Method for predicting gas permeability of all-composite pressure vessels
By combining the permeability prediction model of the gas barrier film and the carbon fiber layer, the problem of permeability prediction of multi-layer heterogeneous structures in all-composite containers is solved, achieving efficient and accurate gas permeability prediction and meeting engineering design requirements.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTHEAST UNIV
- Filing Date
- 2025-07-23
- Publication Date
- 2026-07-31
AI Technical Summary
Existing gas permeation prediction models are difficult to effectively couple the complex variables of multilayer heterogeneous structures in all-composite containers, resulting in huge deviations between the prediction results and the actual values, which cannot meet the accuracy requirements of engineering design.
A gas barrier film permeability prediction model and a carbon fiber layer permeability prediction model were adopted, combined with a gas permeability coefficient prediction model for heterogeneous laminated materials. The influence of the orientation, crystallinity and number of carbon fiber layers on permeability of the gas barrier nanoparticle filler was considered to establish a corresponding prediction model.
It improves the accuracy and systematicness of gas permeability prediction for all-composite pressure vessels, enabling rapid and accurate prediction of gas permeability coefficients after product molding, thus meeting the precision requirements of engineering design.
Smart Images
Figure CN120853731B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of gas permeability measurement technology, and in particular to a method for predicting the gas permeability of an all-composite pressure vessel. Background Technology
[0002] With the widespread application of cryogenic fuels such as liquid hydrogen, liquid oxygen, and liquid methane in aerospace, new energy, and other fields, lightweight and highly safe storage and transportation containers have become a key technological bottleneck. All-composite liquid hydrogen pressure vessels, with their high specific strength, low thermal conductivity, and excellent fatigue performance, are considered a core solution for next-generation cryogenic storage tanks. However, hydrogen molecules, as the smallest gas molecules in nature (kinetic diameter approximately 0.289 nm), are highly susceptible to penetrating composite laminate structures under ultra-low temperatures (-253°C) and high-strain conditions in pressure vessels, leading to permeable leaks. Such leaks not only cause fuel loss but also pose a potential explosion risk, severely restricting their engineering applications.
[0003] The core challenge currently facing this technology lies in the complexity of the permeation mechanism in multilayer heterogeneous structures and the lack of systematic prediction methods. All-composite containers typically consist of alternating layers of carbon fiber reinforced polymer (CFRP) and a gas barrier film (semi-crystalline polymer). When gas passes through this complex structure, its diffusion path is significantly influenced by multiple factors: the orientation, content (φ), and geometry (e.g., lamellar aspect ratio α, arrangement parameter σ) of the nanoparticles added to the gas barrier film strongly alter the tortuosity (τ) of gas diffusion; simultaneously, changes in the crystallinity (β) of the gas barrier film caused by the composite material curing process directly affect the gas's solubility (S); furthermore, differences in the number of carbon fiber layups (n) lead to varying degrees of microcrack defects, the cumulative effect of which exhibits nonlinear characteristics. Existing gas permeation prediction models, such as those based on pure polymer theory, struggle to effectively couple these complex variables across scales, resulting in significant deviations between predicted and actual values, failing to meet the accuracy requirements of engineering design. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a method for predicting the gas permeability of all-composite material pressure vessels, aiming to improve the accuracy of prediction results and meet the precision requirements of engineering design.
[0005] The technical solution adopted in this invention is as follows: This invention provides a method for predicting the gas permeability of a fully composite pressure vessel. The fully composite material includes stacked carbon fiber layers and a gas barrier film. The carbon fiber layers include one or more stacked carbon fiber unit layers. The prediction method includes the following steps: A gas permeability prediction model for the gas barrier membrane is used to solve for the gas permeability coefficient of the gas barrier membrane and obtain the first predicted value. A carbon fiber layer permeability prediction model is used to solve for the gas permeability coefficient of the carbon fiber layer and obtain a second predicted value. Based on the first and second predicted values, the gas permeability coefficient prediction model for heterogeneous laminated structural materials is used to solve for the gas permeability coefficient of the all-composite material, and a third predicted value is obtained. The gas leakage rate of the pressure vessel is calculated based on the third predicted value; The establishment of the gas barrier film permeation performance prediction model includes: comprehensively considering the influence of the amount and orientation of the gas barrier nanoparticles and the crystallinity on the permeation performance, and establishing corresponding prediction models under different conditions. The carbon fiber layer permeability prediction model considers the influence of the number of different carbon fiber unit layers on the permeability coefficient, and the calculation formula is as follows: ,in, P n For carbon fiber unit layers n The gas permeability coefficient of the carbon fiber layer is Φ D As a defect factor, Φ n The influence factor of the number of strata. P 0 The gas permeability coefficient of carbon fiber without any defects.
