A hematoma drug dissolution simulation method and system based on a multiphase flow model

Abstract

This invention belongs to the field of medical simulation and computational fluid dynamics technology, and discloses a simulation method and system for hematoma drug dissolution based on a multiphase flow model. The method includes: constructing a three-phase fluid system comprising a solid hematoma phase, a liquid hematoma phase, and a drug phase; distributing and calculating the mass conservation equations to construct the mass conservation equations; constructing the momentum conservation equations for the mixture and combining them with the relative velocity equations to construct a phase transition dynamics model; constructing a constitutive model; constructing an interphase force model; simulating and predicting the dynamic evolution of the hematoma morphology using a phased control strategy; dividing the dynamic evolution law using the mass conservation equations, the momentum conservation equations for the mixture, the phase transition dynamics model, the constitutive model, the interphase force model, and the hematoma dissolution time phase division results; and calculating the complete control equations using a pressure implicit operator splitting algorithm to obtain the spatiotemporal distribution of volume fraction, velocity field, and pressure field. This invention overcomes the limitations of single-phase flow or simplified two-phase flow models.

Description

Technical Field

[0001] This invention relates to the field of medical simulation and computational fluid dynamics, and in particular to a simulation method and system for drug dissolution of hematoma based on a multiphase flow model. Background Technology

[0002] Intracranial hematoma is a serious neurological disorder, and its treatment primarily includes craniotomy and minimally invasive hematoma evacuation. In recent years, drug-induced hematoma lysis has gained widespread attention as a novel minimally invasive treatment. This technique involves injecting a dissolving agent into the hematoma cavity, gradually liquefying the solid hematoma, and then removing the liquid hematoma through aspiration. The drug-induced hematoma lysis process is a complex multi-physics coupling process involving the interaction of three different states of matter: solid hematoma, liquid hematoma, and the drug. This process includes multiple physical processes such as drug permeation and diffusion in porous media, phase transitions caused by the chemical reaction between the drug and the solid hematoma, and the flow and aspiration of the liquid hematoma.

[0003] However, current research on drug dissolution processes in hematomas mainly relies on clinical experience and simplified mathematical models, lacking a systematic description of multiphase flow, mass transfer, and phase transition processes. Existing simulation methods typically employ single-phase flow or simple two-phase flow models, which cannot accurately capture the dynamic processes of solid-liquid phase transitions and are also insufficient to describe the coupling mechanisms of drug permeation, chemical reactions, and selective aspiration.

[0004] Therefore, how to provide a simulation method and system for drug dissolution of hematoma based on a multiphase flow model is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a simulation method and system for drug dissolution of hematoma based on a multiphase flow model, in order to solve the problems mentioned above in the prior art.

[0006] According to a first aspect of the present invention, a simulation method for drug dissolution of hematoma based on a multiphase flow model is provided.

[0007] In one embodiment, the simulation method for drug dissolution of hematoma based on a multiphase flow model includes: using the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase as evolution axes, and embedding the evolution axes into the physical mechanism of hematoma dissolution to construct a three-phase fluid system; using a set of mass conservation equations to perform distribution calculations on the three-phase fluid system to construct the mass conservation equation of the three-phase fluid system; based on the three-phase fluid system and the mass conservation equation, constructing a momentum conservation equation for the mixture; using the momentum conservation equation and the relative velocity equation for the mixture to describe the infiltration process of the drug phase in the porous solid hematoma phase medium; based on the three-phase fluid system and the mass conservation equation, constructing a phase transition kinetic model to describe the phase transition process of solid hematoma transforming into liquid hematoma; based on the three-phase fluid system, A constitutive model is constructed using the mass conservation equation and phase transition kinetics model. An interphase force model is constructed based on the three-phase fluid system and momentum conservation equation. A pre-constructed dynamic evolution model of hematoma morphology is used to simulate and predict the dynamic evolution of the hematoma dissolution process. A phased control strategy is employed to divide the dynamic evolution process, resulting in the hematoma dissolution time phase division. Based on the mass conservation equation, the mixture momentum conservation equation, the phase transition kinetics model, the constitutive model, the interphase force model, and the hematoma dissolution time phase division, a complete set of control equations is generated. A pressure implicit operator splitting algorithm is used to numerically calculate the complete set of control equations, obtaining the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, thus achieving simulation of drug dissolution in hematoma.

[0008] In one embodiment, the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase are used as the evolution axis, and the evolution axis is embedded into the physical mechanism of hematoma dissolution to construct a three-phase fluid system. This includes: dividing the dissolution time of the solid hematoma phase, liquid hematoma phase, and drug phase into a drug injection stage, a phase transition dissolution stage, and a liquid aspiration stage, obtaining the hematoma dissolution time stage division results; based on the hematoma dissolution time stage division results, using the osmosis mechanism and Darcy's law, clarifying the drug's permeation into the solid hematoma through the porous medium, obtaining the drug permeation and initial phase transition law; based on the drug permeation and initial phase transition law, using... The chemical reaction mechanism and phase transition process were analyzed, and Arrhenius dynamics were used to clarify the drug consumption characteristics. The contact efficiency function was used to control the pre-set transformation of solid hematoma into liquid hematoma, thus obtaining the solid-liquid phase transition and drug consumption law. Using the mass conservation mechanism and following the stoichiometric relationship, the mass balance relationship between the solid-liquid phase transition and drug consumption law was obtained, thus obtaining the mass conservation constraints of each phase. Based on the time stage division results and the mass conservation constraints of each phase, a three-phase dynamic evolution axis was generated. The evolution axis was then embedded into the osmosis mechanism, chemical reaction mechanism, phase transition process and mass conservation mechanism and integrated to obtain a three-phase fluid system.

[0009] In one embodiment, the mass conservation equations are used to perform distribution calculations on a three-phase fluid system to construct the mass conservation equations for the three-phase fluid system. This includes: setting the basic parameters of the solid hematoma phase and constructing the mass conservation relationship for the solid hematoma phase; calculating the time-varying and spatial migration of the mass conservation relationship to obtain the solid hematoma phase mass conservation equation; defining solid-liquid phase transition source terms based on the solid hematoma phase mass conservation equation, and associating them with the basic reaction rate constant, drug volume fraction, and contact efficiency function to obtain the phase transition source term calculation rules; setting a preset range for the critical drug volume fraction based on the phase transition source term calculation rules, constructing a contact efficiency function, and inputting the contact efficiency function into the phase transition source term calculation rules to obtain the complete solid hematoma phase mass conservation equation; and setting... The basic parameters of the liquid hematoma phase and the drug phase are determined. Based on the liquid hematoma phase, the mass conservation relationship of the liquid hematoma phase is constructed by combining the aspiration source term and using the calculation rules of the phase change source term. The mass conservation relationship of the liquid hematoma phase is calculated by defining the aspiration source term, resulting in a complete mass conservation equation for the liquid hematoma phase. Based on the drug phase, the injection source term, and the drug consumption term, a preset range of stoichiometric coefficients is set, and the mass conservation relationship of the drug phase is constructed by combining the calculation rules of the phase change source term. The mass conservation relationship of the drug phase is calculated by defining the logic of the drug consumption term, resulting in a complete mass conservation equation for the drug phase. The mass conservation equations of the solid hematoma phase, the liquid hematoma phase, and the drug phase are integrated to obtain the mass conservation equation for the three-phase fluid system.

[0010] In one embodiment, a momentum conservation equation for a mixture is constructed based on a three-phase fluid system and the mass conservation equation. The momentum conservation equation and relative velocity equation are used to describe the infiltration process of the drug phase in a porous solid hematoma phase medium. This includes: constructing a momentum conservation equation for a mixture based on a three-phase fluid system and the mass conservation equation, comprising local acceleration, convection, pressure gradient, effective stress, gravity, interphase force, and momentum source terms; and defining the effective stress tensor as the dynamic viscosity of the mixture, the identity matrix, and the matrix transpose based on the momentum conservation equation. The calculation yields the complete momentum conservation equation for the mixture. For the drug phase permeation process in porous hematoma media, a relative velocity equation is constructed, establishing the relationship between the permeability tensor, drug dynamic viscosity, drug phase pressure, and drift velocity. Based on the relative velocity equation, the permeability tensor is determined using the Kalman model. Combining the basic permeability, the cube of porosity, and the square of the solid hematoma volume fraction, the increasing law of permeability is obtained. This increasing law of permeability is integrated into the relative velocity equation, which can describe the permeation process of the drug phase in porous solid hematoma media.

