CN113128097A - Method for simulating and predicting heat transfer performance of porous nanofiber medium - Google Patents
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Abstract
The invention belongs to the technical field of fiber membrane performance simulation, and particularly relates to a method for simulating and predicting heat transfer performance of a porous nanofiber medium, which is characterized in that parametric modeling is carried out on the basis of the real geometric structure of an electrostatic spinning PU porous fiber membrane and real fiber measurement data thereof serving as parameters, and a highly simulated 3D geometric model is established; importing the built 3D geometric model into finite element simulation software, and performing mesh division by using tetrahedral units; and adding a laminar flow physical field to generate an apparent velocity, and adding a heat transfer physical field to perform heat transfer simulation analysis. The method takes the real fiber structure as the setting basis of simulation parameters to carry out 3D modeling simulation, the simulation result and the experimental result have better consistency, and the simulation and prediction method for the heat transfer performance of the invention is verified to be capable of meeting the simulation process of 3D geometric modeling and heat transfer of the electrostatic spinning PU nanofiber membrane, and has important theoretical significance for the research of the heat transfer performance of the porous fiber membrane medium.
Description
Technical Field
The invention belongs to the technical field of fiber membrane performance simulation, and particularly relates to a method for simulating and predicting heat transfer performance of a porous nanofiber medium.
Background
In recent years, with the progress of scientific technology, the depth of scientific research is expanding. Computer simulation techniques are increasingly used in the scientific research field. The computer simulation can collect and analyze various complex data, can comprehensively and effectively research physical phenomena, and adjust and optimize test parameters; therefore, economic loss and waste of manpower and material resources are reduced, the limit of experimental conditions can be broken through, errors caused by human factors are reduced, and reliable theoretical basis is provided for application of various materials. The development of the computer simulation technology provides great assistance for the research and development design of various materials, so that the research by the computer simulation technology in the research of various physical properties of textiles becomes the focus of more and more textile researchers. The geometric model construction of the textile fabric and the finite element analysis of various properties of the fabric are hot spots of research in recent years.
The description of the real geometric structure of the fabric such as the porous fiber membrane is the basis of the simulation research on the performance of the fabric, which directly determines the accuracy of the simulation result. The exploration of the modeling technical means begins from the thirties in the twentieth century, and various methods with practicability have been developed so far. The method mainly comprises the following steps: generating random long fibers in a three-dimensional space by utilizing a computer programming language to form a 3D geometric model; performing morphological processing on acquired image data by using imaging technologies such as Magnetic Resonance Imaging (MRI), Digital Volume Imaging (DVI), X-ray tomography imaging (X-CT) and the like, and further constructing a 3D geometric model based on real material related information; the method comprises the steps of obtaining information such as fiber diameter, porosity and the like of the porous fiber fabric through various experimental measuring instruments, and directly constructing the porous fiber fabric by using relevant special software.
The basic method for simulating the heat transfer of the fabric finite element is to firstly construct a heat transfer simulation model under relevant conditions, and then to accurately calculate and simulate the heat transfer performance of different physical fields and different samples by using finite element simulation software and utilizing relevant theories of the finite element method. With the continuous deepening of relevant theoretical research and practical exploration, the fabric heat transfer model is gradually expanded from a theoretical model to a two-dimensional geometric simulation model and then to a three-dimensional simulation model. A plurality of researchers at home and abroad utilize finite element simulation technology when researching the heat transfer performance of the fabric. With the development of finite element simulation technology, the model for researching the heat transfer performance of the fabric gradually transits from the initial two-dimensional numerical model to the 3D geometric model which is close to the real fabric structure. The heat transfer performance of the fabric is researched by utilizing finite element simulation, so that the fine research on the heat transfer process of the fabric is facilitated, and a reliable theoretical basis is provided for the prediction of the heat performance of the fabric.
