Method for simulating, analyzing and estimating influence of nanofluid on promotion of methane hydrate reaction

Abstract

The invention relates to the technical field of hydrate reaction data analysis and estimation, and particularly discloses a method for simulating, analyzing and estimating the influence of nanofluid on promoting methane hydrate reaction, which comprises the following steps: acquiring four-phase basic parameters, synchronously constructing a four-phase flow model, and basically setting the four-phase flow model, the method comprises the following steps of: inputting the heat conductivity coefficient into ANSYS Fluent software, calculating, outputting and analyzing a methane hydrate basic generation efficiency index, analyzing and obtaining a nanofluid heat conductivity coefficient correction value, analyzing a methane hydrate reaction promotion efficiency index, gradually increasing a nanofluid concentration value, and estimating a function relationship between each nanofluid heat conductivity coefficient and the methane hydrate reaction promotion efficiency index. According to the method, the change of the heat conductivity coefficient of the nanofluid under different concentrations is accurately tracked by constructing the four-phase flow model, carrying out basic setting and focusing on the promotion effect of the nanofluid, so that the promotion effect of the nanofluid is more scientific and intuitive, and a data basis is provided for predicting reaction effects under different nanofluid conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrate reaction data analysis and estimation, and in particular to a method for simulating, analyzing and estimating the effect of nanofluids on promoting methane hydrate reaction. Background Art

[0002] Methane hydrate, as a potential clean energy source, has attracted much attention due to its huge reserves and high energy density. It forms under appropriate temperature and pressure conditions and consists of methane molecules enclosed in a cage-like structure formed by water molecules. However, the formation and decomposition processes of methane hydrate are affected by multiple factors, and its kinetics and thermodynamics are relatively complex, limiting its development and utilization in practical energy applications. In recent years, nanofluids have been introduced into the methane hydrate reaction system due to their unique thermophysical properties. In the study of methane hydrate reactions, simulation analysis has become an important research method. The simulation analysis methods in existing technologies are mainly based on the principles of computational fluid dynamics (CFD), using various CFD software to construct multiphase flow models for analysis.

[0003] For example, the invention patent with announcement number CN109271679B announces a method for simulating the influence of an external electric field on the formation and decomposition of methane hydrate. The method uses computer simulation software to build a model, establish a methane hydrate unit cell, melt the hydrate unit cell at high temperature to obtain a gas-liquid mixed phase, and then superimpose the methane hydrate unit cell and the gas-liquid mixed phase to obtain an initial configuration; set simulation parameters, obtain a stable configuration through energy minimization and pre-equilibrium simulation, and apply electric fields of different intensities and frequencies for molecular simulation; obtain molecular trajectory coordinate information through molecular dynamics calculation, and perform image analysis and computational analysis on the molecular trajectory coordinates.

[0004] For example, the invention patent with announcement number CN116821635B announces a method for estimating marine natural gas hydrate resources based on an organic matter degradation model. It takes into account the influence of environmental factors such as the sedimentation rate of natural gas hydrate formation in marine sediments, organic matter flux, and environmental oxygen content. It evaluates the activity of organic matter in sediments through a continuous organic matter degradation model based on log-normal distribution, and further simulates the degradation process of organic matter in sediments. Then, the methane-producing area in the sediment is judged based on the sulfate profile of the sediment pore water, and the resource amount of biogenic natural gas hydrate is evaluated.

[0005] However, during the implementation of the embodiments of the present application, the present application discovered that the above technology has at least the following technical problems: The current simulation analysis method for methane hydrate reaction promotion lacks in-depth analysis of the complex interaction relationship between the nanofluid phase, water phase, methane phase and hydrate phase when constructing the model, as well as the analysis and adjustment of the dynamic changes of the physical properties of the nanofluid at different nanofluid concentrations. This reduces the accuracy and reliability of the simulation analysis of methane hydrate reaction promotion. At the same time, the evaluation indicators for nanofluid-promoted reactions are relatively simple and static, making it impossible to systematically study the reaction promotion effect at different nanofluid concentrations. Summary of the Invention

[0006] In response to the deficiencies of the prior art, the present invention provides a method for simulating, analyzing and estimating the effect of nanofluids on promoting methane hydrate reactions, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a simulation analysis and estimation method for the influence of nanofluids on promoting methane hydrate reaction, including: S1, obtaining the basic parameters of four phases, the four phases including nanofluid phase, water phase, methane phase and hydrate phase, and synchronously constructing a four-phase flow model.

[0008] S2, perform basic settings for the four-phase flow model, input the four-phase flow model into ANSYS Fluent software, synchronously input the basic parameters of the four phases and set the basic parameters of the nanofluid phase to zero, output the basic reaction effect parameters of methane hydrate, and analyze the basic methane hydrate formation efficiency indicators.

[0009] S3, obtaining basic characteristic data of the nanofluid, analyzing the fusion coefficient of the nanofluid characteristics, and thus obtaining a correction value of the thermal conductivity of the nanofluid.

[0010] S4. In ANSYS Fluent software, input the basic parameters of the four phases and the modified value of the thermal conductivity of the nanofluid, output the methane hydrate reaction effect parameters, and analyze the methane hydrate reaction promotion efficiency index in combination with the basic methane hydrate formation efficiency index.

[0011] S5, gradually increasing the nanofluid concentration value, analyzing the thermal conductivity corresponding to each nanofluid concentration value, and inputting it into ANSYS Fluent software, thereby estimating the functional relationship between the thermal conductivity of each nanofluid and the methane hydrate reaction promotion efficiency index.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a simulation analysis and estimation method for the effect of nanofluids on promoting methane hydrate reactions. During the simulation process, by constructing a four-phase flow model and making reasonable basic settings, the simulation is more focused on the promotion of the reaction by the nanofluid, eliminating the influence of other factors. By analyzing the fusion coefficient of the nanofluid characteristics to obtain the thermal conductivity correction value, the thermal conductivity change of the nanofluid at different concentrations is accurately tracked, making the heat transfer simulation more realistic, providing a scientific numerical basis for the thermodynamic and kinetic processes of methane hydrate formation, and through the methane hydrate reaction promotion efficiency index, the promotion effect of the nanofluid on the reaction is made more scientific and intuitive. At the same time, by estimating the functional relationship between the thermal conductivity and the reaction promotion efficiency index, a data basis can be provided for predicting the reaction effect under different nanofluid conditions.

