Preparation method of PVDF piezoelectric catalytic material with hollow tubular structure

Abstract

The application discloses a preparation method of a PVDF piezoelectric catalytic material with a hollow tubular structure, and particularly relates to the field of piezoelectric catalytic material preparation, and comprises the following steps: by applying drying, dispersion, shearing, degassing and viscosity adjustment treatment to PVDF particle raw material data, a forming preparation dispersion liquid data is constructed to provide a basic material system for a subsequent disturbance forming stage; by applying a periodic disturbance field on the basis of initial mold filling data, a dynamic disturbance maintaining data is generated by dynamically inducing chain segment secondary orientation, and the data is used as an intermediate state of inverse fatigue structure formation. By applying pressure and speed control flow processing and periodic disturbance field induction processing according to the forming preparation dispersion liquid data, chain segment arrangement and local stress distribution are dynamically and synchronously controlled, dynamic solidification freezing and preparation path closed-loop optimization are combined, and the stable construction of the inverse fatigue effect of the hollow tubular PVDF piezoelectric catalytic material under a periodic water pressure disturbance environment is realized.

Description

Technical Field

[0001] This invention relates to the field of piezoelectric catalytic material preparation technology, and more specifically, to a method for preparing PVDF piezoelectric catalytic material with a hollow tubular structure. Background Technology

[0002] As the application of piezoelectric catalytic materials in environmental pollution control and green energy conversion continues to expand, traditional sheet-like PVDF piezoelectric catalysts have exposed serious local stress concentration problems under periodic fluid impact or water pressure pulsation environment, leading to microcrack initiation, surface peeling and continuous decay of piezoelectric performance, which in turn accelerates material structure fatigue and significantly reduces catalytic activity, restricting its long-term stable application in dynamic liquid phase environment.

[0003] Especially under continuous high-frequency pulsating loading, traditional materials cannot coordinate external deformation and internal piezoelectric response modes, resulting in uncontrollable deformation energy conversion efficiency and easily amplifying fatigue failure effects.

[0004] Existing fabrication technologies mainly focus on morphological optimization and lack a systematic fabrication path modeling mechanism for regulating structural mechanical response and functional synergy, which is not conducive to fundamentally solving the problem of fatigue degradation.

[0005] Therefore, it is urgent to establish a novel method for preparing PVDF piezoelectric catalytic materials that can achieve the reverse fatigue effect under periodic water pressure disturbance, based on a hollow tubular structure as the core, combined with the flexibility and stress dispersion mechanism of PVDF, and through mathematical model derivation of the preparation path and control of structural parameters. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for preparing PVDF piezoelectric catalytic materials with hollow tubular structures. By applying controlled pressure and speed flow treatment and periodic disturbance field induction treatment based on the data of the molding preparation dispersion, the chain segment arrangement and local stress distribution are dynamically and synchronously regulated. Combined with dynamic solidification freezing and closed-loop optimization of the preparation path, the hollow tubular PVDF piezoelectric catalytic materials are stably constructed under periodic water pressure disturbance environment to reverse fatigue effect, thereby solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure, comprising:

[0008] By applying drying, dispersion, shearing, degassing and viscosity adjustment treatments to PVDF particle raw material data, a molding preparation dispersion data is constructed, providing a basic material system for the subsequent perturbation molding stage;

[0009] By applying a periodic disturbance field on the basis of the initial filling data, the secondary orientation of the chain segments is dynamically induced, and the dynamic disturbance retention data is generated as the intermediate state of the inverse fatigue structure formation;

[0010] By dynamic curing and cooling control processing, the chain arrangement state in the dynamic disturbance retention data is frozen, and the stress release and final data are formed;

[0011] By applying disturbance activation processing and performing piezoelectric function detection, inverse fatigue initial state activation data and function verification data are extracted, and primary fatigue test data are generated for performance characteristic evaluation;

[0012] Through dynamic deformation feature extraction and path deviation correction processing, self-adaptive closed-loop optimization of the preparation process is realized, and the final optimized preparation path data is output.

[0013] In a preferred embodiment, PVDF raw material data is collected, which includes particle size distribution, initial moisture content parameter, melt index parameter, and PVDF raw material data is used to characterize the basic state of PVDF piezoelectric catalytic raw material;

[0014] The PVDF raw material data is applied to the temperature control drying operation, and the drying curve parameters are solved in the temperature control drying operation to generate dry PVDF particle data. The dry PVDF particle data is introduced into the solvent system, and shear stirring processing is performed to form homogeneous dispersion liquid data;

[0015] The homogeneous dispersion liquid data is applied to shear pretreatment, the initial orientation direction of the chain segment is identified, and the chain segment primary orientation dispersion liquid data is generated. The chain segment primary orientation dispersion liquid data is introduced into the vacuum standing step to exclude the internal bubbles, and the degassing homogeneous dispersion liquid data is formed;

[0016] The degassing homogeneous dispersion liquid data is applied to viscosity adjustment processing, the solvent ratio is adjusted, and the target viscosity dispersion liquid data is output. The storage temperature and time of the target viscosity dispersion liquid data are controlled, and the molding preparation dispersion liquid data is output.