In the conventional method for researching the thermal property of the electrospun porous fiber membrane, a repetitive physical experiment is adopted, but the traditional method has the problems of high cost, low efficiency and the like. Introduction of computer simulation into the study of the heat transfer performance of electrospun porous fibrous membranes is beneficial to alleviating the problems. The description is carried out according to the real geometric structure of the fabric such as the porous fiber membrane and the like, so that not only is the basis for ensuring the authenticity and the accuracy of a simulation result ensured, but also the basis for carrying out simulation on the performance of the fabric is also provided. Therefore, the method has great practical significance in exploring a scheme of 3D geometric modeling of the electrospun porous fiber membrane and applying the 3D geometric model of the electrospun porous fiber membrane to finite element simulation research of the heat transfer performance of the electrospun porous fiber membrane.
Disclosure of Invention
Based on the defects in the prior art, the invention provides a method for simulating and predicting the heat transfer performance of the porous nanofiber medium.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for simulation and prediction of heat transfer performance of porous nanofiber media, comprising the steps of:
(1) selecting a solvent to prepare a spinning solution according to the solubility parameter, and performing electrostatic spinning to obtain a porous nanofiber membrane;
(2) obtaining structural parameters of the porous nanofiber membrane, including the thickness, porosity, air permeability and average diameter of single fibers of the fiber membrane;
(3) establishing a 3D geometric model by utilizing Digimat-FE according to the porosity of the fiber membrane and the average diameter of single fibers; setting the geometric dimension of the 3D geometric model according to the thickness of the fiber membrane; changing the orientation of single fibers by setting random algorithm seeds with different two-dimensional orientations to generate 3D geometric models with different single fiber distribution positions;
(4) importing 3D geometric models with different single fiber distribution positions into ANSYS Workbench, carrying out Boolean operation, combining the models into a whole, connecting all geometric bodies in the 3D geometric models into a grid common node, and constructing to obtain a 3D geometric model;
(5) introducing the constructed 3D geometric model into COMSOL, setting material properties of a fiber domain and an air domain, then carrying out mesh division, and then respectively carrying out the step (6) and the step (7);
(6) adding a laminar flow physical field to the 3D geometric model, setting boundary conditions, and then performing steady-state analysis to solve the simulated air permeability; if the simulated air permeability and the air permeability of the fiber membrane are within the target error threshold, turning to the step (7);
(7) adding a heat transfer physical field to the 3D geometric model, setting boundary conditions, performing transient analysis, obtaining heat flux and temperature gradient in the heat transfer direction in the 3D geometric model through finite element simulation, performing heat transfer simulation, and calculating the heat conductivity coefficient of the 3D geometric model.
Preferably, in the step (1), the solute of the spinning solution is polyurethane, polyacrylonitrile, polyvinyl alcohol, ethylene oxide or polyamide.
Preferably, in the step (1), the parameters of electrostatic spinning include:
the spinning speed is 0.2-5.0 ml/h, the receiving distance is 10-30 cm, the spinning voltage is 5-30 kV, the ambient temperature is 25 +/-5 ℃, and the ambient relative humidity is 40 +/-5%.
Preferably, in the step (2), the thicknesses of the porous nanofiber membrane at different positions are measured, and the average value is taken as the thickness of the fiber membrane;
measuring the diameters of a plurality of single fibers according to the scanning electron microscope photo of the porous nanofiber membrane, and calculating the average value of the diameters as the average diameter of the single fibers;
the porosity of the fiber membrane is calculated by using a volume-density method to calculate the effective volume fraction of the porous fiber membrane, and the volume-density method has the following calculation formula:
wherein, VfIs volume fraction, m is mass of the porous fiber membrane, h is thickness of the porous fiber membrane, S is area of the porous fiber membrane, and ρ is density of the porous fiber membrane.
