Energy efficiency prediction method for ion salt-gradient power generation under temperature gradient

Abstract

An energy efficiency prediction method of ion salt gradient power generation under temperature gradient is disclosed. Based on the control equation and boundary condition of ion salt gradient power generation under temperature gradient, the number of influence parameters is determined, the input parameters and output energy efficiency are measured, and the dimensionless database of ion salt gradient power generation under temperature gradient is constructed by using dimensionless quantity dimension analysis. The input parameters and output energy efficiency of the dimensionless database are analyzed by correlation coefficient. The input parameters with high correlation coefficient of output energy efficiency are retained, and the input parameters with low correlation coefficient of output energy efficiency are removed. The dimensionless correlation of ion salt gradient power generation under temperature gradient is fitted by using multiple linear regression fitting. Finally, the dimensionless correlation is corrected to improve the accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ion salt differential power generation, and particularly relates to an energy efficiency prediction method for ion salt differential power generation under a temperature gradient. BACKGROUND

[0002] Ion salt differential power generation is a process in which ion energy carriers are driven by a concentration gradient to migrate through a nano-selective film to form an ion current and a potential difference, and then convert Gibbs free energy into electrical energy. Ion salt differential power generation technology is a typical green and clean energy, which has the advantages of high power density, large energy storage, and sustainable development. In the physical process of ion salt differential power generation under a temperature gradient, not only the temperature gradient can be used as a source of power to drive ion migration, but the temperature also greatly changes the physical property parameters and affects the power generation efficiency. Ion salt differential power generation under a temperature gradient involves concentration field, potential field, velocity field, and temperature field. It contains many influencing parameters, such as channel parameters, working medium parameters, and heat transfer parameters. The performance parameters mainly include diffusion potential, power, and efficiency.

[0003] The existing research on ion salt differential power generation mainly investigates the influence of nano-channel materials and single parameter changes, and some effective conclusions and general physical cognition have been obtained. For example, a larger nano-channel radius can obtain higher ion flux, but affects the selectivity and thus the performance. Increasing the concentration can increase the number of ion transport, but the thickness of the double-layer becomes smaller, thus affecting the performance. In addition, the current ion salt differential power generation formula is too ideal, and the parameters considered are incomplete. For the theoretical formula of diffusion potential, the influence of nano-channel structure and surface charge density is not considered. For the efficiency prediction formula, the cation migration number is not an input parameter, and the ion salt differential power generation efficiency cannot be accurately predicted by input parameters. Moreover, the power prediction correlation of ion salt differential power generation has not been proposed. Therefore, the energy efficiency prediction problem of ion salt differential power generation under a temperature gradient needs to be solved. A quantitative energy efficiency prediction correlation can clarify the relationship between input parameters and output performance, and has reference value for ion salt differential power generation research.

[0004] The information disclosed in the background section merely serves to enhance the understanding of the background of the present application, and thus can include information that does not constitute prior art that is already known to those of ordinary skill in the art. SUMMARY

[0005] The present application provides an energy efficiency prediction method for ion salt differential power generation under temperature gradient, which is clear about the control equation of ion salt differential power generation under temperature gradient, and further obtains all the considered influence parameters.

[0006] The object of the present application is achieved by the following technical solutions.

[0007] The energy efficiency prediction method for ion salt differential power generation under temperature gradient comprises the following steps.

[0008] Step S100: determining input parameters and output parameters based on the control equation and boundary conditions of ion salt differential power generation under temperature gradient, and measuring the input parameters and output parameters to construct a database of ion salt differential power generation under temperature gradient.

[0009] Poisson equation:

[0010] Nernst-Planck equation:

[0011] Continuity equation:

[0012] Navier-Stokes equation:

[0013] Fluid energy equation:

[0014] Solid energy equation:

[0015] Nanochannel surface boundary condition:

[0016] Nanochannel left end boundary condition: T=T a , c i =C h ,

[0017] Nanochannel right end boundary condition: T=T b, c i =C1,

[0018] wherein, is a partial differential operator, ε is a dielectric constant, φ is an electric potential, F is a Faraday constant, c i is an ion concentration of the i-th ion, z i is a valence charge number of the i-th ion, D i is a diffusion coefficient of the i-th ion, α i is a reduced Soret coefficient of the i-th ion, J i is an ion flux of the i-th ion, wherein i=1 represents a cation, i=2 represents an anion, u is a velocity, R g is a universal gas constant, T is a temperature, p is a pressure, μ is a dynamic viscosity, a is a thermal diffusivity, k f is a fluid thermal conductivity, k s is a solid thermal conductivity, σ f is an electrical conductivity, σ is a nanochannel surface charge density, T a is a left end temperature, T b is a right end temperature, C h is a high concentration side concentration, C1 is a low concentration side concentration, the input parameters include a dielectric constant, a high concentration, a low concentration, a left end temperature, a right end temperature, a nanochannel surface charge density, a nanochannel length L, a nanochannel radius R, a cation diffusion coefficient, an anion diffusion coefficient, a cation reduced Soret coefficient, an anion reduced Soret coefficient, a dynamic viscosity, a thermal diffusivity, a fluid thermal conductivity, an electrical conductivity, a solid thermal conductivity, a Faraday constant and a universal gas constant, and the output parameters include a diffusion potential E diff , a power P and an efficiency η.

[0019] Step S200: using dimensionless quantity dimension analysis, obtaining a dimensionless expression form of the input parameters and the output parameters, and constructing a dimensionless database of ion salt difference power generation under a temperature gradient;

[0020] Step S300: performing a correlation coefficient analysis on the input parameters and the output parameters of the dimensionless database, retaining the input parameters with a correlation coefficient higher than a predetermined value with respect to the output parameters, and eliminating the input parameters with a correlation coefficient lower than a predetermined value with respect to the output energy efficiency;

[0021] Step S400: using a multiple linear regression fitting, fitting a dimensionless correlation formula of ion salt difference power generation under a temperature gradient based on the dimensionless database;

[0022] Step S500: modifying the dimensionless correlation formula, adding the input parameters eliminated in step S300 to the dimensionless correlation formula in step S400 with a predetermined coefficient, until the accuracy meets the requirements.

[0023] The method, the temperature gradient ion salt difference power generation includes nanochannel, left storage pool, right storage pool and solid part.

[0024] The method, the nanochannel is single straight nanochannel, left storage pool has high concentration salt solution, right storage pool has low concentration salt solution, temperature gradient size and direction of both ends of storage pool can be selected according to actual needs.

[0025] The method, the dimensionless input parameter is as follows,

[0026]

[0027]

[0028] The dimensionless output parameter is as follows:

[0029]

[0030] Wherein, Π j The jth dimensionless input parameter, j=1~12, T m The average temperature, ΔT is temperature difference, E diff * The dimensionless diffusion potential, P max * The dimensionless power.

[0031] The method, the predetermined value is 0.2,

[0032] The method, the correlation coefficient of dimensionless input parameter Π1 to Π7 to dimensionless output parameter is greater than 0.2, the correlation coefficient of dimensionless input parameter Π8 to H 12 To dimensionless output parameter is less than 0.2, dimensionless input parameter Π8 to H12 is eliminated;

[0033] The method, the prediction correlation of dimensionless diffusion potential, dimensionless power and efficiency is respectively as follows,

[0034]

[0035]

[0036] 1gη=-0.48lgΠ1-0.74lgΠ2+0.73lgΠ3+1.27lgΠ4+0.34lgΠ5-2.07lgΠ6+0.14lgΠ7-1.44。

[0037] The method, the prediction correlation of dimensionless diffusion potential, dimensionless power and efficiency after correction is respectively as follows,

[0038]

[0039]

[0040] lgη = -0.48lgΠ1-0.74lgΠ2+0.73lgH3+1.27lgH4+0.34lgΠ5-2.07lgΠ6+0.14lgΠ7-0.005Π8+0.005Π9+0.005Π 10 -0.005Π 11 +0.005Π 12 -1.44.

