Dynamic fractal micro-channel topological structure, thermal control method and system

Abstract

The invention discloses a dynamic fractal micro-channel topological structure, a thermal control method and a thermal control system, belongs to the technical field of electromechanical servo thermal control, and aims to solve key challenges such as insufficient dynamic heat source distribution adaptability, poor reliability and lack of multi-physical field collaborative optimization in the prior art. Through the dynamic fractal generation algorithm, the functional gradient composite material and the multi-physics field coupling optimization strategy, the heat dissipation efficiency, adaptability and reliability of the micro-channel system are remarkably improved, and important technical support is provided for deep space exploration tasks.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical servo thermal control technology, and in particular to a bionic thermal control structure design method for an electromechanical servo system in a microgravity or outer space environment. Background Art

[0002] With the rapid development of aerospace technology, electromechanical servo products, as key actuators of spacecraft, often need to be directly exposed to the outside world in the space environment and face harsh extreme conditions. Unlike flight control and other components that can be internally integrated and protected, electromechanical servo products usually cannot achieve effective integrated protection due to their functional requirements and structural characteristics. Therefore, their thermal management requirements have become a core problem that restricts their performance.

[0003] In the space environment, electromechanical servo products must maintain stable and reliable operation under a wide temperature fluctuation range (-150℃~+200℃), microgravity environment and high power density. These harsh environmental conditions place extremely high demands on the thermal management performance of the product.

[0004] The existing flow channel thermal control technology and its shortcomings are as follows: 1) Limitations of fixed bifurcation mode: The tree-like fractal flow channel proposed in the existing patent adopts a fixed bifurcation mode and cannot respond to the dynamic heat source distribution of the servo motor in real time. This static design is prone to uneven heat flow distribution when facing power density fluctuations, which can cause local overheating or insufficient heat dissipation, resulting in low thermal management efficiency. Similarly, some bionic designs (such as fish scale bionic structures) have also failed to break through the bottleneck of static design and are difficult to adapt to thermal management requirements under complex working conditions.

[0005] 2) Fractal algorithm does not consider the thermal stress coupling effect of materials: Some patents propose a fractal algorithm to optimize the flow channel topology, but its design does not fully consider the thermal stress coupling effect of materials. In practical applications, thermal stress concentration will cause fatigue damage to the flow channel structure, affecting the reliability and service life of the system.

[0006] 3) Lack of thermal-mechanical synergistic optimization: In the aerospace environment, the flow channel structure needs to reduce weight and improve vibration resistance as much as possible while ensuring heat dissipation performance. Therefore, the dynamic mechanism should be thermally-mechanically optimized to meet the requirements of aerospace products for lightweight and high reliability.

[0007] These problems seriously restrict the actual application effect of microfluidic thermal management technology in aerospace electromechanical servo products. Summary of the invention

[0008] In view of the shortcomings of the prior art, such as insufficient dynamic adaptability, reliability problems, and multi-physical field collaborative optimization, the present invention proposes a dynamic fractal microfluidic topology structure, thermal control method and system. By real-time monitoring of heat source distribution, combining bionic fractal feature extraction, machine learning prediction and dynamic fractal generation algorithm, adjusting the flow channel bifurcation angle and width, and using functional gradient composite materials and multi-physical field coupling optimization, efficient thermal management is achieved.

[0009] The objective of the present invention is achieved through the following technical solutions: In the first aspect, a dynamic fractal microfluidic channel topological structure is provided, including a multi-stage pipeline connected end to end in sequence, each stage of the pipeline corresponds to a level of working temperature distribution; the bifurcation angle of the pipeline is designed using the Fibonacci sequence, and the flow channel width of the pipeline decreases exponentially to form a pressure gradient; the pipeline adopts a three-layer composite structure with a functional gradient design.

[0010] Preferably, the three-layer composite structure includes, from inside to outside, a self-lubricating layer, a high thermal conductivity layer, and a thermal expansion regulating layer.