Preferably, in the step (3), the digital-FE software is started, Analysis is newly built, project type is selected for thermal, RVE type is selected for 3D, after the Material project is newly built, Phase1 is added into Microtexture as a continuous fiber Phase, and Phase2 is added into an air Phase; inputting the effective volume fraction of a fiber membrane and the average diameter of single fibers, setting the fiber phase direction as 2D random, setting the peripheral size of a 3D geometric model as 20 x 40 μm, setting the minimum relative volume as 0.5, selecting an allowable intercross option, changing random seeds to generate the 3D geometric model with different single fiber distribution positions, then selecting export Geometry in a Geometry interface, exporting the generated geometric model, and selecting the export format as xmt _ txt.
Preferably, in the step (4), the geometric model derived in the step (3) is opened in ANSYS Workbench software, a geometric shape is selected, and the geometric model enters a Design Modeler of a preprocessing platform, and the analysis unit is selected to be micrometer; and selecting a command Form New Part, combining the command Form New Part into a whole, connecting all the geometric bodies into a grid common node, and constructing to obtain the 3D geometric model.
Preferably, in the step (5), the material property of the fiber domain is set to polyurethane, and the material property of the air domain is set to air; and meshing the 3D geometric model by adopting tetrahedral units.
Preferably, in the mesh division process, the fiber domain division size is selected to be coarsened, the maximum unit size is set to be 2.06 μm, the minimum unit size is set to be 0.617 μm, the maximum unit growth rate is 1.2, the curvature factor is 0.7, and the narrow region growth rate is 0.6; the air domain division size was selected as "very coarse", the maximum cell size was set to 6.79 μm, the minimum cell size was set to 1.44 μm, the maximum cell growth rate was 1.4, the curvature factor was 1, and the narrow region growth rate was 0.3.
Preferably, in the step (6), the boundary conditions set for the laminar flow physical field are: adding an upper surface, a lower surface, pressure and a flowing direction to enable pressure difference to exist between the upper surface and the lower surface, and setting four vertical surfaces as non-slip walls;
calculating the apparent velocity in the steady state analysis to feed back the simulated air permeability, wherein the apparent velocity is as follows:
wherein u is the apparent velocity,
for the pressure gradient in the flow direction, κ is the permeability and μ is the kinetic viscosity of air. Preferably, in the step (7), the boundary conditions set for the heat transfer physical field are:
τ>at the time of 0, the number of the first,
wherein tau is a time constant, n is a normal direction of a boundary surface of the 3D geometric model, lambda is a thermal conductivity coefficient, w represents the boundary surface of the 3D geometric model, and t is time; f (tau) is a functional relation with a time constant as an independent variable;
the heat conductivity coefficient is:
wherein z is the direction of heat flux transfer; q ″)zHeat flux in the direction of heat flux transfer;
is the temperature gradient in the heat flux transfer direction.
Compared with the prior art, the invention has the following technical effects:
the invention prepares the spinning solution according to the solubility parameter, and spins the porous fiber membrane by the electrostatic spinning process, and the obtained porous fiber membrane has good overall appearance, uniform thickness and basically no string of beads;
after the thickness, the effective volume fraction and the average diameter of single fibers of the porous fiber membrane are respectively measured through experiments, a 3D geometric model of the porous fiber membrane for finite element simulation is established according to the real appearance characteristics of the porous fiber membrane randomly arranged in the two-dimensional direction; the 3D geometric model of the porous fiber membrane is subjected to meshing by adopting non-structural units (tetrahedral units), and a laminar flow physical field is added to test the apparent speed of the 3D geometric model, so that the structural accuracy of the 3D geometric model of the porous fiber membrane is verified, and the simulation value and the experimental value realize better consistency; and simulation analysis is carried out on the heat transfer process of the porous fiber membrane by utilizing finite element simulation, and comparison analysis verifies that the error between a simulation result and an experimental result is small, so that the feasibility and the reliability of the heat transfer performance simulation prediction method are demonstrated.