[0011] In one embodiment, a phase transition kinetic model is constructed based on a three-phase fluid system and the mass conservation equation to describe the phase transition process from solid to liquid hematoma. This includes: obtaining the solid-liquid phase transition process from solid to liquid hematoma using the Arrhenius reaction kinetic model based on the three-phase fluid system and the mass conservation equation, thus obtaining an initial phase transition kinetic model; simplifying the initial phase transition kinetic model using isothermal conditions and combining it with the effective reaction rate constant to obtain a phase transition reaction rate expression; obtaining a characterization method for the phase transition reaction rate based on the phase transition reaction rate expression and combining it with saturation concentration, specific surface area, and product inhibition effect; defining the phase transition conversion rate based on the phase transition reaction rate characterization method and combining it with the time cumulative relationship between the initial solid hematoma volume fraction and the phase transition reaction rate to obtain a quantitative characterization of the phase transition process; and constructing a phase transition kinetic model based on the phase transition reaction rate expression, the characterization method of the phase transition reaction rate, and the quantitative characterization of the phase transition process.

[0012] In one embodiment, constructing a constitutive model based on a three-phase fluid system, the mass conservation equation, and a phase transition kinetics model includes: constructing a mixture density model based on incompressible fluids; integrating the volume fractions and densities of the solid hematoma phase, liquid hematoma phase, and drug phase using a volume-weighted average method to obtain a mixture density model; based on the mixture density model, and combining the continuous phase viscosity, the maximum packing fraction of the solid hematoma, and the Einstein coefficient to describe the influence of solid particles on the mixture viscosity to obtain a mixture viscosity model; based on the mixture viscosity model, combining the effects of molecular diffusion coefficient, comprehensive pore structure, tortuosity, and solid hematoma volume fraction, and using the Stokes-Einstein equation to calculate and integrate the molecular diffusion coefficient to obtain an effective diffusion coefficient model; based on the effective diffusion coefficient model, using the modified Kalman-Kozani equation to calculate the characteristic particle size, specific surface area, shape factor, and solid hematoma volume fraction to obtain a permeability model; and constructing a constitutive model based on the phase transition kinetics model, the mixture density model, the mixture viscosity model, the effective diffusion coefficient model, and the permeability model.

[0013] In one embodiment, constructing an interphase force model based on a three-phase fluid system and the momentum conservation equation includes: dragging force representing the momentum transfer between different phases due to velocity differences; calculating the dilute phase flow using a residual temperature model and the dense phase flow using an Ergenz model; and obtaining a dragging force model based on the calculation results and a smooth transition function; obtaining a virtual mass force model based on the additional mass effect generated during phase acceleration and combining the virtual mass coefficient, drug phase volume fraction, and phase acceleration difference; and constructing an interphase force model based on the dragging force model and the virtual mass force model.

[0014] In one embodiment, a pre-constructed dynamic evolution model of hematoma morphology is used to simulate and predict the three-phase fluid system, obtaining the dynamic evolution law of the hematoma dissolution process. A phased control strategy is then used to divide the dynamic evolution law into stages, resulting in the hematoma dissolution time stage division. This includes: using a level set function for interface reconstruction and regularization; at several time steps, a re-initialization method based on the Hamiltonian-Jacobi equation is used to restore the sign distance characteristic of the level set function, obtaining the numerical accuracy of interface tracking; using a morphological diffusion bidirectional coupling mechanism, the permeability field at the interface position is dynamically adjusted, and the effective diffusion coefficient is adjusted by combining the local curvature of the interface, constructing a consistent mapping relationship between the solid hematoma volume fraction and the level set function, thus obtaining the hematoma morphological dynamic evolution model; using the hematoma morphological dynamic evolution model to simulate and predict the three-phase fluid system, obtaining the dynamic evolution law of the hematoma dissolution process; and using a phased control strategy to divide the dynamic evolution law into stages, thus obtaining the hematoma dissolution time stage division.

[0015] In one embodiment, a complete set of governing equations is generated based on the mass conservation equation, the mixture momentum conservation equation, the phase transition kinetic model, the constitutive model, the interphase force model, and the hematoma dissolution time stage division results. The complete set of governing equations is then numerically calculated using a pressure implicit operator splitting algorithm to obtain the spatiotemporal distributions of the volume fraction, velocity field, and pressure field of each phase, thereby simulating hematoma drug dissolution. This includes: generating a complete set of governing equations to be numerically calculated based on the mass conservation equation, the mixture momentum conservation equation, the phase transition kinetic model, the constitutive model, the interphase force model, the hematoma morphology dynamic evolution model, and a staged control strategy; based on the complete set of governing equations, the volume fraction of each phase is explicitly updated using the pressure implicit operator splitting algorithm, and the updated phase volume fractions are normalized to obtain the normalized phase fraction field; the phase fraction field is solved using the momentum prediction equation to obtain the predicted velocity field, and the predicted velocity field is assembled and the pressure equation is solved to obtain the pressure field. The predicted velocity field is corrected using the pressure field to obtain the corrected velocity field. An outer-layer iteration is performed on the repeated momentum prediction, pressure equation solution, and corrected velocity field. An inner-layer correction loop is performed after the iteration results meet the convergence criterion to obtain the velocity-pressure coupled calculation results. Based on the velocity-pressure coupled calculation results, the convection and diffusion terms of the complete control equations are processed using corresponding discretization schemes, and the gradient of the control equations is calculated using the least squares method to obtain the spatially discretized control equations. Based on the spatially discretized control equations, a multidimensional general explicit unconstraint algorithm is used to correct the flux of the phase volume fraction, and iterative solutions are used to determine the flux constraint factor, obtaining the basis for phase volume fraction calculation. Based on the phase volume fraction calculation basis, a numerical calculation system is constructed according to the range of values ​​for the control Courant number, diffusion number, and reaction Damk-Köhler number. Based on the numerical calculation system, the complete control equations are numerically calculated to obtain the spatiotemporal distributions of the volume fraction, velocity field, and pressure field of each phase.

[0016] According to a second aspect of the present invention, a simulation system for drug dissolution of hematoma based on a multiphase flow model is provided.

[0017] In one embodiment, the hematoma drug dissolution simulation system based on a multiphase flow model includes:

[0018] The three-phase fluid construction module is used to utilize the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase as the evolution axis, and embed the evolution axis into the physical mechanism of hematoma dissolution to construct a three-phase fluid system.

[0019] The mass conservation modeling module is used to perform distribution calculations on a three-phase fluid system using the mass conservation equations to construct the mass conservation equations for the three-phase fluid system.

[0020] The momentum conservation building module is used to construct the momentum conservation equation of a mixture based on the three-phase fluid system and the mass conservation equation; and to describe the permeation process of the drug phase in a porous solid hematoma phase medium using the momentum conservation equation of the mixture and the relative velocity equation.

[0021] The phase transition dynamics modeling module is used to construct a phase transition dynamics model based on a three-phase fluid system and the mass conservation equation to describe the phase transition process of solid hematoma transforming into liquid hematoma;

[0022] The constitutive modeling module is used to construct constitutive models based on three-phase fluid systems, mass conservation equations, and phase transition dynamics models.

[0023] The interphase interaction force modeling module is used to construct interphase interaction force models based on three-phase fluid systems and momentum conservation equations.

[0024] The hematoma evolution segmentation module is used to simulate and predict the three-phase fluid system using a pre-built dynamic evolution model of hematoma morphology, obtain the dynamic evolution law of the hematoma dissolution process, and use a staged control strategy to divide the dynamic evolution law into stages, thus obtaining the hematoma dissolution time stage segmentation results.