[0013] (2) This invention constructs a four-phase flow model that comprehensively considers the nanofluid phase, water phase, methane phase, and hydrate phase. It combines the mass conservation, momentum conservation, and energy conservation equations to present a more comprehensive picture of the physical behavior and interactions of each phase in the reaction system, avoiding the one-sidedness of the simulation of multiphase interactions. In the simulation analysis, the model can track the generation, consumption, and transmission of each phase in detail, and clearly show the impact of the fluid flow state on the mixing, contact, and reaction heat distribution of the reactants. Through simulations using this model, it is possible to predict the promotion effect of methane hydrate under different conditions.

[0014] (3) The present invention obtains a corrected value for the thermal conductivity of the nanofluid by analyzing the nanofluid characteristic fusion coefficient. The nanofluid characteristic fusion coefficient can fully reflect the characteristic state of the nanofluid. By obtaining a corrected value for the thermal conductivity based on this coefficient, the variation in the thermal conductivity of the nanofluid under different characteristic combinations can be accurately considered, making the numerical value of the nanofluid thermal conductivity during the simulation more accurate, thereby improving the accuracy of the methane hydrate reaction simulation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0016] Figure 1 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Reference Figure 1 As shown, the present invention provides a method for simulating, analyzing and estimating the effect of nanofluids on promoting methane hydrate reaction, comprising the following steps: S1, obtain the basic parameters of the four phases, including nanofluid phase, water phase, methane phase and hydrate phase, and simultaneously build a four-phase flow model.

[0019] In this embodiment, the four-phase basic parameters are obtained, and the specific process is as follows: The four-phase basic parameters specifically include four-phase mass conservation parameters, four-phase momentum conservation parameters and four-phase energy conservation parameters.

[0020] The four-phase mass conservation parameters include the density, velocity and mass source phase of the four phases.

[0021] It should be understood that density reflects the mass of each phase of matter per unit volume. The difference in density of different phases affects their distribution and flow characteristics in the reflection system. Velocity reflects the speed and direction of movement of each phase of matter in space. The mass source phase is used to indicate the rate of mass generation or consumption in each phase due to processes such as chemical reactions, which determines the change of the mass of each phase over time.

[0022] The four-phase momentum conservation parameters include the pressure, dynamic viscosity and interphase force of the four phases.

[0023] It is important to understand that pressure is the force per unit area within each phase and at the phase interface, and dynamic viscosity characterizes the magnitude of the internal friction within the fluid that hinders its flow, affecting the fluidity and stability of the movement.

[0024] The interphase force describes the interaction between different phases due to contact, friction, chemical reaction, etc. It should be noted that the interphase force refers to the force between the analyzed phase and other phases. Taking the nanofluid phase as an example, the interphase force of the nanofluid phase refers to the force between the nanofluid phase and the other three phases.

[0025] The four-phase energy conservation parameters include the internal energy, thermal conductivity and interphase heat exchange of the four phases.

[0026] It's important to understand that internal energy is the sum of the energy within each phase of a substance. It's closely related to the temperature and state of the substance, reflecting the energy level of each phase. Thermal conductivity measures the ability of each phase to conduct heat and determines the rate at which heat is transferred within it.

[0027] The interphase heat exchange amount refers to the amount of heat transferred between different phases due to the temperature difference. It should be noted that the interphase heat exchange amount refers to the heat exchange amount between the analyzed phase and other phases. Taking the nanofluid phase as an example, the interphase heat exchange amount of the nanofluid phase refers to the heat exchange amount between the nanofluid phase and the other three phases.

[0028] It should be noted that the four-phase energy conservation parameters are extracted from a pre-set methane hydrate reaction research database.

[0029] In this embodiment, a four-phase flow model is constructed simultaneously, and the specific process is as follows: The mass conservation equation is constructed based on the four-phase mass conservation parameters.

[0030] The momentum conservation equation is constructed based on the four-phase momentum conservation parameters.

[0031] The energy conservation equation is constructed based on the four-phase energy conservation parameters.

[0032] The mass conservation equation, momentum conservation equation and energy conservation equation are combined as a four-phase flow model. In a specific embodiment, the mass conservation equations corresponding to the four phases are obtained as follows: , in, is the density of the i-th phase, is the velocity of the i-th phase, is the mass source term of phase i, t is the reaction time, ,in, Corresponding to the nanofluid phase, For the water phase, Corresponding to the methane gas phase, corresponding to the hydrate phase.

[0033] It is important to understand that is the partial derivative of density with respect to time, which is used to characterize the rate of change of density with time at a fixed point in space. For example, in a fluid flow system, for a specific position, if , indicating that the density of the i-th phase at this point increases with time. When the volume remains constant, the mass of the i-th phase at this point increases. If , indicating that the density of the i-th phase at this point decreases with time. When the volume remains constant, the mass of the i-th phase at this point decreases. If , which means that the density of phase i at this point does not change with time and is in a stable state.

[0034] The divergence of the product of density and velocity represents the net outflow or inflow of mass flux per unit volume. According to the divergence theorem , is the volume, is the density, For speed, is the closed surface of the volume, is the surface normal vector. The physical meaning of the divergence theorem can be understood as the net mass flux through a closed surface S. When , it means that there is a net mass outflow in the control volume of phase i, which may be the fluid outflowing from the area; when When , it means that the net mass of phase i flows into the control volume, that is, the matter is converging to this area; when When , it indicates that the mass flowing into and out of the area is equal, and it is in a state of mass balance.