Drawings
FIG. 1 is a schematic view of an electrospinning apparatus according to an embodiment of the present invention;
FIG. 2 is a scanning electron microscope image of a real electrospun PU porous fibrous membrane of an embodiment of the present invention;
FIG. 3 is a random algorithmic seed, long fiber root, and effective volume fraction of a 3D geometric model in an embodiment of the present invention;
FIG. 4 is a cross-section (a) and an electron microscope cross-sectional view (b) of a 3D geometric model in an embodiment of the present invention;
FIG. 5 is a 3D geometric model diagram of an embodiment of the present invention;
FIG. 6 is a simulation domain and boundary conditions for solving apparent velocity for a 3D geometric model of an embodiment of the present invention;
FIG. 7 is a velocity field of dry air solved by a laminar physical field in a 3D geometric model of an embodiment of the present invention; (a) x-z cross-sectional velocity, (b) y-z cross-sectional velocity;
FIG. 8 is a meshing of a 3D geometric model of an embodiment of the present invention; (a) a grid of a fiber domain, (b) a grid of an air domain, (c) a grid mass distribution cloud map, (d) a grid mass distribution histogram;
FIG. 9 is a boundary condition definition diagram of a 3D geometric model according to an embodiment of the present invention;
FIG. 10 is a cloud plot (in K) of the simulated temperature distribution at 25 ℃ for a sample of an embodiment of the present invention; (a) at 0 s; (b) at 40 s; (c)80 s; (d) at 120 s; (e) at 160 s;
FIG. 11 is an iso-surface temperature distribution plot at the end of the test for a sample of an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further explained by the following specific examples.
The method for simulating and predicting the heat transfer performance of the porous nanofiber medium comprises the following steps:
(1) preparing a DMF/acetone or DMF/BuAc mixed solution of Polyurethane (PU) according to the solubility parameter; obtaining clear and transparent spinning solution without bubbles after magnetic stirring for 12 hours; pouring the spinning solution into a micro injection pump, fixing the micro injection pump, and starting electrostatic spinning after setting related spinning parameters; and after the porous nanofiber membrane is spun, putting the porous nanofiber membrane into a vacuum drying oven for drying and storing to obtain the PU porous nanofiber membrane.
The spinning solution adopts different solvents, the solvents are the mixture of DMF and acetone or the mixture of DMF and BuAc, and the volume ratio of DMF to acetone is 4: 1, the volume ratio of DMF to acetone is 2: 1, volume ratio of DMF to BuAc 2: 3. the composite solvent is adopted, so that the obtained PU porous nanofiber membrane has larger fiber size difference, and the accuracy of simulation prediction is improved.
As shown in fig. 1, the electrostatic spinning device comprises a micro injection pump, a spinning groove, a receiving plate and a high voltage, which can be referred to the prior art; the spinning parameters were as follows: the spinning speed is 0.6 ml/h, the receiving distance is 15 cm, the spinning voltage is 15 kilovolts, and the spinning time is 4 hours; the environmental conditions were: the temperature environment is 25 +/-5 ℃, and the relative humidity is 40 +/-5%. In addition, the spinning speed is 0.5-1.0 ml/h, the receiving distance is 10-30 cm, the spinning voltage is 5-30 kV, the spinning time is 2-8 hours, and the spinning speed can be freely selected according to the practical application in the range.
(2) Scanning the real electrospun PU porous nanofiber membrane under a scanning electron microscope to obtain a scanning electron microscope image of the PU porous nanofiber membrane, as shown in FIG. 2;
(3) measuring structural parameters of the PU porous nanofiber membrane, including the thickness of the fiber membrane, the average diameter of single fibers in the fiber membrane, the porosity of the fiber membrane and the air permeability of the fiber membrane;
measuring the thicknesses of different positions of the porous nanofiber membrane and calculating the average value of the thicknesses as the thickness of the fiber membrane;
measuring the diameters of a plurality of single fibers according to the scanning electron microscope photo of the porous nanofiber membrane, and calculating the average value of the diameters as the average diameter of the single fibers;
the porosity of the fiber membrane is calculated by using a volume-density method to calculate the effective volume fraction of the porous fiber membrane, and the volume-density method has the following calculation formula:
wherein, VfIs volume fraction, m is mass of the porous fiber membrane, h is thickness of the porous fiber membrane, S is area of the porous fiber membrane, and ρ is density of the porous fiber membrane.
