A heat transfer performance analysis method based on a medium-deep buried tube heat exchanger

Abstract

The present invention provides a method for analyzing the heat transfer performance of a medium-deep buried tube heat exchanger, which relates to the technical field of geothermal energy development. The method includes: obtaining the initial data of the rock and soil mass to construct a non-uniform random distribution series of the rock and soil mass, calculating the thermal conductivity of the rock and soil mass with a normal distribution, screening out sample data from the detected data, then constructing a POD low-order model based on the sample data, capturing the optimal basis functions for energy contribution from the sample data, further calculating the projection of the fluid temperature field sample in the basis function space to obtain spectral coefficients, thereby reconstructing the temperature field to obtain the reconstructed drilling fluid temperature, and obtaining the final drilling fluid temperature after screening; through the projection on the orthogonal basis vectors and the reconstruction of the fluid temperature field, the present invention realizes precise dimensionality reduction processing of system data, simplifies the physical model, improves the calculation efficiency, effectively reduces the order of magnitude of the traditional algorithm conditions, and realizes the effective analysis of the heat transfer performance of the medium-deep buried tube heat exchanger.

Description

Technical Field

[0001] The present invention relates to the technical field of geothermal energy development, and more particularly, to a method for analyzing the heat transfer performance of a medium-deep buried pipe heat exchanger. Background Art

[0002] During the heat transfer process of a medium-deep buried pipe heat exchanger, the porosity changes with the drilling depth and is a scalar. However, for the sake of simplifying the model and facilitating calculations, in existing geothermal models, the physical parameter of soil thermal conductivity is considered to be uniformly distributed and is usually treated as a constant value in calculations, resulting in a large difference between theoretical predictions and actual engineering problems. In actual geotechnical engineering problems, the soil is restricted by many factors such as its own density, surface structure, surrounding environment, and climate, leading to variable soil layer structures and non-uniform distribution of thermal conductivity. In addition, the uniformity of the soil layer has an uncertainty within the range of 6%-10%, which has exceeded the acceptable reliability limit. Borehole measurement data shows that the variation of the geothermal conductivity of the rock and soil at different depths of the buried pipe approximately follows a normal distribution, increases with the increase of the drilling depth, and reaches a peak at about half of the drilling depth. After that, as the drilling depth increases, the geothermal conductivity of the rock and soil changes in the opposite direction. It can be asserted that at deeper depths of the borehole, the mean value of the soil thermal conductivity presents a constant value, the fluctuation range of the surrounding soil thermal conductivity is limited, it changes in a symmetric trend, and the fluctuation is small. Therefore, its non-uniformity is usually generated by a random sequence in mathematics in engineering problems.

[0003] In the early stage, Kitanids and Bean et al. proposed two effective methods for generating random numbers: the simulation calculation method and the Kriging method. The simulation calculation method mainly conducts mathematical modeling based on the correlation of actual system data, uses a computer program to simulate the established model, and gives analysis and evaluation of the simulation results; the Kriging method is a statistical method that starts from the statistical perspective and uses variable correlation to perform unbiased and optimal estimation of the data within the region. The two methods have a common idea: both can generate random sequence numbers. In comparison, the simulation calculation method better meets the actual needs and can be widely applied in various fields. Although the above methods both rely on random process sampling, can generate random sequence numbers, and can obtain numerical solutions of non-uniform soil thermal conductivity, they are both subject to certain limitations when solving different working conditions, such as low calculation efficiency, poor accuracy, and slow algorithm convergence, etc., and cannot effectively analyze the heat transfer performance of a medium-deep buried pipe heat exchanger, thereby reducing the development progress of geothermal energy. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for analyzing the heat transfer performance of a medium-deep buried pipe heat exchanger, which can solve the problems mentioned in the background art.