[0035] The momentum conservation equation is obtained as follows: , in, is the density of the i-th phase, is the velocity of the i-th phase, is the pressure of phase i, is the dynamic viscosity of phase i, is the force between the i-th phase and other phases, t is the reaction time, ,in, Corresponding to the nanofluid phase, For the water phase, Corresponding to the methane gas phase, corresponding to the hydrate phase.

[0036] It is important to understand that is the partial derivative of momentum with respect to time, which represents the rate of change of the fluid momentum of phase i with time at a fixed point in space. , indicating that the momentum of the fluid of phase i at this point is changing. In steady flow, that is, When , it means that the momentum of the fluid in phase i at this point does not change with time and is in a stable momentum state.

[0037] is called the convection term and represents the change in momentum due to the flow of the fluid in phase i. It reflects the change in momentum due to the change in velocity and direction of the fluid as it moves from one location to another.

[0038] is the pressure gradient, which indicates the pressure per unit volume. , the pressure gradient will produce a force on the fluid, causing the fluid to flow from the high-pressure area to the low-pressure area.

[0039] is the viscous force term, which represents the force due to the viscosity of the fluid. For Newtonian fluids, the viscous force is proportional to the velocity gradient and describes the force generated by the interaction between adjacent layers of the fluid due to the viscosity within the fluid.

[0040] The energy conservation equation is obtained as follows: , in, is the density of the i-th phase, is the internal energy of phase i, is the velocity of the i-th phase, is the thermal conductivity of the i-th phase, is the temperature of the i-th phase, is the heat exchange amount between phase i and other phases, t is the reaction time, ,in, Corresponding to the nanofluid phase, For the water phase, Corresponding to the methane gas phase, corresponding to the hydrate phase.

[0041] It is important to understand that It is the partial derivative of the internal energy per unit volume with respect to time, which indicates the rate of change of the internal energy of the i-th phase with time at a fixed point in space. , indicating that the internal energy at this point is increasing. , indicating that the internal energy at this point is decreasing. Under steady state, that is, , indicating that the internal energy at this point does not change with time and is in a state of energy balance.

[0042] The change in internal energy due to fluid flow is called the convection term of energy. It describes the change in energy caused by the flow of fluid, which carries internal energy from one location to another.

[0043] is the heat conduction term, according to Fourier's law ( is the heat flux density, is the thermal conductivity, is temperature), It represents the net heat flow into or out of a unit volume by heat conduction. It describes the heat transfer due to temperature gradient. , indicating that there is a net heat flow into the area, which will increase the temperature of the area; when , indicating that there is a net flow of heat out of the area, which will reduce the temperature of the area.

[0044] S2, perform basic settings for the four-phase flow model, input the four-phase flow model into ANSYS Fluent software, synchronously input the basic parameters of the four phases and set the basic parameters of the nanofluid phase to zero, output the basic reaction effect parameters of methane hydrate, and analyze the basic methane hydrate formation efficiency indicators.

[0045] It should be noted that ANSYS Fluent (Computational FluidDynamics Software) is primarily used in the field of computational fluid dynamics, simulating and analyzing complex physical phenomena such as fluid flow, heat transfer, and chemical reactions. It offers powerful multi-physics simulation capabilities, a rich set of physical models and solvers, and convenient pre- and post-processing functions.

[0046] In this embodiment, basic settings are made for the four-phase flow model, including: (1) The nanofluid phase, water phase, methane phase, and hydrate phase are assumed to be continuous media.

[0047] It should be understood that under the continuous medium assumption, each phase is regarded as continuously distributed in space, and continuous functions can be used to describe physical quantities, so that the mathematical methods can be used to analyze and solve using the solver of ANSYS Fluent software.

[0048] (2) It is assumed that there is a clear and continuous phase interface between the phases, and mass, momentum and energy exchange occurs at the phase interface.

[0049] (3) It is assumed that methane hydrate formation reaction only generates hydrate from liquid water and methane gas.

[0050] (4) It is assumed that methane has no solubility effect in water and no water vapor exists during the generation process.

[0051] (5) For the nanofluid phase, it is assumed that the nanoparticles are uniformly dispersed in the base fluid, the agglomeration of the nanoparticles is not considered, and the nanoparticles will not undergo obvious sedimentation in the nanofluid due to gravity.

[0052] It should be understood that the above setting processes (3), (4) and (5) are all used to simplify the reaction model and exclude the influence of other factors. That is, this model only analyzes the promoting effect of nanofluids on the methane hydrate formation reaction.

[0053] In this embodiment, the basic methane hydrate formation efficiency index is analyzed, and the specific analysis process is as follows: The four-phase flow model and the basic parameters of the four phases were input into the ANSYS Fluent software, and the basic parameters of the nanofluid phase were set to zero. The basic reaction effect parameters of methane hydrate were calculated and output using the solver of the ANSYS Fluent software.

[0054] It should be noted that the basic parameters of the nanofluid phase are set to zero in order to establish a baseline situation, that is, the state of methane hydrate formation without nanofluid promotion.

[0055] It should also be noted that in a specific embodiment, the numerical algorithm used by the ANSYS Fluent software solver is the finite volume method. This method divides the computational domain into a series of non-overlapping control volumes, with each grid node surrounded by a control volume. By integrating conservation equations (such as mass conservation, momentum conservation, and energy conservation equations) through the control volumes, partial differential equations are converted into algebraic equations for solution. During the integration process, flux terms in the equations (such as mass flux, momentum flux, and energy flux) are calculated using the control volume surfaces. This approach ensures the conservation of physical quantities within each control volume and across the entire computational domain.