[0017] In a preferred embodiment, pressure and speed controlled flow processing is performed according to the molding preparation dispersion liquid data, the flow trajectory is guided along the hollow tube mold axis, and the space form of the mold cavity is continuously accumulated and filled, and the initial filling data is generated;

[0018] The initial filling data is applied to the filling uniformity detection processing, the filling rate and filling pressure parameters in the initial filling data are counted, and the filling detection data is generated.

[0019] In a preferred embodiment, it is judged whether the mold filling detection data meets the preset mold filling uniformity standard, if yes, the periodic disturbance processing is applied, if not, the injection flow rate parameter corresponding to the preliminary mold filling data is corrected and the pressure and speed controlled flow processing step is returned to be re-executed;

[0020] The preliminary mold filling data is applied to the periodic disturbance field loading processing, the disturbance frequency parameter and the disturbance amplitude parameter are regulated, and the dynamic disturbance forming data is output. The dynamic disturbance forming data is applied to the chain segment secondary orientation detection processing, the chain segment arrangement direction feature in the dynamic disturbance forming data is extracted, and the chain segment orientation detection data is generated;

[0021] It is judged whether the chain segment orientation detection data meets the set chain segment orientation standard, if yes, the dynamic solidification processing is applied, if not, the periodic disturbance field parameter is corrected and the periodic disturbance field loading processing is returned;

[0022] The dynamic disturbance forming data meeting the chain segment orientation standard is applied to the disturbance duration maintaining processing, the disturbance field action duration is maintained, and the dynamic disturbance maintaining data is generated.

[0023] In a preferred embodiment, the dynamic disturbance maintaining data is applied to the dynamic solidification processing, the solidification temperature curve is set in the dynamic solidification processing, and the dynamic solidification start data is output. The dynamic solidification start data is applied to the chain segment internal stress freezing detection, the freezing degree index is solved, and the freezing detection data is generated;

[0024] It is judged whether the freezing detection data meets the internal stress freezing standard, if yes, the cooling processing is introduced, if not, the solidification time and temperature are adjusted and the solidification step is returned;

[0025] The dynamic solidification start data is applied to the cooling control processing, the cooling rate and gradient are set, and the cooling shaping data is generated. The cooling shaping data is applied to the cooling uniformity detection, the temperature distribution is counted, and the cooling detection data is output;

[0026] It is judged whether the cooling detection data meets the cooling uniformity standard, if yes, the residual stress release step is entered, if not, the cooling curve is adjusted and returned to the cooling processing. The cooling shaping data meeting the cooling uniformity standard is applied to the surface residual stress release processing, and the stress released shaping data is generated.

[0027] In a preferred embodiment, the stress released shaping data is applied to the low amplitude disturbance pre-activation processing, and the inverse fatigue initial state activation data is constructed. The inverse fatigue initial state activation data is applied to the preliminary piezoelectric response detection, the piezoelectric response amplitude and response rate are extracted, and the preliminary piezoelectric detection data is generated;

[0028] determining whether the preliminary piezoelectric detection data meets the inverse fatigue starting standard, if yes, entering the function verification stage, if not, modifying the disturbance amplitude parameter and returning to the activation step; applying the inverse fatigue initial state activation data to the microstructure stability detection, counting the grain size and chain segment orientation degree, and constructing the microstructure detection data;

[0029] determining whether the microstructure detection data meets the stability standard, if yes, registering as function verification passed data, if not, returning to the surface stress release step and processing in a loop;

[0030] applying the function verification passed data to the primary fatigue cycle test, loading the pulse cycle number, and outputting the primary fatigue test data; applying the primary fatigue test data to the fatigue performance extraction processing, and generating the fatigue performance characteristic data.

[0031] In a preferred embodiment, the fatigue performance characteristic data is applied to the dynamic deformation response detection, the natural frequency and strain recovery rate are extracted, and the dynamic deformation characteristic data is output; the dynamic deformation characteristic data is introduced into the preparation path model, the path deviation index is solved, and the preparation path deviation data is formed;

[0032] determining whether the preparation path deviation data is within the set tolerance range, if yes, outputting the final preparation path data, if not, applying the path reverse correction processing; applying the preparation path deviation data to the path reverse correction processing, modifying the disturbance frequency, the mold cavity parameter and the solidification window, and generating the preparation path correction data;

[0033] applying the preparation path correction data to the preparation process adjustment processing, outputting the preparation process optimization data, applying the preparation process optimization data to the process convergence detection, counting the performance stability, and generating the process convergence detection data;

[0034] determining whether the process convergence detection data meets the convergence standard, if yes, outputting the final optimized preparation path, if not, returning to the path reverse correction processing and performing the loop optimization.