[0005] The technical solution of the present invention is as follows:

[0006] In a first aspect, the present application provides a method for analyzing the heat transfer performance of a medium-deep buried tube heat exchanger, which includes the following steps:

[0007] S1. Obtain the initial data of the rock and soil mass to construct a non-uniform random distribution series of the rock and soil mass, and calculate the thermal conductivity of the rock and soil mass with a normal distribution;

[0008] S2. Screen out sample data from the detected data through the thermal conductivity of the rock and soil mass;

[0009] S3. Construct a POD low-order model based on the sample data, and capture the basis function with the optimal energy contribution from the sample data. The calculation process includes:

[0010]

[0011] In the formula, is the sample matrix, T * (x, y, t) is the instantaneous medium temperature at different nodes, (x, y) represents the spatial coordinates, t is the time coordinate, T * (x, y, t a ) represents the instantaneous medium temperature at time a, T * (x, y, t b ) represents the instantaneous medium temperature at time b, E (a,b) is the defined truncated free element, E T is the orthogonal matrix constructed by the truncated free element, λ i is the i-th eigenvalue, is the eigenvector corresponding to the i-th eigenvalue of the k-th group of samples, is the fluid temperature of the k-th group of samples, is the component element of the basis function vector, is the basis function;

[0012] S4. Calculate the projection of the fluid temperature field sample in the basis function space according to the basis function with the optimal energy contribution to obtain the spectral coefficient;

[0013] S5. Reconstruct the temperature field using the spectral coefficient and then output the drilling fluid temperature;

[0014] S6. Evaluate whether the numerical solution of the drilling fluid temperature meets the preset conditions. If so, output the final result. If not, return to step S3 until the conditions are met to output the final result.

[0015] Furthermore, in step S1, the calculation formula of the thermal conductivity of the rock and soil mass includes:

[0016]

[0017] Wherein, f(x) is the normal distribution density function of the thermal conductivity of the rock and soil mass, μ is the mean value, σ is the standard deviation, and x and e are both constants.

[0018] Further, in step S3, the above-mentioned basis function with the optimal energy contribution includes that the contribution rate of the eigenvalue reaches 99.99% and / or the cumulative contribution rate of the eigenvalue reaches 98%, and its expression is:

[0019]

[0020] Wherein, λ i represents the i-th eigenvalue, λ k represents the k-th eigenvalue, and L represents the number of basis function vectors.

[0021] Further, in step S4, the expression for calculating the spectral coefficient is:

[0022]

[0023] Wherein, c k is the spectral coefficient, T * is the instantaneous medium temperature, is the component element of the basis function vector, is the projection of the temperature field sample in the basis function space.

[0024] Further, in step S5, the above-mentioned calculation formula for the temperature of the drilling fluid includes:

[0025]

[0026] Wherein, T ** (x, y, t) is the temperature of the drilling fluid output by the final system, c i (t) represents the spectral coefficient at time t, is the component element of the basis function vector, and M is the reconstructed basis function.

[0027] Further, in step S6, the calculation formula for evaluating whether the preset condition of the temperature of the drilling fluid is satisfied is:

[0028]

[0029] Wherein, ε is the relative error of the numerical solution of the temperature of the drilling fluid, is the temperature of the drilling fluid obtained by using the POD reduction technique in the low-order model, and T C-N is the temperature of the drilling fluid calculated by using the traditional C-N format.

[0030] In a second aspect, the present application provides an electronic device, including:

[0031] A memory for storing one or more programs;

[0032] Processor;

[0033] When one or more of the above programs are executed by the above processor, a heat transfer performance analysis method based on a medium-deep buried tube heat exchanger as described in any one of the above first aspects is implemented.

[0034] In a third aspect, the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a heat transfer performance analysis method based on a medium-deep buried tube heat exchanger as described in any one of the above first aspects is implemented.