[0025] The numerical calculation and solution module is used to generate a complete set of governing equations based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition kinetic model, the constitutive model, the interphase interaction force model, and the results of the hematoma dissolution time stage division. The module then uses the pressure implicit operator splitting algorithm to perform numerical calculations on the complete set of governing equations, and obtains the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, so as to realize the simulation of drug dissolution in hematoma.

[0026] According to a third aspect of the present invention, a computer device is provided.

[0027] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0028] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0029] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0030] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0031] 1. This invention constructs a three-phase flow model of solid hematoma, liquid hematoma and drug, which accurately describes the multiphase flow, mass transfer and phase change coupling mechanism in the hematoma dissolution process, overcoming the limitations of existing single-phase flow or simplified two-phase flow models.

[0032] 2. This invention achieves quantitative characterization of solid-liquid phase transition processes and improves simulation accuracy by establishing a phase transition model based on Arrhenius kinetics and designing source terms that consider contact efficiency. Combined with constitutive models such as the Krieger-Dougherty viscosity model and the Kozeny-Carman permeability model, it accurately describes the rheological properties of high-solids-content suspension systems and the permeability characteristics of porous media.

[0033] 3. This invention employs a phased control strategy to accurately simulate the timing process of drug injection, phase transition dissolution, and liquid aspiration, closely aligning with clinical procedures. Simultaneously, it utilizes the PIMPLE and MULES algorithms to ensure the stability and boundedness of the numerical solution, making it suitable for handling discontinuities and localized high gradients caused by phase transitions. This provides a theoretical basis for optimizing clinical treatment parameters, contributing to improved hematoma clearance rates and patient prognosis.

[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0036] Figure 1 This is a flowchart illustrating an exemplary embodiment;

[0037] Figure 2 This is a system principle block diagram illustrated according to an exemplary embodiment;

[0038] Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0039] Figure 4 This is a schematic diagram of the structure of a three-phase fluid system according to an exemplary embodiment;

[0040] Figure 5 This is a schematic diagram illustrating a phased control strategy according to an exemplary embodiment;

[0041] Figure 6 This is a flowchart illustrating the PIMPLE algorithm according to an exemplary embodiment. Detailed Implementation

[0042] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0043] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0044] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0045] Figure 1 An embodiment of a hematoma drug dissolution simulation method based on a multiphase flow model of the present invention is shown.

[0046] In this optional embodiment, the hematoma drug dissolution simulation method based on a multiphase flow model includes:

[0047] Step S101: Using the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase as the evolution axis, and embedding the evolution axis into the physical mechanism of hematoma dissolution, a three-phase fluid system is constructed.

[0048] Step S102: Use the mass conservation equations to perform distribution calculations on the three-phase fluid system in order to construct the mass conservation equations for the three-phase fluid system.

[0049] Step S103: Based on the three-phase fluid system and the mass conservation equation, construct the momentum conservation equation of the mixture; use the momentum conservation equation of the mixture and the relative velocity equation to describe the permeation process of the drug phase in the porous solid hematoma phase medium.

[0050] Step S104: Based on the three-phase fluid system and the mass conservation equation, a phase transition kinetic model is constructed to describe the phase transition process of solid hematoma transforming into liquid hematoma;

[0051] Step S105: Construct a constitutive model based on the three-phase fluid system, the mass conservation equation, and the phase transition dynamics model;

[0052] Step S106: Based on the three-phase fluid system and the momentum conservation equation, construct an interphase force model;

[0053] Step S107: Using the pre-constructed dynamic evolution model of hematoma morphology, the three-phase fluid system is simulated and predicted to obtain the dynamic evolution law of the hematoma dissolution process. The dynamic evolution law is divided into stages using a phased control strategy to obtain the results of the hematoma dissolution time stage division.

[0054] Step S108: Based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition kinetic model, the constitutive model, the interphase interaction force model, and the results of the hematoma dissolution time stage division, a complete set of control equations is generated. The pressure implicit operator splitting algorithm is used to perform numerical calculations on the complete set of control equations to obtain the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, so as to realize the simulation of drug dissolution of hematoma.

[0055] In this optional embodiment, the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase are used as the evolution axis, and the evolution axis is embedded into the physical mechanism of hematoma dissolution to construct a three-phase fluid system. This includes: dividing the dissolution time of the solid hematoma phase, liquid hematoma phase, and drug phase into a drug injection stage, a phase transition dissolution stage, and a liquid aspiration stage, obtaining the hematoma dissolution time stage division results; based on the hematoma dissolution time stage division results, using the osmosis mechanism and Darcy's law, clarifying the drug's permeation into the solid hematoma through the porous medium, obtaining the drug permeation and initial phase transition law; based on the drug permeation and initial phase transition law, using... The chemical reaction mechanism and phase transition process were analyzed, and Arrhenius dynamics were used to clarify the drug consumption characteristics. The contact efficiency function was used to control the pre-set transformation of solid hematoma into liquid hematoma, thus obtaining the solid-liquid phase transition and drug consumption law. Using the mass conservation mechanism and following the stoichiometric relationship, the mass balance relationship between the solid-liquid phase transition and drug consumption law was obtained, thus obtaining the mass conservation constraints of each phase. Based on the time stage division results and the mass conservation constraints of each phase, a three-phase dynamic evolution axis was generated. The evolution axis was then embedded into the osmosis mechanism, chemical reaction mechanism, phase transition process and mass conservation mechanism and integrated to obtain a three-phase fluid system.

[0056] Specifically, such as Figure 4 As shown in Table 1, the physical process of drug dissolution of hematoma is divided into three stages:

[0057] Table 1. Drug dissolution process of hematoma

[0058] Time period Phase Name Main physical processes 0-3s Drug injection Drug penetration, initial phase transition 3-7s Phase change dissolution Solid-liquid phase transition, drug consumption 7-10s Liquid suction Removal of liquid hematoma

[0059] The physical mechanisms of hematoma dissolution include: Osmosis: The drug permeates into the solid hematoma through a porous medium, following Darcy's law. Chemical reaction: A dissolution reaction occurs upon contact between the drug and the solid hematoma, with the reaction rate following Arrhenius kinetics. Phase transition: 20-30% of the solid hematoma transforms into a liquid state; the phase transition is controlled by a contact efficiency function. Mass conservation: The drug is consumed in the reaction, following stoichiometry.

[0060] In this optional embodiment, the mass conservation equations are used to perform distribution calculations on the three-phase fluid system to construct the mass conservation equations for the three-phase fluid system. This includes: setting the basic parameters of the solid hematoma phase and constructing the mass conservation relationship of the solid hematoma phase; calculating the time variation and spatial migration of the mass conservation relationship to obtain the solid hematoma phase mass conservation equation; defining the solid-liquid phase transition source term based on the solid hematoma phase mass conservation equation, and associating it with the basic reaction rate constant, drug volume fraction, and contact efficiency function to obtain the phase transition source term calculation rules; setting a preset range for the critical drug volume fraction based on the phase transition source term calculation rules, constructing the contact efficiency function, and inputting the contact efficiency function into the phase transition source term calculation rules to obtain the complete solid hematoma phase mass conservation equation; and setting... The basic parameters of the liquid hematoma phase and the drug phase are determined. Based on the liquid hematoma phase, the mass conservation relationship of the liquid hematoma phase is constructed by combining the aspiration source term and using the calculation rules of the phase change source term. The mass conservation relationship of the liquid hematoma phase is calculated by defining the aspiration source term, resulting in a complete mass conservation equation for the liquid hematoma phase. Based on the drug phase, the injection source term, and the drug consumption term, a preset range of stoichiometric coefficients is set, and the mass conservation relationship of the drug phase is constructed by combining the calculation rules of the phase change source term. The mass conservation relationship of the drug phase is calculated by defining the logic of the drug consumption term, resulting in a complete mass conservation equation for the drug phase. The mass conservation equations of the solid hematoma phase, the liquid hematoma phase, and the drug phase are integrated to obtain the mass conservation equation for the three-phase fluid system.

