A performance simulation processing method and system applied to a friction nanogenerator

Abstract

This invention discloses a performance simulation processing method and system for triboelectric nanogenerators. The method involves collecting physical parameter data sets and structural parameter data sets of the triboelectric nanogenerator device; defining material regions, motion boundaries, and load interfaces in the three-dimensional geometric model of the triboelectric nanogenerator; setting external load parameter data sets and environmental frequency data sets as coupling boundary conditions in a multiphysics coupling model; performing finite element numerical simulation to obtain output characteristic data sets, generating key parameter data subsets; constructing a multi-objective optimization model based on the key parameter data subsets and output characteristic data sets, and setting optimization objective weights and constraints; obtaining an optimized structural parameter data set; updating the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator, and performing judgments until the convergence criterion is met. This invention can significantly improve the targeting of structural design and the optimization convergence efficiency.

Description

Technical Field

[0001] This invention relates to the field of triboelectric nanogenerator technology, and in particular to a performance simulation processing method and system for triboelectric nanogenerators. Background Technology

[0002] Triboelectric nanogenerators exhibit completely different dynamic performance from electromagnetic generators, and are expected to compensate for the poor performance of electromagnetic generators in low-frequency scenarios.

[0003] Two challenges in the dynamics of triboelectric nanogenerators limit their application: they are generally considered to be suitable for loads with high resistance, but with low output current, which greatly limits their application scenarios; and currently, there is a lack of a universally applicable or significantly advantageous structural design method, meaning that technological convergence has not yet been achieved, which has dispersed research efforts.

[0004] Therefore, a structural design method that can fully utilize the dynamic characteristics of triboelectric nanogenerators is proposed. Its core lies in making targeted adjustments to the structural design based on the load and ambient frequency, thereby achieving adaptation to the load and environment. Summary of the Invention

[0005] One objective of this invention is to propose a performance simulation processing method and system for triboelectric nanogenerators, which can significantly improve the targeting of structural design and optimize convergence efficiency.

[0006] A performance simulation processing method for triboelectric nanogenerators according to an embodiment of the present invention includes:

[0007] S1. Collect the physical parameter data set and structural parameter data set of the triboelectric nanogenerator device;

[0008] S2. Construct a three-dimensional geometric model of a triboelectric nanogenerator based on the physical parameter data set and the structural parameter data set, and define material regions, motion boundaries and load interfaces in the three-dimensional geometric model of the triboelectric nanogenerator;

[0009] S3. Using the three-dimensional geometric model of the triboelectric nanogenerator, a multi-physics coupling model of electric field-mechanical motion-charge transfer is established, and the external load parameter data set and the environmental frequency data set are set as coupling boundary conditions in the multi-physics coupling model;

[0010] S4. Perform finite element numerical simulation based on the multiphysics coupling model to obtain an output characteristic data set, wherein the output characteristic data set includes at least transient voltage, transient current and transient power;

[0011] S5. Perform parameter sensitivity analysis on the output characteristic data set to generate a key parameter data subset. Construct a multi-objective optimization model based on the key parameter data subset and the output characteristic data set, and set the optimization objective weights and constraints.

[0012] S6. Call the dream optimization algorithm to iteratively solve the multi-objective optimization model and obtain the set of optimized structural parameter data;

[0013] S7. Update the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator, and make judgments until the convergence criterion is met.

[0014] Optionally, the physical parameter data set includes the surface charge density, dielectric constant, and thickness of the friction layer material, and the structural parameter data set includes the length, width, number of stacked units, and stacking method of the friction layer units.

[0015] Optionally, S2 includes:

[0016] S21. Determine the length, width, thickness, and relative permittivity of the upper friction material region of each triboelectric nanogenerator unit in the three-dimensional geometric model of the triboelectric nanogenerator;

[0017] S22. Stack multiple triboelectric nanogenerator units in the three-dimensional geometric model of the triboelectric nanogenerator in sequence according to the thickness direction. Set all triboelectric nanogenerator units in the three-dimensional geometric model of the triboelectric nanogenerator to have the same excitation period and phase synchronization motion characteristics in the simulation environment.

[0018] S23. Based on the thickness of the upper friction material region, the thickness of the lower friction material region, the relative permittivity of the upper friction material region, the relative permittivity of the lower friction material region, and the vacuum permittivity in the three-dimensional geometric model of the triboelectric nanogenerator, calculate the equivalent weighted average permittivity.

[0019] S24. Divide the material region in the three-dimensional geometric model of the triboelectric nanogenerator. The material region includes an upper friction material region and a lower friction material region. Set the thickness and relative permittivity for the upper friction material region and the lower friction material region in the three-dimensional geometric model of the triboelectric nanogenerator, respectively.

[0020] S25. Set the motion boundary and load interface in the three-dimensional geometric model of the triboelectric nanogenerator;

[0021] The motion boundary is used to simulate the relative periodic motion between the friction layers. The period is based on the excitation period in the environmental frequency data set. The motion boundary is used to drive the charge separation and electric field distribution changes between the friction layers, thereby realizing the periodic change of the voltage signal in the output characteristic data set.

[0022] Under a given load and motion input cycle The parameters must satisfy the following conditions:

[0023] ;

[0024] in, The vacuum permittivity, is a parameter related to the motion input function, where N is the number of triboelectric nanogenerators, and L and W are the length and width of the triboelectric nanogenerators, respectively. and These represent the thicknesses of the friction layer units. and is the relative permittivity.

[0025] Optionally, S3 includes:

[0026] S31. Construct a multiphysics coupling model of electric field-mechanical motion-charge transfer;

[0027] S32. Establish the electric potential field distribution equation and the mechanical motion equation in the multiphysics coupling model of electric field-mechanical motion-charge transfer;

[0028] S33. Set up surface charge migration boundary conditions and external load parameter data set in the electric field-mechanical motion-charge migration multiphysics coupling model;

[0029] S34. Introduce an environmental frequency data set into the electric field-mechanical motion-charge migration multiphysics coupling model, and integrate the potential distribution equation, mechanical motion function, surface charge migration boundary conditions, circuit boundary conditions, and environmental frequency excitation function into the electric field-mechanical motion-charge migration multiphysics coupling model.

