computational domain model of the target heat transfer surface and divide the mesh, and establish a volumetric fluid volume (VOF) model based on the computational domain model to initialize the physical property parameters of the continuous water phase and discrete oil droplet particles and the flow field boundary conditions, and finally generate and output the initial flow field data.

[0015] It should be stated to those skilled in the art that the “computational domain model of the target heat exchange surface” described in this invention refers to the “fluid domain model” or “physical solution space” used to solve multiphase flows in actual engineering or fluid mechanics numerical simulations; at the same time, the “volume fluid volume (VOF) model” is the same as the “VOF (Volume of Fluid) model” conventionally expressed in the art, and the two are completely identical in physical connotation and logical application.

[0016] Specifically, the data processing logic within the flow field initialization and mesh construction module 1 is completed sequentially by four sub-modules. First, the geometric computational domain construction sub-module is responsible for constructing the computational domain model of the target heat transfer surface. To balance the computational accuracy and time cost of cross-scale transient calculations, the system typically extracts representative and periodic local ripple segments as the computational space. For example... Figure 2 As shown in the figure, this diagram illustrates the single-cycle trapezoidal corrugated channel computational domain model generated by the geometric computational domain construction submodule. The figure clearly defines the geometric topological characteristics of the computational fluid domain, specifically including the distribution sequence of the 3mm trapezoidal corrugated structure. To simulate a realistic industrial heat exchange environment, the left cross-section of the computational domain is defined as the velocity inlet, used to set the inflow velocity of the continuous water phase; the right cross-section is defined as the pressure outlet, used to maintain the pressure balance of the flow field. Inside the channel, the upper and lower walls, composed of titanium plates, define the physical boundaries of the fluid domain. Furthermore, the figure visually presents the distribution of DPM oil droplets introduced by the particle jet injection submodule in the initial stage of the flow field. This model provides a complete geometric foundation and physical boundary constraints for subsequent cross-scale simulations. Within this computational domain model, the macroscopic flow of the fluid strictly follows the law of conservation of mass, and the corresponding equation is as follows: ; in, This indicates the density of the mixed fluid. Indicates the physical solution time. This represents the velocity vector of the mixed fluid in the flow field. For example... Figure 2As shown in the figure, this diagram clearly illustrates the computational domain of a single-cycle trapezoidal corrugated channel on the target heat exchange surface. The system generates a seamless three-dimensional computational space based on this geometric boundary. Subsequently, the spatial mesh discrete submodule receives the constructed computational domain model, meshes it, and transforms it into a discrete node array composed of numerous tiny control volumes, thus obtaining a meshed computational domain model. To ensure that the meshing accuracy meets the requirements of cross-scale interface tracking, such as... Figure 3 As shown in the figure, this diagram visually illustrates the details of the local meshing of the meshed computational domain model and the mesh refinement of the near-wall boundary layer.

[0017] After obtaining the spatial discrete domain, the property model building submodule is activated to establish a volumetric fluid volume (VOF) model based on the meshed computational domain model and initialize the property parameters of the continuous water phase and discrete oil droplets. The core of establishing the VOF model lies in solving the transport equations for the volume fractions of each phase in a fixed Eulerian grid to achieve accurate tracking of the oil-water free interface. The transport control equations of this model are as follows: ; in, This represents the volume fraction of the continuous oil phase within the grid cell. Based on this mathematical and physical framework, the system further injects actual physical properties into the flow field, specifically completing the initial configuration of values ​​such as the density and dynamic viscosity of the continuous water phase, as well as the density, dynamic viscosity, and surface tension of the discrete oil droplets.

[0018] Finally, the flow field boundary configuration submodule defines constraints on the physical environment surrounding the computational domain to initialize the flow field boundary conditions. Combined with the configured physical property parameters, it packages and outputs the initial flow field data. Through the rigorous integration of the above steps, the initial flow field data, including the mesh topology, interphase interface tracking framework, fluid property set, and physical boundary constraints, is completed and transmitted as a complete basic environment data package to downstream modules, thus ensuring data continuity in the cross-scale coupled simulation process.