[0035] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0036] A heat transfer performance analysis method based on a medium-deep buried tube heat exchanger provided by the present invention, by means of the POD technology principle, takes the temperature field as the main variable for dimensionality reduction modeling. Considering the influence of the non-uniformity of the geothermal conductivity of the rock and soil mass on the transient heat transfer performance of the buried tube heat exchanger, a new heat transfer model of the buried tube heat exchanger is established to realize the reconstruction of the fluid temperature field, and then the accurate dimensionality reduction processing of the system data is realized, simplifying the physical model, improving the calculation efficiency and accuracy, effectively reducing the order of magnitude of the traditional algorithm conditions, avoiding the appearance of too many parameters and non-convergent simulation results due to the non-uniform change of the geothermal conductivity of the rock and soil, and this method is easy to implement, can provide scientific numerical guarantee for the exploration, development and utilization of oil and gas and geothermal resources, greatly improving the optimization level in the process of new energy utilization and generating significant social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a step diagram of a heat transfer performance analysis method based on a medium-deep buried tube heat exchanger of the present invention;

[0039] Figure 2 It is a flowchart of a heat transfer performance analysis method based on a medium-deep buried tube heat exchanger of the present invention;

[0040] Figure 3 It is an effect diagram of the random distribution of the non-uniformity of the rock and soil mass;

[0041] Figure 4 It is a curve graph of the frequency distribution of the fluid temperature of the soil non-uniformity;

[0042] Figure 5 Reconstruction and error map of the drilling outlet fluid temperature;

[0043] Figure 6 Schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0044] Icons: 101, memory; 102, processor; 103, communication interface. Detailed implementation manners

[0045] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0046] The following will describe in detail some implementation manners of the present application with reference to the accompanying drawings.

[0047] Embodiment 1

[0048] Please refer to Figure 1-2 , which is a step diagram and a flowchart of a heat transfer performance analysis method based on a medium-deep buried tube heat exchanger provided by an embodiment of the present application.

[0049] The present application provides a heat transfer performance analysis method based on a medium-deep buried tube heat exchanger, which includes the following steps:

[0050] S1. Obtain the initial data of the rock and soil mass to construct a non-uniform random distribution series of the rock and soil mass, and calculate the thermal conductivity of the rock and soil mass with a normal distribution;

[0051] S2. Screen out the sample data from the detection data through the thermal conductivity of the rock and soil mass;

[0052] S3. Construct a POD low-order model based on the sample data, and capture the basis functions with the optimal energy contribution from the sample data;

[0053] S4. Calculate the projection of the fluid temperature field sample in the basis function space according to the basis functions with the optimal energy contribution to obtain the spectral coefficients;

[0054] S5. Reconstruct the temperature field using the spectral coefficients and then output the drilling fluid temperature;

[0055] S6. Evaluate whether the numerical solution of the drilling fluid temperature meets the preset conditions. If so, output the final result. If not, return to step S3 until the conditions are met to output the final result.

[0056] As a preferred embodiment, in step S1, the calculation formula of the thermal conductivity of the rock and soil mass includes:

[0057]

[0058] In the formula, f(x) is the normal distribution density function of the thermal conductivity of the rock and soil mass, μ is the mean value, σ is the standard deviation, and x and e are both constants.

[0059] As a preferred embodiment, in step S3, the process of constructing a POD low-order model based on the sample data and capturing the basis function with the optimal energy contribution from the sample data includes:

[0060] Select 50 samples at different time layers. The samples are linearly independent, and the step length between adjacent samples needs to be long enough. Then, decompose the instantaneous medium temperature T * (x, y, t) at different nodes to obtain the sample matrix formula:

[0061]

[0062] To avoid generating a singular matrix and achieve accurate and efficient optimal orthogonal decomposition of the sample matrix, define the truncated free element:

[0063]

[0064] E T is a 50×50 order symmetric square matrix, and solve the eigenvalues λ and eigenvectors α of the square matrix E T :

[0065]

[0066] In the formula, is the sample matrix, T * (x, y, t) is the instantaneous medium temperature at different nodes, (x, y) represents the spatial coordinates, t is the spatial coordinate, T * (x, y, t a ) represents the instantaneous medium temperature at time a, T * (x, y, t b ) represents the instantaneous medium temperature at time b, E (a,b) is the defined truncated free element, E T is the orthogonal matrix constructed by the truncated free element, λ i is the i-th eigenvalue, is the eigenvector corresponding to the i-th eigenvalue of the k-th group of samples, is the fluid temperature of the k-th group of samples, is the component element of the basis function vector, is the basis function.

[0067] It should be noted that taking up to 50 groups of bases can optimize the energy contribution rate.