[0035] In a preferred embodiment, according to the molding preparation dispersion liquid data, the flow shear rate, local chain segment energy density and chain segment rotation orientation are dynamically controlled through the pressure and speed control flow processing and the periodic disturbance field loading operation, so as to form the dynamic disturbance retention data under the time and space synergistic driving; the total chain segment orientation evolution function of the disturbance auxiliary molding process is set as:

[0036]

[0037] wherein the local disturbance enhancement term is defined as:

[0038]

[0039] wherein is the perturbation assisted segment orientation evolution function; is the mold inner space coordinate position variable; is the perturbation applied time variable; is the local flow velocity gradient tensor; is the local flow shear rate tensor; is the local segment stretch energy density; is the regularization term; is the flow shear master control index; is the local perturbation enhancement term; is the perturbation frequency; is the perturbation amplitude; is the perturbation initial phase offset; is the Laplacian of the local pressure field; is the local pressure relaxation modulation factor; in the above equation is the perturbation amplification master control index.

[0040] In a preferred embodiment, by setting the dynamic solidification temperature curve and freezing kinetics, combined with the local segment freezing damping and internal stress dissipation rate, the solidification segment arrangement state and prestressed structure are formed, and the surface residual stress is released by heat flow control during the cooling process, and the stress release post-setting data is generated;

[0041] The overall model of freezing kinetics and cooling stress release is:

[0042]

[0043] where the cooling stress release term is:

[0044]

[0045] where is the freezing, stress release coupling completion function; is the local temperature field variable; is the process time variable of solidification freezing and cooling stress release; is the local internal stress tensor; is the local shear rate tensor; represents the temperature dependent segment viscous damping coefficient; is the regularization term; is the freezing dynamic response index; represents the local residual stress release degree function in the cooling process; is the temperature gradient tensor; is the temperature Laplacian tensor; is the heat flow regularization term; is the cooling stress release index.

[0046] In a preferred embodiment, the preparation path deviation index is constructed according to the fatigue performance characteristic data, combined with the local strain resilience rate, the fatigue gain rate and the natural frequency mismatch rate, and through gradient strengthening and nonlinear alternating step optimization, the preparation path parameters are dynamically corrected and converged to the final optimized preparation path;

[0047] Let the overall error convergence kinetics equation of the preparation path be:

[0048]

[0049] The path correction iteration formula is:

[0050]

[0051] The dynamic step adjustment is:

[0052]

[0053] Wherein is the kth round of preparation path comprehensive deviation function; is the kth round of strain resilience rate deviation; is the kth round of fatigue gain rate deviation; is the kth round of natural frequency deviation; is the target strain resilience rate; is the target fatigue gain rate; is the target natural frequency; is the comprehensive deviation enhancement index; is the kth round of preparation path parameter set; is the gradient of the preparation path parameter to the path deviation function; is the dynamic step factor; is the dynamic step modulation factor.

[0054] Technical effects and advantages of the present application:

[0055] By applying pressure and speed control flow and periodic disturbance processing on the basis of the molding preparation dispersion liquid data, the secondary orientation of the chain segment is dynamically induced, the local stress concentration in the periodic water pressure impact is dispersed, and the reverse fatigue performance of the hollow tubular PVDF material in the dynamic liquid phase environment is continuously enhanced;

[0056] Through dynamic disturbance maintaining data, dynamic solidification processing and cooling control processing are applied, the chain segment arrangement is frozen and the surface residual stress is released synchronously, the hollow tube wall structure stability is improved, and the long-term consistency of the piezoelectric catalytic effect under the long-period water pressure pulsation is ensured;

[0057] By applying low-amplitude perturbation pre-activation processing, inducing local activation structure inside the chain segment, combining piezoelectric function detection and microstructure detection to extract inverse fatigue initial state activation data, establishing a sustainable enhancement piezoelectric response mechanism, supporting energy conversion in dynamic environment;

[0058] By constructing preparation path deviation index based on fatigue performance characteristic data, applying dynamic deformation response detection and path reverse correction processing, forming a dynamic self-adaptive closed-loop optimization path, improving the consistency of chain segment orientation and the matching of micro-mechanical properties in the forming process;

[0059] By introducing disturbance synchronous induction, dynamic freezing stabilization and path optimization iteration throughout the process, the structure morphology, chain order and piezoelectric output of the material are cooperatively stable in the periodic pulsating impact environment, and the application breadth of PVDF piezoelectric catalytic material in dynamic liquid phase is expanded. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 The method steps flowchart of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0062] With reference to the drawings in the description Figure 1 , an embodiment of the present application is a PVDF piezoelectric catalytic material preparation method with a hollow tubular structure, comprising:

[0063] By applying drying, dispersion, shearing, degassing and viscosity adjustment processing to the PVDF particle raw material data, a dispersion liquid data for forming preparation is constructed to provide a basic material system for the subsequent disturbance forming stage;

[0064] By applying a periodic disturbance field on the basis of the preliminary mold filling data, the secondary orientation of the chain segment is dynamically induced, and the dynamic disturbance retention data is generated as the intermediate state of the inverse fatigue structure formation;

[0065] By dynamic curing and cooling control processing, the chain segment arrangement state in the dynamic disturbance retention data is frozen, forming the stress release and setting data, which is used to ensure the stability of the inverse fatigue effect structure;

[0066] By applying disturbance activation processing and performing piezoelectric function detection, inverse fatigue initial state activation data and function verification pass data are extracted, and primary fatigue test data is generated for performance characteristic evaluation;

[0067] Through dynamic deformation feature extraction and path deviation correction processing, the preparation process is realized adaptive closed-loop optimization, and the final optimized preparation path data is output.