[0011] In a second aspect, a dynamic fractal microfluidic channel thermal control method is provided, comprising the following steps: S1. Real-time collection of temperature distribution data of each working area; S2, combining machine learning models to predict the spatial distribution of heat sources; S3, generating a microfluidic channel topology structure through a dynamic fractal generation algorithm, and dynamically generating a preliminary microfluidic channel bifurcation angle and channel width according to the prediction results in S2; wherein the microfluidic channel topology structure includes a multi-stage pipeline connected end to end, and each stage of the pipeline corresponds to a first-stage working temperature distribution; the bifurcation angle of the pipeline is bifurcated using the Fibonacci sequence, and the channel width of the pipeline decreases exponentially to form a pressure gradient; the pipeline adopts a three-layer composite structure with a functional gradient design; S4, performing functional gradient design on the pipeline of the microfluidic channel topological structure, and designing the pipeline into a three-layer composite structure; S5, comprehensively considering the heat flow distribution, fluid flow and material mechanics factors, simulating the microchannel topology structure designed in step S4, performing multi-physics field collaborative optimization, adjusting the channel structure, and obtaining optimized channel topology parameters; S6. Generate the final microchannel bifurcation angle and channel width according to the optimized channel topology parameters; S7. Based on the final microchannel bifurcation angle and channel width, the microchannel bifurcation angle and channel width are adjusted by the execution component.

[0012] Preferably, the step S1 further comprises preprocessing the collected temperature distribution data, wherein the preprocessing comprises cleaning, filtering, denoising and standardization.

[0013] Preferably, in step S7, a piezoelectric driver is used to adjust the microchannel bifurcation angle, and a memory alloy is used to adjust the channel width.

[0014] Preferably, the bifurcation angle of the pipeline is designed using the Fibonacci sequence, including: The initial angle is 34.5°, and the adjustment step size Δθ=20.5°.

[0015] Preferably, the flow channel width of the pipeline decreases exponentially to form a pressure gradient, including: The flow channel width decreases according to the following formula: , where n is the number of flow channel pipelines, is the initial width.

[0016] In a third aspect, a dynamic fractal microfluidic channel thermal control system is provided, comprising: Data acquisition and preprocessing module, used to collect temperature distribution data of each working area in real time; Heat source distribution prediction module, used to predict the spatial distribution of heat sources in combination with machine learning models; The dynamic fractal module generates a microchannel topological structure through a dynamic fractal generation algorithm, and dynamically generates a preliminary microchannel bifurcation angle and channel width according to the prediction results in the heat source distribution prediction module; wherein the microchannel topological structure includes a multi-stage pipeline connected end to end, and each stage of the pipeline corresponds to a first-stage working temperature distribution; the bifurcation angle of the pipeline is bifurcated using the Fibonacci sequence, and the channel width of the pipeline forms a pressure gradient in an exponentially decreasing manner; the pipeline adopts a three-layer composite structure with a functional gradient design; A functional gradient design module, used for performing functional gradient design on the pipeline of the microfluidic channel topology structure, and designing the pipeline into a three-layer composite structure; A multi-physics field collaborative optimization module is used to comprehensively consider heat flow distribution, fluid flow and material mechanics factors, simulate the microchannel topology structure designed in step S4, perform multi-physics field collaborative optimization, adjust the channel structure, and obtain optimized channel topology parameters; A flow channel topology adjustment module is used to generate the final microchannel bifurcation angle and flow channel width according to the optimized flow channel topology parameters; The adjustment execution module is used to adjust the microchannel bifurcation angle and the flow channel width through the execution component based on the final microchannel bifurcation angle and the flow channel width.

[0017] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the dynamic fractal microfluidic channel thermal control method as described in the second aspect is implemented.

[0018] In a fifth aspect, an electronic device is provided, comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and when the processor executes the computer instructions, the dynamic fractal microfluidic channel thermal control method as described in the second aspect is implemented.

[0019] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention forms a dynamic fractal generation algorithm by real-time monitoring of heat source distribution and combining a bionic fractal microchannel topology generation mechanism based on bionics, fractal geometry and dynamic optimization algorithm to adjust the channel topology (bifurcation angle and channel width) to achieve efficient thermal management and fluid transport. Then, through the combination of dynamic topology reconstruction and functional gradient composite materials, an efficient and reliable thermal management solution is achieved. It has the following advantages: 1) Improved heat dissipation efficiency: Compared with the traditional tree-like flow channel design, the heat dissipation efficiency is improved by 30%~50%.

[0021] 2) Improved heat source positioning accuracy: The heat source positioning accuracy is improved by 20%, and the heat flow propagation prediction error is less than 5%.