Specifically, the thickness of the measured fiber film was 38.7 μm, the average diameter of the single fibers was 1.643 μm as measured by Image-Pro Plus software, and the effective volume fraction (porosity) of the electrospun PU porous fiber film was 54.7% as measured by volume-density calculation, as shown in fig. 3.
(4) Testing the heat conductivity coefficient of the PU porous nanofiber membrane by using a thermal constant analyzer;
specifically, the test instrument used was TPS2500The model Hot Disk thermal constant analyzer is based on the transient plane heat source method. The input power adopted in the experimental test is 0.6W, the measurement time is 160s, the experimental measurement is carried out at the room temperature of 25 ℃, and the result of the experimental measurement calculation is as follows: the thermal conductivity coefficient is 0.162W/(m.K), the surface temperature rise is 4.11K, the characteristic time of the total ratio is 0.466, and the average deviation is 3.050 multiplied by 10-4K。
(5) Starting Digimat-FE software, entering a main software interface to newly establish an Analysis, and taking the average diameter of the single fibers and the porosity of the fiber membrane counted in the step (3) as a basic basis for establishing a 3D geometric model; changing the orientation of single fibers by setting random algorithm seeds with different two-dimensional orientations, and generating a 3D geometric model with different single fiber distribution positions in a parameterization manner;
specifically, starting Digimat-FE software, entering a software main interface to create an Analysis, selecting thermal project type and 3D RVE type, adding Phase1 as a continuous fiber Phase and Phase2 as an air Phase in Microtexture after creating a Material project; and inputting the fiber with an effective volume fraction of 54.7%, an average fiber diameter of 1.643, a fiber phase direction set to be 2D random, and an overall geometric peripheral dimension set to be 20 × 20 × 40 μm3Setting the minimum relative volume to be 0.5, selecting an allowable interbody crossing option, and changing random seeds to generate 3D geometric models with long fibers facing different directions, wherein random algorithm seeds, single fiber roots (namely long fiber roots) and effective volume fraction values of the 3D geometric models are shown in FIG. 3; and then selecting export Geometry in the Geometry interface, and exporting the generated geometric model, wherein the format is selected to be xmt _ txt.
(6) Importing the generated 3D geometric model into ANSYS Workbench, entering a main interface for preprocessing, namely performing Boolean operation, merging into a whole, connecting all geometric bodies in the 3D geometric model into a grid common node, and constructing to obtain the 3D geometric model;
specifically, opening a derived geometric model comprising a fiber phase and an air phase in ANSYS Workbench software, entering a main interface, selecting a geometric shape, entering a preprocessing platform Design Modeler, and selecting a micrometer analysis unit; importing a geometric model generated in Digimat-FE software, selecting a command Form New Part, combining the geometric model into a whole, connecting all geometric bodies into a grid common node, and constructing to obtain a 3D geometric model; after the 3D geometric model is constructed, the storage path storage material RVE model is selected, and the generated 3D geometric model is shown in fig. 4 in a cross-sectional view and fig. 5 in a 3D geometric model view.