[0019] In one specific embodiment, after the flow field initialization is completed, the system sequentially activates the DPM discrete phase trajectory tracking module 2. This module is mainly used to establish a discrete phase DPM model based on the initial flow field data, and to track the motion trajectory of the discrete oil droplet particles within the computational domain model through the discrete phase DPM model, so as to obtain the collision state data of the discrete oil droplet particles and the target heat exchange surface.

[0020] It should be stated to those skilled in the art that the "discrete oil droplet particles" described in this invention correspond to "discrete phase particles" in the Lagrange coordinate system in the context of numerical calculation; the "wall collision characteristics" specifically cover the physical characteristics such as the spatial coordinates, normal momentum, tangential momentum and particle size distribution of the particles when they collide with the wall.

[0021] Specifically, the execution logic of the DPM discrete phase trajectory tracking module 2 is completed collaboratively by the following four sub-modules. First, the discrete phase model establishment sub-module establishes a discrete phase DPM model based on the received initial flow field data. The core of establishing the discrete phase DPM model lies in solving the spatial position change of each discrete oil droplet particle by integrating the particle force balance equation in the Lagrange coordinate system. The corresponding particle motion control equation is as follows: ; in, This represents the velocity of discrete oil droplet particles. This represents the fluid drag force experienced by a unit mass of oil droplet. The density of the discrete oil droplets and the density of the continuous water phase are given. This is the additional force source term experienced by the particle.

[0022] Subsequently, the particle injection submodule begins execution, used to inject discrete oil droplet particles in a discrete state into the computational domain model through the discrete phase DPM model. In the actual simulation settings, this submodule uses surface injection based on the inlet geometry of the computational domain model and initializes the particle mass flow rate according to a preset oil content. Figure 4 As shown in the figure, this diagram intuitively demonstrates the spatial distribution and initial velocity vector of discrete oil droplet particles in the initial stage of inlet injection, ensuring that the discrete phase enters the flow field in a form that conforms to physical reality.

[0023] Next, the force and trajectory solving submodule is activated to calculate the interaction forces experienced by the injected discrete oil droplet particles during flow, thereby obtaining the trajectory of the discrete oil droplet particles. In the complex shear flow field of the corrugated channel, the additional force source term plays a crucial deflection role, with a focus on calculating the Saffman lift force caused by the velocity gradient. This force directly drives the particles to migrate laterally towards the wall. Based on the real-time solution of the above mechanical equilibrium, as shown... Figure 5 As shown, the Figure 3 The system visually displays the streamlines of discrete oil droplet particles, clearly reflecting the complex movement paths of particles under the influence of mainstream carrying, eddy current entrainment, and lift.

[0024] Finally, the collision state extraction submodule intervenes to monitor and extract the collision features of the discrete oil droplet particles with the target heat exchange surface in real time based on the calculated motion trajectory, thereby obtaining the collision state data. This submodule applies wall-film logic at the boundary of the target heat exchange surface; when the particle trajectory is detected to intersect the wall boundary, it immediately calculates the normal Weber number of the particle to assess the collision energy. Figure 6 As shown, this figure presents the collision state data on the target heat exchange surface in the form of a scatter plot, including the normal velocity distribution and particle kinetic energy information at each impact point. This high-fidelity microscopic data is fully encapsulated, providing a precise Lagrangian input source for subsequent cross-scale phase transition triggering.

[0025] During the simulation, understanding the force balance of oil droplets in the flow channel and the resulting trajectory deflection is key to understanding the oil film formation mechanism. The interactive tools below allow you to adjust the fluid velocity and droplet size to observe how Saffman lift alters the droplet trajectory and affects its collision frequency with the wall.