[0022] 3) Improved reliability: A multi-layer functional gradient composite structure is proposed. The material performance gradient function is designed according to the heat source distribution and flow requirements. By optimizing material performance and structural design, the durability and service life of the system are significantly improved.

[0023] 4) Lightweight: Dynamic fractal microfluidic channel topology generation is combined with multi-physics field collaborative optimization to optimize the channel structure to reduce weight and improve the system's vibration resistance and reliability while meeting heat dissipation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a dynamic fractal microfluidic channel thermal control method shown in an embodiment of the present invention; Figure 2 A schematic diagram of a dynamic fractal microfluidic channel topological structure according to an embodiment of the present invention; Figure 3 A dynamic fractal flow chart of an embodiment of the present invention; Figure 4 A system working flow diagram is shown for an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present application for the above-mentioned problems below should all be the contributions made by the inventor to the present application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0027] In view of the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows: Reference Figure 1 In an exemplary embodiment, a dynamic fractal microfluidic channel thermal control method is provided, comprising the following steps: S1. Real-time collection of temperature distribution data of each working area; S2, combining machine learning models to predict the spatial distribution of heat sources; S3, generating a microchannel topology structure through a dynamic fractal generation algorithm, and dynamically generating a preliminary microchannel bifurcation angle and channel width according to the prediction results in S2; S4, performing functional gradient design on the pipeline of the microfluidic channel topological structure, and designing the pipeline into a three-layer composite structure; S5, comprehensively considering the heat flow distribution, fluid flow and material mechanics factors, simulating the microchannel topology structure designed in step S4, performing multi-physics field collaborative optimization, adjusting the channel structure, and obtaining optimized channel topology parameters; S6. Generate the final microchannel bifurcation angle and channel width according to the optimized channel topology parameters; S7. Based on the final microchannel bifurcation angle and channel width, the microchannel bifurcation angle and channel width are adjusted by the execution component.

[0028] Specifically, in step S1, the servo system operating data (operating temperature distribution) and heat flux density and other data are collected in real time through sensors, and preprocessing such as data cleaning, filtering, denoising and standardization is performed to ensure the accuracy and consistency of the input data, thereby providing reliable basic data for subsequent heat source distribution prediction and dynamic fractal generation algorithms.

[0029] In step S2, a heat source distribution prediction model is established by using a machine learning model (such as a convolutional neural network) in combination with the working conditions to predict the spatial distribution of the heat source in real time. The trained model is embedded in the microfluidic thermal control center, and the heat source data is collected and preprocessed in real time. The heat source spatial distribution results are output to facilitate the subsequent dynamic adjustment of the microfluidic bifurcation angle and channel width according to the prediction results.

[0030] The prediction results of the heat source spatial distribution provide input parameters for the dynamic fractal generation algorithm in step S3, calculate the preliminary topological adjustment parameters (bifurcation angle), and formulate a path optimization plan. Figure 3 The specific dynamic fractal generation process is given.

[0031] Figure 3 In the bionic fractal feature extraction, plant veins are used as bionic objects to extract their fractal features (such as self-similarity and branching angle). The fractal features of leaf veins are transformed into mathematical models to provide a theoretical basis for dynamic fractal generation, and the following are obtained: Fractal dimension D: , where N(r) is the number of branches at scale r.

[0032] Branching angle θ: .

[0033] Figure 3 The topological structure generated by the dynamic fractal algorithm is mainly dynamic bifurcation angle and dynamic width adjustment, such as Figure 2 As shown, a dynamic fractal microfluidic topology is provided, including a multi-stage pipeline connected end to end, and each stage of the pipeline corresponds to a first-stage working temperature distribution. The dynamic bifurcation angle is designed by using the Fibonacci sequence. Since the Fibonacci sequence is widely present in nature, such as the leaf arrangement of plants and the number of petals of flowers, the ratio of its two adjacent items is close to the golden ratio (about 1.618), which has good self-similarity and optimization characteristics. In the microfluidic design, the Fibonacci sequence is used to dynamically adjust the bifurcation angle, which can simulate the efficient material transport mechanism in nature.