[0030] Optionally, S4 includes:

[0031] S41. Input the multi-physics coupling model of electric field-mechanical motion-charge migration into the finite element simulation system. Based on the material properties and boundary conditions in the three-dimensional geometric model of the triboelectric nanogenerator, as well as the potential field distribution equation, mechanical motion equation, surface charge migration boundary condition and circuit boundary condition in the multi-physics coupling model of electric field-mechanical motion-charge migration, set the initial simulation conditions.

[0032] S42. In a finite element simulation system, calculate the transient voltage at the output port of a triboelectric nanogenerator based on the potential function;

[0033] S43. In the finite element simulation system, calculate the transient current at the output port of the triboelectric nanogenerator based on the transient voltage and the external load resistance;

[0034] S44. In the finite element simulation system, the transient power at the output port of the triboelectric nanogenerator is calculated based on the transient voltage and transient current. The transient power is estimated using the following formula.

[0035] ;

[0036] in, The surface charge density of the friction material. It is a parameter related to the structural form and motion input function, obtained through experimentation or calculation. The weighted harmonic average of the dielectric constant of the material:

[0037] ;

[0038] S45. Summarize the transient voltage, transient current and transient power of the triboelectric nanogenerator to form an output characteristic data set.

[0039] Optionally, S5 includes:

[0040] S51. Perform parameter sensitivity analysis on the output characteristic data set, and determine the degree of influence of the input parameters on the output performance of the triboelectric nanogenerator by calculating the response amplitude of each input parameter change to the output characteristic data set.

[0041] S52. In the parameter sensitivity analysis, the surface charge density, relative permittivity, friction layer thickness, friction layer length, friction layer width, stacking quantity, and external load resistance of the friction layer material are used as input parameters. The standardized sensitivity coefficient of each input parameter to the output characteristic data set is measured by the parameter influence matrix.

[0042] S53. Perform eigenvalue decomposition on the parameter influence matrix, analyze the contribution rate of each input parameter in the parameter sensitivity analysis, and extract the input parameters that have a significant impact on the output performance of the triboelectric nanogenerator based on the preset contribution rate threshold, forming a subset of key parameter data;

[0043] S54. Construct a multi-objective optimization model based on the output characteristic data set and the key parameter data subset;

[0044] S55. Set physical and structural constraints in a multi-objective optimization model.

[0045] Optionally, S6 includes:

[0046] S61. Initialize the dream optimization algorithm, using each design variable in the key parameter data subset as the search dimension of the dream optimization algorithm to form a design variable vector;

[0047] S62. In the dream optimization algorithm, the optimization process is divided into the cognitive stage, the perturbation stage, and the reality reinforcement stage;

[0048] S63. In the cognitive stage, the fitness value of each individual is calculated based on the comprehensive optimization objective function of the multi-objective optimization model;

[0049] S64. During the perturbation phase, each element of the design variable vector is subjected to a nonlinear jump according to the dream perturbation mechanism;

[0050] S65. During the reality reinforcement phase, calculate the dream quality score for each individual. The dream quality score determines whether the individual has a high-quality dream or a low-quality dream. When the dream quality score is less than the dream quality threshold, the individual is identified as a low-quality dream individual. Low-quality dream individuals are replaced by re-initialization or random perturbation.

[0051] S66. After each iteration, perform constraint checks on all design variable vectors. If any design variable vector does not meet the constraints in the multi-objective optimization model, then correct the fitness value of the design variable vector through the penalty correction function.

[0052] S67. Determine if the termination condition is met. If the termination condition is met, terminate the dream optimization algorithm; otherwise, return to the cognitive stage to continue the next iteration.

[0053] S68. Output the final optimal design variable vector as the optimized structural parameter data set.

[0054] Optionally, the process involves making judgments until the convergence criterion is met, including: updating the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator, re-executing the finite element numerical simulation, generating a verification data set, determining that the verification data set meets the preset convergence criterion, outputting the optimized design scheme and the predicted performance data set; otherwise, returning to step S5 to continue iterative optimization, re-performing parameter sensitivity analysis on the current key parameter data subset and the output characteristic data set, reconstructing the multi-objective optimization model, and executing the dream optimization algorithm iterative process until the verification data set meets the convergence criterion.

[0055] The present invention also relates to a system for performing the above-described performance simulation processing method applied to triboelectric nanogenerators, comprising:

[0056] The simulation modeling module is used to collect physical parameter data sets and structural parameter data sets of the triboelectric nanogenerator device, construct a three-dimensional geometric model of the triboelectric nanogenerator, and define material regions, motion boundaries and load interfaces in the three-dimensional geometric model;

[0057] The multiphysics coupling modeling module is used to establish a multiphysics coupling model of electric field-mechanical motion-charge transfer based on the three-dimensional geometric model, and to set the external load parameter data set and the environmental frequency data set as boundary conditions.

[0058] The finite element simulation module is used to perform finite element numerical simulations under the multiphysics coupling model to obtain a set of output characteristic data.

[0059] The parameter analysis and optimization module is used to perform parameter sensitivity analysis on the output characteristic data set, form a key parameter data subset, and construct a multi-objective optimization model based on the key parameter data subset and the output characteristic data set, and set the objective weights and constraints.

[0060] The Dream Optimization module is used to call the Dream Optimization algorithm to iteratively solve the multi-objective optimization model, obtain the optimized structural parameter data set, and update, simulate and verify the three-dimensional geometric model based on the optimized structural parameter data set until the convergence criterion is met, and output the optimized design scheme and the predicted performance data set.

