A dynamic fractal microchannel topological structure, thermal control method and system

Abstract

The present invention discloses a dynamic fractal microchannel topological structure, a thermal control method and a system, belonging to the technical field of electromechanical servo thermal control, aiming at the key challenges existing in the prior art, such as insufficient adaptability to dynamic heat source distribution, reliability problems and lack of multi-physical field collaborative optimization. The present invention significantly improves the heat dissipation efficiency, adaptability and reliability of the microchannel system through a dynamic fractal generation algorithm, a functionally graded composite material and a multi-physical field coupling optimization strategy, providing important technical support for deep space exploration missions.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical servo thermal control, and particularly 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 space technology, as a key actuator of a spacecraft, electromechanical servo products often need to be directly exposed in the space environment and face harsh extreme conditions. Different from components such as flight control that can be internally integrated for protection, due to their functional requirements and structural characteristics, electromechanical servo products usually cannot achieve effective integrated protection. Therefore, their thermal management requirements have become the core problem restricting the performance of these products.

[0003] In the space environment, electromechanical servo products must operate stably and reliably under large temperature fluctuation ranges (-150°C to +200°C), microgravity environments, and high power density conditions. These harsh environmental conditions pose extremely high requirements for the thermal management performance of the products.

[0004] The existing flow channel thermal control technologies and their deficiencies are as follows:

[0005] 1) Limitations of the fixed bifurcation mode: The tree-shaped fractal flow channels proposed in existing patents adopt a fixed bifurcation mode and cannot respond in real time to the dynamic heat source distribution of the servo motor. This static design is prone to uneven heat flow distribution when facing power density fluctuations, leading to problems such as 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 meet the thermal management requirements under complex working conditions.

[0006] 2) The fractal algorithm does not consider the material thermal stress coupling effect: Some patents have proposed a fractal algorithm to optimize the flow channel topology, but their designs do not fully consider the material thermal stress coupling effect. In practical applications, thermal stress concentration will cause fatigue damage to the flow channel structure, affecting the reliability and service life of the system.

[0007] 3) Lack of thermal-mechanical co-optimization: In the space environment, while ensuring the heat dissipation performance, the flow channel structure needs to minimize weight and improve vibration resistance as much as possible. Therefore, the dynamic mechanism should be thermally-mechanically co-optimized to meet the requirements of space products for lightweight and high reliability.

[0008] These problems have severely restricted the actual application effect of microchannel thermal management technology in aerospace electromechanical servo products. Summary of the Invention

[0009] In view of the deficiencies in the prior art, such as insufficient dynamic adaptability, reliability issues, and the lack of collaborative optimization of multiple physical fields, the present invention proposes a dynamic fractal microchannel topological structure, a thermal control method, and a system. By real-time monitoring of the heat source distribution, combined with bionic fractal feature extraction, machine learning prediction, and dynamic fractal generation algorithms, the bifurcation angle and width of the flow channels are adjusted, and functional gradient composite materials and multi-physical field coupling optimization are utilized to achieve efficient thermal management.

[0010] The object of the present invention is achieved through the following technical solutions:

[0011] In the first aspect, a dynamic fractal microchannel topological structure is provided, including multi-stage pipelines connected in sequence from head to tail, with each stage of pipeline corresponding to a stage 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 functional gradient design.

[0012] Preferably, the three-layer composite structure includes a self-lubricating layer, a high thermal conductivity layer, and a thermal expansion control layer from the inside to the outside in sequence.

[0013] In the second aspect, a dynamic fractal microchannel thermal control method is provided, including the following steps:

[0014] S1. Real-time collect the temperature distribution data of each working area;

[0015] S2. Combine a machine learning model to predict the spatial distribution of the heat source;

[0016] S3. Generate a microchannel topological structure through a dynamic fractal generation algorithm, and dynamically generate preliminary microchannel bifurcation angles and flow channel widths according to the prediction results in S2; among them, the microchannel topological structure includes multi-stage pipelines connected in sequence from head to tail, with each stage of pipeline corresponding to a stage 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 functional gradient design;

[0017] S4. Perform functional gradient design on the pipelines of the microchannel topological structure, and design the pipelines as a three-layer composite structure;

[0018] S5. Considering heat flow distribution, fluid flow, and material mechanics factors comprehensively, simulate the microchannel topological structure designed in step S4, perform multi-physical field collaborative optimization, and adjust the flow channel structure to obtain optimized flow channel topological parameters;

[0019] S6. Generate the final microchannel bifurcation angles and flow channel widths according to the optimized flow channel topological parameters;

[0020] S7. Based on the final microchannel bifurcation angle and channel width, adjust the microchannel bifurcation angle and channel width through an actuator.

