CN104156539B - Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology - Google Patents
Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology Download PDFInfo
- Publication number
- CN104156539B CN104156539B CN201410419212.3A CN201410419212A CN104156539B CN 104156539 B CN104156539 B CN 104156539B CN 201410419212 A CN201410419212 A CN 201410419212A CN 104156539 B CN104156539 B CN 104156539B
- Authority
- CN
- China
- Prior art keywords
- electrode
- gas transport
- curvature factor
- small cubes
- fuel cell
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Inert Electrodes (AREA)
Abstract
The invention discloses a kind of solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology, concretely comprise the following steps：Build the equivalent volume unit of electrode；The physical property of small cubes is randomly assigned, according to porosity ε requirement, part small cubes are randomly assigned as hole for the randomness being distributed according to solid portion in electrode and aperture sections, remaining to be appointed as solid, complete the structure of porous electrode；Based on finite element method and effective media theory, effective diffusion cofficient D is calculatedeff；Based on capillary model, by strict theory deduction, Curvature factor is obtained.Randomness and finite element technique of the present invention based on distribution of pores, overcome the defect of experimental method and ball method of piling, and a kind of simple, efficient, accurate new method is provided for the prediction of solid oxide fuel cell electrode gas transport Curvature factor.
Description
Technical field
The invention belongs to fuel cell field, and in particular to a kind of solid oxide fuel cell electrode gas transport curvature
Factor prediction method.
Background technology
Fuel cell has obtained countries in the world as the forth generation new-generation technology after water power, thermoelectricity, nuclear power
Pay attention to.It is considered as a kind of fuel electricity of most market prospects as the SOFC of third generation fuel cell
Pond.Since " eight or five ", " 95 ", " 15 ", Eleventh Five-Year Plan plan, China continuously subsidizes SOFC technology
Research and development.National high-tech research plan (863 Program) and state key basic research development plan (973 plan) are also to solid oxygen
Compound fuel cell is subsidized for many years.Although SOFC technology has obtained fast development in recent years, but solid
Research of the randomness, complexity, no regularity of oxide fuel cell electrode pore structure to its gas transport Curvature factor
Stern challenge is proposed, hence it is imperative that a kind of learning gas transmission Curvature factor is simple, accurately and efficiently new square
Method.
Solid oxide fuel cell electrode is loose structure, when battery works, fuel gas from electrode surface along
Reaction is participated in the triple line region that pore structure is transferred to electrolyte annex, and reaction product then need to be transferred to electricity along pore structure
Then pole surface is discharged.Because the randomness, complexity, no regularity of electrode pore structure cause gas transport path not
Straight line, but it is very tortuous.In order to characterize the tortuous of gas path, parameter-curvature (τ) is defined.Curvature passes for gas
The ratio of defeated actual path length and air line distance, Curvature factor (τ2) be curvature square.Curvature gets over the actual road of atmospheric
Footpath is longer, and the concentration polarization of battery is bigger.Therefore electrode curvature factor pair optimized contact scheme is reduced, the raising of battery performance is non-
It is often necessary.
At present, the method for obtaining the electrode curvature factor mainly has experimental method and spheric granules free accumulation method.Experimental method is
Utilize the technologies such as SEM (SEM), synchronous X-ray nanometer tomographic, Focused Ion Beam tomoscan (FIB)
Two-dimentional tomoscan is carried out to electrode sample, then two-dimentional tomography picture is synthesized to the electrode structure of solid again.Though experimental method
So can be true and careful reconstruct the pore structure of electrode, but exist it is costly, rely on a large amount of high-end devices, structure
Quality depends critically upon the resolution ratio of image and the reprocessing analysis of data, time-consuming (needs to scan hundreds and thousands of pictures, picture
Post processing it is also very complicated) the shortcomings of.To overcome the disadvantage of above-mentioned experimental method, propose to build using spheric granules free accumulation method
Electrode structure is to study its property, and spheric granules free accumulation method is once by a spheric granules above cuboid container
Random site falls, and when this particle is contacted with container bottom or is contacted with other three or more than three particles, particle reaches
Equilbrium position, the position of particle is fixed, then another spheric granules of free-falling again, and substantial amounts of spheric granules is piled into electrode
Model.This method is although fairly simple, quick, but there are following three major issues：(1) the maximum pore rate of electrode model is
0.45, therefore the situation that porosity is more than 0.45 can not be calculated；(2) because Part-spherical particle is apart from closer, but do not have again
There is contact, cause the electrode model mesh generation difficulty that spheric granules is built than larger, or even mesh generation can not be completed；(3)
The Curvature factor that the electrode model built using spheric granules is calculated deviation ratio compared with experiment value is larger.