[0061] The beneficial effects of this invention are:

[0062] (1) This invention integrates the physical parameter data set and structural parameter data set of the triboelectric nanogenerator into a three-dimensional geometric model, and fully describes the coupling behavior of the triboelectric layer material, charge migration, structural morphology and external load in the multi-physics coupling model. Combined with the output characteristic data set, parameter sensitivity analysis is performed to automatically screen the key parameters that have the greatest impact on performance. Then, a multi-objective optimization model and constraints are constructed to form a closed-loop process of parameter acquisition, simulation, optimization and verification, which can significantly improve the pertinence of structural design and optimization convergence efficiency.

[0063] (2) In the process of multi-objective optimization, the present invention introduces a dream optimization algorithm with three stages of cognition-perturbation-reality reinforcement. The dream memory factor, dream perturbation factor and dream quality scoring mechanism jointly drive the search individual to dynamically adjust between the global optimum and the population mean. Combined with adaptive penalty correction to maintain the feasibility of physical constraints, it can effectively overcome the local extreme value trap of parameter space, realize leap convergence and maintain diversity. In the high-dimensional parameter optimization scenario of triboelectric nanogenerator, it improves the global search capability of the optimal structural parameters. Simulation shows that the average convergence speed is improved by about 21%, and the output power of the optimal solution is improved by more than 14%.

[0064] (3) This invention directly maps the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator. Relying on the multi-physics finite element simulation system of electric field-mechanical motion-charge migration, it realizes fully automated verification from structural update to performance prediction. By performing convergence criterion detection on the output characteristic data set and optimization results, the automatic feedback and iteration of model parameters are realized, avoiding a large number of repetitive manual operations in the traditional experiment-simulation separation process. Test results show that the automatic closed-loop optimization process can shorten the design verification cycle by more than 30% under complex structure and low-frequency load matching conditions, and significantly improve the reliability of simulation prediction and engineering application value. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0066] Figure 1 This is a flowchart of a performance simulation processing method for triboelectric nanogenerators proposed in this invention. Detailed Implementation

[0067] Example 1: Reference Figure 1 A performance simulation method for triboelectric nanogenerators includes:

[0068] S1. Collect the physical parameter data set and structural parameter data set of the triboelectric nanogenerator device;

[0069] In this embodiment, the physical parameter data set includes the surface charge density, dielectric constant, and thickness of the friction layer material, and the structural parameter data set includes the length, width, number of stacked units, and stacking method of the friction layer units.

[0070] S2. Construct a three-dimensional geometric model of a triboelectric nanogenerator based on the physical parameter data set and the structural parameter data set, and define the material region, motion boundary and load interface in the three-dimensional geometric model of the triboelectric nanogenerator;

[0071] In this embodiment, S2 includes:

[0072] S21. Determine the length, width, thickness, and relative permittivity of the upper friction material region of each triboelectric nanogenerator unit in the three-dimensional geometric model of the triboelectric nanogenerator;

[0073] The length, width, thickness, and relative permittivity are described by the length of each triboelectric nanogenerator unit in the three-dimensional geometric model of the triboelectric nanogenerator, the width of each triboelectric nanogenerator unit in the three-dimensional geometric model of the triboelectric nanogenerator, the thickness of the upper triboelectric material region in the three-dimensional geometric model of the triboelectric nanogenerator, and the relative permittivity of the upper triboelectric material region in the three-dimensional geometric model of the triboelectric nanogenerator, respectively. At the same time, the thickness and relative permittivity of the lower triboelectric material region are determined.

[0074] S22. Stack multiple triboelectric nanogenerator units in the three-dimensional geometric model of the triboelectric nanogenerator in sequence according to the thickness direction. Set all triboelectric nanogenerator units in the three-dimensional geometric model of the triboelectric nanogenerator to have the same excitation period and phase synchronization motion characteristics in the simulation environment.

[0075] The number of stacks is based on the number of stacks in the structural parameter data set.

[0076] S23. Based on the thickness of the upper friction material region, the thickness of the lower friction material region, the relative permittivity of the upper friction material region, the relative permittivity of the lower friction material region, and the vacuum permittivity in the three-dimensional geometric model of the triboelectric nanogenerator, calculate the equivalent weighted average permittivity.

[0077] The equivalent weighted average dielectric constant represents the overall synthetic response of the multilayer dielectric of the triboelectric nanogenerator device to the electric field distribution. The equivalent weighted average dielectric constant is calculated by multiplying the sum of the thickness of the upper triboelectric material region and the thickness of the lower triboelectric material region by the ratio of the thickness of the upper triboelectric material region divided by the relative dielectric constant of the upper triboelectric material region plus the sum of the thickness of the lower triboelectric material region divided by the relative dielectric constant of the lower triboelectric material region, and then multiplying the product by the vacuum dielectric constant to obtain the equivalent weighted average dielectric constant.

[0078] ;

[0079] in, The vacuum permittivity, , These are the relative permittivity of the upper and lower friction layers, respectively. , To correspond to the thickness of the friction layer, It is the equivalent weighted average dielectric constant.

[0080] S24. Divide the material region in the three-dimensional geometric model of the triboelectric nanogenerator. The material region includes an upper friction material region and a lower friction material region. Set the thickness and relative permittivity for the upper friction material region and the lower friction material region in the three-dimensional geometric model of the triboelectric nanogenerator, respectively.

[0081] All settings are based on a set of physical parameter data to ensure the accurate representation of material properties in the three-dimensional geometric model of the triboelectric nanogenerator.

[0082] S25. Set the motion boundary and load interface in the three-dimensional geometric model of the triboelectric nanogenerator;

[0083] The motion boundary is used to simulate the relative periodic motion between the friction layers. The period is based on the excitation period in the environmental frequency data set. The motion boundary is used to drive the charge separation and electric field distribution changes between the friction layers, thereby realizing the periodic change of the voltage signal in the output characteristic data set.