[0061] Specifically, the mass conservation equations for the solid hematoma phase are as follows: ;in, This indicates the volume fraction of solid hematoma. The density of the solid hematoma is represented by t, which represents time. This represents gradient operation. Indicates the velocity of solid hematoma. This represents the solid-liquid phase transition source term. Specifically, the phase transition source term is: ;in, Let represent the basic reaction rate constant, αd represent the drug volume fraction, and f_contact represent the contact efficiency function. The contact efficiency function is specifically: f_contact = 1 - exp(-αd / αd,crit); where αd andcrit represent the critical drug volume fraction, ranging from 0.01 to 0.05. This function ensures that the contact probability is low at very low drug concentrations, and the contact efficiency increases with increasing drug concentration, exhibiting a saturation effect. The mass conservation equation for the liquid hematoma phase is specifically: ;in, This indicates the volume fraction of the liquid hematoma. Indicates the density of liquid hematoma. This indicates the velocity of the liquid hematoma, and S_suction represents the aspiration source term. The aspiration source term specifically includes:

[0062] Where A_pipe represents the cross-sectional area of ​​the suction tube, U_suction represents the suction velocity, and V_cell represents the volume of the computational cell. The mass conservation equation for the drug phase is as follows: Where ρd represents drug density, Ud represents drug velocity, D_eff represents effective diffusion coefficient, Cd represents drug concentration, S_injection represents injection source term, and Rd represents drug consumption term. The drug consumption term is specifically as follows:

[0063] ;in, This represents the stoichiometric coefficient, with a value range of 0.1-0.5.

[0064] In this optional embodiment, a momentum conservation equation for the mixture is constructed based on the three-phase fluid system and the mass conservation equation. The description of the drug phase's permeation process in a porous solid hematoma phase medium using the mixture momentum conservation equation and the relative velocity equation includes: constructing a mixture momentum conservation equation based on the three-phase fluid system and the mass conservation equation, comprising local acceleration terms, convection terms, pressure gradient terms, effective stress terms, gravity terms, interphase force terms, and momentum source terms; and defining the effective stress tensor as the mixture's dynamic viscosity, identity matrix, and matrix transformation. The complete momentum conservation equation for the mixture is obtained through operation. For the infiltration process of the drug phase in porous hematoma media, a relative velocity equation is constructed. The difference between the permeability tensor, drug dynamic viscosity, drug phase pressure, and drift velocity is established to obtain the relative velocity equation. Based on the relative velocity equation, the permeability tensor is determined using the Kalman model. Combining the basic permeability, the cube of porosity, and the square of the solid hematoma volume fraction, the increasing law of permeability is obtained. This increasing law of permeability is integrated into the relative velocity equation, which can describe the infiltration process of the drug phase in porous solid hematoma media.

[0065] Specifically, the momentum conservation equation and the momentum equation for a mixture are as follows:

[0066] ;in, Indicates the density of the mixture. Indicates the velocity of the mixture. This represents the tensor product operation, where p represents pressure, τ_eff represents the effective stress tensor, g represents gravitational acceleration, F_inter represents interphase force, and S_momentum represents the momentum source term. The effective stress tensor is specifically: ;in, Let I represent the dynamic viscosity of the mixture, T represent the identity matrix, and I represent the matrix transpose operation. The relative velocity equation describes the drug permeation process in porous hematoma media: Where K_perm represents the permeability tensor, μd represents the drug dynamic viscosity, pd represents the drug phase pressure, and U_drift represents the drift velocity. The permeability tensor is based on the Kozeny-Carman model, specifically: ;in, Indicates the basic penetration rate. Porosity expressed as the cube of its value represents the flow space. This represents the effect of solid surface area. The model describes the physical law governing the increase in permeability as the solid dissolves.

[0067] In this optional embodiment, a phase transition kinetic model is constructed based on a three-phase fluid system and the mass conservation equation to describe the phase transition process from solid to liquid hematoma. This includes: obtaining the solid-liquid phase transition process from solid to liquid hematoma using the Arrhenius reaction kinetic model based on the three-phase fluid system and the mass conservation equation, thus obtaining an initial phase transition kinetic model; simplifying the initial phase transition kinetic model using isothermal conditions and combining it with the effective reaction rate constant to obtain a phase transition reaction rate expression; obtaining a characterization method for the phase transition reaction rate based on the phase transition reaction rate expression and combining it with saturation concentration, specific surface area, and product inhibition effect; defining the phase transition conversion rate based on the phase transition reaction rate characterization method and combining it with the time cumulative relationship between the initial solid hematoma volume fraction and the phase transition reaction rate to obtain a quantitative characterization of the phase transition process; and constructing a phase transition kinetic model based on the phase transition reaction rate expression, the characterization method of the phase transition reaction rate, and the quantitative characterization of the phase transition process.

[0068] Specifically, the phase transition kinetics model in this invention uses the Arrhenius reaction kinetics model to describe the solid-liquid phase transition process: ;in, This represents the rate of solid-liquid phase transition reaction (1 / s). The exponential factor (1 / s) is given, Ea represents the activation energy (J / mol), R = 8.314 J / (mol·K) represents the universal gas constant, T_abs represents the absolute temperature (K), and Cd represents the drug concentration. C_ref represents the reference concentration. , where n represents the reaction order (usually 0.5-2). The simplified form under isothermal conditions is: ;in, This represents the effective reaction rate constant, and the `min()` function ensures that the reaction rate does not exceed physical limits. The extended reaction rate model considers concentration and contact area.

[0069] Where C_sat represents the saturation concentration. dp represents the specific surface area and dp represents the characteristic particle size.

[0070] Represents the product suppression function. This represents the suppression constant. The phase transition conversion rate is specifically: ;in, This represents the initial solid hematoma volume fraction.

[0071] In this optional embodiment, the constitutive model is constructed based on a three-phase fluid system, the mass conservation equation, and a phase transition kinetics model, including: constructing a mixture density model based on incompressible fluids; integrating the volume fractions and densities of the solid hematoma phase, liquid hematoma phase, and drug phase using a volume-weighted average method to obtain a mixture density model; based on the mixture density model, and combining the continuous phase viscosity, the maximum packing fraction of the solid hematoma, and the Einstein coefficient to describe the influence of solid particles on the mixture viscosity to obtain a mixture viscosity model; based on the mixture viscosity model, combining the effects of molecular diffusion coefficient, comprehensive pore structure, tortuosity, and solid hematoma volume fraction, and using the Stokes-Einstein equation to calculate and integrate the molecular diffusion coefficient to obtain an effective diffusion coefficient model; based on the effective diffusion coefficient model, using the modified Kalman-Kozani equation to calculate the characteristic particle size, specific surface area, shape factor, and solid hematoma volume fraction to obtain a permeability model; and constructing a constitutive model based on the phase transition kinetics model, mixture density model, mixture viscosity model, effective diffusion coefficient model, and permeability model.

[0072] Specifically, for incompressible fluids, the constitutive model and mixture density model employ a volume-weighted average: The Krieger-Dougherty model, a viscosity model for mixtures, considers the influence of solid particles. ;in, Indicates the viscosity of the continuous phase. The maximum packing fraction is represented by [η]≈2.5, which represents the Einstein coefficient. The effective diffusion coefficient takes into account tortuosity and pore structure.

[0073] ;in, The molecular diffusion coefficient can be calculated using the Stokes-Einstein equation (i.e., the Stokes-Einstein equation):

[0074] ;in, Let r_molecule represent the Boltzmann constant, and r_molecule represent the molecular radius. The permeability model uses the modified Carman-Kozeny equation:

[0075] ;in, f_shape represents the specific surface area (i.e., spherical particles), and f_shape represents the shape factor (i.e., 1.0 for spherical particles and 0.8 for irregular particles).

[0076] In this optional embodiment, the interphase force model is constructed based on the three-phase fluid system and the momentum conservation equation, including: drag force representing the momentum transfer between different phases due to velocity differences, and using the residual temperature model to calculate the dilute phase flow state, using the Ergen model to calculate the dense phase flow state, and based on the calculation results, combined with a smooth transition function, to obtain the drag force model; according to the additional mass effect generated during phase acceleration, and combined with the virtual mass coefficient, drug phase volume fraction and phase acceleration difference, to obtain the virtual mass force model; and constructing the interphase force model based on the drag force model and the virtual mass force model.