[0056] The basic reaction effect parameters of methane hydrate include the formation rate, final formation amount, induction time and formation temperature standard deviation of methane hydrate.

[0057] It should be understood that the induction time refers to the period of time from when the reaction system reaches the initial set conditions until methane hydrate begins to form and can be detected during the nanofluid-promoted methane hydrate reaction.

[0058] It should be noted that when the generation rate is faster, more methane and water react to form methane hydrate per unit time, which may shorten the induction time and often result in a larger final generation amount. A larger final generation amount means that a large number of reactions have occurred, which is usually associated with a faster generation rate. The induction time is the preparatory stage before the reaction begins. If the induction time is long and the reaction starts slowly, the generation rate will be low in the early stage and the final generation amount may also be limited. The standard deviation of the generation temperature reflects the temperature uniformity of the system. If the standard deviation is large, it means that the temperature fluctuates greatly, which may interfere with the reaction kinetics, resulting in an unstable generation rate, affecting the reaction process and ultimately affecting the final generation amount. If the system temperature is relatively stable, that is, the standard deviation of the generation temperature is small, it is conducive to maintaining a stable generation rate and ensuring that the reaction proceeds in the direction of generating more methane hydrate. These parameters are inseparable from each other, and the comprehensive analysis has a high degree of integration and accuracy.

[0059] In a specific embodiment, the formation rate of methane hydrate is calculated by a solver by simultaneously solving the mass conservation equation and the energy conservation equation, the final formation amount is calculated by the solver based on the mass conservation equation, the induction time is calculated by the solver by simultaneously solving the mass conservation equation and the energy conservation equation, and the standard deviation of the formation temperature is calculated by the solver based on the energy conservation equation.

[0060] It is important to note that while the basic reaction parameters can be calculated using the mass and energy conservation equations, the establishment of the momentum conservation equation is essential when studying methane hydrate reactions. The entire reaction system is a complex multiphase system, in which the transfer and changes of mass, energy, and momentum are intertwined. The mass conservation equation focuses on tracking the production, consumption, and transfer of each phase of matter, the energy conservation equation focuses on the conversion and transfer of energy and its driving effect on the reaction, and the momentum conservation equation describes the motion of each phase within the system and the transfer of momentum between them. In the actual methane hydrate reaction scenario, the flow state of the fluid has a profound impact on the mixing and contact of the reactants, as well as the distribution of the reaction heat. The momentum conservation equation provides a dynamic perspective on the entire reaction system, ensuring the accuracy and completeness of the simulation. Without the momentum conservation equation, the description of the reaction system will be one-sided, unable to fully present the reaction of fluid flow to the mass and energy changes during the reaction process, and difficult to accurately simulate the comprehensive influence of each phase on the methane hydrate reaction under complex flow and interaction, and thus unable to truly restore the coupling relationship between various physical quantities in the actual reaction process.

[0061] According to the basic reaction effect parameters of methane hydrate, the basic methane hydrate generation efficiency index is obtained through analysis and processing. The basic methane hydrate generation efficiency index is used to characterize the methane hydrate generation effect when no nanofluid is added.

[0062] In a specific embodiment, the basic methane hydrate formation efficiency index is obtained through analysis and processing, and the specific analysis method is as follows: The basic reaction reference effect parameters stored in the reference database are extracted, including the reference generation rate, reference final generation amount, reference induction time and reference generation temperature standard deviation of methane hydrate.

[0063] According to the methane hydrate basic reaction effect parameters and basic reaction reference effect parameters, the basic methane hydrate formation efficiency index is obtained through analysis and processing.

[0064] In a specific embodiment, by analyzing the basic methane hydrate formation efficiency index, the methane hydrate formation effect when no nanofluid is added can be clearly characterized, providing a benchmark data reference for subsequent research on the promoting effect of nanofluid on the reaction, making the comparative analysis of methane hydrate formation under different conditions more scientific and accurate, and making the entire simulation analysis process more reasonable and precise.

[0065] Extract the preset generation rate weight, final generation amount weight, induction time weight and generation temperature standard deviation weight in the database. For example, the extraction method is to form a mapping set with the generation rate, final generation amount, induction time and generation temperature standard deviation of methane hydrate and their corresponding weights respectively. When the weight needs to be extracted, the generation rate, final generation amount, induction time and generation temperature standard deviation of methane hydrate obtained in real time are input into the corresponding mapping set respectively, so as to extract the generation rate weight, final generation amount weight, induction time weight and generation temperature standard deviation weight.

[0066] The specific analysis method of the basic methane hydrate formation efficiency index is as follows: , in, It is the basic generation efficiency index of methane hydrate. is the formation rate of methane hydrate, is the final amount of methane hydrate produced, is the induction time of methane hydrate, is the standard deviation of the formation temperature of methane hydrate, is the reference generation rate of methane hydrate, is the reference final production amount of methane hydrate, is the reference induction time of methane hydrate, is the standard deviation of the reference formation temperature of methane hydrate, is the generation rate weight, is the final generated weight, is the induction time weight, To generate the temperature standard deviation weight, is a natural constant.

[0067] In this embodiment, a pre-set methane hydrate basic generation efficiency index threshold is extracted from the database. If the methane hydrate basic generation efficiency index is less than or equal to the methane hydrate basic generation efficiency index threshold, it indicates that the computing power of the ANSYS Fluent software may be insufficient, resulting in the simulation process being unable to accurately reflect the actual physical process, causing the calculation results to deviate from the normal range and become invalid. At this time, it is necessary to start a verification simulation and determine the executable conditions based on the verification simulation results.

[0068] In a specific embodiment, the verification simulation is started, and the specific method is as follows: Get the initial set number of grids.