[0026] Furthermore, in a specific embodiment, as the collision state data is continuously output, the system sequentially activates the multi-state collision condition determination module 3. This module, acting as the logical hub connecting microscopic Lagrange particle tracking and macroscopic Eulerian interface evolution, is primarily used to determine the wall state at the point of impact of discrete particles based on the collision state data transmitted in the preceding steps. Specifically, this module establishes branch judgment logic based on the multiphase flow field properties within the grid cell: when there is no continuous oil phase under the volumetric fluid flow (VOF) model at the impact point, local characteristic thickness data is obtained; when the continuous oil phase already exists within the grid cell at the impact point, the volume fraction data of the continuous oil phase within the grid cell is obtained. It is hereby declared to those skilled in the art that the "multi-state collision condition" described in this specification and claims aims to cover two distinctly different physical environments at the spatial location where a macroscopic oil-water interface exists when a discrete oil droplet particle touches a solid wall boundary.

[0027] In specific implementation, the data processing flow of the multi-state collision condition determination module 3 is completed by four closely linked sub-modules. First, the collision location positioning sub-module receives the collision state data. This sub-module extracts the three-dimensional spatial coordinate information of the collision event and accurately locates the bottom-level grid cell at the point of impact of the discrete oil droplet particles through a spatial geometric mapping algorithm. The moment the positioning is completed, the continuous phase existence detection sub-module immediately intervenes to detect whether there is a continuous oil phase under the volumetric fluid volume (VOF) model within the located grid cell, in order to confirm the wall state at the current collision point. In the underlying numerical logic, this detection process is mainly achieved by reading whether the scalar value of the oil phase volume fraction within the located grid cell is zero, thereby strictly dividing the complex physical collision environment into two determination states: "no continuous oil phase" and "existing continuous oil phase".

[0028] When the continuous phase presence detection submodule confirms that the wall surface is devoid of the continuous oil phase, the system logic branch directs to the wall film thickness evaluation submodule. In this state, due to the lack of macroscopic oil phase adsorption and fusion, the discrete oil droplet particles adhere to the target heat exchange surface in the form of a microscopic Lagrangian wall film. The wall film thickness evaluation submodule performs dynamic calculations based on the adhesion and accumulation behavior of the discrete oil droplet particles on the wall surface. By recording the total mass and spreading area of ​​the captured particles within the local grid region in real time, it obtains local characteristic thickness data. This local characteristic thickness data accurately quantifies the degree of local aggregation of dispersed oil droplets at the microscopic level before they transform into a macroscopic continuous oil phase.

[0029] Conversely, when the continuous phase existence detection submodule confirms that the wall state indicates the presence of the continuous oil phase within the grid cell, the system logic branch directs to the phase volume fraction extraction submodule. The corresponding physical scenario is that subsequent discrete oil droplets directly collide with the already preliminarily formed continuous oil film surface. Therefore, the phase volume fraction extraction submodule directly penetrates the Eulerian phase solution domain, retrieves and extracts the field variable matrix of the VOF model at that specific grid node, to obtain the volume fraction data of the continuous oil phase within the grid cell. This volume fraction data objectively characterizes the degree to which the current grid space is occupied by the continuous oil film. Through the above-mentioned parallel state determination and data extraction mechanism, the system completes the collection of variables for the preconditions of cross-scale phase transitions without omission.

[0030] Furthermore, in a specific embodiment, following the judgment results of the preceding module on the flow field and wall properties, the system sequentially activates the DPM-to-VOF state transition trigger module 4. This module, as the decision-making core connecting the microscopic particle and macroscopic fluid interface evolution, is mainly used to, based on the aforementioned local characteristic thickness data and volume fraction data, to terminate the Lagrangian trajectory tracking of the corresponding discrete oil droplet particles in the discrete phase DPM model in real time and dynamically, and ultimately obtain phase transition command data used to drive the cross-scale physical quantity transfer. It is hereby declared to those skilled in the art that the "state transition" and "phase transition" described in this application are equivalent technical concepts in the context of numerical models and multiphase flow calculations; both refer to the process by which an oil droplet transitions from a discrete point mass or local wall film state under the Lagrangian framework to a continuous phase interface state under the Eulerian framework, bridging mathematical and physical properties.