The content of the invention
Goal of the invention：In order to overcome the above-mentioned deficiencies of the prior art, the invention provides a kind of solid oxide fuel electricity
Pond electrode gas transmission Curvature factor Forecasting Methodology.
Technical scheme：Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology of the present invention,
Comprise the following steps：
1) the equivalent volume unit of electrode is built
The equivalent volume unit of electrode is piled into using small cubes, the length of side of small cubes is electrode solids and hole
Characteristic length；
Solid oxide fuel cell electrode is made up of solid portion and aperture sections, and solid portion and aperture sections
Distribution there is randomness.Electrode can be thus equivalent to by solid small cubes and the random accumulation of hole small cubes
Into.Microscopic dimensions due to electrode size much larger than hole or solid portion, so only needing to be piled into using small cubes
The equivalent volume unit of electrode.Electrode model is built different from spheric granules, electrode model is built using small cubes, is not present
Especially narrow region, grid division of being more convenient for.
2) it is randomly assigned the physical property of small cubes
The randomness being distributed according to solid portion in electrode and aperture sections, it is according to porosity ε requirement, part is small vertical
Cube at random be appointed as hole, it is remaining to be appointed as solid, complete the structure of porous electrode；
3) finite element method is based on, carrying out loading equation, mesh generation and border to equivalent elementary volume, volume element is set, and calculates
Go out effective diffusion cofficient Deff
The gas transport differential equation is solved using finite element method, then gas flow J to equivalent volume list
The upper surface of member is integrated acquisition total gas flow rate Jtotal；Based on effective media theory, having for porous electrode is further obtained
Imitate diffusion coefficient Deff；
4) capillary model and effective diffusion cofficient D are combinedeff, obtain curvature rate factor τ2。
The step 2) it is specially with equivalent volume unit of the method in small cubes dense accumulation for assigning physical property at random
In, there are N number of small cubes altogether, when porosity is ε, [ε * N] individual small cubes are just randomly assigned to the physical property of hole, will be surplus
Under [(1- ε) * N] individual physical property for being assigned to solid.
The step 3) in effective diffusion cofficient DeffComputing formula is：
Wherein L is the length of the equivalent volume unit in gas transport direction, and A is the equivalent vertical with gas transport direction
The cross-sectional area of product unit, cup(cdown) for equivalent volume unit upper (lower) surface gas concentration.
The step 4) it is specially to be based on capillary model, obtain effective diffusion cofficient DeffWith intrinsic diffusion coefficient D pass
It is to be：
Further according to formula：
Calculate curvature factors τ2, wherein ε is porosity, the effective diffusion cofficient DeffBy step 3) obtain.
Beneficial effect：Compared with the existing technology, the present invention reconstructs electrode, therefore low cost using computer technology, is not required to
High-end devices are wanted, it is simple and convenient, overcome the defect of experimental method.The present invention can both calculate the smaller situation of porosity,
The situation that electrode porosity is more than 0.45 can be calculated, because the present invention can randomly select enough small cubes for hole
Gap.The electrode that the present invention is built is not in narrow region, therefore mesh generation is very easy.By with experimental data
Contrast, the Curvature factor that the present invention is calculated is accurately reliable.Therefore patent of the present invention overcomes the disadvantage of spheric granules free accumulation method
Disease, for solid oxide fuel cell electrode gas transport Curvature factor prediction provide it is a kind of simple, efficiently, it is accurately new
Method.
Brief description of the drawings
The equivalent volume unit figure that Fig. 1 builds for the present invention；
The porous electrode figure that Fig. 2 builds for the present invention；
Fig. 3 is mesh generation result figure of the present invention；
Fig. 4 is result of calculation figure of the present invention；
Fig. 5 result of calculations of the present invention and experiment value comparison diagram.