[0006] The further technical solution is as follows: The establishment of the gas barrier membrane permeation performance prediction model includes: Without considering the change in crystallinity, the first model, the second model and the third model are established to characterize the gas permeability coefficient of the gas barrier film when the orientation of the gas barrier nanoparticle filler is ordered, the orientation normal of the filler is perpendicular to the gas escape direction and the slit resistance of the filler is considered, and the orientation of the gas barrier nanoparticle filler is disordered. Without considering the changes in the amount and orientation of the gas barrier nanoparticles, a fourth and a fifth model are established to characterize the gas permeability coefficient of the gas barrier film in the semi-crystalline polymer state when the crystallinity is less than or equal to a set threshold. The fourth and fifth models are coupled with the first to third models in sequence to obtain six prediction models for the gas permeability coefficient of the barrier film under different conditions.
[0007] The calculation formula for the first model is: The calculation formula for the second model is: The calculation formula for the third model is as follows: Of the three formulas above, PThis represents the predicted gas permeability coefficient. This represents the volume percentage of the gas-barrier nanoparticles. Where σ is the orientation angle, and σ is a constant related to the geometry and arrangement of the packing material. α This is the ratio of lamellar length to thickness. P a1 The gas permeability coefficient represents the gas barrier film without the addition of gas barrier nanoparticles.
[0008] The calculation formula for the fourth model is as follows: The calculation formula for the fifth model is as follows: In the above two formulas, P β represents the predicted gas permeability coefficient, and β represents the crystallinity. P a2 The permeability coefficient represents the gas permeability coefficient of a completely amorphous substance.
[0009] The fourth model is coupled with the first, second, and third models respectively to obtain prediction models for the gas permeability coefficient of the barrier film when the crystallinity is less than a set threshold, the packing material is arranged in an ordered manner, the packing material orientation normal is perpendicular to the gas escape direction and there is slit resistance between the packing materials, and the packing material orientation is disordered. The calculation formulas are as follows: , The fifth model is coupled with the first, second, and third models respectively to obtain gas permeability prediction models for gas-barrier films when the crystallinity is greater than or equal to the set threshold, the packing material is arranged in an ordered manner, the packing material orientation normal is perpendicular to the gas escape direction and there is slit resistance between the packing materials, and the packing material orientation is disordered. The calculation formulas are as follows: .
[0010] The set threshold is 20%.
[0011] The calculation formula for the gas permeability coefficient prediction model of the heterogeneous laminated structure material is as follows: In the formula, The gas permeability coefficient of the composite material is given. For the first j The thickness of the barrier film. For the first j The gas permeability coefficient of the gas barrier film is assigned the first predicted value during the solution process; For the first i The thickness of the carbon fiber layer, For the first i The gas permeability coefficient of the carbon fiber layer is assigned the second predicted value during the solution process; L The thickness of the composite material is given.
[0012] No. i The thickness of the carbon fiber layer is calculated using the following formula: ,in, m k For the first i The number of carbon fiber unit layers contained in the carbon fiber layer. m The number of carbon fiber unit layers in the composite material.
[0013] The calculation of the container's gas leakage rate based on the third predicted value includes: The gas leakage rate of the test container made of the aforementioned all-composite material is calculated using the following formula. J : ,in, A , These represent the inner surface area and internal pressure of the container under test, respectively. L The thickness of the composite material is the wall thickness of the container under test. Atmospheric pressure.
[0014] Solving for the carbon fiber unit layer is n The gas permeability coefficient of the carbon fiber layer P n ,include: Before product molding, the gas permeability coefficients of the three different structures of the all-composite material and the carbon fiber layer were tested. Substituting these values into the calculation formula of the carbon fiber layer permeability prediction model, the nonlinear least squares method was used for calculation, and the results were obtained iteratively. Φ D , Φ n , P 0 Based on the solution obtained Φ D , Φ n , P 0 ,calculate P n .