[0084] The load interface is used as the boundary condition for the output current. The boundary condition is based on the external load resistance in the external load parameter data set. The load interface is used to realize the physical coupling between the three-dimensional geometric model of the triboelectric nanogenerator and the external circuit model.

[0085] The upper friction material region, the lower friction material region, the motion boundary and the load interface are all integrated into the three-dimensional geometric model of the triboelectric nanogenerator. This ensures that the three-dimensional geometric model of the triboelectric nanogenerator can completely and accurately reflect all geometric dimensions, physical parameters, material properties and boundary conditions in multiphysics simulation, and provides a unified structural input framework for multiphysics coupling simulation.

[0086] Under a given load and motion input cycle The parameters must satisfy the following conditions:

[0087] ;

[0088] in, The vacuum permittivity, is a parameter related to the motion input function, where N is the number of triboelectric nanogenerators, and L and W are the length and width of the triboelectric nanogenerators, respectively. and These represent the thicknesses of the friction layer units. and is the relative permittivity.

[0089] S3. A multi-physics coupling model of electric field-mechanical motion-charge transfer is established using a three-dimensional geometric model of a triboelectric nanogenerator. In the multi-physics coupling model, the set of external load parameter data and the set of environmental frequency data are set as coupling boundary conditions.

[0090] In this embodiment, S3 includes:

[0091] S31. Construct a multiphysics coupling model of electric field-mechanical motion-charge transfer;

[0092] The multiphysics coupling model of electric field-mechanical motion-charge migration takes the relative motion of the friction layer as the driving source, and the electric field change, mechanical motion response and surface charge migration behavior work together.

[0093] S32. Establish the electric potential field distribution equation and the mechanical motion equation in the multiphysics coupling model of electric field-mechanical motion-charge transfer;

[0094] The electric potential field distribution equation is used to describe the electric potential distribution at different spatial locations and times. The electric potential distribution is affected by both the dielectric constant distribution and the free charge density. The electric potential field distribution equation is realized by the divergence of the product of the dielectric constant distribution and the electric potential gradient at different locations in space, which is equal to the negative free charge density.

[0095] The mechanical motion equation is used to describe the relative motion between the friction layers. The distance between the friction layers changes with time according to a sine function. The maximum amplitude of the distance between the friction layers and the excitation period together determine the spatial position of the friction layer at any time. The mechanical motion equation serves as the mechanical boundary input to drive the periodic change of the electromotive force.

[0096] S33. Set up surface charge migration boundary conditions and external load parameter data set in the electric field-mechanical motion-charge migration multiphysics coupling model;

[0097] The surface charge migration boundary condition is used to describe the difference between the projected values ​​of the normal electric displacement vectors on the upper and lower sides of the friction layer interface, which is equal to the surface charge density. The surface charge density changes with time, and the surface charge migration boundary condition reflects the jump of the electric field distribution at the friction layer interface.

[0098] The external load resistance in the external load parameter data set is used as a circuit boundary condition. The circuit boundary condition realizes the connection between the model potential output terminal and the circuit solution domain, and provides a physical basis for the loop current path.

[0099] S34. Introduce an environmental frequency data set into the electric field-mechanical motion-charge migration multiphysics coupling model, and integrate the potential distribution equation, mechanical motion function, surface charge migration boundary conditions, circuit boundary conditions, and environmental frequency excitation function into the electric field-mechanical motion-charge migration multiphysics coupling model.

[0100] In the environmental frequency dataset, the excitation frequency and excitation period are reciprocals of each other. The excitation frequency is used to control the periodicity of the mechanical motion function, ensuring that the model input is consistent with the actual environment.

[0101] The electric potential distribution equation, mechanical motion function, surface charge transfer boundary conditions, circuit boundary conditions, and environmental frequency excitation function are all integrated into the electric field-mechanical motion-charge transfer multiphysics coupling model to form a performance simulation model that can achieve full spatial and temporal coupling, providing a unified and complete physical modeling framework for finite element simulation calculations.

[0102] S4. Perform finite element numerical simulation based on the multiphysics coupling model to obtain the output characteristic data set, which includes at least transient voltage, transient current and transient power;

[0103] In this embodiment, S4 includes:

[0104] S41. Input the multi-physics coupling model of electric field-mechanical motion-charge migration into the finite element simulation system. Based on the material properties and boundary conditions in the three-dimensional geometric model of the triboelectric nanogenerator, as well as the potential field distribution equation, mechanical motion equation, surface charge migration boundary condition and circuit boundary condition in the multi-physics coupling model of electric field-mechanical motion-charge migration, set the initial simulation conditions.

[0105] S42. In a finite element simulation system, calculate the transient voltage at the output port of a triboelectric nanogenerator based on the potential function;

[0106] The transient voltage is the potential difference at the output port of the triboelectric nanogenerator at any point in time.

[0107] S43. In the finite element simulation system, calculate the transient current at the output port of the triboelectric nanogenerator based on the transient voltage and the external load resistance;

[0108] The transient current is the current value at the output port of the triboelectric nanogenerator at any time point. The transient current is obtained by dividing the transient voltage by the external load resistance, which is the load resistance in the external load parameter data set.

[0109] S44. In the finite element simulation system, the transient power at the output port of the triboelectric nanogenerator is calculated based on the transient voltage and transient current. The transient power is estimated using the following formula.

[0110] ;

[0111] in, The surface charge density of the friction material. It is a parameter related to the structural form and motion input function, obtained through experimentation or calculation. The weighted harmonic average of the dielectric constant of the material:

[0112] ;

[0113] S45. Summarize the transient voltage, transient current and transient power of the triboelectric nanogenerator to form an output characteristic data set.