(7) Importing the constructed 3D geometric model into COMSOL, respectively setting material properties of a fiber domain and an air domain for the 3D geometric model, and then performing mesh division;
specifically, the material property of the fiber domain is set to polyurethane, and the material property of the air domain is set to air; meshing the 3D geometric model by using a tetrahedral unit; in the 3D geometric model of the porous fiber membrane, long fibers on the same horizontal plane are crossed with each other and partially overlapped in the vertical direction, so that the 3D geometric model of the porous fiber membrane is divided into meshes by adopting non-structural units (tetrahedral units), and the minimum unit size is set to be the average radius of the long fibers according to the diameter of the long fibers in the model, thereby effectively reducing the occurrence of false alarms; in the COMSOL mesh division process, the division size of the fiber domain is selected to be coarsened, the maximum unit size is set to be 2.06 mu m, the minimum unit size is set to be 0.617 mu m, the maximum unit growth rate is 1.2, the curvature factor is 0.7, and the narrow region growth rate is 0.6; the air domain division size was selected as "very coarse", the maximum cell size was set to 6.79 μm, the minimum cell size was set to 1.44 μm, the maximum cell growth rate was 1.4, the curvature factor was 1, and the narrow region growth rate was 0.3, and the resulting grid division was as shown in fig. 8.
(8) Adding a laminar flow physical field to the 3D geometric model, setting boundary conditions, and then performing steady-state analysis to solve the simulated air permeability; comparing and analyzing the air permeability calculated in the step (8) with the air permeability measured in the step (3), if the air permeability calculated in the step (8) and the air permeability of the fiber membrane are within a target error threshold, verifying that the 3D geometric model meets the requirements, and turning to the step (9);
specifically, the boundary conditions set for the laminar flow physical field are: adding an upper surface, a lower surface, pressure and a flowing direction to enable pressure difference to exist between the upper surface and the lower surface, and setting four vertical surfaces as non-slip walls; as shown in fig. 6, the lower surface is a pressure inlet, the pressure is 0Pa, the flow direction is normal flow, and the pressure is set to suppress backflow; the upper surface is a pressure outlet, the pressure is 100Pa, and the other settings are the same as the lower surface.
Calculating the apparent velocity in the steady state analysis to feed back the simulated air permeability, wherein the apparent velocity is as follows:
where u is the superficial velocity (i.e., the volumetric flow rate per cross-sectional area),
for the pressure gradient in the flow direction, κ is the permeability (expressing the transport properties of the porous medium to the fluid) and μ is the kinetic viscosity of air.
The apparent velocity can reflect the permeability, and further reflect the complexity of the microstructure in the porous medium through the size of the apparent velocity.
In addition, the flow characteristics of the gas (drying air) are determined by the continuity of the vector form and the momentum conservation equation (Navier-Stokes):
wherein the content of the first and second substances,
ρ, p and μ are velocity (m/s) and density (kg/m), respectively3) Pressure (Pa) and dynamic viscosity (Pa · s), n is the normal direction of the boundary surface of the 3D geometric model, FnIs the mass force (N) in the normal direction,
is the unit tensor that is,
the change is indicated by a change in the value of,
indicating the change in velocity in the normal direction.
The process of calculating the velocity field of the drying air solved for the laminar physical field was simulated in the COMSOL software as shown in fig. 7.
(9) Adding a heat transfer physical field to the 3D geometric model, setting boundary conditions, performing transient analysis, obtaining heat flux and temperature gradient in the heat transfer direction in the 3D geometric model through finite element simulation, performing heat transfer simulation, and calculating the heat conductivity coefficient of the 3D geometric model.
Specifically, the boundary conditions are used to describe the temperature of the boundary surface of the heat-conducting object, the heat flux distribution, the heat exchange with the surrounding environment, and the like. In order to simulate the experimental environment during material testing, the lower surface of the 3D geometric model is set as a boundary condition; for transient thermal analysis, the relationship for the boundary conditions is:
τ>at the time of 0, the number of the first,
wherein tau is a time constant, n is a normal direction of a boundary surface of the 3D geometric model, lambda is a thermal conductivity coefficient, w represents the boundary surface of the 3D geometric model, and t is time; f (tau) is a functional relation with a time constant as an independent variable;
in addition, the control equation of the porous fiber membrane heat transfer model is as follows:
wherein rho is the material density of the porous fiber membrane and has the unit of kg/m3；CpThe specific heat capacity of the porous fiber membrane material is expressed by J/(kg. K); mu.stransIs a velocity vector with the unit of m/s; t is the temperature; qhThe heat source inside the model is represented by W; qcThe total heat of the convective heat exchange between the upper surface of the porous fiber membrane and the surrounding environment;
thermal conductivity, also known as thermal conductivity, refers to the ability of a given material to conduct/transfer heat, expressed as λ, and expressed in units of W/(m.K). The thermal conductivity is generally calculated by the following formula:
wherein z is the direction of heat flux transfer; q ″)zThe magnitude of heat flux in the heat flux transfer direction, W/m2；
The temperature gradient in the heat flux transfer direction is given in K/m.