[0034] The nth term of the Fibonacci sequence is:

[0035] In the present invention, the bifurcation angle θ n The dynamic adjustment can be expressed as: , where θ0 is the initial angle. In the present invention, the simulation calculation adopts the initial angle θ0=34.5°, and the adjustment step Δθ=20.5°. That is, the initial angle: 34.5°, the intermediate angle: 55°, the final angle: 89.5°, the dynamic adjustment range: 34.5° → 55° → 89.5°, and the final simulation result is the best overall. A too small bifurcation angle will complicate the flow path and increase resistance. A too large bifurcation angle will cause the flow channel to be too concentrated, affecting the heat dissipation uniformity. The bifurcation angle is dynamically adjusted by the Fibonacci sequence, and the optimal heat dissipation performance can be achieved under different heat source conditions.

[0036] The dynamic flow channel width decreases exponentially to form a pressure gradient. The exponential decrease design is inspired by the fractal structure in nature, such as tree branches and blood vessels. Through the exponential decrease method, the flow channel width can be reasonably distributed at different levels, taking into account the heat dissipation efficiency and material utilization, which helps to maintain the stability of the flow rate and avoid local flow stagnation or overload.

[0037] Initial width: W0 (can be set according to specific needs) Decreasing formula: , where n is the number of flow channel levels.

[0038] The deceleration rate is 0.3 (can be optimized through experiments or simulations, and 0.3 is the best in this invention) For example: When n=0, W n =W0 (initial width).

[0039] When n=1, W n =W0×e −0.3 ≈0.7408W0.

[0040] When n=2, W n =W0×e −0.6 ≈0.5488W0.

[0041] Figure 3 The dynamic fractal microfluidic channel topology structure step aims to minimize the heat flow propagation path length L and maximize the heat dissipation efficiency η, generating the optimal width and bifurcation angle, namely: minL, maxη, where: , , Q 散 is the heat dissipation flow, Q 总 is the total heat flow.

[0042] Furthermore, combined with Figure 2In step S4, the functional gradient design is mainly to achieve multi-physical field coordinated optimization through the distribution of functional gradient materials. The pipeline of the present invention adopts a three-layer composite structure of functional gradient design. The three-layer composite structure includes a self-lubricating layer 14, a high thermal conductivity layer 13, and a thermal expansion regulating layer 12 from the inside to the outside.

[0043] Preferably, the outer thermal expansion regulating layer 12 is made of silicon carbide fiber reinforced aluminum-based composite material, which has high rigidity, low thermal expansion coefficient and corrosion resistance, effectively inhibits deformation and cracking caused by thermal stress, and adapts to extreme temperature changes. The middle high thermal conductivity layer 13 uses a boron nitride thermal conductive coating, which has high thermal conductivity and quickly transfers heat. The inner self-lubricating layer 14 uses a polyetheretherketone (PEEK) self-lubricating layer, which has excellent wear resistance and self-lubricating properties, and reduces fluid flow resistance. Material performance gradient design principle: According to the heat source distribution and flow requirements, the performance gradient of the flow channel material (such as thermal conductivity and strength) is designed. The gradient function expression is as follows: , where k(x) is the thermal conductivity, σ(x) is the intensity, and f(x) is the position-dependent weight function.

[0044] In a microgravity environment, the multi-level pressure gradient design of the three-layer structure of the present invention effectively overcomes the problem of fluid flow stagnation.

[0045] In step S5, the multi-physics field collaborative optimization realizes the collaborative optimization of flow channel topology, material properties and system parameters through thermal-mechanical-fluid coupling simulation. On the premise of meeting the heat dissipation performance, the flow channel structure is optimized to reduce weight and improve the vibration resistance and reliability of the system.

[0046] Finally, after obtaining the final microchannel bifurcation angle and channel width, the microchannel bifurcation angle and channel width are adjusted by the execution component. Specifically, the corresponding channel topology adjustment instructions are generated to control the execution of the actuator, such as Figure 3 The medium-bend dynamic actuator 8 is made of piezoelectric material. By applying a control voltage, the piezoelectric ceramic sheet is driven to bend, thereby changing the bifurcation angle and controlling the memory alloy to adjust the flow channel width. The simulation results show that the performance of the present invention is significantly improved compared with the existing structure, as shown in Table 1 below.

[0047]

[0048] It can be seen that the present invention can improve the overall performance of system thermal control by combining dynamic fractal microfluidic channel topology generation with multi-physical field collaborative optimization.