[0021] Preferably, in step S1, preprocessing is further performed on the collected temperature distribution data, and the preprocessing includes cleaning, filtering, denoising, and normalization.

[0022] Preferably, in step S7, a piezoelectric actuator is used to adjust the microchannel bifurcation angle, and a shape memory alloy is used to adjust the channel width.

[0023] Preferably, the bifurcation angle of the pipeline is designed by using the Fibonacci sequence, including:

[0024] The initial angle is 34.5°, and the adjustment step Δθ = 20.5°.

[0025] Preferably, the channel width of the pipeline forms a pressure gradient in an exponentially decreasing manner, including:

[0026] The channel width decreases according to the following formula:

[0027] , where n is the number of levels of the channel pipeline, is the initial width.

[0028] In a third aspect, a dynamic fractal microchannel thermal control system is provided, including:

[0029] A data acquisition and preprocessing module for real-time acquisition of temperature distribution data in each working area;

[0030] A heat source distribution prediction module for predicting the spatial distribution of the heat source in combination with a machine learning model;

[0031] A dynamic fractal module that generates a microchannel topology 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 topology includes multiple levels of pipelines connected end to end in sequence, and each level of pipeline corresponds to a level of working temperature distribution; the bifurcation angle of the pipeline is designed by using the Fibonacci sequence, 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 functionally graded design;

[0032] A functionally graded design module for performing a functionally graded design on the pipelines of the microchannel topology and designing the pipelines into a three-layer composite structure;

[0033] A multi-physical field collaborative optimization module for comprehensively considering heat flow distribution, fluid flow, and material mechanics factors, simulating the microchannel topology designed in step S4, performing multi-physical field collaborative optimization, adjusting the channel structure, and obtaining optimized channel topology parameters;

[0034] A runner topology adjustment module, configured to generate a final microchannel bifurcation angle and a runner width according to the optimized runner topology parameters;

[0035] An adjustment execution module, configured to adjust the microchannel bifurcation angle and the runner width through an execution component based on the final microchannel bifurcation angle and the runner width.

[0036] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the dynamic fractal microchannel thermal control method described in the second aspect is implemented.

[0037] In a fifth aspect, an electronic device is provided, including a memory and a processor. A computer instruction that can run on the processor is stored on the memory, and when the processor runs the computer instruction, the dynamic fractal microchannel thermal control method described in the second aspect is implemented.

[0038] It should be further noted that the technical features corresponding to the above options can be combined or replaced with each other without conflict to form a new technical solution.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The present invention forms a dynamic fractal generation algorithm by real-time monitoring of the heat source distribution and combining a bionic fractal microchannel topology generation mechanism based on bionics, fractal geometry, and dynamic optimization algorithms, and adjusts the channel topology structure (bifurcation angle and channel width) to achieve efficient heat management and fluid transport. Then, through the combination of dynamic topology reconstruction and functionally graded composites, an efficient and reliable heat management solution is achieved. It has the following advantages:

[0041] 1) The heat dissipation efficiency is improved: compared with the traditional tree-shaped runner design, the heat dissipation efficiency is increased by 30% - 50%.

[0042] 2) The heat source positioning accuracy is improved: the heat source positioning accuracy is increased by 20%, and the prediction error of heat flow propagation is less than 5%.

[0043] 3) The reliability is improved: a multi-layer functionally graded composite structure is proposed. According to the heat source distribution and flow requirements, a material property gradient function is designed. By optimizing the material properties and structural design, the durability and service life of the system are significantly improved.

[0044] 4) Lightweight: The combination of dynamic fractal microchannel topology generation and multi-physical field collaborative optimization optimizes the channel structure to reduce weight under the premise of meeting the heat dissipation performance, improving the vibration resistance and reliability of the system. Description of the Drawings

[0045] Figure 1Flowchart of a dynamic fractal microchannel thermal control method shown in an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the topological structure of the dynamic fractal microchannel shown in an embodiment of the present invention;

[0047] Figure 3 Dynamic fractal flowchart shown in an embodiment of the present invention;

[0048] Figure 4 Flowchart of the system operation shown in an embodiment of the present invention. Detailed implementation manners

[0049] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0050] It should be noted that the defects existing in the above prior art solutions are all results obtained by the inventor through practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present application below to address the above problems should be contributions made by the inventor to the present application during the invention creation process, and should not be construed as well-known technical content in the art.