[0015] The beneficial effects of this invention are as follows: This invention, on the one hand, integrates the dual control mechanism of orientation state and crystallinity threshold of gas-barrier nanoparticle fillers to construct a synergistic prediction rule for the permeability coefficient of semi-crystalline polymer gas-barrier films, forming a corresponding prediction model and improving the accuracy of gas permeability coefficient prediction for gas-barrier films. On the other hand, a corresponding prediction model is established for carbon fiber layers. This model uses a layer number exponential function to correct for defects in the theoretically defect-free permeability coefficient, mathematically characterizing the inhibitory effect of increasing the number of layers on microcracks, thus improving the accuracy of gas permeability coefficient prediction for carbon fiber layers. Based on this, the parameters of each layer are integrated based on the series resistance principle to derive the overall permeability coefficient of the laminated structure. Furthermore, the hydrogen leakage rate is calculated by combining the internal pressure and surface area of the container to verify safety. This invention significantly improves the systematic nature and engineering applicability of permeability prediction by synergistically integrating nano-modification mechanisms, crystallinity evolution effects, and layer number inhibition models.
[0016] Verification experiments according to embodiments of the present invention show that the gas permeability coefficient of materials before product molding can be measured experimentally. However, after product molding, it is impossible to measure the gas permeability coefficient experimentally without damaging the composite material structure. The present invention can rapidly predict the gas permeability coefficient of the molded product by solving a corresponding prediction model, achieving high prediction efficiency and accurate results.
[0017] Other features and advantages of the invention will be set forth in the following description or may be learned by practicing the invention. Attached Figure Description
[0018] Figure 1 This is a flowchart of the prediction method according to an embodiment of the present invention.
[0019] Figure 2 This is a schematic diagram showing the non-percolation results of the HDPE film and carbon fiber layer in the sample during the verification experiment of this invention embodiment.
[0020] Figure 3 The XRD curves of the HDPE film before and after curing were obtained in the verification experiment of this invention embodiment.
[0021] Figure 4 The experimental data and predicted data curves of the sample gas permeability coefficient in the verification test of the embodiments of the present invention are presented.
[0022] Figure 5 The microcrack structure of the fiber prepreg itself was verified in the embodiment of the present invention.
[0023] In the image: 1. Microcracks. Detailed Implementation
[0024] The specific embodiments of the present invention are described below with reference to the accompanying drawings.
[0025] See Figure 1This embodiment discloses a method for predicting the gas permeability of a fully composite pressure vessel. The fully composite material includes stacked carbon fiber layers and a gas barrier film. The carbon fiber layers include one or more stacked carbon fiber unit layers. The prediction method includes the following steps: S1. Using a gas barrier membrane permeability prediction model, the gas permeability coefficient of the gas barrier membrane is solved to obtain a first predicted value; S1. Using a carbon fiber layer permeability prediction model, the gas permeability coefficient of the carbon fiber layer is solved to obtain a second predicted value; S3. Based on the first and second predicted values, the gas permeability coefficient prediction model for heterogeneous laminated structural materials is used to solve the gas permeability coefficient of the all-composite material to obtain the third predicted value; S4. Calculate the gas leakage rate of the pressure vessel based on the third predicted value; The establishment of the gas barrier film permeation performance prediction model includes: comprehensively considering the influence of the amount and orientation of the gas barrier nanoparticles and the crystallinity on the permeation performance, and establishing corresponding prediction models under different conditions. The carbon fiber layer permeability prediction model considers the influence of the number of different carbon fiber unit layers on the permeability coefficient, and the calculation formula is as follows: ,in, P n For carbon fiber unit layers n The gas permeability coefficient of the carbon fiber layer is Φ D The defect factor ranges from 0 to 1. Φ n The influence factor of the number of strata. P 0 The gas permeability coefficient of carbon fiber without any defects.
[0026] The carbon fiber layer permeability prediction model describes the nonlinear dispersion effect of microcracks caused by the increase in the number of carbon fiber layers through defect factors and layer number influence factors. The theoretical defect-free permeability coefficient is corrected for defects using a layer number exponential function, mathematically characterizing the inhibitory effect of increasing the number of carbon fiber unit layers on microcracks.