[0068] Collecting PVDF raw material data, the PVDF raw material data includes particle size distribution, initial moisture content parameter, melt index parameter, and the PVDF raw material data is used to characterize the basic state of the PVDF piezoelectric catalytic raw material;

[0069] Applying the PVDF raw material data to the temperature-controlled drying operation, solving the drying curve parameters in the temperature-controlled drying operation, generating the dried PVDF particle data, introducing the dried PVDF particle data into the solvent system, and performing shear stirring processing to form the homogeneous dispersion liquid data;

[0070] Applying the homogeneous dispersion liquid data to shear pretreatment, identifying the initial orientation direction of the chain segment, generating the chain segment primary orientation dispersion liquid data, introducing the chain segment primary orientation dispersion liquid data into the vacuum standing step, and excluding the internal bubbles to form the degassed homogeneous dispersion liquid data;

[0071] Applying the degassed homogeneous dispersion liquid data to viscosity adjustment processing, adjusting the solvent ratio, outputting the target viscosity dispersion liquid data, and controlling the storage temperature and time of the target viscosity dispersion liquid data to output the molding preparation dispersion liquid data.

[0072] According to the molding preparation dispersion liquid data, performing pressure and speed control flow processing, guiding the flow trajectory along the hollow tube mold axial direction, and continuously accumulating and filling according to the mold cavity space form to generate the preliminary mold filling data;

[0073] Applying the preliminary mold filling data to the mold filling uniformity detection processing, counting the filling rate and filling pressure parameters in the preliminary mold filling data, and generating the mold filling detection data.

[0074] Determine whether the mold filling detection data meets the preset mold filling uniformity standard, if yes, apply periodic disturbance processing, if not, modify the injection flow rate parameter corresponding to the preliminary mold filling data and return to the pressure and speed control flow processing step to execute again;

[0075] Applying the preliminary mold filling data to the periodic disturbance field loading processing, adjusting the disturbance frequency parameter and the disturbance amplitude parameter, and outputting the dynamic disturbance molding data; applying the dynamic disturbance molding data to the chain segment secondary orientation detection processing, extracting the chain segment arrangement direction feature in the dynamic disturbance molding data, and generating the chain segment orientation detection data;

[0076] Determine whether the chain segment orientation detection data meets the set chain segment orientation standard, if yes, apply dynamic curing processing, if not, modify the periodic disturbance field parameters and return to the periodic disturbance field loading processing;

[0077] The dynamic disturbance forming data meeting the segment orientation criterion is subjected to a disturbance duration maintaining treatment, maintaining the duration of the disturbance field effect, to generate dynamic disturbance maintaining data.

[0078] The dynamic disturbance maintaining data is subjected to a dynamic solidification treatment, a solidification temperature curve is set in the dynamic solidification treatment, and dynamic solidification start data is output; the dynamic solidification start data is subjected to a segment internal stress freezing detection, a freezing degree index is solved, and freezing detection data is generated;

[0079] It is judged whether the freezing detection data meets the internal stress freezing criterion, if it meets, a cooling treatment is introduced, if it does not meet, the solidification time and temperature are adjusted and returned to the solidification step;

[0080] The dynamic solidification start data is subjected to a cooling control treatment, a cooling rate and gradient are set, and cooling shaping data is generated; the cooling shaping data is subjected to a cooling uniformity detection, a temperature distribution is counted, and cooling detection data is output;

[0081] It is judged whether the cooling detection data meets the cooling uniformity criterion, if it meets, it enters a residual stress release step, if it does not meet, the cooling curve is adjusted and returned to the cooling treatment; the cooling shaping data meeting the cooling uniformity criterion is subjected to a surface residual stress release treatment, and stress release after shaping data is generated.

[0082] The stress release after shaping data is subjected to a low amplitude disturbance pre-activation treatment, and inverse fatigue initial state activation data is constructed; the inverse fatigue initial state activation data is subjected to a preliminary piezoelectric response detection, a piezoelectric response amplitude and response rate are extracted, and preliminary piezoelectric detection data is generated;

[0083] It is judged whether the preliminary piezoelectric detection data meets the inverse fatigue start criterion, if it meets, it enters a function verification phase, if it does not meet, the disturbance amplitude parameter is corrected and returned to the activation step; the inverse fatigue initial state activation data is subjected to a microstructure stability detection, a crystal size and segment orientation degree are counted, and microstructure detection data is constructed;

[0084] It is judged whether the microstructure detection data meets the stability criterion, if it meets, it is registered as function verification pass data, if it does not meet, it returns to the surface stress release step and is processed in a loop;

[0085] The function verification pass data is subjected to a primary fatigue cycle test, a number of pulsating cycles is loaded, and primary fatigue test data is output; the primary fatigue test data is subjected to a fatigue performance extraction treatment, and fatigue performance characteristic data is generated.