[0051] In response to the technical problems pointed out in the background art, the embodiments provided by the present invention are as follows:

[0052] Refer to Figure 1 , in an exemplary embodiment, a dynamic fractal microchannel thermal control method is provided, including the following steps:

[0053] S1. Real-time collect the temperature distribution data of each working area;

[0054] S2. Combine a machine learning model to predict the spatial distribution of heat sources;

[0055] S3. Generate a microchannel topological structure through a dynamic fractal generation algorithm, and dynamically generate a preliminary microchannel bifurcation angle and channel width according to the prediction result in S2;

[0056] S4. Perform a functional gradient design on the pipeline of the microchannel topological structure, and design the pipeline as a three-layer composite structure;

[0057] S5. Considering the heat flux distribution, fluid flow, and material mechanics factors comprehensively, simulate the microchannel topological structure designed in step S4, conduct multi-physical field collaborative optimization, adjust the channel structure, and obtain the optimized channel topological parameters;

[0058] S6. Generate the final microchannel bifurcation angle and channel width according to the optimized channel topological parameters;

[0059] S7. Based on the final microchannel bifurcation angle and channel width, adjust the microchannel bifurcation angle and channel width through the actuator.

[0060] Specifically, in step S1, the working condition data (working temperature distribution) and heat flux density data of the servo system are collected in real time through sensors, and preprocessing such as data cleaning, filtering, denoising, and standardization is carried out to ensure the accuracy and consistency of the input data, providing reliable basic data for the subsequent heat source distribution prediction and dynamic fractal generation algorithm.

[0061] 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, the heat source spatial distribution is predicted in real time, the trained model is embedded in the microchannel heat control center, the heat source data is collected in real time and preprocessed, and the heat source spatial distribution result is output, which is convenient for dynamically adjusting the microchannel bifurcation angle and channel width according to the prediction result.

[0062] The prediction result of the heat source spatial distribution provides input parameters for the dynamic fractal generation algorithm in step S3, calculates the preliminary topological adjustment parameters (bifurcation angle), and formulates a path optimization scheme. The following combines Figure 3 to give the specific dynamic fractal generation process.

[0063] Figure 3 In, the bionic fractal feature extraction takes plant leaf veins as the bionic object and extracts its fractal features (such as self-similarity, branch angle). The fractal features of the leaf veins are transformed into a mathematical model, providing a theoretical basis for dynamic fractal generation, and obtaining:

[0064] Fractal dimension D:

[0065] , where N(r) is the number of branches at scale r.

[0066] Branch angle θ:

[0067] .

[0068] Figure 3 In, the topological structure generated by the dynamic fractal algorithm is mainly dynamic bifurcation angle and dynamic width adjustment, such as Figure 2As shown, a dynamic fractal microchannel topological structure is provided, including multi-stage pipelines connected in sequence from start to end, and each stage of pipeline corresponds to a stage of working temperature distribution. Among them, the dynamic bifurcation angle is designed by using the Fibonacci sequence. Since the Fibonacci sequence widely exists in nature, such as the phyllotaxis arrangement of plants and the number of petals of flowers. The ratio of its adjacent two terms approaches the golden ratio (about 1.618), and it has good self-similarity and optimization characteristics. In the microchannel design, using the Fibonacci sequence to dynamically adjust the bifurcation angle can simulate the efficient mass transport mechanism in nature.

[0069] The nth term of the Fibonacci sequence is:

[0070]

[0071] In the present invention, the dynamic adjustment of the bifurcation angle θ n can be expressed as:

[0072] , where θ0 is the initial angle. In the simulation calculation of the present invention, the initial angle θ0 = 34.5° is adopted, and the adjustment step size 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°. The final simulation result is comprehensively optimal. If the bifurcation angle is too small, it will lead to the complication of the flow path and increase the resistance. If the bifurcation angle is too large, the flow channels will be too concentrated, affecting the heat dissipation uniformity. By dynamically adjusting the bifurcation angle with the Fibonacci sequence, the optimal heat dissipation performance can be achieved under different heat source conditions.

[0073] The dynamic channel width forms a pressure gradient in an exponential decreasing manner. The exponential decreasing design inspiration comes from the fractal structures in nature, such as tree branches, blood vessels, etc. By the exponential decreasing method, a reasonable distribution of the channel width can be achieved at different levels, taking into account the heat dissipation efficiency and material utilization rate, which helps to maintain the stability of the flow velocity and avoid local flow stagnation or overload.

[0074] Initial width: W0 (which can be set according to specific requirements)

[0075] Decreasing formula:

[0076] , where n is the number of channel levels.