[0049] It should be noted that the specific values ​​selected in this embodiment (such as the initial angle, adjustment step size and deceleration rate, etc.) are not to be construed as limitations on the present application. In practical applications, the initial angle, adjustment step size and deceleration rate can be adaptively adjusted according to specific circumstances.

[0050] Based on the same inventive concept as the method embodiment, a dynamic fractal microfluidic channel thermal control system is provided, comprising: Data acquisition and preprocessing module, used to collect temperature distribution data of each working area in real time; Heat source distribution prediction module, used to predict the spatial distribution of heat sources in combination with machine learning models; The dynamic fractal module generates a microchannel topological structure through a dynamic fractal generation algorithm, and dynamically generates a preliminary microchannel bifurcation angle and channel width according to the prediction results in the heat source distribution prediction module; wherein the microchannel topological structure includes a multi-stage pipeline connected end to end, and each stage of the pipeline corresponds to a first-stage working temperature distribution; the bifurcation angle of the pipeline is bifurcated using the Fibonacci sequence, and the channel width of the pipeline forms a pressure gradient in an exponentially decreasing manner; the pipeline adopts a three-layer composite structure with a functional gradient design; A functional gradient design module, used for performing functional gradient design on the pipeline of the microfluidic channel topology structure, and designing the pipeline into a three-layer composite structure; A multi-physics field collaborative optimization module is used to comprehensively consider heat flow distribution, fluid flow and material mechanics factors, simulate the microchannel topology structure designed in step S4, perform multi-physics field collaborative optimization, adjust the channel structure, and obtain optimized channel topology parameters; A flow channel topology adjustment module is used to generate the final microchannel bifurcation angle and flow channel width according to the optimized flow channel topology parameters; The adjustment execution module is used to adjust the microchannel bifurcation angle and the flow channel width through the execution component based on the final microchannel bifurcation angle and the flow channel width.

[0051] Specifically, Figure 4 As shown, the working principle and process of this system are as follows: The control center is the CPU of the entire system, which performs data processing and calculation. The data acquisition and preprocessing module combines the sensors of the real-time monitoring and feedback module to collect data such as the working temperature distribution and heat flux density of the servo system in real time, and performs preprocessing such as filtering, denoising and standardization. The heat source distribution prediction module uses machine learning models (such as convolutional neural networks) to predict the spatial distribution of heat sources based on the temperature distribution of each working area collected by existing sensors and combined with the working conditions. According to the heat source distribution prediction results, it provides input parameters for the dynamic fractal module, calculates the preliminary topological adjustment parameters (bifurcation angle), and formulates a path optimization plan. The functional gradient design module is a three-layer gradient material design. The multi-physics field collaborative optimization module will comprehensively consider the heat flux distribution, fluid flow and material mechanics factors, and give the optimized material performance gradient and thermal management plan. The flow channel topology adjustment module generates the final flow channel topology adjustment plan based on the optimized flow channel topology parameters. Finally, the flow channel topology adjustment instruction is executed by the adjustment execution module to complete the actual change of the flow channel structure. Specifically, the piezoelectric driver is used to adjust the bifurcation angle, and the memory alloy is used to adjust the flow channel width.

[0052] Furthermore, the system also includes a real-time monitoring and feedback module for real-time monitoring of the system operation status and feeding back the results to the data acquisition and preprocessing module to achieve dynamic real-time adjustment.

[0053] Based on the same inventive concept as the method embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the dynamic fractal microfluidic thermal control method provided by the embodiment of the present invention is implemented. Based on such an understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0054] Based on the same inventive concept as the method embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and when the processor executes the computer instructions, the dynamic fractal microfluidic channel thermal control method provided by the embodiment of the present invention is executed.

[0055] The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0056] Embodiments of the subject matter and functional operations described in this specification may be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver device for execution by the data processing device.

[0057] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuits, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits), and the apparatus can also be implemented as special purpose logic circuits.

[0058] Processors suitable for executing computer programs include, for example, general and / or special microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more large-capacity storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to this large-capacity storage device to receive data from it or to transmit data to it, or both. However, the computer does not necessarily have such a device. In addition, the computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, just to name a few.