[0086] The fatigue performance characteristic data is subjected to a dynamic deformation response detection, a natural frequency and strain recovery rate are extracted, and dynamic deformation characteristic data is output; the dynamic deformation characteristic data is introduced into a preparation path model, a path deviation index is solved, and preparation path deviation data is formed;

[0087] determining whether the preparation path deviation data is within a set tolerance range, if it is, outputting the final preparation path data, if it is not, applying path reverse correction processing; applying path reverse correction processing to the preparation path deviation data, correcting the disturbance frequency, mold cavity parameters and solidification window, generating preparation path correction data;

[0088] applying preparation path correction data to preparation process adjustment processing, outputting preparation process optimization data, applying process convergence detection to preparation process optimization data, counting performance stability, generating process convergence detection data;

[0089] determining whether the process convergence detection data meets the convergence standard, if it does, outputting the final optimized preparation path, if it does not, returning to path reverse correction processing and performing cyclic optimization.

[0090] It should be noted that for the formula structure involved in the present scheme, the dimensionless term can be used as a proportional or structural adjustment factor. When combined with quantities with units, it only plays a numerical scaling role and does not introduce new physical dimensions, so it will not change or confuse the unit system of the whole expression; Such combination of "dimensionless term and quantity unit term" can be understood as a complex structure expression form commonly used in mathematical and physical modeling, which meets the principle of dimensional consistency and has a clear physical interpretation basis;

[0091] Secondly, in the formula structure of the present scheme, if it involves multiple variable terms with different physical units, including but not limited to time, mass or energy variables, their joint occurrence is to express the cooperative modeling relationship of multiple physical mechanisms. Each variable can be mapped by a function, combined by a ratio, or adjusted by a unit. The unit is clear, the meaning is clear, and the whole expression meets the principle of dimensional consistency and the common norm of engineering modeling;

[0092] In the present scheme, if design constants, weights, adjustment factors, threshold parameters, proportionality coefficients, etc. are designed, they are all adjustable control parameters for different application environments. Their values depend on the target device configuration, data input characteristics and performance optimization goals. In the implementation phase, they are converged within a reasonable range through model verification, performance constraints or engineering calibration, etc. Although such parameters do not have a unique value, they have a clear adjustment logic and calculation path, and belong to the deterministic setting process in engineering implementation. The purpose of such setting is to ensure that the scheme has both general adaptability and reproducibility and operability, without affecting its technical clarity and implementability;

[0093] According to the molding preparation dispersion liquid data, through pressure and speed control flow processing and periodic disturbance field loading operation, the flow shear rate, local chain segment energy density and chain segment rotation orientation are dynamically adjusted, so as to form dynamic disturbance retention data under the driving of time and space cooperation; Let the total chain segment orientation evolution function of the disturbance auxiliary molding process be:

[0094]

[0095] where the local disturbance enhancement term is defined as:

[0096]

[0097] where is the disturbance assisted molding segment orientation evolution function, which is used to reflect the time and space variation of the local segment arrangement direction; is the spatial coordinate position variable in the mold; is the disturbance application time variable; is the local flow velocity gradient tensor; is the local flow shear rate tensor; is the local segment stretch energy density; is the minimum regularization term, which is used to prevent the phenomenon of division by zero; is the flow shear master index, which is used to reflect the nonlinear sensitivity of the segment to the flow response; is the local disturbance enhancement term, which couples and forms a dynamic superimposed disturbance effect; is the disturbance frequency, which is used to control the rate of periodic disturbance variation; is the disturbance amplitude, which is used to control the disturbance intensity; is the disturbance initial phase offset; is the Laplace operator of the local pressure field, which represents the degree of local variation of the pressure field; is the local pressure relaxation modulation factor; in the above formula is the disturbance amplification master index, which is used to reflect the degree of nonlinear response of the segment to the disturbance superposition field;

[0098] Explanation of the components of the above formula:

[0099] Part represents the master effect of flow shear and stretch coupling on segment arrangement, and the shear tensor and stretch energy density jointly dominate the initial rotation and arrangement trend of the local segment;

[0100] Part represents the dynamic influence of local disturbance superposition and pressure field relaxation on the correction of segment arrangement, which ensures that the disturbance effect is coordinated and synchronized with the local flow variation.