Embodiment
With 15 × 15 × 15 equivalent volume unit, (through research, the length of side of sample X, Y and Z-direction is at least the present embodiment
During 15 units, the requirement of equivalent volume unit is just met) exemplified by, calculate the Curvature factor τ that porosity is ε=0.2~0.52,
And the correctness of the present invention is verified compared with experiment value.
Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology specific implementation of the present invention is such as
Under：
With porosity ε=0.3, exemplified by the calculating of Z-direction Curvature factor：
The first step：Build the equivalent volume unit of electrode
Solid oxide fuel cell electrode is made up of solid portion and aperture sections, and solid portion and aperture sections
Distribution there is randomness.Electrode can be thus equivalent to by solid small cubes and the random accumulation of hole small cubes
Into.Microscopic dimensions due to electrode size much larger than hole or solid portion, so only needing to be piled into using small cubes
The equivalent volume unit of electrode.First do not consider hole in the case of, with small cubes dense packing into 15 × 15 × 15 it is equivalent
Elementary volume, volume element, has 3375 (N) individual small cubes, as shown in Figure 1 altogether.
Second step：It is randomly assigned the physical property of small cubes
The randomness being distributed according to solid portion in electrode and aperture sections, according to the requirement of porosity ε=0.3, by [ε *
N=0.3*3375]=1013 small cubes are assigned to the physical property of hole, and the diffusion coefficient of gas wherein is intrinsic diffusion coefficient
D.Due to consideration that the Curvature factor of electrode is unrelated with the transport mechanism of gas, thus the concrete numerical value also with D is unrelated, for letter
D is set to 1 for the sake of list.Remaining 3375-1013=2362 small cubes are assigned to the physical property of solid, i.e. gas to be passed through,
As shown in Fig. 2 wherein the small cubes of grey are hole, white small cubes are solid.
3rd step：Based on finite element method, loading equation, mesh generation and border are carried out to equivalent elementary volume, volume element are set,
Calculate effective diffusion cofficient Deff, as shown in Figure 3.
Due to consideration that the Curvature factor of electrode is unrelated with the transport mechanism of gas, determine so have chosen simplest Fick
The transmission of rule description gas.The transmission equation of gas in the electrodes is：
▽ J=▽ (- D ▽ c)
J is the flow of gas in formula, and D is the intrinsic diffusion coefficient of gas,cFor gas concentration.
The concentration of gas is set in the upper surface of equivalent volume unit as cup(1mol/m3), set the dense of gas in its lower surface
Spend for cdown(0mol/m3), it is zero that other borders, which are set to normal direction gas flow,.
Because electrode is formed by small cubes accumulation, therefore without especially narrow region, mesh generation is easier.It is first
Structured grid division first is carried out to a small cubes, then using copy function, the mesh generation of other small cubes is completed.
As a result it is as shown in Figure 3.
The above-mentioned differential equation is solved using finite element method, Gas concentration distribution is obtained as shown in Figure 4.J pairs
The upper surface of equivalent volume unit is integrated acquisition total gas flow rate Jtotal.Based on effective media theory, and then obtain porous
The effective diffusion cofficient D of electrodeeff。
Wherein L is the length of the equivalent volume unit in gas transport direction, and A is the equivalent vertical with gas transport direction
The cross-sectional area of product unit.
4th step：Calculate the Curvature factor of gas transport
Based on capillary model, by strict theory deduction, the relation of effective diffusion cofficient and intrinsic diffusion coefficient is obtained
For：
As long as with porosity ε divided by effective diffusion cofficient DeffWith intrinsic diffusion coefficient D ratio, you can easily ask
Go out Curvature factor τ2。
Utilize same method calculating X, the Curvature factor τ of Y-direction2, while song when can also calculate other porositys
Rate factor τ2。
As shown in figure 5, compared for the Curvature factor τ that present invention during different porosities is calculated2And experimental measurements, demonstrate
The accuracy of the inventive method.