[0041] Beneficial effects

[0042] The present application simplifies the physical parameters and the correlation expression form through dimensional analysis and correlation analysis of the ion salt differential power generation database under temperature gradient; the energy efficiency prediction expression of the ion salt differential power generation under temperature gradient obtained by the present application is a quantitative expression, which not only reflects the influence size of the input parameters on the output parameters, but also can be called by experiments and simulations in the field of ion salt differential power generation; the energy efficiency prediction expression of the ion salt differential power generation under temperature gradient obtained by the present application is the most basic and universal physical scene, which can be further modified and expanded, and is suitable for more parameters and more complex physical working conditions; the energy efficiency prediction expression of the ion salt differential power generation under temperature gradient obtained by the present application can realize the beneficial effects of high prediction accuracy and high speed-up ratio, and is suitable for engineering application reference.

[0043] The description is only a summary of the technical solutions of the present application, in order to make the technical means of the present application clearer and more understandable, to the extent that the contents of the description can be implemented by those skilled in the art, and in order to make the said and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are exemplified. BRIEF DESCRIPTION OF DRAWINGS

[0044] Various other advantages and benefits of the present application will become apparent to those of ordinary skill in the art, upon reading the following detailed description of the preferred embodiments. The accompanying drawings illustrate the preferred embodiments and, although a certain detail is shown in such drawings, it is to be understood that many other arrangements can be utilized and that the present application is not to be limited to the embodiments disclosed herein. Rather, the present application is to cover all modifications and alternative methods and materials made in accordance with the spirit and scope of the present application. Like numbers refer to like elements throughout.

[0045] In the drawings:

[0046] Figure 1 A flowchart of the energy efficiency prediction method of ion salt differential power generation under temperature gradient provided for an embodiment of the present disclosure is shown in the figure;

[0047] Figure 2 A simulation working condition schematic diagram of the energy efficiency prediction method of the ion salt differential power generation under a temperature gradient provided for another embodiment of the present disclosure;

[0048] Figure 3 A non-dimensional input-output parameter correlation coefficient analysis diagram of the energy efficiency prediction method of the ion salt differential power generation under a temperature gradient provided for another embodiment of the present disclosure;

[0049] Figures 4(a) to 4(c) A regression analysis diagram of the energy efficiency prediction correlation of the energy efficiency prediction method of the ion salt differential power generation under a temperature gradient provided for another embodiment of the present disclosure.

[0050] The present application will be further explained in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0051] The present application will be further explained in conjunction with the accompanying drawings and embodiments. Figures 1 to 4(c) The specific embodiments of the present application will be described in more detail with reference to the accompanying drawings. Although the specific embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be accurately conveyed to those skilled in the art.

[0052] It should be noted that certain terms are used throughout the specification and claims which refer to particular components. As one skilled in the art will appreciate, the terms used can be substituted with other terms that have the same meaning. The specification and claims are not to be limited by the terms used in the specification and claims, but are to be given the full scope of the components as set forth in the claims. As used throughout the specification and claims, "comprise" or "include" is an open term that should be interpreted as "including but not limited to." The subsequent description describes preferred embodiments of the present application for the purpose of illustrating the general principles of the present application, and is not intended to limit the scope of the present application. The scope of the present application is defined by the appended claims.

[0053] For the sake of understanding the embodiments of the present application, the following will be further explained and described with reference to the accompanying drawings in several specific embodiments, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present application.

[0054] In one embodiment, as shown in FIG. 1, a method for predicting the energy efficiency of ion salt differential power generation under a temperature gradient is disclosed, which comprises the following steps: Figure 1

[0055] ​Step S100: Determine input parameters and output parameters based on the control equation and boundary conditions of ion-salt difference power generation under temperature gradient, measure the input parameters and output parameters to construct a database of ion-salt difference power generation under temperature gradient, wherein the control equation and boundary conditions of ion-salt difference power generation under temperature gradient are:

[0056] Poisson's equation:

[0057] Nernst-Planck equation:

[0058] Continuity equation:

[0059] Navier-Stokes equations:

[0060] Fluid energy equation:

[0061] Solid energy equation:

[0062] Nanochannel surface boundary conditions:

[0063] Boundary condition at the left end of the nanochannel: T = T a , c i =C h ,

[0064] Boundary condition at the right end of the nanochannel: T = T b , c i =C1,