[0027] This embodiment considers two aspects. First, after adding gas-barrier nanoparticles to the gas-barrier film, within a certain content range, the gas permeability coefficient of the gas-barrier film will decrease as the content of the gas-barrier nanoparticles increases. Second, as the gas-barrier film solidifies with the composite material, its crystallinity also changes, affecting the gas permeability coefficient. Therefore, by combining the dual control mechanism of the orientation state of the gas-barrier nanoparticle filler and the crystallinity threshold, a synergistic prediction rule for the permeability coefficient of the semi-crystalline polymer gas-barrier film is constructed, forming a corresponding prediction model and improving the accuracy of the gas permeability coefficient prediction. Third, based on the presence of microcracks in the carbon fiber composite material itself, the permeability performance of carbon fiber composites with different layer numbers is predicted to characterize the impact of these microcracks on permeability performance. A corresponding prediction model—a layer-dependent microcrack suppression model—is established. This model uses a layer number exponential function to correct for defects in the theoretically defect-free permeability coefficient, mathematically representing the suppression effect of increasing the number of layers on microcracks, thereby improving the accuracy of the carbon fiber layer gas permeability coefficient prediction. Based on the first two aspects, the parameter values of the gas permeability coefficient prediction model for integrated heterogeneous laminated structural materials based on Fick's first law are corrected. That is, the first and second predicted values are substituted into the model calculation formula to finally obtain an accurate prediction of the gas permeability coefficient of the all-composite pressure vessel.
[0028] As a preferred embodiment, the establishment of the gas barrier film permeation performance prediction model includes: Ignoring changes in crystallinity, for gas barrier films containing gas barrier nanoparticles, differential modeling is performed based on the orientation state of the filler: a first model, a second model, and a third model are established to characterize the gas permeability coefficient of the gas barrier film when the gas barrier nanoparticle filler is oriented in an ordered manner, when the filler orientation normal is perpendicular to the gas escape direction and there is slit resistance between the fillers, and when the gas barrier nanoparticle filler is oriented in a disordered manner. Without considering the changes in the amount and orientation of the gas barrier nanoparticles, a fourth and a fifth model are established to characterize the gas permeability coefficient of the gas barrier film in the semi-crystalline polymer state when the crystallinity is less than or equal to a set threshold. The fourth and fifth models are coupled with the first to third models in sequence to obtain six prediction models for the gas permeability coefficient of the barrier film under different conditions.
[0029] As a specific implementation method: When the particulate filler is oriented in an ordered manner, a predictive relationship is constructed based on the volume ratio of nanoparticles, the length-to-thickness ratio of lamellar crystals, and the angle between the flux direction and the thickness of the lamellar crystals to obtain the first model. The calculation formula is as follows: When the normal of the particulate packing is perpendicular to the gas escape direction and the slit resistance of the packing is considered, a constant related to the geometric arrangement of the packing is introduced for correction, and the second model is constructed. Its calculation formula is as follows: When the particulate filler is completely disordered, a specific empirical function is used to correlate the particle ratio with the length-to-thickness ratio to construct the third model, the calculation formula of which is: Of the three formulas above, P This represents the predicted gas permeability coefficient. This represents the volume percentage of the gas-barrier nanoparticles. Where σ is the orientation angle, and σ is a constant related to the geometry and arrangement of the packing material. α This is the ratio of lamellar length to thickness. P a1 The gas permeability coefficient represents the gas barrier film without the addition of gas barrier nanoparticles.
[0030] The effect of quantitative curing process on crystallinity is investigated. When the crystallinity is below 20%, a linear relationship between the permeability coefficient and crystallinity is established based on the Maxwell model, thus obtaining the fourth model. The calculation formula is as follows: When the crystallinity is not less than 20%, it is converted to a cube root relation according to the Bruggeman model, and then the fifth model is established. The calculation formula is as follows: In the above two formulas, P β represents the predicted gas permeability coefficient, and β represents the crystallinity. P a2 The permeability coefficient represents the gas permeability coefficient of a completely amorphous substance.