[0031] In specific implementation, the data flow and logical judgment within the DPM-to-VOF state transition trigger module 4 are completed collaboratively by four sub-modules. First, the phase transition threshold comparison sub-module acts as a condition screening hub, comparing the received local feature thickness data with a preset thickness threshold in the system, and simultaneously comparing the received volume fraction data with a preset volume fraction threshold in the system, comprehensively evaluating the evolution degree of the two different wall states, and thus obtaining the comparison result. Once a critical condition for cross-scale evolution is found, the trajectory solution truncation sub-module immediately intervenes. This sub-module is used to, based on the comparison result, immediately terminate the motion trajectory tracking calculation of the corresponding discrete oil droplet particles in the discrete phase DPM model within the current solution time step when it is detected that the local feature thickness data reaches the preset thickness threshold, or the volume fraction data exceeds the preset volume fraction threshold. This allows for precise separation and locking of discrete oil droplet particles from the large discrete particle group, preventing them from continuing to participate in subsequent flow field iteration calculations as independent discrete phases.

[0032] After successfully intercepting and locking the particles that trigger the transformation conditions, the system precisely allocates subsequent instruction generation tasks based on the specific threshold type met. On one hand, when there is no continuous oil phase in the collision region, the first phase change instruction generation submodule is used to respond when the comparison result shows that the local feature thickness data reaches the preset thickness threshold. At this time, physically, this is characterized by the accumulation and growth of the microscopic Lagrange wall film adhering to the heat exchange surface to a sufficient thickness, possessing the physical basis for evolving into a macroscopic continuous fluid. This submodule then generates and obtains the first phase change instruction data characterizing the wall film transformation for the locked discrete oil droplet particles. On the other hand, when an oil film already exists in the collision region, the second phase change instruction generation submodule is used to respond when the comparison result shows that the volume fraction data exceeds the preset volume fraction threshold. This physical process characterizes the subsequent floating discrete oil droplets directly colliding with and merging into the already highly saturated continuous oil phase region, resulting in instantaneous fusion. This submodule then generates and obtains the second phase change instruction data characterizing the fusion behavior for these locked discrete oil droplet particles. Ultimately, the first phase transition command data and the second phase transition command data are packaged and encapsulated together to form a complete phase transition command data, providing a clear object pointer and operation password for subsequent source term conservation transfer.

[0033] Furthermore, the cross-scale simulation prediction system for oil droplet swarm adhesion and film formation on the heat exchange surface provided in this embodiment includes a DPM-to-VOF source term dynamic coupling module 5 responsible for the rigorous transfer of cross-scale physical quantities. The first step executed by this module is completed by the phase transition command parsing submodule. In this embodiment, the phase transition command data issued by the preceding stage is received through this submodule. The phase transition command data is a low-level control beacon generated when the microscopic oil droplets meet specific physical transformation conditions. In this embodiment, based directly on the command data, all tracking nodes in the Lagrange coordinate system are globally traversed within the current calculation time step to accurately lock the discrete oil droplet particles that trigger the transformation. Under a rigorous transient calculation framework, this embodiment forcibly locks tens of thousands of tiny discrete oil droplet particles that meet the interface phase transition boundary conditions within a single scan cycle of 0.001 seconds, thereby ensuring the spatial accuracy and physical uniqueness of the cross-scale tracking object.

[0034] After locking onto the target object, this embodiment extracts and removes microscopic physical quantities through a discrete parameter stripping submodule. This embodiment extracts the mass and velocity of the discrete oil droplet particles that trigger the transformation. In this process, the extracted mass is derived from the product of the initial density and particle size / volume assigned when the discrete phase is established, and the extracted velocity is derived from the instantaneous three-dimensional velocity vector obtained by integrating the force differential equation. After extracting the above physical quantities, this embodiment completely removes the extracted mass and velocity from the discrete phase DPM model. The essence of this removal operation is to cancel the corresponding particle's data index in the Lagrange tracking matrix and cut off all subsequent force analysis and trajectory deduction.

[0035] Next, this embodiment utilizes the equivalent source term conversion submodule to transform the microscopic stripping quantity into the input quantity of the macroscopic continuous domain. This embodiment first converts and calculates the extracted quality into an equivalent quality source term.