[0069] The methane hydrate basic generation efficiency index is subtracted from the methane hydrate basic generation efficiency index threshold to obtain the methane hydrate basic generation efficiency deviation index. The required grid number reduction value is extracted based on the methane hydrate basic generation efficiency deviation index. The extraction method is as follows: the grid number reduction value corresponding to each basic generation efficiency deviation index interval stored in the database is extracted, and the grid number reduction value corresponding to the interval in which the methane hydrate basic generation efficiency deviation index is located is mapped and extracted, and recorded as the required grid number reduction value.

[0070] It should be noted that if the required mesh number reduction value extracted exceeds the tolerable range set by the ANSYS Fluent software, the subsequent analysis and processing will be based on its maximum tolerable reduction value.

[0071] Adjust the mesh number setting according to the initial setting mesh number and the required mesh number reduction value, and start the verification simulation.

[0072] In a specific example, suppose that in a simulation, the initial grid count is set to 5000. Analysis shows that the required grid count needs to be reduced to 1000. Therefore, when adjusting the grid count, the grid count is adjusted from 5000 to 4000. The simulation is then rerun in ANSYS Fluent using the adjusted grid settings.

[0073] In a specific embodiment, the executable condition is determined based on the verification simulation result, and the specific method is as follows: The basic methane hydrate formation efficiency index is obtained through re-analysis and recorded as the basic methane hydrate formation efficiency verification index. If the basic methane hydrate formation efficiency verification index is still less than or equal to the basic methane hydrate formation efficiency index threshold, the executable condition judgment result is recorded as unavailable for further execution, and a warning message is generated simultaneously to provide a prompt.

[0074] If the methane hydrate basic generation efficiency verification index is greater than the methane hydrate basic generation efficiency index threshold, the executable condition judgment result is recorded as executable, and the methane hydrate basic generation efficiency verification index is used as the methane hydrate basic generation efficiency index, thereby continuing the methane hydrate reaction analysis.

[0075] S3, obtaining basic characteristic data of the nanofluid, analyzing the fusion coefficient of the nanofluid characteristics, and thus obtaining a correction value of the thermal conductivity of the nanofluid.

[0076] In this embodiment, the nanofluid characteristic fusion coefficient is analyzed, and the specific analysis process is as follows: Obtain basic characteristic data of nanofluids, including the average particle size of nanoparticles, the average surface area of ​​nanoparticles, the average surface charge of nanoparticles and nanofluid concentration.

[0077] It is important to understand that the basic characteristic data of nanofluids influence each other. For example, a decrease in the average particle size will lead to a significant increase in the specific surface area of ​​the nanoparticles, and thus a significant increase in the average surface area, which makes the interaction between the particles and the base fluid and between the particles stronger. On the one hand, a larger average surface area provides more active sites for the formation of methane hydrates, promoting the reaction. On the other hand, the average surface charge plays a key role in this. When the charge number is large, the electrostatic repulsion between particles increases, which can effectively prevent particle agglomeration, help the nanoparticles to be evenly dispersed in the nanofluid, and maintain the stability of the nanofluid concentration. A higher nanofluid concentration will increase the chances of collisions between particles. If the average surface charge number is not enough to overcome the attraction between particles, the particles are prone to agglomeration, thereby increasing the average particle size and reducing the average surface area.

[0078] It should be noted that the basic characteristic data of nanofluids were extracted from a pre-set methane hydrate reaction research database.

[0079] Nanofluids consist of a base fluid and nanoparticles.

[0080] The nanofluid characteristic fusion coefficient is obtained by analyzing and processing the obtained basic characteristic data of the nanofluid, and the nanofluid characteristic fusion coefficient is used to characterize the characteristic state of the nanofluid.

[0081] In a specific embodiment, the nanofluid characteristic fusion coefficient is obtained by the following method: The nanofluid reference characteristic data stored in the reference database is extracted, including the reference average particle size of the nanoparticles, the reference average surface area of ​​the nanoparticles, the reference average surface charge number of the nanoparticles and the nanofluid reference concentration.

[0082] The average particle size weight, average surface area weight, average surface charge weight and concentration weight pre-set in the database are extracted. It should be noted that the average particle size weight, average surface area weight, average surface charge weight and concentration weight are all in the range of 0 to 1. In actual extraction, the extraction method is, for example, to construct a mapping set one by one with the average particle size of nanoparticles, the average surface area of ​​nanoparticles, the average surface charge number of nanoparticles and the concentration of nanofluids and their corresponding average particle size weights, average surface area weights, average surface charge number weights and concentration weights, and input the average particle size of nanoparticles, the average surface area of ​​nanoparticles, the average surface charge number of nanoparticles and the concentration of nanofluids obtained in real time into the corresponding mapping sets, thereby extracting the average particle size weight, average surface area weight, average surface charge number weight and concentration weight.

[0083] In a specific embodiment, the nanofluid characteristic fusion coefficient is specifically expressed as: , in, is the nanofluid characteristic fusion coefficient, is the average particle size of the nanoparticles, is the average surface area of ​​the nanoparticles, is the average surface charge of the nanoparticles, is the nanofluid concentration, is the reference average particle size of nanoparticles, is the reference average surface area of ​​the nanoparticles, is the reference average surface charge of the nanoparticles, is the nanofluid reference concentration, is the average particle size weight, is the average surface area weight, is the average surface charge weight, is the concentration weight.

[0084] In this embodiment, the corrected value of the thermal conductivity of the nanofluid is obtained, and the specific analysis process is as follows: The thermal conductivity correction factor corresponding to each characteristic fusion coefficient interval stored in the database is extracted, and the thermal conductivity correction factor corresponding to the interval where the nanofluid characteristic fusion coefficient is located is mapped and extracted, and recorded as the nanofluid thermal conductivity correction factor.