[0031] As a specific implementation, the fourth model is coupled with the first model, the second model, and the third model respectively to obtain a gas permeability prediction model for the gas barrier film when the crystallinity is less than a set threshold, the packing orientation is ordered, the packing orientation normal is perpendicular to the gas escape direction and there is slit resistance between the packings, and the packing orientation is disordered. The calculation formulas are as follows: , As a specific implementation, the fifth model is coupled with the first model, the second model, and the third model respectively to obtain a gas permeability prediction model for the gas barrier film when the crystallinity is greater than or equal to the set threshold, the packing orientation is ordered, the packing orientation normal is perpendicular to the gas escape direction and there is slit resistance between the packings, and the packing orientation is disordered. The calculation formulas are as follows: .
[0032] The following further explains the derivation process of the calculation formulas for the first to third models, as well as the fourth and fifth models, and the derivation process of the calculation formulas for the six permeability coefficient prediction models under different conditions.
[0033] I. Constructing the first relative coefficient model: , In the formula, D , S , P The diffusion coefficients (cm) of the gas barrier film after adding gas barrier nanoparticles are shown below. 2 / s, solubility coefficient, cm 3 / (cm 3 ·Pa), and gas permeability coefficient, cm 3 ·cm / (cm 2 ·s·Pa), and have P = D × S ; represents the tortuosity factor; D a1 , S a2 , P a1 The values represent the diffusion coefficient, solubility coefficient, and gas permeability coefficient of the gas barrier film without added gas barrier nanoparticles, respectively. D r , S r , P r These are the relative diffusion coefficient, relative solubility coefficient, and relative permeability coefficient, respectively. In gas-barrier nanoparticles, gas molecules are essentially insoluble; therefore, the relative solubility coefficient is low. S r This can be expressed by the following empirical formula: (1) For gas barrier films with ordered filler orientation, the tortuosity factor is calculated using the following formula: (2), where, The orientation angle is defined as the angle between the flux direction (gas escape direction) and the lamellar thickness direction. For a barrier film where the normal direction of the packing orientation is perpendicular to the gas escape direction, the tortuosity factor is calculated using the following formula, considering the slit resistance between the packings: (3) In the formula, the constant σ related to the geometry and arrangement of the filler is specifically defined as follows: if the nanofiller is a two-dimensional structure, it is the distance between two layers on the same plane divided by its thickness; if the nanofiller is a three-dimensional structure, it is the cross-sectional area of the void region perpendicular to the diffusion flux direction divided by the cross-sectional area of the non-void region. For gas barrier films with disordered filler orientation, the tortuosity factor is expressed by the following empirical formula: (4) Substitute equations (1) to (4) into the first relative coefficient model to obtain the first model to the third model.
[0034] II. Constructing the Second Relative Coefficient Model: , In this formula, D , S , P The diffusion coefficients, in cm⁻¹, are those of the gas barrier film without added gas barrier nanoparticles. 2 / s, solubility coefficient, cm 3 / (cm 3 ·Pa), and gas permeability coefficient, cm 3 ·cm / (cm 2 ·s·Pa), and have P = D × S ; represents the tortuosity factor; D a2 , S a2 , P a2 These are the diffusion coefficient, solubility coefficient, and gas permeability coefficient of a completely amorphous substance, respectively. D r , S r , P r These are the relative diffusion coefficient, relative solubility coefficient, and relative permeability coefficient, respectively.
[0035] The molecular chain density in the crystalline region is high, making it difficult for gas molecules to dissolve. Therefore, assuming that the crystalline region is completely insoluble in gas molecules, the relative solubility coefficient Sr can be expressed by the following empirical formula: (5) For the tortuosity factor τ, when the crystallinity is less than 20%, the Maxwell model is used for calculation: (6) When the crystallinity is greater than 20%, the Bruggeman model is used for calculation: (7) Substitute equations (5) to (7) into the second relative coefficient model to obtain the fourth and fifth models.