[0101] By setting dynamic curing temperature curve and freezing kinetics, combining with local segment freezing damping and internal stress dissipation rate, curing segment arrangement state and prestressed structure are formed, surface residual stress is released by heat flow control in cooling process, and stress release post-setting data are generated;

[0102] The overall model of freezing kinetics and cooling stress release is:

[0103]

[0104] The cooling stress release term is:

[0105]

[0106] Wherein is the freezing and stress release coupling completion function, which is used to describe the coordination degree of segment arrangement freezing and stress release; is the local temperature field variable; is the process time variable of curing freezing and cooling stress release; is the local internal stress tensor, which evolves with time u; is the local shear rate tensor; represents the temperature-dependent segment viscous damping coefficient; is the minimum regularization term, which is used to prevent division by zero phenomenon; is the freezing dynamic response index, which is used to reflect the nonlinear response degree of freezing process to stress dissipation; represents the local residual stress release degree function in cooling process; is the temperature gradient tensor; is the temperature Laplace tensor; is the heat flow regularization term, which is used to prevent division by zero phenomenon; is the cooling stress release index, which is used to describe the nonlinear characteristics of residual stress release process;

[0107] Component explanation:

[0108] In , as the first term is used to describe the nonlinear evolution of segment freezing process with time, and the shear dissipation and internal stress accumulation are cooperatively driven to freeze;

[0109] In , as the second term is used to describe the local residual stress release degree in cooling process, and to inhibit the secondary micro-cracks or structural relaxation induced by cooling.

[0110] According to the fatigue performance characteristic data, a preparation path deviation index is constructed, combined with the local strain resilience rate, the fatigue gain rate and the natural frequency mismatch rate, and through gradient strengthening and nonlinear alternating step optimization, the preparation path parameters are dynamically corrected and converged to the final optimized preparation path;

[0111] The preparation path overall error convergence dynamics equation is:

[0112]

[0113] The path correction iteration formula is:

[0114]

[0115] The dynamic step adjustment is:

[0116]

[0117] Wherein is the kth round of preparation path comprehensive deviation function; is the kth round of strain resilience rate deviation; is the kth round of fatigue gain rate deviation; is the kth round of natural frequency deviation; is the target strain resilience rate; is the target fatigue gain rate; is the target natural frequency; is the comprehensive deviation enhancement index, which is used to strengthen the deviation convergence strength; is the kth round of preparation path parameter set, which includes perturbation frequency, injection flow rate and cooling curve parameters; is the gradient of the preparation path parameters to the path deviation function; is a dynamic step factor, which is dynamically scaled according to the deviation amount in actual application; is a dynamic step modulation factor.

[0118] Need to be explained as a whole, this scheme is aimed at the fatigue failure problem of traditional sheet-shaped PVDF piezoelectric catalytic material in dynamic liquid phase environment caused by local stress concentration, based on the systematic regulation of chain segment arrangement and stress evolution mechanism, a set of hollow tubular structure as the core of the reverse fatigue material preparation method is established;

[0119] The scheme first performs drying, shear dispersion, degassing and viscosity adjustment processing on the PVDF particle raw material data to form a molding preparation dispersion liquid data with stable flowability and chain segment preliminary orientation characteristics, providing a basic system for subsequent pressure and speed flow processing and periodic disturbance application;

[0120] In the forming stage, the pressure and speed control flow process is performed according to the forming preparation dispersion liquid data, guiding the continuous accumulation and filling along the hollow tube mold axis, and through the synchronous superposition of the periodic disturbance field in the mold filling process, the flow shear rate, the local segment energy density and the segment rotation orientation are dynamically controlled, the dynamic disturbance retention data is formed, and the spatial synchronous induction of the secondary orientation of the segment is realized.

[0121] In the curing stage, the dynamic disturbance retention data is applied to the dynamic curing process, the segment arrangement state is dynamically frozen through the curing temperature curve, and the residual stress is released through the cooling control process to generate the stress release post-setting data, so that the freezing and stability of the segment internal stress and the continuity of the hollow tube wall structure are ensured.

[0122] In the function activation stage, the low-amplitude disturbance pre-activation process is applied according to the stress release post-setting data to induce the activation of the internal structure of the segment, and the inverse fatigue initial state activation data and the function verification pass data are extracted through piezoelectric response detection and microstructure detection, thereby providing a basis for fatigue performance characteristic evaluation.

[0123] In the preparation path closed loop stage, the preparation path deviation index is constructed based on the fatigue performance characteristic data, the strain rebound rate, the fatigue gain rate and the natural frequency mismatch rate are combined, the dynamic deformation response detection and the path reverse correction process are applied, the path parameters are converged through the dynamic step control mechanism, and finally the optimized preparation path data meeting the requirements of inverse fatigue is output.

[0124] The whole scheme adopts the logic design of disturbance-assisted forming, dynamic freezing curing, low-amplitude activation and path closed loop optimization, which is to build the segment order and prestress system from the beginning of the forming stage in the periodic water pressure disturbance environment, continuously strengthen the piezoelectric response and fatigue resistance through dynamic synchronous excitation, fundamentally overcome the structural bottleneck of easy failure of traditional materials, and ensure the synchronization of segment orientation, micro stability and macro morphology in the preparation process, so as to realize the inverse fatigue working mode of “the more impact, the more active”, and meet the high-efficiency and stable operation demand in long-term dynamic liquid phase application.