[0036] The specific conversion calculation process of the equivalent mass source term is as follows: In this embodiment, firstly, in the bottom mesh space, the set of all discrete oil droplet particles that trigger the conversion within the same control volume within the current time step is counted, where the control volume refers to the three-dimensional Eulerian mesh element after the spatial mesh is discretized; then, the mass of any discrete oil droplet particle in the set is accumulated and summed to obtain the total conversion mass within the current control volume; finally, the total conversion mass is divided by the product of the volume of the control volume and the time step to obtain the equivalent mass source term per unit volume per unit time.

[0037] While performing the mass conservation conversion, this embodiment simultaneously converts and calculates the equivalent momentum source term through the equivalent source term conversion submodule.

[0038] The specific conversion calculation process of the equivalent momentum source term is as follows: In this embodiment, for the set of discrete oil droplet particles that trigger conversion within the same control body, the mass of any discrete oil droplet particle in the set and the instantaneous three-dimensional velocity of the particle are extracted, and the two are multiplied to obtain the momentum vector of a single particle; then, the momentum vectors of all particles in the set are vector-accumulated and summed to obtain the total converted momentum received by the current control body; finally, the total converted momentum is also divided by the product of the volume of the control body and the time step to obtain the equivalent momentum source term in the three-dimensional coordinate system. Taking a specific interface evolution condition as an example, when the set contains 50 converted particles and the average impact velocity is 0.8 m / s, this embodiment smoothly converts the microscopic discrete particle impact momentum into a macroscopic momentum thrust source on the continuous oil film boundary through the above-mentioned physical conservation conversion logic, thereby providing a driving force that fully conforms to the basic laws of fluid mechanics for the secondary spreading of the continuous interface.

[0039] Finally, this embodiment completes the physical fusion loop of cross-scale information through the control equation update submodule. In this embodiment, the equivalent mass source term and the equivalent momentum source term obtained above are directly added to the control equations of the volumetric fluid volume (VOF) model.

[0040] In this embodiment, the equivalent mass source term is imported as the mass generation term on the right-hand side of the continuity equation, and the equivalent momentum source term is imported as the volume force source term on the right-hand side of the Navier-Stokes momentum conservation equation. This addition mechanism forces the macroscopic fluid to fully absorb the mass and momentum transferred from the microscopic domain in the subsequent implicit iterative solution stage. In a complete interphase coupled flow field iteration, this embodiment performs 20 to 30 internal convergence calculations to ensure that the pressure, velocity, and volume fraction distribution of the entire flow field after adding the source terms achieve absolute numerical conservation. Through the above closed-loop calculation operation of this embodiment, the original discrete particle group is integrated into the continuous oil film in the form of fluid volume sources, realizing the dynamic cross-scale coupled analysis of microscopic oil droplet collision with the wall to grow into a macroscopic oil film.

[0041] Specifically, the calculation formulas for the equivalent mass source term and the equivalent momentum source term are as follows: ; in, For the equivalent quality source term, For the equivalent momentum source term, To control the volume, For time step, It is the collection of discrete oil droplet particles that trigger transformation within the control volume within a time step. Let be the mass of any discrete oil droplet particle within the set. Let be the velocity of any discrete oil droplet particle within the set.

[0042] Furthermore, the cross-scale simulation prediction system for oil droplet adhesion and film formation on the heat exchange surface provided in this embodiment includes a VOF oil film interface evolution analysis module 6 responsible for the final macroscopic fluid interface morphology deduction. This embodiment first calculates the required mixture properties parameters for solving the governing equations through a property parameter homogenization submodule. Mixture properties parameters refer to the fluid physical property parameters obtained by spatially weighted averaging based on the proportion of each phase in order to solve a single momentum equation in gas-liquid or liquid-liquid multiphase fluid dynamics calculations, since two or even multiple phases of fluid exist simultaneously within the same discrete grid cell. In this embodiment, the mixture properties parameters to be solved include volume average density and volume average viscosity. This embodiment forces the homogenization calculation data processing at the initial stage of each time step for micro-grid cells with a side length of 0.02 mm to ensure that the flow field after the cross-scale phase transition has accurate and physically absolutely conserved basic thermodynamic and kinetic property input boundaries.