[0036] III. Coupling Process The relative solubility coefficient of gas barrier films with added nanofillers and controlled crystallinity S r Expressed by the following formula: (8) For nanoparticle barrier films with a crystallinity of less than 20% and ordered filler orientation, the tortuosity factor is calculated using the following formula: (9) For a gas barrier film with a crystallinity of less than 20% and a filler orientation normal perpendicular to the gas escape direction, considering the slit resistance between fillers, the tortuosity factor is calculated using the following formula: (10) For nanoparticle barrier films with crystallinity less than 20% and disordered filler orientation, the tortuosity factor is expressed by the following empirical formula: (11) For nanoparticle barrier films with a crystallinity greater than or equal to 20% and ordered filler orientation, the tortuosity factor is calculated using the following formula: (12) For a gas barrier film with a crystallinity greater than or equal to 20% and the normal direction of the filler orientation perpendicular to the gas escape direction, considering the slit resistance between the fillers, the tortuosity factor is calculated using the following formula: (13) For nanoparticle barrier films with a crystallinity greater than or equal to 20% and disordered filler orientation, the tortuosity factor is expressed by the following empirical formula: (14) Substituting equations (8) to (14) into the second relative coefficient model yields the prediction formulas for the permeability coefficient of the barrier film under different conditions, as shown in Table 1 below: Table 1. Prediction formulas for the permeability coefficient of gas barrier films under different conditions.
[0037] The above describes the construction process of the first to fifth models, as well as the corresponding prediction models under the six conditions.
[0038] As a specific implementation method, the calculation formula for the gas permeability coefficient prediction model of the heterogeneous laminated structure material in this embodiment is as follows: In the formula, The gas permeability coefficient of the composite material is the calculated third predicted value. For the first j The thickness of the barrier film. For the first j The gas permeability coefficient of the gas barrier film is assigned the first predicted value during the solution process; For the first i The thickness of the carbon fiber layer, For the first i The gas permeability coefficient of the carbon fiber layer is assigned the second predicted value during the solution process; L The thickness of the composite material is given.
[0039] Among them, considering that the thickness of the semi-crystalline polymer after curing is the same as that before curing, the first i The thickness of the carbon fiber layer is calculated using the following formula: in, m k For the first iThe number of carbon fiber unit layers contained in the carbon fiber layer. m The number of carbon fiber unit layers in the composite material.
[0040] As a specific implementation method, based on the third predicted value, the gas leakage rate of the test container using the all-composite material is calculated using the following formula. J : ,in, A , These represent the inner surface area and internal pressure of the container under test, respectively. L The thickness of the composite material is the wall thickness of the container under test. Atmospheric pressure.
[0041] The material properties are assessed based on the calculated gas leakage rate. For example, if the leakage rate exceeds 6 mL / (h·L), the material does not meet the requirements and needs to be modified again. Otherwise, the material is considered acceptable.
[0042] The effectiveness of the proposed solution is further verified by the following experiments. The experimental verification process is as follows: Step 1: Prepare the all-composite material sample according to the designed lamination method. Specific steps include: a) Mold preparation: Apply a release agent to the molding platform surface, dry, and then cover with a release cloth; b) Layup assembly: Manually lay up carbon fiber prepreg and high-density polyethylene (HDPE) film on the release cloth according to the designed stacking order; c) Encapsulation: After layup, cover with a release cloth, place the entire assembly in a vacuum bag, seal, and continuously apply vacuum; d) Hot pressing curing: Place the encapsulated body in a 180℃ forced-air drying oven for 2 hours to cure. (The remaining text appears to be incomplete and requires further context.) Figure 5 As shown, the fiber prepreg used has the microcracks mentioned above.
[0043] Five samples were designed, and the layup sequence is shown in Table 2 below: Table 2 Layup sequence of all composite material samples
[0044] In Table 2, 0 indicates that the layup direction is 0° relative to the principal axis of the material. 0 represents a single layer of carbon fiber prepreg. The subscript of 0 indicates the number of carbon fiber prepreg layers. For example, 07 represents 7 layers of carbon fiber prepreg, i.e., a carbon fiber unit layer. HDPE indicates that an HDPE film is laid in this layer. For example, 07-HDPE-07 means that an HDPE film is sandwiched between two carbon fiber layers, and each carbon fiber layer contains 7 layers of carbon fiber prepreg. The HDPE film does not contain nano-barrier particles.