[0068] As a preferred implementation manner, in step S3, the basis functions with the optimal energy contribution include that the contribution rate of the eigenvalue reaches 99.99% and / or the cumulative contribution rate of the eigenvalue reaches 98%, and its expression is:

[0069]

[0070] In the formula, λ i represents the i-th eigenvalue, λ k represents the k-th eigenvalue, and L represents the number of basis function vectors.

[0071] As a preferred implementation manner, in step S4, the expression for calculating the spectral coefficient is:

[0072]

[0073] In the formula, c k is the spectral coefficient, T * is the instantaneous medium temperature, is the component element of the basis function vector, is the projection of the temperature field sample in the basis function space.

[0074] As a preferred implementation manner, in step S5, the calculation formula for the drilling fluid temperature includes:

[0075]

[0076] In the formula, T ** (x, y, t) is the drilling fluid temperature output by the final system, c i (t) represents the spectral coefficient at time t, is the component element of the basis function vector, and M is the reconstructed basis function.

[0077] As a preferred implementation manner, in step S6, the calculation formula for evaluating whether the preset condition of the drilling fluid temperature is satisfied is:

[0078]

[0079] In the formula, ε is the relative error of the numerical solution of the drilling fluid temperature, is the drilling fluid temperature obtained by using the POD reduction technique in the low-order model, and T C-N is the drilling fluid temperature calculated by using the traditional C-N format.

[0080] Example 2

[0081] Please refer to Figure 3 , Figure 3It is the effect diagram of the random distribution of the heterogeneity of rock and soil masses, showing that the soil porosity of soil layers at different depths is not evenly distributed, and its variation law conforms to the normal distribution curve. The first step in the generation process is to generate two sets of random sequence numbers that follow a uniform distribution, and then convert the random numbers that follow a uniform distribution into two independent random variables that follow a normal distribution. The thermal conductivity of rock and soil can be obtained from these two sets of random numbers, and random sequence numbers can also be generated by the randn function in the MATLAB program.

[0082] Example 3

[0083] Please refer to Figure 4 , Figure 4 It is the frequency distribution curve diagram of the fluid temperature of soil heterogeneity, which describes the frequency distribution result of the fluid temperature at the drilling outlet under the action of non-uniform thermal conductivity of rock and soil. According to the relevant knowledge of probability statistics, the degree of dispersion satisfies a small probability event (probability less than 5%), which conforms to the reliability of 95% of the actual engineering implementation plan, and it exactly verifies the reliability of the project and the economic feasibility.

[0084] Example 4

[0085] Please refer to Figure 5 , Figure 5 It gives the reconstructed diagram of the fluid temperature at the outlet after the geothermal energy exchanger has taken heat for 120 days at a depth of 2000m of the drilling. It can be seen from the figure that the error between the predicted value of the low-order model and the traditional algorithm (C-N format) is very small, and the relative error is within 0.08%. Moreover, the calculation speed is fast and the efficiency is high, indicating that the POD algorithm proposed in the present invention is skillfully integrated with the random sampling technology, and can completely realize the rapid prediction of the fluid temperature field.

[0086] Example 5

[0087] Please refer to Figure 6 , Figure 6 It is a schematic structural block diagram of an electronic device provided by an embodiment of the present application.

[0088] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, the processor 102, and the communication interface 103 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used to communicate signaling or data with other node devices.

[0089] Among them, the memory 101 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

[0090] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0091] It can be understood that the structure shown in the figure is only schematic. A heat transfer performance analysis method based on a medium-deep buried tube heat exchanger may also include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Each component shown in the figure can be implemented by hardware, software, or a combination thereof.