Claims (4)
1. a kind of solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology, it is characterised in that including as follows
Step：
1) the equivalent volume unit of electrode is built
The equivalent volume unit of electrode is piled into using small cubes, the length of side of small cubes is the feature of electrode solids and hole
Length；
2) it is randomly assigned the physical property of small cubes
The randomness being distributed according to solid portion in electrode and aperture sections, according to porosity ε requirement, by part small cubes
It is random to be appointed as hole, it is remaining to be appointed as solid, complete the structure of porous electrode；
3) finite element method is based on, carrying out loading equation, mesh generation and border to equivalent elementary volume, volume element is set, and has calculated
Imitate diffusion coefficient Deff
The gas transport differential equation is solved using finite element method, then gas flow J to equivalent elementary volume, volume element
Upper surface is integrated acquisition total gas flow rate Jtotal；Based on effective media theory, effective expansion of porous electrode is further obtained
Dissipate coefficient Deff；
4) capillary model and effective diffusion cofficient D are combinedeff, obtain Curvature factor τ2。
2. solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology according to claim 1, it is special
Levy and be, the step 2) it is specially with equivalent volume unit of the method in small cubes dense accumulation for assigning physical property at random
In, there are N number of small cubes altogether, when porosity is ε, [ε * N] individual small cubes are just randomly assigned to the physical property of hole, will be surplus
Under [(1- ε) * N] individual physical property for being assigned to solid.
3. solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology according to claim 1, it is special
Levy and be, the step 3) in effective diffusion cofficient DeffComputing formula is：
Wherein L is the length of the equivalent volume unit in gas transport direction, and A is the equivalent volume list vertical with gas transport direction
The cross-sectional area of member, cup、cdownThe respectively gas concentration on the upper and lower surface of equivalent volume unit.
4. solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology according to claim 1, it is special
Levy and be, the step 4) it is specially to be based on capillary model, obtain effective diffusion cofficient DeffWith intrinsic diffusion coefficient D pass
It is to be：
Further according to formula：
Calculate Curvature factor τ2, wherein ε is porosity, the effective diffusion cofficient DeffBy step 3) obtain.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410419212.3A CN104156539B (en) | 2014-08-22 | 2014-08-22 | Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410419212.3A CN104156539B (en) | 2014-08-22 | 2014-08-22 | Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104156539A CN104156539A (en) | 2014-11-19 |
| CN104156539B true CN104156539B (en) | 2017-07-14 |
Family
ID=51882037
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410419212.3A Active CN104156539B (en) | 2014-08-22 | 2014-08-22 | Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104156539B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184056B (en) * | 2015-08-14 | 2017-10-17 | 江苏科技大学 | SOFC electrostatic spinning electrode nanoparticle, which exceedes, oozes probability computational methods |
| CN108153935B (en) * | 2017-11-30 | 2021-05-04 | 东南大学 | Method for constructing porous structure of cementing material |
| CN112578008B (en) * | 2020-12-03 | 2022-11-29 | 江苏科技大学 | Performance analysis method for three-dimensional microstructure of ternary composite electrode of proton ceramic fuel cell |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102437358A (en) * | 2011-11-30 | 2012-05-02 | 中国科学技术大学 | Three-layered structured oxide fuel cell supported by stainless steel and preparation method thereof |
| CN102903940A (en) * | 2012-10-22 | 2013-01-30 | 天津工业大学 | Asymmetric composite anode of microtuble-type solid oxide fuel cell and preparation method of asymmetric composite anode |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5834914B2 (en) * | 2011-12-29 | 2015-12-24 | Toto株式会社 | Solid oxide fuel cell |
-
2014
- 2014-08-22 CN CN201410419212.3A patent/CN104156539B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102437358A (en) * | 2011-11-30 | 2012-05-02 | 中国科学技术大学 | Three-layered structured oxide fuel cell supported by stainless steel and preparation method thereof |