[0045] Step 2: After the sample has cured, remove it and check whether the HDPE film has permeated into the carbon fiber layer. If permeation occurs, it does not conform to the heterogeneous laminated structure model. If no permeation occurs, it indicates that the prepared sample can be predicted using the heterogeneous laminated structure model. Figure 2 As shown, the HDPE film (white) was removed from the cured carbon fiber layer (black in the image). No adhesion or penetration occurred between the two, indicating no seepage. The crystallinity of the cured HDPE film was then measured to predict the permeability coefficient.
[0046] The HDPE film used to prepare the sample was tested. The crystallinity of the film was obtained by X-ray diffraction. The experimental results are as follows: Figure 3 As shown. Figure 3 (a) is the X-ray diffraction image of the thin film before testing. Figure 3 Image (b) shows the X-ray diffraction pattern of the film after curing. Integral analysis of the spectra reveals that the crystallinity of the film before testing was 57.18%, and after curing, it was 64.67%, both exceeding 20%. The permeability coefficient of the film before curing was 7.66 × 10⁻⁶. -16 mol / (m·s·Pa). Referring to Table 1, the formula was used to predict the permeability coefficient after curing, resulting in a permeability coefficient of 5.74 × 10⁻⁶. - 16 mol / (m·s·Pa).
[0047] Step 3: Solve for the unknowns in the calculation formula of the carbon fiber layer permeability prediction model: The parameter values (gas permeability coefficient and number of carbon fiber layers) obtained from testing three different samples—CFRP14, CFRP14-HDPE1, and CFRP7-HDPE4—were substituted into the calculation formula to... Φ D , Φ n , P 0 The solution is then performed. Experimental values for the gas permeability coefficients of different samples are as follows: Figure 4 As shown. They are respectively: , , The calculation was performed using the nonlinear least squares method, without constrained boundary conditions, and after 1000 iterations, the solution was obtained. , Φ D =0.98、 Φ n =2.55.
[0048] Step 4: Based on the calculation results obtained in Step 3, the permeability coefficient of carbon fiber prepregs with different numbers of carbon fiber unit layers is predicted, and the results are shown in Table 3.
[0049] Table 3. Predicted permeability coefficients of carbon fiber prepregs containing different numbers of carbon fiber unit layers.
[0050] Then, the permeability coefficient of the composite material with added thin film was predicted according to the heterolaminated structure model. The predicted results were compared with the experimental results, for example... Figure 4 As shown, the gas permeability coefficient of CFRP12-HDPE2 and CFRP10-HDPE3 was predicted using a prediction model, and the prediction results are as follows: , The actual test results were: , P can be obtained. CFRP12-HDPE2 P CFRP10-HDPE3 The prediction errors were 5.18% and 20.37%, respectively.
[0051] It will be understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for predicting the gas permeability coefficient of an all-composite pressure vessel, wherein the all-composite material comprises stacked carbon fiber layers and a gas barrier film, the carbon fiber layers comprising one or more stacked carbon fiber unit layers, characterized in that... The method includes the following steps: A gas permeability prediction model for the gas barrier membrane is used to solve for the gas permeability coefficient of the gas barrier membrane and obtain the first predicted value. A carbon fiber layer permeability prediction model is used to solve for the gas permeability coefficient of the carbon fiber layer and obtain a second predicted value. Based on the first and second predicted values, the gas permeability coefficient prediction model for heterogeneous laminated structural materials is used to solve for the gas permeability coefficient of the all-composite material, and a third predicted value is obtained. The gas leakage rate of the pressure vessel is calculated based on the third predicted value; The establishment of the gas barrier film permeation performance prediction model includes: comprehensively considering the influence of the amount and orientation of the gas barrier nanoparticles and the crystallinity on the permeation performance, and establishing corresponding prediction models under different conditions. The carbon fiber layer permeability prediction model considers the influence of the number of different carbon fiber unit layers on the permeability coefficient, and the calculation formula is as follows: ， in, P n For carbon fiber unit layers n The gas permeability coefficient of the carbon fiber layer is Φ D As a defect factor, Φ n The layer number influence factor. P 0 The gas permeability coefficient of carbon fiber without any defects; The establishment of the gas barrier membrane permeation performance prediction model includes: Without considering the change in crystallinity, the first model, the second model and the third model are established to characterize the gas permeability coefficient of the gas barrier film when the orientation of the gas barrier nanoparticle filler is ordered, the orientation normal of the filler is perpendicular to the gas escape direction and the slit resistance of the filler is considered, and the orientation of the gas barrier nanoparticle filler is disordered. Without