[0043] Regarding the specific data processing and conversion of the aforementioned volume-average density and volume-average viscosity, this embodiment employs a rigorous linear weighted average algorithm based on phase volume fraction. Specifically, this embodiment extracts the volume fraction of the continuous oil phase within the current grid cell as the core weighting factor, with its value strictly defined between 0 and 1. This parameter originates from the transient scalar field of the Eulerian grid after the preceding spatial discretization. When calculating the volume-average density, this embodiment multiplies the density of the continuous oil phase by its volume fraction, and then sums this product with the density of the continuous water phase multiplied by its water phase volume fraction (i.e., the value 1 minus the continuous oil phase volume fraction) to obtain the final volume-average density. When calculating the volume-average viscosity, the exact same mathematical mapping process is used: multiplying the dynamic viscosity of the continuous oil phase by its volume fraction, and then adding the dynamic viscosity of the continuous water phase multiplied by its water phase volume fraction, yields the final volume-average viscosity. As a quantitative anchor point, this embodiment extracts an initial constant density of 850 kg / m³ and a dynamic viscosity of 0.04 Pascals-second for the continuous oil phase; and an initial density of 998 kg / m³ and a dynamic viscosity of 0.001 Pascals-second for the continuous aqueous phase. When the volume fraction of the continuous oil phase within a certain wall grid cell is detected to be 0.6, this embodiment accurately calculates the volume average density within that grid as 909.2 kg / m³ and the volume average viscosity as 0.0244 Pascals-second using the aforementioned weighted logic.

[0044] After obtaining accurate mixing parameters, this embodiment performs dynamic evolution deduction of the macroscopic continuous interface through the phase interface tracking solution submodule. Based on the aforementioned calculated mixing parameters, this embodiment solves the volume fraction transport equation in the multiphase flow control equation in the underlying computational domain model to analyze the coalescence and spreading process of the continuous oil phase. The coalescence and spreading process refers to the hydrodynamic interface evolution phenomenon in which, after tiny oil droplets meet the thickness or volume fraction threshold conditions and transform into a continuous phase, the oil spontaneously expands laterally and merges longitudinally on the target heat exchange surface under the combined control of fluid shear force and surface tension, resulting in lateral area expansion and longitudinal thickness fusion. In solving this transport equation, this embodiment extracts flux data on the mesh interface and calculates the mass transfer scalar of the continuous oil phase between adjacent control volumes using a high-resolution geometric interface reconstruction algorithm.

[0045] To ensure absolute conservation of momentum when microscopic droplets merge into the macroscopic liquid film, this embodiment simultaneously activates the flow field momentum coupling solution submodule. This embodiment combines the equivalent mass source term and equivalent momentum source term generated in the previous cross-scale transformation process and directly adds them to the source term matrix of the momentum control equations, which includes pressure and velocity fields, to perform coupled calculations of flow field momentum transfer. In the data processing logic of this coupled calculation, this embodiment reads the momentum compensation value generated after the transformation and stripping of microscopic particles and applies it as an additional continuous volume force term to the corresponding boundary grid nodes. At the boundary of the first layer of the wall grid, this embodiment applies an equivalent momentum source term stimulus with an average magnitude of 0.005 Newtons per cubic meter to the control equations. This direct injection of the momentum source term allows the underlying Eulerian continuous flow field to sense the real physical impact force from the discrete phase colliding with the wall, thereby driving the generation of a physically realistic internal velocity gradient and secondary flow within the existing continuous oil film on the wall.