[0125] The above only describes the preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for preparing a PVDF piezoelectric catalytic material having a hollow tubular structure, characterized by, Comprise: By applying drying, dispersion, shearing, degassing and viscosity adjustment processes to the PVDF particle raw material data, a molding preparation dispersion liquid data is constructed to provide a basic material system for the subsequent disturbance molding stage; By applying a periodic disturbance field to the initial mold filling data, the chain segment secondary orientation is dynamically induced to generate dynamic disturbance retention data as an intermediate state for the formation of inverse fatigue structure; By dynamic curing and cooling control processes, the chain segment arrangement state in the dynamic disturbance retention data is frozen to form the post-stress release setting data; By applying disturbance activation processing and executing piezoelectric function detection, inverse fatigue initial state activation data and function verification pass data are extracted to generate primary fatigue test data for performance characteristic evaluation; Through dynamic deformation feature extraction and path deviation correction processing, the preparation process is realized self-adaptive closed-loop optimization, and the final optimized preparation path data is output.

2. The PVDF piezoelectric catalytic material preparation method with a hollow tubular structure according to claim 1, characterized in that: Collecting PVDF raw material data, the PVDF raw material data including particle size distribution, initial moisture content parameter, melt index parameter, the PVDF raw material data for representing the basic state of the PVDF piezoelectric catalytic raw material; Applying temperature control drying operation to the PVDF raw material data, solving the drying curve parameters in the temperature control drying operation, generating dried PVDF particle data, introducing the dried PVDF particle data into the solvent system, and executing shearing stirring processing to form homogeneous dispersion liquid data; Applying shearing pretreatment to the homogeneous dispersion liquid data, identifying the chain segment initial orientation direction, generating chain segment primary orientation dispersion liquid data, executing vacuum standing step for the chain segment primary orientation dispersion liquid data to exclude internal bubbles, and forming degassed homogeneous dispersion liquid data; Applying viscosity adjustment processing to the degassed homogeneous dispersion liquid data, adjusting the solvent ratio, outputting target viscosity dispersion liquid data, controlling storage temperature and time for the target viscosity dispersion liquid data, and outputting molding preparation dispersion liquid data.

3. The PVDF piezoelectric catalytic material preparation method with a hollow tubular structure according to claim 2, characterized in that: According to the molding preparation dispersion liquid data, executing pressure and speed controlled flow processing, guiding the flow trajectory along the hollow tube mold axial direction, and continuously accumulating and filling according to the mold cavity space form to generate initial mold filling data; Applying mold filling uniformity detection processing to the initial mold filling data, counting the filling rate and filling pressure parameters in the initial mold filling data, and generating mold filling detection data.

4. The PVDF piezoelectric catalytic material preparation method with a hollow tubular structure according to claim 3, characterized in that: Judging whether the mold filling detection data meets the preset mold filling uniformity standard, if yes, applying periodic disturbance processing, if not, modifying the injection flow rate parameter corresponding to the initial mold filling data and returning to the pressure and speed controlled flow processing step for re-execution; Applying periodic disturbance field loading processing to the initial mold filling data, adjusting the disturbance frequency parameter and disturbance amplitude parameter, and outputting dynamic disturbance molding data; The dynamic disturbance forming data is applied to the segment secondary orientation detection process to extract the segment arrangement direction characteristics in the dynamic disturbance forming data, and segment orientation detection data is generated; It is judged whether the segment orientation detection data meets the set segment orientation standard. If it meets, dynamic solidification processing is applied. If it does not meet, the periodic disturbance field parameter is modified and the periodic disturbance field loading process is returned; The dynamic disturbance forming data meeting the set segment orientation standard is applied to the disturbance duration maintenance processing to maintain the duration of the disturbance field effect, and dynamic disturbance maintenance data is generated.

5. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 4, characterized in that: The dynamic disturbance maintenance data is applied to the dynamic solidification processing, a solidification temperature curve is set in the dynamic solidification processing, and dynamic solidification start data is output; the dynamic solidification start data is applied to the segment internal stress freezing detection, and a freezing degree index is solved to generate freezing detection data; It is judged whether the freezing detection data meets the internal stress freezing standard. If it meets, cooling processing is introduced. If it does not meet, the solidification time and temperature are adjusted and the solidification step is returned; The dynamic solidification start data is applied to the cooling control processing, a cooling rate and gradient are set, and cooling shaping data is generated; the cooling shaping data is applied to the cooling uniformity detection, the temperature distribution is counted, and cooling detection data is output; It is judged whether the cooling detection data meets the cooling uniformity standard. If it meets, the residual stress release step is entered. If it does not meet, the cooling curve is adjusted and returned to the cooling processing; The cooling shaping data meeting the cooling uniformity standard is applied to the residual stress release processing to generate stress release shaping data.

6. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 5, characterized in that: The stress release shaping data is applied to the low-amplitude disturbance pre-activation processing to construct inverse fatigue initial state activation data; The inverse fatigue initial state activation data is applied to the preliminary piezoelectric response detection to extract the piezoelectric response amplitude and response rate, and preliminary piezoelectric detection data is generated; It is judged whether the preliminary piezoelectric detection data meets the inverse fatigue start standard. If it meets, the function verification phase is entered. If it does not meet, the disturbance amplitude parameter is modified and the activation step is returned; The inverse fatigue initial state activation data is applied to the microstructure stability detection to count the grain size and segment orientation degree, and microstructure detection data is constructed; It is judged whether the microstructure detection data meets the stability standard. If it meets, it is registered as function verification passed data. If it does not meet, the residual stress release step is returned and the processing is cycled; The function verification passed data is applied to the primary fatigue cycle test, the number of pulse cycles is loaded, and primary fatigue test data is output; The primary fatigue test data is applied to the fatigue performance extraction processing to generate fatigue performance characteristic data.

7. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 6, characterized in that: The fatigue performance characteristic data is applied to dynamic deformation response detection, natural frequency and strain resilience rate are extracted, and dynamic deformation characteristic data is output; the dynamic deformation characteristic data is introduced into the preparation path model, the path deviation index is solved, and preparation path deviation data is formed; It is judged whether the preparation path deviation data is located in the set tolerance range, if it is consistent, the final preparation path data is output, if it is not consistent, the path reverse correction processing is applied; The preparation path deviation data is applied to the path reverse correction processing, the disturbance frequency, the mold cavity parameter and the curing window are corrected, and the preparation path correction data is generated; The preparation path correction data is applied to the preparation process adjustment processing, the preparation process optimization data is output, the preparation process optimization data is applied to the process convergence detection, the performance stability is counted, and the process convergence detection data is generated; It is judged whether the process convergence detection data meets the convergence standard, if it meets, the final optimized preparation path is output, if it does not meet, the path reverse correction processing is returned and the loop optimization is executed.

8. The method of claim 7, wherein the PVDF piezoelectric catalytic material with a hollow tubular structure is prepared by: According to the molding preparation dispersion liquid data, the flow processing and the periodic disturbance field loading operation are controlled by pressure and speed, the flow shear rate, the local chain segment energy density and the chain segment rotation orientation are dynamically controlled, and the dynamic disturbance retention data is formed under the time and space coordinated driving; the total chain segment orientation evolution function of the disturbance auxiliary molding process is: Wherein the local disturbance enhancement term is defined as: wherein is a perturbation assisted forming segment orientation evolution function; is a mold interior spatial coordinate position variable; is a perturbation application time variable; is a local flow velocity gradient tensor; is a local flow shear rate tensor; is a local segment stretch energy density; is a regularization term; is a flow shear master control index; is a local perturbation enhancement term; is a perturbation frequency; is a perturbation amplitude; is a perturbation initial phase offset; is a Laplacian operator of the local pressure field; is a local pressure relaxation modulation factor; in the above equation is a perturbation amplification master control index.

9. The method of claim 8, wherein the PVDF piezoelectric catalytic material with a hollow tubular structure is prepared by: By setting the dynamic curing temperature curve and freezing kinetics, combining the frozen damping and internal stress dissipation rate of local chain segments, the chain segment arrangement state is cured and the prestressed structure is formed, the surface residual stress is released by heat flow control in the cooling process, and the stress release post-setting data is generated; The overall model of freezing kinetics and cooling stress release is: Wherein the cooling stress release term is: where is the frozen, stress release coupled completion function; is the local temperature field variable; is the process time variable for solidification freezing and cooling stress release; is the local internal stress tensor; is the local shear rate tensor; denotes the temperature dependent segmental viscous damping coefficient; is the regularizing term; is the frozen dynamic response exponent; denotes the local residual stress release function during cooling; is the temperature gradient tensor; is the temperature Laplacian tensor; is the heat flow regularizing term; is the cooling stress release exponent.

10. The method of claim 9, wherein the PVDF piezoelectric catalytic material with a hollow tubular structure is prepared by: According to the fatigue performance characteristic data, the path deviation index is constructed, the local strain resilience rate, the fatigue gain rate and the natural frequency mismatch rate are combined, the preparation path parameters are dynamically corrected by gradient strengthening and nonlinear alternating step optimization, and the final optimized preparation path is converged; The preparation path overall error convergence kinetics equation is: The path correction iteration formula is: Wherein the dynamic step adjustment is: wherein is the kth round manufacturing path synthesis bias function; is the kth round strain recovery bias; is the kth round fatigue gain bias; is the kth round natural frequency bias; is the target strain recovery; is the target fatigue gain; is the target natural frequency; is the synthesis bias enhancement index; is the kth round manufacturing path parameter set; is the gradient of the manufacturing path parameter with respect to the path bias function; is the dynamic step size factor; is the dynamic step size modulation factor.

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