| CN102903940A (en) * | 2012-10-22 | 2013-01-30 | 天津工业大学 | Asymmetric composite anode of microtuble-type solid oxide fuel cell and preparation method of asymmetric composite anode |
Non-Patent Citations (2)
| Title |
|---|
| Observation of atomic scale compositional and displacive modulations;Fengxia We等;《Journal of Solid State Chemistry》;20130731;第203卷;全文 * |
| 阳极孔隙率对固体氧化物燃料电池性能影响的数值分析;于建国等;《硅酸盐学报》;20100228;第38卷(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104156539A (en) | 2014-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106767687B (en) | A method of utilizing remote sensing moisture measurement beach elevation | |
| CN104156539B (en) | Solid oxide fuel cell electrode gas transport Curvature factor Forecasting Methodology | |
| Jiang et al. | Hydrochemical characteristics and water quality evaluation of rivers in different regions of cities: a case study of Suzhou City in Northern Anhui Province, China | |
| Kan et al. | Study on applicability of conceptual hydrological models for flood forecasting in humid, semi-humid semi-arid and arid basins in China | |
| CN106248702A (en) | In influence factor's detection method in a kind of lithium ion battery self discharge | |
| CN114139371B (en) | Multiphase and multi-scale modeling method and system for lithium ion battery electrode | |
| CN103542998B (en) | Sand wind measuring device and sand wind measuring method through sand wind measuring device | |
| Shi et al. | Electrochemical atomic force microscopy study on the dynamic evolution of lithium deposition | |
| Rhazaoui et al. | Towards the 3D modeling of the effective conductivity of solid oxide fuel cell electrodes–II. Computational parameters | |
| Wan et al. | Continuum level transport and electro-chemo-mechanics coupling—solid oxide fuel cells and lithium ion batteries | |
| You et al. | Export of dissolved organic carbon from the source region of Yangtze river in the Tibetan Plateau | |
| Fang et al. | Standard deviation quantitative characterization and process optimization of the pyramidal texture of monocrystalline silicon cells | |
| Caleffi et al. | Ag/MgO nanoparticles via gas aggregation nanocluster source for perovskite solar cell engineering | |
| Yue et al. | Relationship between landform development and lake water recharge in the Badain Jaran Desert, China | |
| Jin et al. | Climate adaptability construction technology of historic conservation areas: The case study of the Chinese–Baroque historic conservation area in Harbin | |
| Wang et al. | Infiltration characteristics and spatiotemporal distribution of soil moisture in layered soil under vertical tube irrigation | |
| Tian et al. | The effect of different mixed organic solvents on the properties of p (OPal-MMA) gel electrolyte membrane for lithium ion batteries | |
| Chen et al. | Mechanistic Exploration of Dendrite Growth and Inhibition for Lithium Metal Batteries | |
| Moussaoui | Microstructural optimization of Solid Oxide Cells: a coupled stochastic geometrical and electrochemical modeling approach applied to LSCF-CGO electrode | |
| CN112908427A (en) | High-throughput screening method for rare earth nickelate material point defects | |
| Niu et al. | In Situ Electrochemical Derivation of Sodium-Tin Alloy as Sodium-Ion Energy Storage Devices Anode with Overall Electrochemical Characteristics | |
| Xie et al. | Positive Effects of Highly Graphitized Porous Carbon Loaded with PbO on Cycle Performance of Negative Plates of Lead-Acid Batteries | |
| Liu et al. | Moisture Migration and Recharge Pattern of Low-Permeability Thick Cohesive Soil in Northern Margin of the Jianghan Plain | |
| Zeng et al. | Influence of Various Urban Morphological Parameters on Urban Canopy Ventilation: A Parametric Numerical Study | |
| Yao et al. | Provenance, Depositional Environment, and Paleoclimatic Conditions of a Near-Source Fan Delta: A Case Study of the Permian Jiamuhe Formation in the Shawan Sag, Junggar Basin |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20141119 Assignee: Jiangsu Jingqi Technology Co.,Ltd. Assignor: JIANGSU University OF SCIENCE AND TECHNOLOGY Contract record no.: X2020980007286 Denomination of invention: Prediction method of curvature factor of electrode gas transport in solid oxide fuel cells Granted publication date: 20170714 License type: Common License Record date: 20201029 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EC01 | Cancellation of recordation of patent licensing contract |
Assignee: Jiangsu Jingqi Technology Co.,Ltd. Assignor: JIANGSU University OF SCIENCE AND TECHNOLOGY Contract record no.: X2020980007286 Date of cancellation: 20201223 |
|
| EC01 | Cancellation of recordation of patent licensing contract |