[0065] in, is the partial differential operator, ε is the dielectric constant, φ is the electric potential, F is the Faraday constant, c i is the ion concentration of the i-th ion, z i is the valence charge of the i-th ion, D i is the diffusion coefficient of the i-th ion, α i is the simplified Soret coefficient of the i-th ion, J i is the ion flux of the i-th ion, where i=1 represents a cation, i=2 represents an anion, u is the velocity, and R g is the universal gas constant, T is temperature, p is pressure, μ is dynamic viscosity, a is thermal diffusivity, k f is the thermal conductivity of the fluid, k s is the solid thermal conductivity, σ f is the conductivity, σ is the surface charge density of the nanochannel, T a is the temperature at the left end, T b is the right end temperature, C hFor high concentration side concentration, C1 is low concentration side concentration, input parameters include dielectric constant, high concentration, low concentration, left end temperature, right end temperature, nanochannel surface charge density, nanochannel length L, nanochannel radius R, cation diffusion coefficient, anion diffusion coefficient, cation simplified Soret coefficient, anion simplified Soret coefficient, dynamic viscosity, thermal diffusivity, fluid thermal conductivity, electrical conductivity, solid thermal conductivity, Faraday constant and universal gas constant, output parameters include diffusion potential E diff , power P and efficiency η;

[0066] Step S200: using dimensionless quantity dimension analysis, obtaining dimensionless expression form of input parameters and output parameters, and constructing dimensionless database of ion salt difference power generation under temperature gradient;

[0067] Step S300: correlation coefficient analysis is carried out on the input parameters and output parameters of the dimensionless database, the input parameters with correlation coefficient higher than the predetermined value are retained, and the input parameters with correlation coefficient lower than the predetermined value are eliminated;

[0068] Step S400: using multiple linear regression fitting, fitting dimensionless correlation formula of ion salt difference power generation under temperature gradient based on dimensionless database;

[0069] Step S500: correcting the dimensionless correlation formula, adding the input parameters eliminated in step S300 to the dimensionless correlation formula in step S400 with predetermined coefficient until the accuracy meets the requirements.

[0070] Further, according to the step S100 of the construction method, as shown in Figure 2 The sample model of ion salt difference power generation under temperature gradient is composed of nanochannel 1, left reservoir 2, right reservoir 3 and solid part 4. Among them, the nanochannel 1 is a single straight nanochannel as a selective permeation channel, the left reservoir 2 and the right reservoir 3 respectively have high and low concentration, high and low temperature salt solution, and the solid part 4 considers the heat conduction of the nanochannel part. Assuming that the nanochannel is negatively charged, under the driving of concentration gradient, cations migrate directionally, selectively pass through the nanochannel 1, form a potential difference between the left reservoir 2 and the right reservoir 3, and generate electricity. The process is regulated by temperature gradient.

[0071] Furthermore, in step S100 of the construction method, the input parameters are dielectric constant, high concentration, low concentration, left end temperature, right end temperature, nanochannel surface charge density, nanochannel length L, nanochannel radius R, cation diffusion coefficient, anion diffusion coefficient, cation simplified Soret coefficient, anion simplified Soret coefficient, dynamic viscosity, thermal diffusivity, fluid thermal conductivity, electrical conductivity, solid thermal conductivity, Faraday constant, universal gas constant, a total of 19. The output parameter is the diffusion potential E diff , power P, efficiency η, a total of 3.

[0072] Furthermore, in step S200 of the construction method, the dimensionless parameters can be converted into dimensionless parameters based on the dimensionless analysis, thereby reducing the number of parameters. The dimensionless input parameters are as follows:

[0073]

[0074]

[0075] The dimensionless output parameters are as follows:

[0076]

[0077] Among them, Π j is the jth dimensionless input parameter (j = 1 to 12), T m is the average temperature, ΔT is the temperature difference, E diff * is the dimensionless diffusion potential, P max * is the dimensionless power. The number of input parameters is reduced from 19 to 12, significantly simplifying the parameters. The dimensionless form also makes the parameters more universal.