[0046] Finally, this embodiment completes the final data aggregation and encapsulation of the entire multi-scale multiphase fluid dynamics calculation through the evolution result output submodule. This embodiment comprehensively summarizes the coalescence and spreading process data obtained from the above-mentioned volume fraction transport equation analysis, as well as the momentum coupling calculation results including source term forced compensation, and finally obtains and outputs the continuous oil film morphology and oil phase volume fraction evolution results of the target heat exchange surface. The evolution results not only include a three-dimensional coordinate point cloud set describing the geometric topological characteristics of the continuous phase interface in three-dimensional space, but also cover a quantitative scalar field matrix characterizing the degree of oil phase enrichment in different regions of the heat exchange plate surface. This embodiment performs physical information writing operations at a frequency of 500 calculation time steps, completely transcribing the three-dimensional velocity vector, transient pressure distribution, and local oil film thickness distribution data accurate to 0.01 mm on the entire flow field grid into the terminal result storage array. Through the deep integration of the above modules and data processing flow in the temporal and spatial dimensions, this embodiment thoroughly realizes the quantitative and high-fidelity simulation prediction of the entire cycle of adhesion and film formation of small oil droplet groups on the heat exchange surface under complex shear flow environment.

[0047] Specifically, the mixture properties parameters include volume average density and volume average viscosity, wherein the calculation formulas for the volume average density and the volume average viscosity are as follows: ; in, The volume average density is... The volume average viscosity is [value missing]. The density of the continuous oil phase is given. The density of the continuous aqueous phase, This refers to the volume fraction of the continuous oil phase. The dynamic viscosity of the continuous oil phase is given by [the value]. The dynamic viscosity is the viscosity of the continuous aqueous phase.

[0048] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0049] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A cross-scale simulation and prediction system for oil droplet cluster adhesion and film formation on a heat exchange surface, characterized in that, include: The flow field initialization and mesh building module is used to construct the computational domain model of the target heat exchange surface and generate a mesh, and to establish a volumetric fluid volume (VOF) model based on the computational domain model to initialize the physical property parameters of the continuous water phase and discrete oil droplet particles and the flow field boundary conditions to obtain the initial flow field data. The DPM discrete phase trajectory tracking module is used to establish a discrete phase DPM model based on the initial flow field data, and to track the motion trajectory of the discrete oil droplet particles within the computational domain model through the discrete phase DPM model, so as to obtain the collision state data of the discrete oil droplet particles and the target heat exchange surface. The multi-state wall impact condition determination module is used to determine the wall state at the impact point based on the collision state data. When there is no continuous oil phase under the volumetric fluid volume (VOF) model at the impact point, local feature thickness data is obtained; when the continuous oil phase already exists in the grid cell at the impact point, the volume fraction data of the continuous oil phase in the grid cell is obtained. The DPM-to-VOF state transition trigger module is used to terminate the trajectory tracking of the corresponding discrete oil droplet particles in the discrete phase DPM model based on the local feature thickness data and the volume fraction data, and to obtain phase transition command data. The DPM-to-VOF source term dynamic coupling module is used to remove the mass and velocity of the discrete oil droplet particles that trigger the transformation from the discrete phase DPM model based on the phase transition command data, and convert the removed mass and velocity into equivalent mass source terms and equivalent momentum source terms, so as to add them to the control equations of the volumetric fluid VOF model. The VOF oil film interface evolution analysis module is used to solve the governing equations to obtain the continuous oil film morphology and oil phase volume fraction evolution results of the target heat exchange surface.

2. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 1, characterized in that, The flow field initialization and mesh construction module includes: The geometric computational domain construction submodule is used to construct the computational domain model of the target heat transfer surface; The spatial grid discrete submodule is used to receive the computational domain model and divide the computational domain model into a grid to obtain a gridded computational domain model. The physical property model establishment submodule is used to establish a volumetric fluid volume (VOF) model based on the meshed computational domain model and initialize the physical property parameters of the continuous water phase and discrete oil droplet particles. The flow field boundary configuration submodule is used to initialize the flow field boundary conditions and, in conjunction with the physical property parameters, output the initial flow field data.

3. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 1, characterized in that, The DPM discrete phase trajectory tracking module includes: The discrete phase model establishment submodule is used to establish a discrete phase DPM model based on the initial flow field data. The particle injection submodule is used to inject discrete oil droplet particles in a discrete state into the computational domain model through the discrete phase DPM model. The force and trajectory solving submodule is used to calculate the interaction forces on the injected discrete oil droplet particles in order to obtain the motion trajectory of the discrete oil droplet particles. The collision state extraction submodule is used to extract the collision features between the discrete oil droplet particles and the target heat exchange surface based on the motion trajectory, so as to obtain the collision state data.

4. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 1, characterized in that, The multi-state collision condition determination module includes: The collision location submodule is used to receive the collision state data in order to locate the grid cell; The continuous phase existence detection submodule is used to detect whether there is a continuous oil phase under the volumetric fluid volume (VOF) model within the located grid cell, in order to confirm the wall state. The wall film thickness evaluation submodule is used to calculate local feature thickness data based on the adhesion and accumulation behavior of discrete oil droplet particles on the wall surface when the wall surface is in a state without the continuous oil phase. The phase volume fraction extraction submodule is used to extract the volume fraction data of the continuous oil phase in the grid cell when the wall state is such that the continuous oil phase already exists in the grid cell.

5. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 1, characterized in that, The DPM-to-VOF state transition triggering module includes: The phase transition threshold comparison submodule is used to compare the received local feature thickness data with a preset thickness threshold, and to compare the received volume fraction data with a preset volume fraction threshold to obtain the comparison result. The trajectory solving truncation submodule is used to terminate the trajectory tracking of the corresponding discrete oil droplet particles in the discrete phase DPM model when the preset thickness threshold or the preset volume fraction threshold is reached, based on the comparison results, so as to lock the discrete oil droplet particles. The first phase transition instruction generation submodule is used to obtain first phase transition instruction data for locked discrete oil droplet particles when the comparison result reaches the preset thickness threshold. The second phase transition instruction generation submodule is used to obtain second phase transition instruction data for locked discrete oil droplet particles when the comparison result exceeds the preset volume fraction threshold, wherein the first phase transition instruction data and the second phase transition instruction data together constitute phase transition instruction data.

6. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 1, characterized in that, The DPM-to-VOF source term dynamic coupling module includes: The phase transition instruction parsing submodule is used to lock the discrete oil droplet particles that trigger the transformation based on instruction data; The discrete parameter stripping submodule is used to extract the mass and velocity of the discrete oil droplet particles that trigger the transformation, and remove the extracted mass and velocity from the discrete phase DPM model; The equivalent source term conversion submodule is used to convert the extracted mass and velocity into equivalent mass source terms and equivalent momentum source terms based on the extracted mass and velocity. The control equation update submodule is used to add the equivalent mass source term and the equivalent momentum source term to the control equations of the volumetric fluid volume (VOF) model.

7. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 1, characterized in that, The VOF oil film interface evolution analysis module includes: The physical property parameter homogenization submodule is used to calculate the mixture parameters required to solve the governing equations; The phase interface tracking solution submodule is used to solve the volume fraction transport equation in the governing equation based on the mixture properties parameters, so as to analyze the coalescence and spreading process of the continuous oil phase; The flow field momentum coupling solution submodule is used to combine the equivalent mass source term and the equivalent momentum source term added to the governing equation to perform coupled calculation of flow field momentum transfer; The evolution result output submodule is used to summarize the results of the coalescence and spreading process and the coupling calculation to obtain the continuous oil film morphology and oil phase volume fraction evolution results of the target heat exchange surface.

8. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 6, characterized in that, The calculation formulas for the equivalent mass source term and the equivalent momentum source term are as follows: ； in, For the equivalent quality source term, For the equivalent momentum source term, To control the volume, For time step, It is the collection of discrete oil droplet particles that trigger transformation within the control volume within a time step. Let be the mass of any discrete oil droplet particle within the set. Let be the velocity of any discrete oil droplet particle within the set.

9. The cross-scale simulation and prediction system for oil droplet group adhesion and film formation on a heat exchange surface according to claim 7, characterized in that, The mixture properties parameters include volume average density and volume average viscosity, wherein the calculation formulas for the volume average density and the volume average viscosity are as follows: ； in, The volume average density is... The volume average viscosity is... The density of the continuous oil phase is given. The density of the continuous aqueous phase, This refers to the volume fraction of the continuous oil phase. The dynamic viscosity of the continuous oil phase is given by [the value of the viscosity]. is the dynamic viscosity of the continuous aqueous phase.

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