[0092] In the embodiments provided in the present application, it should be understood that the disclosed method can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the methods and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0093] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0094] If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0095] In summary, a heat transfer performance analysis method for a medium-deep buried tube heat exchanger provided by an embodiment of the present application constructs a non-uniform random distribution series of a geotechnical body by obtaining initial geotechnical data, calculates the thermal conductivity of the geotechnical body with a normal distribution, screens out sample data from the detected data, then constructs a POD low-order model based on the sample data, captures the basis functions with the optimal energy contribution from the sample data, and further calculates the projection of the fluid temperature field sample in the basis function space to obtain spectral coefficients, thereby reconstructing the temperature field to obtain the reconstructed drilling fluid temperature, and obtaining the final drilling fluid temperature after screening, thus realizing precise dimensionality reduction processing of system data, simplifying the physical model, improving the calculation efficiency, effectively reducing the order of magnitude of the traditional algorithm conditions, and realizing effective analysis of the heat transfer performance of the medium-deep buried tube heat exchanger.

[0096] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0097] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A heat transfer performance analysis method based on a medium-deep underground heat exchanger, characterized in that: The following steps are involved: S1. Obtaining initial data of rock and soil mass to construct a non-uniform random distribution column of rock and soil mass, and calculating the normally distributed thermal conductivity of rock and soil mass; S2. Filter out sample data from the test data by using the thermal conductivity of the rock and soil; S3. Build a POD low-order model based on sample data and capture the basis function with the best energy contribution from the sample data. The calculation process includes: In the formula, is the sample matrix, T*(x,y,t) is the instantaneous medium temperature at different nodes, (x,y) represents the spatial coordinate, t is the time coordinate, T * (x,y,t a ) represents the instantaneous medium temperature at time a, T * (x,y,t b ) represents the instantaneous medium temperature at time b, E (a,b) is the defined truncated free element, E T is the orthogonal matrix constructed by truncating free elements, λ i is the i-th eigenvalue, is the eigenvector corresponding to the i-th eigenvalue of the k-th group of samples, is the fluid temperature of the kth group of samples, is the component element of the basis function vector, is the basis function; S4. Calculate the projection of the fluid temperature field sample in the basis function space according to the basis function with the best energy contribution to obtain the spectrum coefficient; S5. Reconstruct the temperature field using the spectral coefficient and output the drilling fluid temperature; S6. Evaluate whether the numerical solution of the drilling fluid temperature meets the preset conditions. If so, output the final result. If not, return to step S3 until the conditions are met to output the final result.

2. A heat transfer performance analysis method based on a medium-deep buried pipe heat exchanger according to claim 1, characterized in that: In step S1, the thermal conductivity of the rock mass is calculated by the formula include: Where f(x) is the normal distribution density function of thermal conductivity of rock and soil, μ is the mean, σ is the standard deviation, and x and e are constants.

3. The heat transfer performance analysis method based on the medium-deep buried pipe heat exchanger according to claim 1, characterized in that: In step S3, the basis function with the best energy contribution includes a characteristic value contribution rate of 99.99% and / or a cumulative characteristic value contribution rate of 98%, and its expression is: In the formula, λ i represents the i-th eigenvalue, λ k represents the kth eigenvalue, and L represents the number of basis function vectors.

4. The heat transfer performance analysis method based on the medium-deep buried pipe heat exchanger according to claim 1, characterized in that: In step S4, the expression for calculating the spectral coefficient is: In the formula, c k is the spectral coefficient, T * is the instantaneous medium temperature, is the component element of the basis function vector, is the projection of the temperature field sample in the basis function space.

5. The heat transfer performance analysis method based on the medium-deep buried pipe heat exchanger according to claim 1, characterized in that: In step S5, the calculation formula of the drilling fluid temperature is include: Where, T ** (x, y, t) is the final system output drilling fluid temperature, c i (t) represents the spectrum coefficient at time t, is the component element of the basis function vector, and M is the reconstruction basis function.

6. The heat transfer performance analysis method based on the medium-deep buried pipe heat exchanger according to claim 1, characterized in that: In step S6, the calculation formula for evaluating whether the drilling fluid temperature meets the preset condition is: Where ε is the relative error of the numerical solution of drilling fluid temperature, In order to obtain the drilling fluid temperature in a low-order model using POD order reduction technology, T C-N is the drilling fluid temperature calculated using the traditional CN format.

7. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, a heat transfer performance analysis method based on a medium-deep buried pipe heat exchanger as described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a heat transfer performance analysis method based on a medium-deep buried pipe heat exchanger is implemented as described in any one of claims 1-6.

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