[0078] In another example, Figure 3 As shown, the present disclosure provides a dimensionless input-output parameter correlation coefficient analysis diagram for ion-salt difference power generation under temperature gradient. In the construction method, the predetermined value is 0.2, and the input parameters with a correlation coefficient higher than the predetermined value for the output parameter are retained, and the input parameters with a correlation coefficient lower than the predetermined value for the output energy efficiency are eliminated. The correlation coefficients of the dimensionless input parameters Π1 to Π7 with the dimensionless output parameters are greater than 0.2, indicating a strong correlation. The dimensionless input parameters Π8 to Π 12 The correlation coefficient of the dimensionless output parameter is less than 0.2, which is not a strong correlation. 12Eliminate and use strong correlation parameters to predict correlation fitting, which is more general. For dimensionless diffusion potential, the correlation coefficients of π5, π2, and π4 are large and have strong correlation. The parameters that make up π5, π2, and π4 have a significant impact on dimensionless diffusion potential. For dimensionless power, the correlation coefficients of π2 and π5 are large and have strong correlation. The parameters that make up π2 and π5 have a significant impact on dimensionless diffusion potential. For efficiency, the correlation coefficients of π5, π2, and π4 are large and have strong correlation. The parameters that make up π5, π2, and π4 have a significant impact on dimensionless diffusion potential. π2 indicates the electrostatic migration ability of ions. The force of ions migrating under concentration gradient is opposite to the force of ions migrating under electrostatic field, so π2 shows a strong negative correlation to dimensionless energy efficiency. π5 indicates the geometric regulation of nanochannels, which can also reflect the strength of ion selectivity to a certain extent, so π5 shows a strong positive correlation to dimensionless energy efficiency.

[0079] In another example, Figures 4(a) to 4(c) As shown, the present disclosure provides a regression analysis diagram of the energy efficiency prediction correlation of ion-salt difference power generation under temperature gradient. The prediction correlation formulas of dimensionless diffusion potential, dimensionless power and efficiency are respectively expressed as follows.

[0080]

[0081]

[0082] lgn=-0.48lgΠ1-0.74lgΠ2+0.73lgΠ3+1.27lgΠ4+0.34lgΠ5-2.07lgΠ6+0.141gΠ7-1.44.

[0083] Coefficient of determination R 2 The correlation coefficient R is a parameter for evaluating the goodness of fit of the regression model, and its expression is Where y is the true predicted value, is the mean value of the predictor variable, is the fitted predicted value. Figures 4(a) to 4(c) It can be seen that the dimensionless diffusion potential, dimensionless power and efficiency R 20.83, 0.86, 0.80, respectively. Correspondingly, the correlation coefficient R of the dimensionless diffusion potential, the dimensionless power and the efficiency are 0.91, 0.93, 0.89, respectively. The conclusion shows that the results predicted by the energy efficiency correlation can be explained by 0.91, 0.93, 0.89, respectively, which meets the engineering requirements. In the expression, the coefficients of the dimensionless prediction correlation reflect the degree of influence of the parameters. For the dimensionless diffusion potential, the diffusion coefficients of the anion and the cation are the most concerned. For the dimensionless power, the surface charge density and the radius of the nanochannel, the dielectric constant, and the temperature are the most concerned. For the efficiency, the temperature at both ends of the nanochannel is the most concerned. In addition, the dimensionless prediction correlation has extensibility. For the input parameters outside the range, the output parameters can also be obtained by prediction with high accuracy.

[0084] In one embodiment, the determination coefficient R of the dimensionless diffusion potential, the dimensionless power and the efficiency prediction correlation 2 are 0.83, 0.86, 0.80, respectively, and the acceleration ratio is as high as 2.07 x 10 7 The energy efficiency correlation of the ion salt gradient power under the temperature gradient has a general significance, and can be modified on this basis to improve the accuracy or be applicable to other physical working conditions.

[0085] To quantitatively measure the prediction effect of the ion salt gradient power energy efficiency prediction correlation under the temperature gradient, so as to realize high accuracy and high acceleration ratio, and effectively guide the actual process of the ion salt gradient power. Taking the power as an example, the performance comparison between the prediction results of the ion salt gradient power energy efficiency prediction correlation and the experimental simulation results is shown in Table 1:

[0086] Table 1 Performance comparison between the correlation prediction results and the experimental simulation results

[0087]

[0088] The average error of the ion salt gradient power energy efficiency prediction correlation under the temperature gradient is 3.28%, and the acceleration ratio is as high as 2.07 x 10 7 This means that by sacrificing 3.28% of the accuracy, the energy efficiency correlation can be used to predict 2.07 x 10 7 times of the working conditions in the time of one experimental simulation working condition, which greatly saves the time cost. The energy efficiency correlation meets the engineering requirements and can be used as a pre-simulation of experiments and simulations to save experimental and computational costs.