[0114] S5. Perform parameter sensitivity analysis on the output characteristic data set to generate a subset of key parameter data. Based on the subset of key parameter data and the output characteristic data set, construct a multi-objective optimization model and set the optimization objective weights and constraints.

[0115] In this embodiment, S5 includes:

[0116] S51. Perform parameter sensitivity analysis on the output characteristic data set, and determine the degree of influence of the input parameters on the output performance of the triboelectric nanogenerator by calculating the response amplitude of each input parameter change to the output characteristic data set.

[0117] S52. In the parameter sensitivity analysis, the surface charge density, relative permittivity, friction layer thickness, friction layer length, friction layer width, stacking quantity, and external load resistance of the friction layer material are used as input parameters. The standardized sensitivity coefficient of each input parameter to the output characteristic data set is measured by the parameter influence matrix.

[0118] The standardized sensitivity coefficient is used to measure the relative contribution of changes in input parameters to changes in the output performance of a triboelectric nanogenerator when other parameters remain constant. The standardized sensitivity coefficient is obtained by multiplying the rate of change of each input parameter by the rate of change of the corresponding output performance and then normalizing the result.

[0119] S53. Perform eigenvalue decomposition on the parameter influence matrix, analyze the contribution rate of each input parameter in the parameter sensitivity analysis, and extract the input parameters that have a significant impact on the output performance of the triboelectric nanogenerator based on the preset contribution rate threshold, forming a subset of key parameter data;

[0120] The key parameter data subset includes material parameters, structural parameters, and load parameters that significantly affect the output performance of triboelectric nanogenerators.

[0121] S54. Construct a multi-objective optimization model based on the output characteristic data set and the key parameter data subset;

[0122] The multi-objective optimization model uses transient voltage objective function, transient current objective function and transient power objective function as optimization objectives, and obtains the comprehensive optimization objective function by weighting and summing the three objective functions according to the objective weight coefficients.

[0123] S55. Set physical and structural constraints in a multi-objective optimization model.

[0124] The physical constraints are defined by the material properties and dielectric constant of the three-dimensional geometric model of the triboelectric nanogenerator, while the structural constraints are defined by the geometric parameters of the triboelectric layer and the external load parameters. All constraints are defined by inequalities to limit the design variables, ensuring that the optimized triboelectric nanogenerator structure meets the requirements of stability and manufacturability within the physical realization range.

[0125] S6. Use the dream optimization algorithm to iteratively solve the multi-objective optimization model and obtain the set of optimized structural parameter data;

[0126] In this embodiment, S6 includes:

[0127] S61. Initialize the dream optimization algorithm, using each design variable in the key parameter data subset as the search dimension of the dream optimization algorithm to form a design variable vector;

[0128] Each element of the design variable vector is a design variable in a subset of key parameter data. The design variables include the surface charge density of the friction layer material, the relative permittivity of the friction layer, the thickness of the friction layer, the length of the friction layer, the width of the friction layer, the number of stacks, and the external load resistance. Each design variable is assigned an initial random value, and the population size and the maximum number of iterations are set.

[0129] S62. In the dream optimization algorithm, the optimization process is divided into the cognitive stage, the perturbation stage, and the reality reinforcement stage;

[0130] In the cognitive stage, a connection is established between dream memory factors and historical optimal solutions. In the perturbation stage, the design variable vector is nonlinearly jumped through dream perturbation factors. In the reality reinforcement stage, dream quality scoring mechanism is used to screen and reinforce dream individuals in the population. The dream quality score is used to determine whether an individual retains the current dream.

[0131] S63. In the cognitive stage, the fitness value of each individual is calculated based on the comprehensive optimization objective function of the multi-objective optimization model;

[0132] The comprehensive optimization objective function is a weighted sum of the transient voltage objective function, transient current objective function, and transient power objective function multiplied by the objective weight coefficients respectively. The value of the comprehensive optimization objective function is used to measure the merits of individual design variable combinations.

[0133] S64. During the perturbation phase, each element of the design variable vector is subjected to a nonlinear jump according to the dream perturbation mechanism;

[0134] In Example 1, the nonlinear jump is specifically as follows: the update method of each design variable is the sum of the difference between the current design variable and the current optimal solution multiplied by the dream memory factor, the difference between the current design variable and the mean of all individuals at the same position in the current population multiplied by the dream enhancement factor, and a random perturbation variable that follows a specific distribution multiplied by the dream perturbation factor. The new design variable value is equal to the sum of the three terms added to the original design variable value.

[0135] ;

[0136] in, Let i be the value of the i-th design variable in the t-th iteration. Let i be the value of the i-th design variable in the (t+1)-th iteration. This is the current optimal solution. Let i be the average value of the i-th design variable in the current population. These are dream memory factors used to maintain an attractive relationship with the optimal solution. This is a dream enhancement factor used to adjust an individual's offset from the group center. This is a dream disturbance factor used to simulate random jumping behavior in dreams. Let be a random perturbation variable that follows the pattern ([-1,1]).

[0137] S65. During the reality reinforcement phase, calculate the dream quality score for each individual. The dream quality score determines whether the individual has a high-quality dream or a low-quality dream. When the dream quality score is less than the dream quality threshold, the individual is identified as a low-quality dream individual. Low-quality dream individuals are replaced by re-initialization or random perturbation.

[0138] The dream quality score uses the difference between each individual's fitness value and the current population mean fitness value as input, and adjusts the sensitivity using a dream quality adjustment coefficient.

[0139] ;

[0140] in, The dream quality score, Let be the fitness value of the i-th individual. This is the average fitness value of all individuals in the current iteration. Dream quality adjustment coefficient.

[0141] S66. After each iteration, perform constraint checks on all design variable vectors. If any design variable vector does not meet the constraints in the multi-objective optimization model, then correct the fitness value of the design variable vector through the penalty correction function.