[0059] It should be understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0060] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A dynamic fractal microfluidic channel topological structure, characterized in that: It comprises a multi-stage pipeline connected end to end in sequence, each stage of the pipeline corresponds to a level of working temperature distribution; the bifurcation angle of the pipeline is designed by adopting the Fibonacci sequence, and the flow channel width of the pipeline decreases exponentially to form a pressure gradient; the pipeline adopts a three-layer composite structure with a functional gradient design.

2. The dynamic fractal microfluidic channel topological structure according to claim 1, characterized in that: The three-layer composite structure includes, from inside to outside, a self-lubricating layer, a high thermal conductivity layer, and a thermal expansion regulating layer.

3. A dynamic fractal microfluidic channel thermal control method, characterized in that: The following steps are involved: S1. Real-time collection of temperature distribution data of each working area; S2, combining machine learning models to predict the spatial distribution of heat sources; S3, generating a microfluidic channel topology structure through a dynamic fractal generation algorithm, and dynamically generating a preliminary microfluidic channel bifurcation angle and channel width according to the prediction results in S2; wherein the microfluidic channel topology structure includes a multi-stage pipeline connected end to end, and each stage of the pipeline corresponds to a first-stage working temperature distribution; the bifurcation angle of the pipeline is bifurcated using the Fibonacci sequence, and the channel width of the pipeline decreases exponentially to form a pressure gradient; the pipeline adopts a three-layer composite structure with a functional gradient design; S4, performing functional gradient design on the pipeline of the microfluidic channel topological structure, and designing the pipeline into a three-layer composite structure; S5, simulating the microchannel topology structure designed in step S4, adjusting the channel structure, and obtaining optimized channel topology parameters; S6. Generate the final microchannel bifurcation angle and channel width according to the optimized channel topology parameters; S7. Based on the final microchannel bifurcation angle and channel width, the microchannel bifurcation angle and channel width are adjusted by the execution component.

4. The dynamic fractal microfluidic channel thermal control method according to claim 3, characterized in that: The step S1 also includes preprocessing the collected temperature distribution data, and the preprocessing includes cleaning, filtering, denoising and standardization.

5. The dynamic fractal microfluidic channel thermal control method according to claim 3, characterized in that: In step S7, a piezoelectric driver is used to adjust the microchannel bifurcation angle, and a memory alloy is used to adjust the channel width.

6. The dynamic fractal microfluidic channel thermal control method according to claim 3, characterized in that: The bifurcation angle of the pipeline is designed by adopting the Fibonacci sequence, including: The initial angle is 34.5°, and the adjustment step size Δθ=20.5°.

7. The dynamic fractal microfluidic channel thermal control method according to claim 3, characterized in that: The flow channel width of the pipeline decreases exponentially to form a pressure gradient, including: The flow channel width decreases according to the following formula: , where n is the number of flow channel pipelines, is the initial width.

8. A dynamic fractal microfluidic channel thermal control system, characterized in that: include: Data acquisition and preprocessing module, used to collect temperature distribution data of each working area in real time; Heat source distribution prediction module, used to predict the spatial distribution of heat sources in combination with machine learning models; The dynamic fractal module generates a microchannel topological structure through a dynamic fractal generation algorithm, and dynamically generates a preliminary microchannel bifurcation angle and channel width according to the prediction results in the heat source distribution prediction module; wherein the microchannel topological structure includes a multi-stage pipeline connected end to end, and each stage of the pipeline corresponds to a first-stage working temperature distribution; the bifurcation angle of the pipeline is bifurcated using the Fibonacci sequence, and the channel width of the pipeline forms a pressure gradient in an exponentially decreasing manner; the pipeline adopts a three-layer composite structure with a functional gradient design; A functional gradient design module, used for performing functional gradient design on the pipeline of the microfluidic channel topology structure, and designing the pipeline into a three-layer composite structure; Multi-physics field collaborative optimization module, used to simulate the designed microfluidic channel topology, adjust the channel structure, and obtain the optimized channel topology parameters; A flow channel topology adjustment module is used to generate the final microchannel bifurcation angle and flow channel width according to the optimized flow channel topology parameters; The adjustment execution module is used to adjust the microchannel bifurcation angle and the flow channel width through the execution component based on the final microchannel bifurcation angle and the flow channel width.

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