[0077] Specifically, the interphase interaction force model and the drag force model describe the momentum transfer between different phases due to the velocity difference: For dilute phase flow The Wen-Yu model (i.e., the residual temperature model) is adopted: For dense phase flow The Ergun model is used: A smooth transition function is used to achieve full-range coverage. ; The virtual mass force model describes the additional mass effect generated during phase acceleration: F_vm = C_vm·αd·ρc·(DUc / Dt - DUd / Dt); where, D is the virtual mass coefficient (i.e., spherical particles), and D / Dt is the material derivative.

[0078] In this optional embodiment, a pre-constructed dynamic evolution model of hematoma morphology is used to simulate and predict the three-phase fluid system, obtaining the dynamic evolution law of the hematoma dissolution process. A phased control strategy is then used to divide the dynamic evolution law into stages, resulting in the hematoma dissolution time stage division. This includes: using a level set function for interface reconstruction and regularization; at several time steps, a re-initialization method based on the Hamiltonian-Jacobi equation is used to restore the sign distance characteristic of the level set function, obtaining the numerical accuracy of interface tracking; using a morphological diffusion bidirectional coupling mechanism, the permeability field at the interface position is dynamically adjusted, and the effective diffusion coefficient is adjusted by combining the local curvature of the interface, constructing a consistent mapping relationship between the solid hematoma volume fraction and the level set function, thus obtaining the hematoma morphology dynamic evolution model; using the hematoma morphology dynamic evolution model to simulate and predict the three-phase fluid system, obtaining the dynamic evolution law of the hematoma dissolution process; and using a phased control strategy to divide the dynamic evolution law into stages, thus obtaining the hematoma dissolution time stage division.

[0079] Specifically, to improve the realism and prediction accuracy of simulating drug diffusion and dissolution processes within hematomas, this invention establishes a dynamic evolution model of hematoma morphology. This model tracks the movement of the hematoma-drug interface using the level set method, achieving bidirectional coupling between hematoma morphological changes and drug diffusion processes. The level set function is defined as follows: Φ(x,t) implicitly describes the hematoma boundary, where Φ=0 represents the position of the hematoma-drug interface. The evolution equation of the level set function is: Where V_interface represents the interface normal velocity, which is determined by both the dissolution reaction rate and the local mass flux. Where M_s represents the molar mass of the solid hematoma, and A_interface represents the interfacial area. This represents the external normal vector. A re-initialization method based on the Hamilton-Jacobi equation is used:

[0080] Where τ represents a pseudo-time variable, This represents the level set function value before reinitialization, with `sign()` being the sign function. The above equation restores Φ to the sign distance function, ensuring the numerical accuracy of interface tracking. A morphology-diffusion bidirectional coupling mechanism exists, where changes in hematoma morphology and drug diffusion are bidirectionally coupled. In the positive coupling aspect, drug diffusion leads to the dissolution of the solid hematoma. In the negative coupling aspect, changes in hematoma morphology alter the spatial distribution of the permeability field and the effective diffusion coefficient, thereby affecting the subsequent diffusion path of the drug. The coupling mechanism is as follows: The permeability field is dynamically adjusted based on the interface position: ; The regularized heaviside function is used to smooth out abrupt changes in physical properties at interfaces.

[0081] Where ε is the interface thickness parameter, typically taken as 2-3 times the mesh scale. For example... Figure 5 As shown, the first stage involves injection on, phase transition on, and aspiration off. The drug is injected at a specified rate, allowing for simultaneous injection and reaction, which is more realistic. The second stage focuses on injection off, phase transition on, and aspiration off. It concentrates on the phase transition process, allowing for large-scale solid-liquid phase transition after the drug is fully distributed. The third stage stops injection and removes the liquid hematoma via aspiration, achieving separation. The influence of changes in tortuosity near the interface on the effective diffusion coefficient is considered. Where κ(x,t) represents the local curvature of the interface, through Calculations are performed, where β is the curvature influence coefficient, ranging from 0.1 to 0.5. This model describes the physical phenomenon of enhanced diffusion at convex surfaces and suppressed diffusion at concave surfaces. Consistent mapping between volume fraction field and level set: Establishing volume fraction α... s With level set function Mapping relationship between them: This mapping ensures the physical consistency between interface evolution and changes in phase volume fraction, avoiding non-physical interpretations in numerical calculations. A phase transition constraint mechanism ensures the total conversion rate does not exceed 30%, where `maxPhaseChange` represents the maximum permissible phase change, calculated using the following formula:

[0082] ;currentPhaseChange represents the current cumulative phase change; pos is a positive function, i.e., pos(x) = max(0,x). The modified reaction rate reactionRate is calculated using the formula reactionRate = pos(maxPhaseChange - currentPhaseChange).

[0083] Dissolution front region identification: The dissolution front region Ω_front is defined based on the gradient of the level set function: Ω_front = {x||Φ(x,t)| < δ_front and Where δ_front is the thickness parameter of the leading edge region, and its value ranges from 3 to 5 times the grid scale. A gradient threshold is used to exclude flat regions. Local micro-aspiration rate control: Within the dissolution front region, the micro-aspiration rate is dynamically adjusted based on the local fluid-filled hematoma formation rate. Where η_coupling is the coupling coefficient, ranging from 0.3 to 0.7, representing the proportion of locally dissolved products that are immediately removed, and χ_front(x,t) is the indicator function for the front region. Dissolution product residence time control: Through a microscale coupling control mechanism, the local residence time τ_residence of the dissolved product is estimated by the following formula: ;in, This represents the volume fraction of the localized hydral hematoma. By adjusting the η_coupling parameter, the residence time of the dissolution products is shortened, improving the efficiency of hydral hematoma removal. A synergistic mechanism of macroscopic stage control and microscale coupling control is established. During the phase change dissolution stage, microscale coupling control continues to operate, achieving dissolution-side clearance. During the hydral aspiration stage, macroscopic aspiration and microscale aspiration are superimposed. When the value is 0 during the phase change dissolution stage, it smoothly transitions to 1 during the hydral aspiration stage, forming a comprehensive clearance effect.

[0084] S_total-suction = S_macro-suction·f_stage(t) + S_micro-suction·(1-f_stage(t)); where S_total-suction is the total suction source term, S_macro-suction is the macroscopic suction source, and f_stage(t) is the stage switching function.

[0085] In this optional embodiment, a complete set of governing equations is generated based on the mass conservation equation, the mixture momentum conservation equation, the phase transition kinetic model, the constitutive model, the interphase force model, and the hematoma dissolution time stage division results. The complete set of governing equations is then numerically calculated using a pressure implicit operator splitting algorithm to obtain the spatiotemporal distributions of the volume fraction, velocity field, and pressure field of each phase. This enables the simulation of hematoma drug dissolution, including: generating a complete set of governing equations to be numerically calculated based on the mass conservation equation, the mixture momentum conservation equation, the phase transition kinetic model, the constitutive model, the interphase force model, the hematoma morphology dynamic evolution model, and a staged control strategy; explicitly updating the volume fraction of each phase using the pressure implicit operator splitting algorithm based on the complete set of governing equations, and normalizing the updated phase volume fractions to obtain the normalized phase fraction field; solving the phase fraction field using the momentum prediction equation to obtain the predicted velocity field; assembling the predicted velocity field and solving the pressure equation to obtain the pressure field. The predicted velocity field is corrected using the pressure field to obtain the corrected velocity field. An outer-layer iteration is performed on the repeated momentum prediction, pressure equation solution, and corrected velocity field. An inner-layer correction loop is performed after the iteration results meet the convergence criterion to obtain the velocity-pressure coupled calculation results. Based on the velocity-pressure coupled calculation results, the convection and diffusion terms of the complete control equations are processed using corresponding discretization schemes, and the gradient of the control equations is calculated using the least squares method to obtain the spatially discretized control equations. Based on the spatially discretized control equations, the phase volume fraction is flux-corrected using a multidimensional general explicit unconstraint algorithm, and iterative solutions are used to determine the flux constraint factor, obtaining the basis for phase volume fraction calculation. Based on the phase volume fraction calculation basis, a numerical calculation system is constructed according to the range of values ​​for the control Courant number, diffusion number, and reaction Damk-Köhler number. Based on the numerical calculation system, the complete control equations are numerically calculated to obtain the spatiotemporal distributions of the volume fraction, velocity field, and pressure field of each phase.