[0085] It is important to understand that the larger the nanofluid property fusion coefficient, the more favorable the characteristic state of the nanofluid is for heat transfer. To more accurately reflect the impact of the nanofluid's different property states on thermal conductivity, the corresponding extracted thermal conductivity correction factor will be larger. Extracting the nanofluid thermal conductivity correction factor based on the nanofluid property fusion coefficient can more accurately account for the changes in thermal conductivity of the nanofluid under different property states. This allows for reasonable corrections to the thermal conductivity based on the actual properties of the nanofluid when simulating the nanofluid-promoted methane hydrate reaction, and thus accurately analyzes the impact of the heat transfer process on reaction parameters such as the methane hydrate formation rate and final yield, improving the accuracy and reliability of the simulation analysis.

[0086] It should be noted that the smaller the average particle size of the nanoparticles, the larger the specific surface area, the stronger the interaction with the base fluid and other particles, which is conducive to heat conduction. The large average surface area provides more heat exchange areas and reaction active sites, promoting heat transfer. The large average surface charge number can prevent particle agglomeration, ensure uniform dispersion of nanoparticles, avoid local thermal resistance, and facilitate uniform heat transfer. A moderate increase in the concentration of nanofluid can increase the chance of particle collision and help heat transfer.

[0087] Extract the thermal conductivity of nanofluids from the four-phase energy conservation parameters.

[0088] The thermal conductivity correction value of the nanofluid is obtained according to the analysis of the thermal conductivity of the nanofluid and the thermal conductivity correction factor of the nanofluid. That is, the thermal conductivity of the nanofluid multiplied by the thermal conductivity correction factor of the nanofluid is equal to the thermal conductivity correction value of the nanofluid.

[0089] S4. In ANSYS Fluent software, input the basic parameters of the four phases and the modified value of the thermal conductivity of the nanofluid, output the methane hydrate reaction effect parameters, and analyze the methane hydrate reaction promotion efficiency index in combination with the basic methane hydrate formation efficiency index.

[0090] In this embodiment, the four-phase basic parameters and the nanofluid thermal conductivity correction value are input, and the methane hydrate reaction effect parameters are output. The specific analysis process is as follows: The thermal conductivity of the nanofluid in the four-phase basic parameters and the corrected value of the thermal conductivity of the nanofluid are superimposed, and the result of the superposition is recorded as the new thermal conductivity of the nanofluid, that is, the thermal conductivity of the nanofluid in the four-phase basic parameters plus the corrected value of the thermal conductivity of the nanofluid is equal to the new thermal conductivity of the nanofluid.

[0091] The new nanofluid thermal conductivity is substituted for the nanofluid thermal conductivity in the four-phase basic parameters, thereby obtaining the new four-phase basic parameters.

[0092] It should be noted that the difference between the new four-phase basic parameters and the four-phase basic parameters is only related to the thermal conductivity of the nanofluid, that is, the new four-phase basic parameters specifically include the new four-phase mass conservation parameters, the new four-phase momentum conservation parameters and the new four-phase energy conservation parameters.

[0093] The new four-phase mass conservation parameters include the density, velocity and mass source phase of the four phases.

[0094] The new four-phase momentum conservation parameters include the pressure, dynamic viscosity and interphase force of the four phases.

[0095] The new four-phase energy conservation parameters include the internal energy of the four phases, the thermal conductivity of the new nanofluid and the interphase heat exchange.

[0096] The new four-phase basic parameters are input into ANSYS Fluent software, and the methane hydrate reaction effect parameters are calculated and output using the solver of ANSYS Fluent software.

[0097] In this embodiment, the methane hydrate reaction promotion efficiency index is analyzed, and the specific analysis process is as follows: The parameters of the methane hydrate promotion reaction include the promotion generation rate of methane hydrate, the final promotion generation amount, the promotion induction time and the standard deviation of the promotion generation temperature.

[0098] According to the analysis and processing of the methane hydrate promotion reaction parameters, the methane hydrate promotion generation indicator parameters are obtained, and the methane hydrate promotion generation indicator parameters are used to characterize the methane hydrate generation effect after the addition of nanofluid.

[0099] In a specific embodiment, the methane hydrate formation promotion indicator parameter is obtained in the following manner: , in, is an indicator parameter for promoting the formation of methane hydrate. is the promotion rate of methane hydrate, is the final amount of methane hydrate generated by promotion, is the promotion induction time of methane hydrate, is the standard deviation of the temperature for promoting the formation of methane hydrate, is the reference generation rate of methane hydrate, is the reference final production amount of methane hydrate, is the reference induction time of methane hydrate, is the standard deviation of the reference formation temperature of methane hydrate, is the generation rate weight, is the final generated weight, is the induction time weight, To generate the temperature standard deviation weight, is a natural constant.

[0100] According to the methane hydrate promotion index parameters and the methane hydrate basic formation efficiency index, the methane hydrate reaction promotion efficiency index is obtained through analysis and processing. The methane hydrate reaction promotion efficiency index is used to characterize the promotion effect of nanofluid on methane hydrate reaction.

[0101] In a specific embodiment, by analyzing the methane hydrate reaction promotion efficiency index, the promoting effect of nanofluids on the methane hydrate reaction can be effectively quantified, providing a clear data judgment criterion for research, which can clearly reflect the different effects produced by different nanofluid conditions in the reaction, and improve the efficiency and accuracy of the research.

[0102] In a specific embodiment, the methane hydrate reaction promotion efficiency index is obtained by: , in, is an index of the methane hydrate reaction promotion efficiency. is an indicator parameter for promoting the formation of methane hydrate. It is the basic generation efficiency index of methane hydrate.

[0103] It should be noted that if the methane hydrate promotion index parameter is less than or equal to the methane hydrate basic generation efficiency index, it means that the current simulation result of the nanofluid's promotion effect on methane hydrate is no promotion effect. At this time, the methane hydrate reaction promotion efficiency index is set to zero, and a prompt message is generated simultaneously for early warning.