In order to simulate the experimental environment during the material test, the boundary conditions of the porous fiber membrane 3D geometric model are set in the manner shown in fig. 9; the heat transfer simulation was performed on the built 3D geometric model by finite element simulation, and in the COMSOL software, the simulated temperature distribution cloud of the 3D geometric model at 25 ℃ is shown in fig. 10. The iso-surface temperature profile of the 3D geometric model at the end of the test is shown in fig. 11. The heat conductivity coefficient of the 3D geometric model is 0.169W/(m.K) and the surface temperature rise is 4.75K calculated by COMSOL software; the heat conductivity coefficient measured by experiments is 0.162W/(m.K), the surface temperature rise is 4.11K, and the difference between the measured heat conductivity coefficient and the heat conductivity coefficient obtained by 3D geometric model simulation calculation is not large, so that the scheme of 3D geometric modeling of the electrostatic spinning porous nanofiber membrane is illustrated, and the 3D geometric model is applied to finite element simulation research of the heat transfer performance of the electrostatic spinning porous nanofiber membrane, and the practical feasibility is great.
In addition, the polyurethane in the embodiment of the invention can be replaced by polyacrylonitrile, polyvinyl alcohol, ethylene oxide or polyamide, and correspondingly, the corresponding solvent can be selected according to the solubility parameter; the method for heat transfer performance simulation prediction of the embodiment of the invention is also applicable.
The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.
Claims (10)
1. A method for simulation and prediction of heat transfer performance of porous nanofiber media is characterized by comprising the following steps:
(1) selecting a solvent to prepare a spinning solution according to the solubility parameter, and performing electrostatic spinning to obtain a porous nanofiber membrane;
(2) obtaining structural parameters of the porous nanofiber membrane, including the thickness, porosity, air permeability and average diameter of single fibers of the fiber membrane;
(3) establishing a 3D geometric model by utilizing Digimat-FE according to the porosity of the fiber membrane and the average diameter of single fibers; setting the geometric dimension of the 3D geometric model according to the thickness of the fiber membrane; changing the orientation of single fibers by setting random algorithm seeds with different two-dimensional orientations to generate 3D geometric models with different single fiber distribution positions;
(4) importing 3D geometric models with different single fiber distribution positions into ANSYS Workbench, carrying out Boolean operation, combining the models into a whole, connecting all geometric bodies in the 3D geometric models into a grid common node, and constructing to obtain a 3D geometric model;
(5) introducing the constructed 3D geometric model into COMSOL, setting material properties of a fiber domain and an air domain, and then performing mesh division;
(6) adding a laminar flow physical field to the 3D geometric model, setting boundary conditions, and then performing steady-state analysis to solve the simulated air permeability; if the simulated air permeability and the air permeability of the fiber membrane are within the target error threshold, turning to the step (7);
(7) adding a heat transfer physical field to the 3D geometric model, setting boundary conditions, performing transient analysis, obtaining heat flux and temperature gradient in the heat transfer direction in the 3D geometric model through finite element simulation, performing heat transfer simulation, and calculating the heat conductivity coefficient of the 3D geometric model.
2. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 1, wherein in the step (1), the solute of the spinning solution is polyurethane, polyacrylonitrile, polyvinyl alcohol, ethylene oxide or polyamide.
3. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 1, wherein in the step (1), the parameters of electrostatic spinning comprise:
the spinning speed is 0.2-5.0 ml/h, the receiving distance is 10-30 cm, the spinning voltage is 5-30 kV, the ambient temperature is 25 +/-5 ℃, and the ambient relative humidity is 40 +/-5%.
4. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 1, wherein in the step (2), the thickness of the porous nanofiber membrane is measured at different positions and averaged to obtain the thickness of the fiber membrane;
measuring the diameters of a plurality of single fibers according to the scanning electron microscope photo of the porous nanofiber membrane, and calculating the average value of the diameters as the average diameter of the single fibers;
the porosity of the fiber membrane is calculated by using a volume-density method to calculate the effective volume fraction of the porous fiber membrane, and the volume-density method has the following calculation formula:
wherein, VfIs volume fraction, m is mass of the porous fiber membrane, h is thickness of the porous fiber membrane, S is area of the porous fiber membrane, and ρ is density of the porous fiber membrane.
5. The method for modeling and predicting the heat transfer performance of a porous nanofiber medium as claimed in claim 4, wherein in step (3), the Digimat-FE software is started, Analysis is newly built, project type is selected thermal, RVE type is selected 3D, Phase1 is added as continuous fiber Phase and Phase2 is added as air Phase in Microtexture after the Material project is newly built; inputting the effective volume fraction of a fiber membrane and the average diameter of single fibers, setting the fiber phase direction as 2D random, setting the peripheral size of a 3D geometric model as 20 x 40 μm, setting the minimum relative volume as 0.5, selecting an allowable intercross option, changing random seeds to generate the 3D geometric model with different single fiber distribution positions, then selecting export Geometry in a Geometry interface, exporting the generated geometric model, and selecting the export format as xmt _ txt.
6. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 5, wherein in the step (4), the geometric model derived in the step (3) is opened in ANSYS Workbench software, and the geometric shape is selected and entered into Design Modeler of preprocessing platform, and the analysis unit is selected as micrometer; and selecting a command Form New Part, combining the command Form New Part into a whole, connecting all the geometric bodies into a grid common node, and constructing to obtain the 3D geometric model.
7. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 6, wherein in step (5), the material property of fiber domain is set as polyurethane, and the material property of air domain is set as air; and meshing the 3D geometric model by adopting tetrahedral units.
8. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 7, wherein in the gridding process, fiber domain division size is selected as "coarsening", the maximum cell size is set to 2.06 μm, the minimum cell size is set to 0.617 μm, the maximum cell growth rate is 1.2, the curvature factor is 0.7, and the narrow region growth rate is 0.6; the air domain division size was selected as "very coarse", the maximum cell size was set to 6.79 μm, the minimum cell size was set to 1.44 μm, the maximum cell growth rate was 1.4, the curvature factor was 1, and the narrow region growth rate was 0.3.
9. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 7, wherein in the step (6), the boundary conditions set for laminar flow physical field are: adding an upper surface, a lower surface, pressure and a flowing direction to enable pressure difference to exist between the upper surface and the lower surface, and setting four vertical surfaces as non-slip walls;
calculating the apparent velocity in the steady state analysis to feed back the simulated air permeability, wherein the apparent velocity is as follows:
wherein u is the apparent velocity,
for the pressure gradient in the flow direction, κ is the permeability and μ is the kinetic viscosity of air.
10. The method for simulation and prediction of heat transfer performance of porous nanofiber medium as claimed in claim 7, wherein in the step (7), the boundary conditions set for the heat transfer physical field are:
τ>at the time of 0, the number of the first,
wherein tau is a time constant, n is a normal direction of a boundary surface of the 3D geometric model, lambda is a thermal conductivity coefficient, w represents the boundary surface of the 3D geometric model, and t is time; f (tau) is a functional relation with a time constant as an independent variable;
the heat conductivity coefficient is:
wherein z is the direction of heat flux transfer; q ″)zHeat flux in the direction of heat flux transfer;
is the temperature gradient in the heat flux transfer direction.
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