[0086] Specifically, such as Figure 6 As shown, this invention employs the PIMPLE algorithm (i.e., PISO-SIMPLE coupling) for time integration, combining the advantages of high time accuracy of the PISO algorithm and good stability of the SIMPLE algorithm. The specific implementation steps of the PIMPLE algorithm are as follows: Based on the velocity field of the previous time step... Sum of phase fractions The volume fraction of each phase is updated using an explicit time-integral scheme. Time discretization is performed using a first-order Euler scheme. ;in, For time step, This represents the quality source term for the i-th phase. After the phase fraction is updated, normalization is performed to ensure... , where j represents the index of each phase The normalized formula for the volume fraction of phase j is: `normalized` refers to the normalization process. The momentum equation is semi-discretized to obtain the matrix form of the momentum prediction equation: ;in, This represents the predicted velocity of the control volume P. This represents the pressure gradient at time step n. The central coefficients include contributions from the time derivative term. And the convection-diffusion term contribution, H(U) is an operator that includes the contributions from neighboring cells and the source term, which can be expressed as: ;in, The neighbor coefficient, S represents the neighboring cell velocity, and S_explicit represents the explicit source term.

[0087] The Gauss-Seidel iterative method (i.e., the Seidel iterative method) solves the above linear equations to obtain the predicted velocity field. Based on the continuity equation Given the constraints, substituting the predicted velocity into the pressure Poisson equation yields the following equation: The momentum prediction equation can be rewritten as: Take the divergence of the above equation and apply the continuity condition. The pressure equation is obtained as follows: The finite volume method is used to spatially discretize the pressure equation, and integration over each control volume yields: Where S_f is the surface vector and a_f is the central coefficient of the surface interpolation. This discrete equation forms a symmetric positive definite coefficient matrix, which is solved iteratively using the preconditional conjugate gradient method (PCG). The velocity field is corrected, and the pressure field is obtained from the solution. Correct the predicted speed: The corrected velocity field satisfies the discrete continuity equation, i.e. Outer iteration and convergence judgment: Perform outer SIMPLE iterations until the convergence criterion is met. ;in, To determine the convergence tolerance of the outer iteration, it is typically taken as... After convergence, an inner-layer PISO correction cycle is typically performed 2-3 times to improve the velocity-pressure coupling accuracy. In the spatial discretization scheme, the convection term ∇·(ρUΦ) is discretized into a surface flux summation form using the finite volume method. Where F_f = ρ_f · U_f · S_f is the surface mass flux. Let be the variable value at the face center. Φ_f is calculated using a second-order upwind scheme (limitedLinear), specifically: Φ_f = Φ_C + ψ(r)·(Φ_D - Φ_C) / 2; where Φ_C is the value at the center of the upstream element, and Φ_D is the value at the center of the downstream element. Let r = (Φ_C - Φ_U) / (Φ_D - Φ_C) be the limiter function, and r = (Φ_C - Φ_U) / (Φ_D - Φ_C) be the ratio of adjacent gradients. The limiter function used in the limitedLinear scheme is: ψ(r) = max(0, min(2r, 1)); this limiter maintains second-order accuracy in smooth regions and automatically degenerates into a first-order upwind scheme near discontinuities, suppressing numerical oscillations. The diffusion term is discretized. Central difference scheme discretization: Where Γ_f represents the diffusion coefficient at the face center, and dV represents the control volume element. Face-centered gradient Calculated by linear interpolation of the center values ​​of adjacent elements: Where Φ_P and Φ_N are the center values ​​of the elements on both sides of the surface, and d_PN is the vector connecting the element centers. The center difference scheme has second-order accuracy and does not introduce numerical diffusion, making it suitable for handling diffusion-dominated problems. For non-orthogonal meshes, an additional non-orthogonal correction term is added to maintain accuracy. Gradient calculation, gradient... Reconstructing using the least squares method, the optimal gradient estimate is obtained by minimizing the following objective function: ; Let be the distance weighting factor. The weighted least squares solution is as follows: ;in, Let be the geometric tensor. The least squares method is robust and has second-order accuracy on unstructured meshes. The MULES algorithm is used to ensure the boundedness of the phase volume fraction, ensuring that 0≤α≤1 always holds. The implementation steps of the MULES algorithm are as follows: The MULES algorithm is based on the idea of ​​flux correction, which imposes a constraint on the convective flux on the control volume surface to ensure that the updated phase volume fraction does not exceed the physical range [0,1]. The algorithm decomposes the surface flux into bounded flux F_f,bounded and anti-diffusion flux F_f,anti-diffusion: F_f = F_f, Where λ_f∈[0,1] is the flux constraint factor, used to control the contribution of the backdiffusion flux. For the control volume P, the explicitly updated phase fraction satisfies:

[0088] To ensure It needs to meet the following requirements: That is, the upper bound condition.

[0089] This refers to the lower bound condition. Flux limitation factor calculation: For each control volume, calculate the total inflow and outflow fluxes. , ; Calculate the maximum change: , Where α_max=1, α_min=0. The flux limiting factor is determined as follows: ; Where ε is a small positive number to prevent division by zero. The final constraint factor is the minimum of the constraint factors of adjacent units: λ_f = min(λ_P, λ_N); Multidimensional expansion and iterative solution: The MULES algorithm uses multiple iterations to handle the mutual influence between fluxes in different directions in multidimensional cases. In each iteration, all control surfaces are traversed, and the constraint factor is updated until convergence. The number of iterations is 3-5, and the convergence criterion is: Numerical stability control: To ensure numerical stability, the following condition must be met: Maximum Courant number: ; Diffusion number: ; Damköhler number of the reaction: The reference values ​​for the physical property parameters used in this invention are shown in Table 2 below:

[0090] Table 2 Reference values ​​for physical property parameters

[0091]

[0092] Boundary conditions are set: the injection boundary conditions are that the drug is injected at a specified rate (Ud=U_injection), the drug phase is pure (αd=1.0), and no solids enter. The suction boundary conditions and selective suction mechanism are discussed. The selective suction mechanism is applied in the following situations: Main suction stage: Macroscopic suction boundary activated, fluid flows out of the computational domain at a specified rate. Microscale coupled suction stage: Local micro-suction is implemented in the dissolution front region, synchronous with the dissolution process. Pressure balance regulation: When the local pressure exceeds a set threshold, pressure release suction is automatically triggered. Simulation steps for the filtration effect: Define the phase permeability coefficient vector. ,in T_l=1 indicates that the solid phase is completely impassable, T_l=1 indicates that the liquid phase is completely permissible, and T_d=1 indicates that the drug phase is completely permissible. Calculate the effective outflow rate for each phase. For the i-th phase, the effective outflow rate is: Where A_outlet represents the suction outlet area and U_mixture represents the overall velocity of the mixture, a solid-phase packing model is established. When the solid phase is blocked by the filter, a packing layer forms near the suction port, with a thickness of... Evolving over time: ;in, Let U_suction,eff be the porosity of the packing layer, and A_filter be the filtration area. After the packing layer forms, the suction resistance increases, and the effective suction rate decreases: U_suction,eff = U_suction,0 / (1 + R_cake / R_0); where, Let K_cake be the packing resistance, K_cake be the packing permeability, and R_0 be the initial pipeline resistance. When the packing thickness exceeds a critical value... In the event of complete blockage, the system will automatically reduce the suction rate or stop suctioning. H_smooth is a smooth step function that enables gradual adjustment of the suction rate. The basic variables used in this invention are defined as shown in Table 3 below:

[0093] Table 3 Definition of basic variables

[0094]

[0095] The multiphase flow model is based on the following assumptions: Incompressible fluid assumption: the density of each phase is constant, simplifying the mass conservation equation; Isothermal process assumption: temperature effects are ignored, and physical property parameters at constant temperature are used; Local thermodynamic equilibrium assumption: there is no temperature difference between phases, and each phase is in thermodynamic equilibrium; Continuous medium assumption: applicable to macroscopic scales, ignoring discontinuities at the molecular level.