[0142] The penalty correction function adds a penalty coefficient multiplied by the sum of squares of all constraint function values ​​that violate the constraints to the fitness value of each design variable vector that does not meet the constraints. The corrected fitness value is used for the next iteration selection.

[0143] S67. Determine if the termination condition is met. If the termination condition is met, terminate the dream optimization algorithm; otherwise, return to the cognitive stage to continue the next iteration.

[0144] The termination condition is that the maximum number of iterations reaches a set value or the change in the comprehensive optimization objective function is less than the convergence threshold in a number of consecutive iterations.

[0145] S68. Output the final optimal design variable vector as the optimized structural parameter data set.

[0146] Each design variable in the optimized structural parameter dataset corresponds to the optimal values ​​of the surface charge density of the friction layer material, the relative permittivity of the friction layer, the thickness of the friction layer, the length of the friction layer, the width of the friction layer, the number of stacks, and the external load resistance under the current load and environmental frequency conditions. The optimized structural parameter dataset is used for performance simulation verification and structural design updates of triboelectric nanogenerators.

[0147] S7. Update the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator and make judgments until the convergence criterion is met.

[0148] In this embodiment, the process of judging until the convergence criterion is met includes: updating the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator, re-executing the finite element numerical simulation, generating a verification data set, outputting the optimized design scheme and the predicted performance data set when the verification data set meets the preset convergence criterion; otherwise, returning to S5 to continue iterative optimization, re-performing parameter sensitivity analysis on the current key parameter data subset and the output characteristic data set, reconstructing the multi-objective optimization model, and executing the dream optimization algorithm iterative process until the verification data set meets the convergence criterion.

[0149] In this embodiment, a system for performing a performance simulation processing method applied to a triboelectric nanogenerator is also provided, comprising:

[0150] The simulation modeling module is used to collect physical parameter data sets and structural parameter data sets of triboelectric nanogenerator devices, construct a three-dimensional geometric model of the triboelectric nanogenerator, and define material regions, motion boundaries and load interfaces in the three-dimensional geometric model;

[0151] The multiphysics coupling modeling module is used to establish a multiphysics coupling model of electric field-mechanical motion-charge transfer based on a three-dimensional geometric model, and to set the external load parameter data set and the environmental frequency data set as boundary conditions.

[0152] The finite element simulation module is used to perform finite element numerical simulations under a multiphysics coupled model to obtain a set of output characteristic data.

[0153] The parameter analysis and optimization module is used to perform parameter sensitivity analysis on the output characteristic data set, form a key parameter data subset, and construct a multi-objective optimization model based on the key parameter data subset and the output characteristic data set, and set the objective weights and constraints.

[0154] The Dream Optimization module is used to call the Dream Optimization algorithm to iteratively solve the multi-objective optimization model, obtain the optimized structural parameter data set, and update, simulate and verify the three-dimensional geometric model based on the optimized structural parameter data set until the convergence criterion is met, and output the optimized design scheme and the predicted performance data set.

[0155] Example 2:

[0156] In the context of triboelectric nanogenerators for low-frequency environmental energy harvesting, a team conducted batch performance optimization simulations on a group of triboelectric nanogenerator devices with different structural parameters. During the implementation, the initial device parameter data were first collected: the surface charge densities of triboelectric layer materials A and B were 2.1 μC / m² and 2.4 μC / m², respectively; the relative permittivity of material A was 2.2, and that of material B was 3.1; the initial thicknesses of the two layers were 0.11 mm and 0.10 mm, respectively; the length of the triboelectric layer was 32 mm; the width was 18 mm; the number of stacked layers was 4; and the external load resistance was 80 MΩ.

[0157] In the first round of baseline performance testing, the researchers applied a periodic 1Hz sinusoidal excitation to the triboelectric nanogenerator and obtained the following output characteristics: maximum transient voltage 301V, maximum transient current 5.8μA, and maximum transient power 3.74μW / cm³.

[0158] To improve the device's output performance and load adaptability, the team conducted simulation optimization using the method of this invention based on the aforementioned parameters. The system automatically constructed a three-dimensional geometric model of the triboelectric nanogenerator by inputting the original parameter set and structural parameter set. After setting periodic boundary conditions, a multiphysics coupling model was generated. Using a finite element simulation platform, the implementers performed parameter sensitivity analysis on all key parameters, extracting the friction layer thickness, stacking quantity, friction layer length, and external load resistance as the main parameters affecting output performance.

[0159] The implementer invoked a dream optimization algorithm to globally optimize the extracted key parameters. The algorithm initially set the design variables to the following ranges: thickness 0.09mm~0.13mm, stacking quantity 2~8, length 28mm~36mm, external load resistance 60MΩ~140MΩ, population size 40, and maximum iteration count 200. After three stages of automatic iteration by the dream optimization algorithm, the simulation system recorded the design parameters and performance of all individuals in each generation. For example, in the 72nd generation, the optimal individual parameters were: friction layer thickness 0.10mm, stacking quantity 7, length 34mm, and external load resistance 115MΩ. At this time, the simulation output characteristics were a maximum voltage of 365V, a maximum current of 7.2μA, and a maximum power density of 5.18μW / cm³. This individual's dream quality score was higher than the dream quality threshold, entering the reality reinforcement stage and becoming a "memory reference" for subsequent evolution. In the 161st generation, another high-quality individual parameter combination is a friction layer thickness of 0.12 mm, a stack number of 6, a length of 35 mm, an external load resistance of 123 MΩ, a maximum output power density of 5.31 μW / cm³, and a maximum current of 7.7 μA.

[0160] After the simulation optimization terminates, the system automatically outputs the optimized structural parameter set and updates it back into the 3D geometric model. The implementer then performs finite element simulation verification on the model. The simulation verification data shows that under 1Hz excitation, the optimized device's output characteristics are: maximum voltage 378V, maximum current 8.0μA, and maximum power density 5.43μW / cm³. The convergence criterion is that the change in the comprehensive optimization target value is less than 0.2%, and the error between the verification data and the optimization stage target is less than 2.5%.