[0077] The decreasing rate is 0.3 (which can be optimized through experiments or simulations. In the present invention, 0.3 is adopted as the optimal value)

[0078] For example:

[0079] When n = 0, W n = W0 (initial width).

[0080] When n = 1, W n = W0×e −0.3 ≈0.7408W0.

[0081] When n = 2, W n = W0×e −0.6 ≈0.5488W0.

[0082] Figure 3 In the dynamic fractal microchannel topological structure step, with the goal of minimizing the heat flow propagation path length L and maximizing the heat dissipation efficiency η, the optimal width and bifurcation angle are generated, that is: minL, maxη, where:

[0083] , , Q 散 is the heat dissipation flow rate, Q 总 is the total heat flow rate.

[0084] Furthermore, in combination with Figure 2 , the functional gradient design in step S4 mainly realizes the multi-physical field collaborative optimization through the distribution of functional gradient materials. The pipeline of the present invention adopts a three-layer composite structure with functional gradient design. The three-layer composite structure sequentially includes a self-lubricating layer 14, a high thermal conductivity layer 13, and a thermal expansion control layer 12 from the inside to the outside.

[0085] Preferably, the outer thermal expansion control layer 12 adopts a silicon carbide fiber reinforced aluminum matrix composite material, which has high rigidity, low thermal expansion coefficient and corrosion resistance, effectively inhibits the deformation and cracking caused by thermal stress, and adapts to extreme temperature changes. The middle high thermal conductivity layer 13 adopts a boron nitride thermal conductivity coating, which has high thermal conductivity and can quickly transfer heat. The inner self-lubricating layer 14 adopts a polyether ether ketone (PEEK) self-lubricating layer, which has excellent wear resistance and self-lubricating performance, and reduces the fluid flow resistance. Material property gradient design principle:

[0086] According to the heat source distribution and flow requirements, the performance gradient (such as thermal conductivity, strength) of the flow channel material is designed. The gradient function expression is as follows:

[0087] , where k(x) is the thermal conductivity, σ(x) is the strength, and f(x) is a position-related weight function.

[0088] In the microgravity environment, the multi-stage pressure gradient design of the three-layer structure of the present invention effectively overcomes the problem of working fluid flow stagnation.

[0089] In step S5, the multi-physical field collaborative optimization realizes the collaborative optimization of the flow channel topology, material properties and system parameters through thermal-fluid-solid coupling simulation. On the premise of meeting the heat dissipation performance, the flow channel structure is optimized to reduce weight, improve the vibration resistance and reliability of the system.

[0090] Finally, after obtaining the final microchannel bifurcation angle and channel width, the microchannel bifurcation angle and channel width are adjusted by an execution component. Specifically, corresponding channel topology adjustment instructions are generated to control the actuator to execute, 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 the shape memory alloy is controlled to adjust the 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.

[0091]

[0092] It can be seen that through the generation of dynamic fractal microchannel topology combined with multi-physical field collaborative optimization, the present invention can improve the overall performance of system thermal control.

[0093] It should be noted that the specific values selected in this embodiment (such as the initial angle, adjustment step, and decreasing rate, etc.) are not construed as limitations to this application. In practical applications, the initial angle, adjustment step, and decreasing rate can be adaptively adjusted according to specific situations.

[0094] Based on the same inventive concept as the method embodiment, a dynamic fractal microchannel thermal control system is provided, including:

[0095] A data acquisition and preprocessing module for real-time collecting temperature distribution data of each working area;

[0096] A heat source distribution prediction module for predicting the spatial distribution of the heat source in combination with a machine learning model;

[0097] A dynamic fractal module that generates a microchannel topology structure through a dynamic fractal generation algorithm and dynamically generates a preliminary microchannel bifurcation angle and channel width according to the prediction result in the heat source distribution prediction module; wherein, the microchannel topology structure includes multiple levels of pipelines connected end to end in sequence, and each level of pipeline corresponds to a level of working temperature distribution; the bifurcation angle of the pipeline is designed with a Fibonacci sequence for bifurcation, and the channel width of the pipeline forms a pressure gradient in an exponential decreasing manner; the pipeline adopts a three-layer composite structure with a functionally graded design;

[0098] A functionally graded design module for performing a functionally graded design on the pipelines of the microchannel topology structure and designing the pipelines into a three-layer composite structure;

[0099] A multi-physical field collaborative optimization module for comprehensively considering heat flow distribution, fluid flow, and material mechanics factors, simulating the microchannel topology structure designed in step S4, performing multi-physical field collaborative optimization, adjusting the channel structure, and obtaining optimized channel topology parameters;

[0100] The flow channel topology adjustment module is used to generate the final micro-channel bifurcation angle and flow channel width according to the optimized flow channel topology parameters;

[0101] The adjustment execution module is used to adjust the micro-channel bifurcation angle and flow channel width through an execution component based on the final micro-channel bifurcation angle and flow channel width.