considering the changes in the amount and orientation of the gas barrier nanoparticles, a fourth and a fifth model are established to characterize the gas permeability coefficient of the gas barrier film in the semi-crystalline polymer state when the crystallinity is less than or equal to a set threshold. The fourth and fifth models are coupled with the first to third models in sequence to obtain six prediction models for the gas permeability coefficient of the barrier film under six conditions. The fourth model is coupled with the first, second, and third models respectively to obtain prediction models for the gas permeability coefficient of the barrier film when the crystallinity is less than a set threshold, the packing material is arranged in an ordered manner, the packing material orientation normal is perpendicular to the gas escape direction and there is slit resistance between the packing materials, and the packing material orientation is disordered. The calculation formulas are as follows: ， ， ， The fifth model is coupled with the first, second, and third models respectively to obtain gas permeability prediction models for gas-barrier films when the crystallinity is greater than or equal to the set threshold, the packing material is arranged in an ordered manner, the packing material orientation normal is perpendicular to the gas escape direction and there is slit resistance between the packing materials, and the packing material orientation is disordered. The calculation formulas are as follows: ， ， ， Of the above six formulas, P This represents the predicted gas permeability coefficient. This represents the volume percentage of the gas-barrier nanoparticles. θ Where σ is the orientation angle, and σ is a constant related to the geometry and arrangement of the packing material. α β represents the ratio of lamellar length to thickness, and β represents crystallinity. P a2 The gas permeability coefficient represents that of a completely amorphous substance; The calculation formula for the gas permeability coefficient prediction model of the heterogeneous laminated structure material is as follows: ， In the formula, The gas permeability coefficient of the composite material is given. For the first j The thickness of the barrier film. For the first j The gas permeability coefficient of the gas barrier film is assigned the first predicted value during the solution process; For the first i The thickness of the carbon fiber layer, For the first i The gas permeability coefficient of the carbon fiber layer is assigned the second predicted value during the solution process; L The thickness of the composite material; n s , n c These represent the number of layers in the gas barrier film and the number of layers in the carbon fiber layer, respectively.
2. The prediction method according to claim 1, characterized in that, The calculation formula for the first model is: ， The calculation formula for the second model is: ， The calculation formula for the third model is as follows: ， Of the three formulas above, P This represents the predicted gas permeability coefficient. This represents the volume percentage of the gas-barrier nanoparticles. θ Where σ is the orientation angle, and σ is a constant related to the geometry and arrangement of the packing material. α This is the ratio of lamellar length to thickness. P a1 The gas permeability coefficient represents the gas barrier film without the addition of gas barrier nanoparticles.
3. The prediction method according to claim 1, characterized in that, The calculation formula for the fourth model is as follows: ， The calculation formula for the fifth model is as follows: ， In the above two formulas, P β represents the predicted gas permeability coefficient, and β represents the crystallinity. P a2 The gas permeability coefficient represents that of a completely amorphous substance.
4. The prediction method according to claim 1, characterized in that, The set threshold is 20%.
5. The prediction method according to claim 1, characterized in that, No. i Thickness of carbon fiber layer Calculate using the following formula: ， in, m i For the first i The number of carbon fiber unit layers contained in the carbon fiber layer. m The number of carbon fiber unit layers in the composite material.
6. The prediction method according to claim 1, characterized in that, The calculation of the container's gas leakage rate based on the third predicted value includes: The gas leakage rate of the test container made of the aforementioned all-composite material is calculated using the following formula. J : ， in, A , These represent the inner surface area and internal pressure of the container under test, respectively. L The thickness of the composite material is the wall thickness of the container under test. Atmospheric pressure.
7. The prediction method according to claim 1, characterized in that, Solving for the carbon fiber unit layer is n The gas permeability coefficient of the carbon fiber layer P n ,include: Before product molding, the gas permeability coefficients of the three different structures of the all-composite material and the carbon fiber layer were tested. Substituting these values into the calculation formula of the carbon fiber layer permeability prediction model, the nonlinear least squares method was used for calculation, and the results were obtained iteratively. Φ D , Φ n , P 0 Based on the solution obtained Φ D , Φ n , P 0 ,calculate P n .