[0161] To demonstrate the differences and advantages of the method of this invention compared with traditional particle swarm optimization methods, the implementers simultaneously conducted comparative experiments with traditional optimization algorithms under the same parameter range and initial samples. During particle swarm optimization, the optimal result appeared in the 194th generation, with parameters of a friction layer thickness of 0.11 mm, a stacking quantity of 6, a length of 33 mm, and an external load resistance of 102 MΩ. The output characteristics were a maximum voltage of 354 V, a maximum current of 6.5 μA, and a maximum power density of 4.66 μW / cm³. The optimization time was 18.7% longer than that of the dream optimization algorithm, and the convergence fluctuation period was significantly longer. Statistical analysis of 30 sets of experiments under different initial conditions showed that using the dream optimization algorithm, the average output power density was 5.21 μW / cm³ with a standard deviation of 0.19 μW / cm³; using the traditional method, the average output power density was 4.61 μW / cm³ with a standard deviation of 0.35 μW / cm³.

[0162] For scenarios with extreme load variations, the implementers adjusted the external load resistance to 140MΩ and re-simulated and optimized the algorithm. The Dream Optimization algorithm once again demonstrated superior adaptability, maintaining the maximum output power density within the range of 5.12~5.38μW / cm³, while the traditional PSO method showed increased fluctuations, with the minimum output power density dropping to 4.15μW / cm³. Experimental results in multiple scenarios all demonstrate that the Dream Optimization algorithm not only improves the convergence speed but also enhances the robustness and adaptability of the final design.

[0163] Throughout the simulation and optimization cycle, implementers observed that traditional methods required approximately 15 manual screenings of design parameters, while this invention only required 2-3 initial manual interventions, with the remainder executed automatically in a closed loop. The final structural design and performance prediction process saved 27% of the overall time compared to traditional methods, with smaller fluctuations in experimental data and wider applicability to various scenarios.

[0164] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for performance simulation processing applied to a friction nanogenerator, characterized in that, include: S1. Collect physical parameter data set and structural parameter data set of the triboelectric nanogenerator device. The physical parameter data set includes the surface charge density, dielectric constant and thickness of the triboelectric layer material. The structural parameter data set includes the length, width, number of stacked units and stacking method of the triboelectric layer unit. S2. Construct a three-dimensional geometric model of a triboelectric nanogenerator based on the physical parameter data set and the structural parameter data set, and define material regions, motion boundaries and load interfaces in the three-dimensional geometric model of the triboelectric nanogenerator; S3. Using the three-dimensional geometric model of the triboelectric nanogenerator, a multi-physics coupling model of electric field-mechanical motion-charge transfer is established, and the external load parameter data set and the environmental frequency data set are set as coupling boundary conditions in the multi-physics coupling model; S4. Perform finite element numerical simulation based on the multiphysics coupling model to obtain an output characteristic data set, wherein the output characteristic data set includes at least transient voltage, transient current and transient power; S5. Perform parameter sensitivity analysis on the output characteristic data set to generate a key parameter data subset. Construct a multi-objective optimization model based on the key parameter data subset and the output characteristic data set, and set the optimization objective weights and constraints. S6. Call the dream optimization algorithm to iteratively solve the multi-objective optimization model and obtain the set of optimized structural parameter data; S7. Update the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator, and make judgments until the convergence criterion is met; S2 includes: S21. Determine the length, width, thickness, and relative permittivity of the upper friction material region of each triboelectric nanogenerator unit in the three-dimensional geometric model of the triboelectric nanogenerator; S22. Stack multiple triboelectric nanogenerator units in the three-dimensional geometric model of the triboelectric nanogenerator in sequence according to the thickness direction. Set all triboelectric nanogenerator units in the three-dimensional geometric model of the triboelectric nanogenerator to have the same excitation period and phase synchronization motion characteristics in the simulation environment. S23. Based on the thickness of the upper friction material region, the thickness of the lower friction material region, the relative permittivity of the upper friction material region, the relative permittivity of the lower friction material region, and the vacuum permittivity in the three-dimensional geometric model of the triboelectric nanogenerator, calculate the equivalent weighted average permittivity. S24. Divide the material region in the three-dimensional geometric model of the triboelectric nanogenerator. The material region includes an upper friction material region and a lower friction material region. Set the thickness and relative permittivity for the upper friction material region and the lower friction material region in the three-dimensional geometric model of the triboelectric nanogenerator, respectively. S25. Set the motion boundary and load interface in the three-dimensional geometric model of the triboelectric nanogenerator; The motion boundary is used to simulate the relative periodic motion between the friction layers. The period is based on the excitation period in the environmental frequency data set. The motion boundary is used to drive the charge separation and electric field distribution changes between the friction layers, thereby realizing the periodic change of the voltage signal in the output characteristic data set. At a given load , and motion input period , the following conditions need to be met between the parameters: ； in, The vacuum permittivity, is a parameter related to the motion input function, where N is the number of triboelectric nanogenerators, and L and W are the length and width of the triboelectric nanogenerators, respectively. and These represent the thicknesses of the friction layer units. and is the relative permittivity.

2. The performance simulation processing method for triboelectric nanogenerators according to claim 1, characterized in that, S3 includes: S31. Construct a multiphysics coupling model of electric field-mechanical motion-charge transfer; S32. Establish the electric potential field distribution equation and the mechanical motion equation in the multiphysics coupling model of electric field-mechanical motion-charge transfer; S33. Set up surface charge migration boundary conditions and external load parameter data set in the electric field-mechanical motion-charge migration multiphysics coupling model; S34. Introduce an environmental frequency data set into the electric field-mechanical motion-charge migration multiphysics coupling model, and integrate the potential distribution equation, mechanical motion function, surface charge migration boundary conditions, circuit boundary conditions, and environmental frequency excitation function into the electric field-mechanical motion-charge migration multiphysics coupling model.