[0089] In another example, the above dimensionless energy efficiency correlation is modified to improve the accuracy. The parameters removed in step S300 are added to the dimensionless correlation in step S400 with smaller coefficients. According to Figure 3correlation analysis, if the removed dimensionless number is positive correlation, 0.005 is added to the dimensionless correlation formula; if the removed dimensionless number is negative correlation, -0.005 is added to the dimensionless correlation formula.

[0090] The modified dimensionless diffusion potential, dimensionless power and efficiency prediction correlation formula is respectively represented as follows,

[0091]

[0092] lgη=-0.48lgΠ1-0.74lgΠ2+0.73lgH3+1.27lgΠ4+0.34lgΠ5-2.07lgH6+0.14lgH7-0.005Π8+0.005H9+0.005Π 10 -0.005Π 11 +0.005Π 12 -1.44

[0093] To quantitatively measure the prediction effect of the modified energy efficiency prediction correlation formula, the above Case 1-5 is still used for prediction, and the performance of the experimental simulation result and the performance of the original correlation formula prediction are compared, and the results are shown in Table 2:

[0094] Table 2 Comparison of prediction results of modified correlation formula

[0095]

[0096] After modifying the correlation formula, the error is reduced from 3.28% to 2.33%, with a decrease of 29%, and the modification effect is significant. As described above, the modified correlation formula can more accurately predict the energy efficiency of ion salt difference power generation under temperature gradient, and has almost no effect on the running time of the correlation formula. In addition, the modification method of the correlation formula is not unique, and other methods can be flexibly selected. Moreover, the modification idea is also applicable to more complex physical conditions of conical channels, pressure difference driving and other parameters. The parameters can be added to the correlation formula obtained in step S400 and modified according to the strength of the parameter correlation.

[0097] The method for constructing the energy efficiency prediction correlation of ion salt gradient power generation under temperature gradient is based on the control equation and boundary condition of ion salt gradient power generation under temperature gradient, determines the number of influence parameters, measures the input parameters and output energy efficiency, and constructs the database of ion salt gradient power generation under temperature gradient. The dimensionless form of input and output parameters is obtained by using dimensionless quantity analysis, and the total number of parameters is reduced by seven. The dimensionless database of ion salt gradient power generation under temperature gradient is constructed by using the obtained dimensionless input parameters and output energy efficiency. The correlation coefficient analysis is performed on the input parameters and output energy efficiency of the dimensionless database. The input parameters with high correlation coefficient of the output energy efficiency are retained, and the input parameters with low correlation coefficient of the output energy efficiency are removed, so that the result is more universal. For the convenience of fitting correlation, the parameters in the sample library are preprocessed by taking logarithm. The dimensionless correlation of ion salt gradient power generation under temperature gradient is fitted by using multiple linear regression fitting. The determination coefficients R2 of the dimensionless diffusion potential, power and efficiency prediction correlation are 0.83, 0.86 and 0.80 respectively, and the acceleration ratio is as high as 2.07×107, which greatly saves the time cost. The energy efficiency correlation has reached the engineering requirement, and can be used as pre-simulation of experiment and simulation to save the experimental cost and calculation cost. The energy efficiency correlation of ion salt gradient power generation under temperature gradient also has general significance, and can be modified on this basis to improve the precision or be applicable to other physical working conditions.

[0098] Although the embodiments of the present application are described above with reference to the drawings, the present application is not limited to the specific embodiments and application fields described above, and the specific embodiments are only illustrative and guiding, but not limiting. Those skilled in the art can make many forms under the guidance of the present specification and without departing from the scope protected by the claims of the present application, which all belong to the protection of the present application.