[0104] S5, gradually increasing the nanofluid concentration value, analyzing the thermal conductivity corresponding to each nanofluid concentration value, and inputting it into ANSYS Fluent software, thereby estimating the functional relationship between the thermal conductivity of each nanofluid and the methane hydrate reaction promotion efficiency index.

[0105] In this embodiment, the nanofluid concentration value is gradually increased, and the thermal conductivity corresponding to each nanofluid concentration value is analyzed. The specific analysis method is: It should be noted that the rule for increasing the nanofluid concentration value is a linear equal-step increase pre-set in the database. In a specific embodiment, for example, the initial concentration is set to 0.5%, and it increases by 0.3% each time, and the upper limit threshold is set to 2.0%.

[0106] The multiple nanofluid concentration values ​​that increase gradually are recorded as each nanofluid concentration value.

[0107] The nanofluid characteristic fusion coefficient corresponding to each nanofluid concentration value is obtained according to the analysis and processing of each nanofluid concentration value, thereby obtaining the nanofluid thermal conductivity correction value corresponding to each nanofluid concentration value.

[0108] In a specific embodiment, the specific method for obtaining the nanofluid characteristic fusion coefficient corresponding to each nanofluid concentration value is as follows: , in, is the nanofluid characteristic fusion coefficient corresponding to the j-th nanofluid concentration value, is the average particle size of the nanoparticles, is the average surface area of ​​the nanoparticles, is the average surface charge of the nanoparticles, is the jth nanofluid concentration, is the reference average particle size of nanoparticles, is the reference average surface area of ​​the nanoparticles, is the reference average surface charge of the nanoparticles, is the nanofluid reference concentration, is the average particle size weight, is the average surface area weight, is the average surface charge weight, is the concentration weight, , j is the number of times the nanofluid concentration value increases, and m is the number of times the nanofluid concentration value increases.

[0109] The thermal conductivity corresponding to each nanofluid concentration value is obtained by analyzing the thermal conductivity correction value of the nanofluid corresponding to each nanofluid concentration value. The specific analysis process is as follows: The thermal conductivity correction factor corresponding to the interval of the nanofluid characteristic fusion coefficient corresponding to each nanofluid concentration value is mapped and extracted, and recorded as the thermal conductivity correction factor of each nanofluid.

[0110] Extract the thermal conductivity of nanofluids from the four-phase energy conservation parameters.

[0111] The thermal conductivity of the nanofluid is multiplied by each nanofluid thermal conductivity correction factor to obtain the nanofluid thermal conductivity correction value corresponding to each nanofluid concentration value.

[0112] The thermal conductivity coefficient corresponding to each nanofluid concentration value is obtained by adding the nanofluid thermal conductivity coefficient correction value corresponding to each nanofluid concentration value to the nanofluid thermal conductivity coefficient in the four-phase basic parameters.

[0113] The functional relationship between the thermal conductivity of each nanofluid and the methane hydrate reaction promotion efficiency index is estimated as follows: A1, obtain the thermal conductivity corresponding to the nanofluid concentration according to the nanofluid concentration value.

[0114] A2, simulate and calculate the reaction effect parameters using ANSYS Fluent software.

[0115] A3, calculate the reaction promotion efficiency index based on the reaction effect parameter.

[0116] A4, sequentially calculating the reaction promotion efficiency index corresponding to each nanofluid concentration value, and obtaining a data set of the nanofluid concentration value and the reaction promotion efficiency index corresponding to the nanofluid concentration value.

[0117] A5, fitting the data set by a data fitting method, thereby obtaining a functional relationship between the thermal conductivity of each nanofluid and the methane hydrate reaction promotion efficiency index.

[0118] In a specific embodiment, the fitting method may be the least squares method.

[0119] In a specific embodiment, assuming that the nanofluid concentration starts from 0.5% and increases in steps of 0.3% to 2%, the corresponding nanofluid thermal conductivities are 0.72 W / (m·K), 0.85 W / (m·K), 0.98 W / (m·K), 1.1 W / (m·K), 1.23 W / (m·K), and 1.35 W / (m·K), and the methane hydrate reaction promotion efficiency indicators are 0.2, 0.35, 0.4, 0.5, 0.6, and 0.7, respectively. Then the data set can be expressed as The least squares method was used to fit the thermal conductivity of the nanofluid and the methane hydrate reaction promotion efficiency index, and the functional relationship was obtained as follows: ,in is an index of the methane hydrate reaction promotion efficiency. is the thermal conductivity of the nanofluid.

[0120] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A simulation analysis and estimation method for the effect of nanofluids on promoting methane hydrate reaction, characterized in that: The following steps are involved: S1, obtaining basic parameters of four phases, including a nanofluid phase, a water phase, a methane phase, and a hydrate phase, and simultaneously constructing a four-phase flow model; S2, perform basic settings for the four-phase flow model, input the four-phase flow model into ANSYS Fluent software, simultaneously input the basic parameters of the four phases and set the basic parameters of the nanofluid phase to zero, output the basic reaction effect parameters of methane hydrate, and analyze the basic methane hydrate formation efficiency indicators; S3, obtaining basic characteristic data of the nanofluid, analyzing the fusion coefficient of the nanofluid characteristics, and thus obtaining a correction value of the thermal conductivity of the nanofluid; S4. In ANSYS Fluent software, input the basic parameters of the four phases and the modified value of the thermal conductivity of the nanofluid, output the methane hydrate reaction effect parameters, and analyze the methane hydrate reaction promotion efficiency index in combination with the basic methane hydrate formation efficiency index; S5, gradually increasing the nanofluid concentration value, analyzing the thermal conductivity corresponding to each nanofluid concentration value, and inputting it into ANSYSFluent software, thereby estimating the functional relationship between the thermal conductivity of each nanofluid and the methane hydrate reaction promotion efficiency index.

2. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 1, characterized in that: The specific process of obtaining the four-phase basic parameters is as follows: The four-phase basic parameters specifically include four-phase mass conservation parameters, four-phase momentum conservation parameters and four-phase energy conservation parameters; The four-phase mass conservation parameters include the density, velocity and mass source phase of the four phases; The four-phase momentum conservation parameters include pressure, dynamic viscosity and interphase force of the four phases; The four-phase energy conservation parameters include the internal energy, thermal conductivity and interphase heat exchange of the four phases.

3. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 2, characterized in that: The specific process of synchronously constructing the four-phase flow model is as follows: Construct the mass conservation equation based on the four-phase mass conservation parameters; Construct momentum conservation equation based on four-phase momentum conservation parameters; Construct the energy conservation equation based on the four-phase energy conservation parameters; The mass conservation equation, momentum conservation equation and energy conservation equation are combined as a four-phase flow model.

4. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 1, characterized in that: The basic setting of the four-phase flow model includes: (1) Assume that the nanofluid phase, water phase, methane phase, and hydrate phase are all continuous media; (2) It is assumed that there is a clear and continuous phase interface between the phases, and mass, momentum and energy exchange occurs at the phase interface; (3) Assume that methane hydrate formation reaction is formed only from liquid water and methane gas; (4) Assume that methane has no solubility effect in water and no water vapor exists during the generation process; (5) For the nanofluid phase, it is assumed that the nanoparticles are uniformly dispersed in the base fluid, the agglomeration of the nanoparticles is not considered, and the nanoparticles will not undergo obvious sedimentation in the nanofluid due to gravity.

5. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 4, characterized in that: The specific analysis process of analyzing the basic methane hydrate formation efficiency index is as follows: The four-phase flow model and the basic parameters of the four phases were input into ANSYS Fluent software, and the basic parameters of the nanofluid phase were set to zero. The basic reaction effect parameters of methane hydrate were calculated and output in the solver of ANSYS Fluent software. The methane hydrate basic reaction effect parameters include the methane hydrate generation rate, final generation amount, induction time and generation temperature standard deviation; According to the methane hydrate basic reaction effect parameters, a methane hydrate basic generation efficiency index is obtained through analysis and processing. The methane hydrate basic generation efficiency index is used to characterize the methane hydrate generation effect when no nanofluid is added.

6. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 1, characterized in that: The specific analysis process of analyzing the nanofluid characteristic fusion coefficient is as follows: Obtain basic characteristic data of nanofluids, including average particle size of nanoparticles, average surface area of ​​nanoparticles, average surface charge of nanoparticles and nanofluid concentration; The nanofluid characteristic fusion coefficient is obtained by analyzing and processing the acquired nanofluid basic characteristic data, and the nanofluid characteristic fusion coefficient is used to characterize the characteristic state of the nanofluid.

7. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 6, characterized in that: The specific analysis process for obtaining the corrected value of the thermal conductivity of the nanofluid is as follows: Extract the thermal conductivity correction factor corresponding to each characteristic fusion coefficient interval stored in the database, and map and extract the thermal conductivity correction factor corresponding to the interval where the nanofluid characteristic fusion coefficient is located, and record it as the nanofluid thermal conductivity correction factor; Extract the thermal conductivity of nanofluids from the four-phase energy conservation parameters; The thermal conductivity correction value of the nanofluid is obtained based on the thermal conductivity of the nanofluid and the thermal conductivity correction factor of the nanofluid.

8. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 1, characterized in that: The four-phase basic parameters and the nanofluid thermal conductivity correction value are input, and the methane hydrate reaction effect parameters are output. The specific analysis process is as follows: The thermal conductivity of the nanofluid in the four-phase basic parameters is superimposed with the corrected value of the thermal conductivity of the nanofluid, and the result of the superposition is recorded as the new thermal conductivity of the nanofluid; Substituting the thermal conductivity of the new nanofluid for the thermal conductivity of the nanofluid in the four-phase basic parameters, thereby obtaining the new four-phase basic parameters; The new four-phase basic parameters are input into ANSYS Fluent software, and the methane hydrate reaction effect parameters are calculated and output using the solver of ANSYS Fluent software.

9. The method for simulating, analyzing and estimating the effect of nanofluids on the methane hydrate reaction according to claim 8, characterized in that: The specific analysis process of analyzing the methane hydrate reaction promotion efficiency index is as follows: The parameters of the methane hydrate promotion reaction include the promotion rate of methane hydrate, the final amount of promotion, the promotion induction time and the standard deviation of the promotion temperature; According to the methane hydrate promotion reaction parameter analysis and processing, a methane hydrate promotion generation indicator parameter is obtained, wherein the methane hydrate promotion generation indicator parameter is used to characterize the methane hydrate generation effect after the addition of the nanofluid; According to the methane hydrate formation promotion index parameter and the methane hydrate basic formation efficiency index, a methane hydrate reaction promotion efficiency index is obtained through analysis and processing. The methane hydrate reaction promotion efficiency index is used to characterize the promotion effect of the nanofluid on the methane hydrate reaction.

10. The method for simulating, analyzing and estimating the effect of nanofluids on promoting methane hydrate reaction according to claim 1, characterized in that: The nanofluid concentration value is gradually increased, and the thermal conductivity corresponding to each nanofluid concentration value is analyzed. The specific analysis method is: The multiple nanofluid concentration values ​​that increase gradually are recorded as each nanofluid concentration value; According to the analysis and processing of each nanofluid concentration value, the nanofluid characteristic fusion coefficient corresponding to each nanofluid concentration value is obtained, thereby obtaining the nanofluid thermal conductivity correction value corresponding to each nanofluid concentration value; The thermal conductivity corresponding to each nanofluid concentration value is obtained by analyzing the thermal conductivity correction value of the nanofluid corresponding to each nanofluid concentration value.

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