3. The performance simulation processing method for triboelectric nanogenerators according to claim 1, characterized in that, S4 includes: S41. Input the multi-physics coupling model of electric field-mechanical motion-charge migration into the finite element simulation system. Based on the material properties and boundary conditions in the three-dimensional geometric model of the triboelectric nanogenerator, as well as the potential field distribution equation, mechanical motion equation, surface charge migration boundary condition and circuit boundary condition in the multi-physics coupling model of electric field-mechanical motion-charge migration, set the initial simulation conditions. S42. In a finite element simulation system, calculate the transient voltage at the output port of a triboelectric nanogenerator based on the potential function; S43. In the finite element simulation system, calculate the transient current at the output port of the triboelectric nanogenerator based on the transient voltage and the external load resistance; S44. In the finite element simulation system, the transient power at the output port of the triboelectric nanogenerator is calculated based on the transient voltage and transient current. The transient power is estimated using the following formula; ； in, The surface charge density of the friction material. It is a parameter related to the structural form and motion input function, obtained through experimentation or calculation. The weighted harmonic average of the dielectric constant of the material: ； S45. Summarize the transient voltage, transient current and transient power of the triboelectric nanogenerator to form an output characteristic data set.

4. The performance simulation processing method for triboelectric nanogenerators according to claim 1, characterized in that, S5 includes: S51. Perform parameter sensitivity analysis on the output characteristic data set, and determine the degree of influence of the input parameters on the output performance of the triboelectric nanogenerator by calculating the response amplitude of each input parameter change to the output characteristic data set. S52. In the parameter sensitivity analysis, the surface charge density, relative permittivity, friction layer thickness, friction layer length, friction layer width, stacking quantity, and external load resistance of the friction layer material are used as input parameters. The standardized sensitivity coefficient of each input parameter to the output characteristic data set is measured by the parameter influence matrix. S53. Perform eigenvalue decomposition on the parameter influence matrix, analyze the contribution rate of each input parameter in the parameter sensitivity analysis, and extract the input parameters that have a significant impact on the output performance of the triboelectric nanogenerator based on the preset contribution rate threshold, forming a subset of key parameter data; S54. Construct a multi-objective optimization model based on the output characteristic data set and the key parameter data subset; S55. Set physical and structural constraints in a multi-objective optimization model.

5. The performance simulation processing method for triboelectric nanogenerators according to claim 1, characterized in that, S6 includes: S61. Initialize the dream optimization algorithm, using each design variable in the key parameter data subset as the search dimension of the dream optimization algorithm to form a design variable vector; S62. In the dream optimization algorithm, the optimization process is divided into the cognitive stage, the perturbation stage, and the reality reinforcement stage; S63. In the cognitive stage, the fitness value of each individual is calculated based on the comprehensive optimization objective function of the multi-objective optimization model; S64. During the perturbation phase, each element of the design variable vector is subjected to a nonlinear jump according to the dream perturbation mechanism; S65. During the reality reinforcement phase, calculate the dream quality score for each individual. The dream quality score determines whether the individual has a high-quality dream or a low-quality dream. When the dream quality score is less than the dream quality threshold, the individual is identified as a low-quality dream individual. Low-quality dream individuals are replaced by re-initialization or random perturbation. S66. After each iteration, perform constraint checks on all design variable vectors. If any design variable vector does not meet the constraints in the multi-objective optimization model, then correct the fitness value of the design variable vector through the penalty correction function. S67. Determine if the termination condition is met. If the termination condition is met, terminate the dream optimization algorithm; otherwise, return to the cognitive stage to continue the next iteration. S68. Output the final optimal design variable vector as the optimized structural parameter data set.

6. The performance simulation processing method for triboelectric nanogenerators according to claim 1, characterized in that, The process involves making judgments until the convergence criterion is met, including: updating the optimized structural parameter data set to the three-dimensional geometric model of the triboelectric nanogenerator, re-executing the finite element numerical simulation, generating a verification data set, and outputting an optimized design scheme and a predicted performance data set when the verification data set meets the preset convergence criterion; otherwise, returning to step S5 to continue iterative optimization, re-performing parameter sensitivity analysis on the current key parameter data subset and the output characteristic data set, reconstructing the multi-objective optimization model, and executing the dream optimization algorithm iterative process until the verification data set meets the convergence criterion.

7. A performance simulation processing system for triboelectric nanogenerators, used to execute the performance simulation processing method for triboelectric nanogenerators as described in any one of claims 1-6, characterized in that, include: The simulation modeling module is used to collect physical parameter data sets and structural parameter data sets of the triboelectric nanogenerator device, construct a three-dimensional geometric model of the triboelectric nanogenerator, and define material regions, motion boundaries and load interfaces in the three-dimensional geometric model; The multiphysics coupling modeling module is used to establish a multiphysics coupling model of electric field-mechanical motion-charge transfer based on the three-dimensional geometric model, and to set the external load parameter data set and the environmental frequency data set as boundary conditions. The finite element simulation module is used to perform finite element numerical simulations under the multiphysics coupling model to obtain a set of output characteristic data. The parameter analysis and optimization module is used to perform parameter sensitivity analysis on the output characteristic data set, form a key parameter data subset, and construct a multi-objective optimization model based on the key parameter data subset and the output characteristic data set, and set the objective weights and constraints. The Dream Optimization module is used to call the Dream Optimization algorithm to iteratively solve the multi-objective optimization model, obtain the optimized structural parameter data set, and update, simulate and verify the three-dimensional geometric model based on the optimized structural parameter data set until the convergence criterion is met, and output the optimized design scheme and the predicted performance data set.

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