Claims

1. A method for predicting the energy efficiency of ion salt differential power generation under temperature gradient, characterized in that, It comprises, Step S100: determining input parameters and output parameters based on control equations and boundary conditions of ion salt difference power generation under temperature gradient, and measuring the input parameters and output parameters to construct a database of ion salt difference power generation under temperature gradient, wherein the control equations and boundary conditions of ion salt difference power generation under temperature gradient are: Poisson equation: Nernst-Planck equation: Continuity equation: Navier-Stokes equations: Fluid energy equation: Solid energy equation: Nanochannel surface boundary conditions: Boundary conditions at the left end of the nanochannel: T = T0, c = C0 a i = C h ,​ Boundary condition at right end of nanochannel: T = T0, c = C1, b i = C1,​ wherein, is the partial differential operator, ε is the permittivity, φ is the electric potential, F is the Faraday constant, c i is the ion concentration of the i-th ion, z i is the valence charge number of the i-th ion, D i is the diffusion coefficient of the i-th ion, α i is the reduced Soret coefficient of the i-th ion, J i is the ion flux of the i-th ion, where i = 1 denotes the cation and i = 2 denotes the anion, u is the velocity, R g is the universal gas constant, T is the temperature, p is the pressure, μ is the dynamic viscosity, a is the thermal diffusivity, k f is the fluid thermal conductivity, k s is the solid thermal conductivity, σ f is the electrical conductivity, σ is the nanochannel surface charge density, T a is the left end temperature, T b is the right end temperature, C h is the high concentration side concentration, C1 is the low concentration side concentration, the input parameters include permittivity, high concentration, low concentration, left end temperature, right end temperature, nanochannel surface charge density, nanochannel length L, nanochannel radius R, cation diffusion coefficient, anion diffusion coefficient, cation reduced Soret coefficient, anion reduced Soret coefficient, dynamic viscosity, thermal diffusivity, fluid thermal conductivity, electrical conductivity, solid thermal conductivity, Faraday constant, and universal gas constant, and the output parameters include diffusion potential E diff , power P, and efficiency η; Step S200: obtaining dimensionless expression forms of the input parameters and output parameters by using dimensionless quantity dimension analysis, and constructing a dimensionless database of ion salt difference power generation under temperature gradient; Step S300: performing correlation coefficient analysis on the input parameters and output parameters of the dimensionless database, retaining the input parameters with a correlation coefficient higher than a predetermined value for the output parameters, and eliminating the input parameters with a correlation coefficient lower than a predetermined value for the output energy efficiency; Step S400: fitting a dimensionless correlation of ion salt difference power generation under temperature gradient based on the dimensionless database by using multiple linear regression fitting; Step S500: correcting the dimensionless correlation, adding the input parameters eliminated in step S300 to the dimensionless correlation in step S400 with a predetermined coefficient until the accuracy meets the requirements.

2. The method of claim 1, wherein, The ion salt difference power generation under temperature gradient comprises a nanochannel, a left liquid storage pool, a right liquid storage pool and a solid part.

3. The method of claim 2, wherein, The nanochannel is a single straight nanochannel, the left liquid storage pool has a high-concentration salt solution, and the right liquid storage pool has a low-concentration salt solution.

4. The method of claim 1, wherein, During the ion salt difference power generation under temperature gradient, the dielectric constant, anion diffusion coefficient, cation diffusion coefficient, dynamic viscosity and conductivity change with the temperature gradient.

5. The method of claim 1, wherein, The dimensionless input parameters are as follows, Pi7 = a1, Pi 10 = a2, The dimensionless output parameters are as follows: where Π j is the jth dimensionless input parameter, j = 1-12, T m is the average temperature, ΔT is the temperature difference, E diff * is the dimensionless diffusion potential, P max * is the dimensionless power.

6. The method of claim 5, wherein, The correlation coefficients of the dimensionless input parameters Pi to Pi7 to the dimensionless output parameter are greater than 0.2, and the dimensionless input parameters Pi8 to Pi 12 The correlation coefficients to the dimensionless output parameter are less than 0.2, and the dimensionless input parameters Pi8 to Pi 12 are eliminated.

7. The method of claim 1, wherein, The predictive correlation of the dimensionless diffusion potential, dimensionless power and efficiency is respectively represented as follows, lgη=-0.48lgΠ1-0.74lgΠ2+0.73lgΠ3+1.27lgΠ4+0.34lgΠ5-2.07lgΠ6+0.14lgΠ7-1.

44.

8. The method of claim 1, wherein, The predictive correlation of the dimensionless diffusion potential, dimensionless power and efficiency is respectively represented as follows,

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