[0096] Figure 2 An embodiment of a hematoma drug dissolution simulation system based on a multiphase flow model according to the present invention is shown.

[0097] In this optional embodiment, the hematoma drug dissolution simulation system based on a multiphase flow model includes:

[0098] The three-phase fluid construction module 201 is used to construct a three-phase fluid system by using the dissolution time stages of the solid hematoma phase, the liquid hematoma phase and the drug phase as the evolution axis and embedding the evolution axis into the physical mechanism of hematoma dissolution.

[0099] The mass conservation modeling module 202 is used to perform distribution calculations on a three-phase fluid system using a set of mass conservation equations to construct the mass conservation equations for the three-phase fluid system.

[0100] The momentum conservation building module 203 is used to construct the momentum conservation equation of the mixture based on the three-phase fluid system and the mass conservation equation; and to use the momentum conservation equation of the mixture and the relative velocity equation to describe the permeation process of the drug phase in the porous solid hematoma phase medium.

[0101] Phase transition dynamics modeling module 204 is used to construct a phase transition dynamics model based on a three-phase fluid system and the mass conservation equation to describe the phase transition process of solid hematoma transforming into liquid hematoma;

[0102] The constitutive modeling module 205 is used to construct a constitutive model based on a three-phase fluid system, the mass conservation equation, and a phase transition dynamics model.

[0103] The interphase interaction force modeling module 206 is used to construct an interphase interaction force model based on a three-phase fluid system and the momentum conservation equation.

[0104] The hematoma evolution division module 207 is used to simulate and predict the three-phase fluid system using a pre-constructed dynamic evolution model of hematoma morphology, obtain the dynamic evolution law of the hematoma dissolution process, and divide the dynamic evolution law into stages using a phased control strategy to obtain the hematoma dissolution time stage division results.

[0105] The numerical calculation and solution module 208 is used to generate a complete set of governing equations based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition kinetic model, the constitutive model, the interphase interaction force model, and the results of the hematoma dissolution time stage division. The module then uses the pressure implicit operator splitting algorithm to perform numerical calculations on the complete set of governing equations to obtain the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, so as to realize the simulation of drug dissolution in hematoma.

[0106] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0107] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0109] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0111] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A simulation method for drug dissolution in hematoma based on a multiphase flow model, characterized in that, include: Using the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase as the evolution axis, and embedding the evolution axis into the physical mechanism of hematoma dissolution, a three-phase fluid system is constructed. The mass conservation equations are used to perform distribution calculations on a three-phase fluid system in order to construct the mass conservation equations for the three-phase fluid system. Based on the three-phase fluid system and the mass conservation equation, a momentum conservation equation for the mixture is constructed; the momentum conservation equation and the relative velocity equation for the mixture are used to describe the infiltration process of the drug phase in a porous solid hematoma phase medium. Based on a three-phase fluid system and the mass conservation equation, a phase transition kinetic model is constructed to describe the phase transition process of solid hematoma transforming into liquid hematoma; A constitutive model is constructed based on a three-phase fluid system, the mass conservation equation, and a phase transition dynamics model. Based on the three-phase fluid system and the momentum conservation equation, a model of interphase interaction force is constructed. Using a pre-constructed dynamic evolution model of hematoma morphology, the three-phase fluid system was simulated and predicted to obtain the dynamic evolution law of the hematoma dissolution process. The dynamic evolution law was divided into stages using a phased control strategy to obtain the results of the hematoma dissolution time stage division. Based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition kinetic model, the constitutive model, the interphase interaction force model, and the results of the hematoma dissolution time stage division, a complete set of governing equations is generated. The pressure implicit operator splitting algorithm is used to numerically calculate the complete set of governing equations to obtain the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, so as to realize the simulation of drug dissolution in hematoma.

2. The method for simulating hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, The three-phase fluid system is constructed by using the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase as the evolution axis and embedding the evolution axis into the physical mechanism of hematoma dissolution. The dissolution time of the solid hematoma phase, liquid hematoma phase, and drug phase is divided into drug injection stage, phase change dissolution stage, and liquid aspiration stage to obtain the hematoma dissolution time stage division results; Based on the hematoma dissolution time stage division results, using the osmosis mechanism and Darcy's law, we clarified that the drug permeates into the solid hematoma through the porous medium and obtained the law of drug permeation and initial phase transition. Based on the laws of drug penetration and initial phase transition, chemical reaction mechanisms and phase transition processes are utilized, combined with Arrhenius dynamics, to clarify drug consumption characteristics. Furthermore, the contact efficiency function is used to control the pre-defined transformation of solid hematoma into liquid hematoma, thereby obtaining the laws of solid-liquid phase transition and drug consumption. By utilizing the mass conservation mechanism and following stoichiometric relationships, the mass balance relationship between solid-liquid phase transition and drug consumption patterns is analyzed, and the mass conservation constraints for each phase are obtained. Based on the time stage division results and the mass conservation constraints of each phase, a three-phase dynamic evolution axis is generated; and the evolution axis is embedded into the permeation mechanism, chemical reaction mechanism, phase change process and mass conservation mechanism and integrated to obtain a three-phase fluid system.

3. The method for simulating hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, The method of using the mass conservation equations to perform distribution calculations on the three-phase fluid system to construct the mass conservation equations for the three-phase fluid system includes: The basic parameters of the solid hematoma phase are set, and the mass conservation relationship of the solid hematoma phase is constructed. The mass conservation relationship is calculated for time change and spatial migration to obtain the mass conservation equation of the solid hematoma phase. Based on the solid hematoma phase mass conservation equation, a solid-liquid phase transition source term is defined, and associated with the basic reaction rate constant, drug volume fraction, and contact efficiency function, to obtain the calculation rules for the phase transition source term; Based on the phase change source term calculation rule, a preset range of critical drug volume fraction values ​​is set, and a contact efficiency function is constructed. The contact efficiency function is then input into the phase change source term calculation rule to obtain the complete solid hematoma phase mass conservation equation. The basic parameters of the liquid hematoma phase and the drug phase are set respectively. Based on the liquid hematoma phase, combined with the aspiration source term, and using the calculation rules of the phase change source term, the mass conservation relationship of the liquid hematoma phase is constructed. The mass conservation relationship of the liquid hematoma phase is calculated by defining the aspiration source term, and the complete mass conservation equation of the liquid hematoma phase is obtained. Based on the drug phase, injection source term, and drug consumption term, a preset range of values ​​for the stoichiometric coefficients is set, and the drug phase mass conservation relationship is constructed in combination with the phase change source term calculation rules. The drug phase mass conservation relationship is calculated using the logic of defining the drug consumption term to obtain the complete drug phase mass conservation equation. By integrating the mass conservation equations for the solid hematoma phase, the liquid hematoma phase, and the drug phase, a mass conservation equation for the three-phase fluid system is obtained.

4. The simulation method for hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, The momentum conservation equation for a mixture is constructed based on a three-phase fluid system and the mass conservation equation. The permeation process of the drug phase in a porous solid hematoma medium is described using the momentum conservation equation and the relative velocity equation of the mixture. Based on the three-phase fluid system and the mass conservation equation, a momentum conservation equation for a mixture is constructed, including local acceleration, convection, pressure gradient, effective stress, gravity, interphase force, and momentum source terms. Based on the momentum conservation equation of the mixture, the effective stress tensor is defined as the dynamic viscosity of the mixture, the identity matrix and the matrix transpose operation, to obtain the complete momentum conservation equation of the mixture; To investigate the permeation process of the drug phase in porous hematoma media, a relative velocity equation was constructed. The relationship between the permeability tensor, drug dynamic viscosity, drug phase pressure, and drift velocity was established to obtain the relative velocity equation. Based on the relative velocity equation, the permeability tensor is determined using the Kalman model. By combining the basic permeability, the cube of porosity, and the square of the solid hematoma volume fraction, the increasing law of permeability is obtained. The increasing law of permeability is then integrated into the relative velocity equation, which can describe the permeation process of the drug phase in a porous solid hematoma phase medium.

5. The method for simulating hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, The phase transition kinetic model, based on a three-phase fluid system and the mass conservation equation, used to describe the phase transition process from solid to liquid hematoma includes: Based on the three-phase fluid system and the mass conservation equation, the solid-liquid phase transition process from solid hematoma to liquid hematoma was obtained using the Arrhenius reaction kinetic model, thus obtaining the initial phase transition kinetic model; The initial phase transition kinetic model is simplified using isothermal conditions, and combined with the effective reaction rate constant, the phase transition reaction rate expression is obtained. Based on the description of phase change reaction rate, and combined with saturation concentration, specific surface area and product inhibition effect, a characterization method for phase change reaction rate is obtained. Based on the phase transition reaction rate characterization method, the phase transition conversion rate is defined, and combined with the time cumulative relationship between the initial solid hematoma volume fraction and the phase transition reaction rate, a quantitative characterization of the phase transition process is obtained. A phase transition kinetic model is constructed based on the description of phase transition reaction rates, the characterization of phase transition reaction rates, and the quantitative characterization of phase transition processes.

6. The simulation method for hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, The constitutive model constructed based on the three-phase fluid system, the mass conservation equation, and the phase transition dynamics model includes: Based on the incompressible fluid, a mixture density model was constructed. The volume fractions and densities of the solid hematoma phase, liquid hematoma phase, and drug phase were integrated using a volume-weighted average method to obtain the mixture density model. Based on the mixture density model, and combined with the continuous phase viscosity, the maximum bulk fraction of solid hematoma and the Einstein coefficient, the influence of solid particles on the mixture viscosity is described, and a mixture viscosity model is obtained. Based on the mixture viscosity model, the effects of molecular diffusion coefficient, comprehensive pore structure, tortuosity and solid hematoma volume fraction are combined, and the Stokes-Einstein equation is used to calculate and integrate the molecular diffusion coefficient to obtain an effective diffusion coefficient model. Based on the effective diffusion coefficient model, the permeability model is obtained by calculating the characteristic particle size, specific surface area, shape factor and solid hematoma volume fraction using the modified Kalman-Kozani equation. A constitutive model is constructed based on the phase transition kinetics model, mixture density model, mixture viscosity model, effective diffusion coefficient model, and permeability model.

7. The simulation method for hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, The method for constructing an interphase force model based on a three-phase fluid system and the momentum conservation equation includes: The drag force represents the momentum transfer between different phases due to the velocity difference. The temperature residual model is used to calculate the dilute phase flow, and the Ergen model is used to calculate the dense phase flow. Based on the calculation results, combined with the smooth transition function, the drag force model is obtained. Based on the additional mass effect generated during phase acceleration, and combined with the virtual mass coefficient, drug phase volume fraction and phase acceleration difference, a virtual mass force model is obtained; An interphase interaction force model is constructed based on the drag force model and the virtual mass force model.

8. The simulation method for drug dissolution of hematoma based on a multiphase flow model according to claim 1, characterized in that, The process utilizes a pre-constructed dynamic evolution model of hematoma morphology to simulate and predict the three-phase fluid system, obtaining the dynamic evolution law of the hematoma dissolution process. A staged control strategy is then used to divide the dynamic evolution law into process stages, resulting in the following hematoma dissolution time stage divisions: The interface is reconstructed and regularized using the level set function. Every certain number of time steps, a re-initialization method based on the Hamilton-Jacobi equation is used to restore the sign distance characteristic of the level set function, thereby obtaining the numerical accuracy of interface tracking. By utilizing the bidirectional coupling mechanism of morphological diffusion, the permeability field at the interface position is dynamically adjusted, and the effective diffusion coefficient is adjusted by combining the local curvature of the interface. A consistent mapping relationship between the solid hematoma volume fraction and the level set function is constructed, and a dynamic evolution model of hematoma morphology is obtained. The dynamic evolution model of hematoma morphology was used to simulate and predict the three-phase fluid system, and the dynamic evolution law of the hematoma dissolution process was obtained. The dynamic evolution law was divided into stages using a phased control strategy, and the time stage division of hematoma dissolution was obtained.

9. The simulation method for hematoma drug dissolution based on a multiphase flow model according to claim 1, characterized in that, Based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition kinetic model, the constitutive model, the interphase interaction force model, and the hematoma dissolution time stage division results, a complete set of governing equations is generated. The pressure implicit operator splitting algorithm is used to numerically calculate the complete set of governing equations to obtain the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, thereby realizing the simulation of drug dissolution in hematoma. Based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition dynamics model, the constitutive model, the interphase interaction force model, the hematoma morphology dynamic evolution model, and the staged control strategy, a complete set of control equations to be numerically calculated is generated. Based on the complete set of control equations, the pressure implicit operator splitting algorithm is used to explicitly update the volume fraction of each phase, and the updated phase volume fraction is normalized to obtain the normalized phase fraction field. The phase fraction field is solved using the momentum prediction equation to obtain the predicted velocity field. The predicted velocity field is then assembled and the pressure equation is solved to obtain the pressure field. The predicted velocity field is then corrected using the pressure field to obtain the corrected velocity field. An outer-layer iteration is performed on the velocity field after the repetitive momentum prediction, pressure equation solution and correction. When the iteration result meets the convergence criterion, an inner-layer correction loop is performed to obtain the velocity-pressure coupling calculation result. Based on the velocity-pressure coupling calculation results, the convection and diffusion terms of the complete control equations are processed by the corresponding discretization schemes, and the gradient of the control equations is calculated by the least squares method to obtain the spatially discretized control equations. Based on the spatially discretized set of control equations, a multidimensional general explicit solution limiter algorithm is used to correct the flux of the phase volume fraction, and iterative solution is used to determine the flux limit factor, thus obtaining the basis for calculating the phase volume fraction. Based on the calculation of phase volume fraction, and according to the range of values ​​of Courant number, diffusion number and reaction Damk-Köhler number, a numerical calculation system is constructed. Based on the numerical calculation system, the complete set of governing equations is numerically calculated to obtain the spatiotemporal distribution of volume fraction, velocity field and pressure field of each phase.

10. A simulation system for drug dissolution of hematoma based on a multiphase flow model, characterized in that, include: The three-phase fluid construction module is used to utilize the dissolution time stages of the solid hematoma phase, liquid hematoma phase, and drug phase as the evolution axis, and embed the evolution axis into the physical mechanism of hematoma dissolution to construct a three-phase fluid system. The mass conservation modeling module is used to perform distribution calculations on a three-phase fluid system using the mass conservation equations to construct the mass conservation equations for the three-phase fluid system. The momentum conservation building module is used to construct the momentum conservation equation of a mixture based on the three-phase fluid system and the mass conservation equation; and to describe the permeation process of the drug phase in a porous solid hematoma phase medium using the momentum conservation equation of the mixture and the relative velocity equation. The phase transition dynamics modeling module is used to construct a phase transition dynamics model based on a three-phase fluid system and the mass conservation equation to describe the phase transition process of solid hematoma transforming into liquid hematoma; The constitutive modeling module is used to construct constitutive models based on three-phase fluid systems, mass conservation equations, and phase transition dynamics models. The interphase interaction force modeling module is used to construct interphase interaction force models based on three-phase fluid systems and momentum conservation equations. The hematoma evolution segmentation module is used to simulate and predict the three-phase fluid system using a pre-built dynamic evolution model of hematoma morphology, obtain the dynamic evolution law of the hematoma dissolution process, and use a staged control strategy to divide the dynamic evolution law into stages, thus obtaining the hematoma dissolution time stage segmentation results. The numerical calculation and solution module is used to generate a complete set of governing equations based on the mass conservation equation, the momentum conservation equation of the mixture, the phase transition kinetic model, the constitutive model, the interphase interaction force model, and the results of the hematoma dissolution time stage division. The module then uses the pressure implicit operator splitting algorithm to perform numerical calculations on the complete set of governing equations, and obtains the spatiotemporal distribution of the volume fraction, velocity field, and pressure field of each phase, so as to realize the simulation of drug dissolution in hematoma.

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