[0102] Specifically, as Figure 4 shown, the working principle and process of this system are as follows:

[0103] 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, based on the temperature distribution of each working area collected by the existing sensors, combines the working conditions and uses a machine learning model (such as a convolutional neural network) to predict the spatial distribution of the heat source, provides input parameters for the dynamic fractal module according to the heat source distribution prediction result, calculates the preliminary topology adjustment parameters (bifurcation angle), and formulates a path optimization plan. The functional gradient design module is designed for three-layer gradient materials. The multi-physical field collaborative optimization module comprehensively considers factors such as heat flow distribution, fluid flow, and material mechanics, and gives the optimized material property gradient and heat management plan. The flow channel topology adjustment module generates the final flow channel topology adjustment plan according to the optimized flow channel topology parameters. Finally, the adjustment execution module executes the flow channel topology adjustment instruction to complete the actual change of the flow channel structure. Specifically, a piezoelectric actuator is used to adjust the bifurcation angle, and a shape memory alloy is used to adjust the flow channel width.

[0104] Furthermore, the system also includes a real-time monitoring and feedback module, which is used to monitor the system operation state in real time and feedback the results to the data acquisition and preprocessing module to achieve dynamic real-time adjustment.

[0105] Based on the same inventive concept as the method embodiments, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, which when executed by a processor, implements the dynamic fractal microchannel thermal control method provided by the embodiments of the present invention. Based on such an understanding, the technical solution of this embodiment, 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 may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0106] Based on the same inventive concept as the method embodiments, an electronic device is provided, including a memory and a processor. The memory stores computer instructions that can run on the processor, and when the processor runs the computer instructions, it executes the dynamic fractal microchannel thermal control method provided by the embodiments of the present invention.

[0107] The processor can be a single-core or multi-core central processing unit or a specific integrated circuit, or an integrated circuit configured to implement one or more of the present invention.

[0108] The embodiments of the subject matter and the functional operations described in this specification can be implemented in the following: 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. The embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more modules in 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 a data processing device. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver device for execution by a data processing device.

[0109] The processing and logical flows described in this specification can be executed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating outputs. The processing and logical flows can also be executed by dedicated logic circuits, such as FPGAs (field-programmable gate arrays) or ASICs (application-specific integrated circuits), and the device can also be implemented as dedicated logic circuits.

[0110] Processors suitable for executing computer programs include, for example, general and / or special-purpose microprocessors, or any other type of central processing unit. Generally, the central processing unit will receive instructions and data from read-only memory and / or 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. Generally, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, etc., or the computer will be operatively coupled to such mass storage devices to receive data therefrom or transfer data thereto, or both. However, a computer is not necessarily required to have such devices. In addition, a computer may 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, to name just a few examples.

[0111] It should be understood that each block in a flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a 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 marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can 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, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0112] The above specific embodiments are detailed descriptions of the present invention. It cannot be determined that the specific embodiments of the present invention are only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions and substitutions can still be made, and all 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 includes a multi-stage pipeline connected end to end, 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 bifurcation angle of the pipeline is designed by adopting the Fibonacci sequence, including: Bifurcation angle θ n The dynamic adjustment can be expressed as: , where θ0 is the initial angle and Δθ represents the adjustment step size; 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; The pipeline adopts a three-layer composite structure with a functional gradient design, and the three-layer composite structure includes a self-lubricating layer, a high thermal conductivity layer, and a thermal expansion regulating layer from the inside to the outside.

2. 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 by adopting 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; the bifurcation angle of the pipeline is bifurcated by adopting the Fibonacci sequence, including: Bifurcation angle θ n The dynamic adjustment can be expressed as: , where θ0 is the initial angle and Δθ represents the adjustment step size; 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; The three-layer composite structure includes, from the inside to the outside, a self-lubricating layer, a high thermal conductivity layer, and a thermal expansion regulating layer; 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; the adjusting the channel structure to obtain optimized channel topology parameters includes: Through thermal-mechanical-fluid coupling simulation, the flow channel topology, material properties and system parameters are optimized in a coordinated manner. Under the premise of meeting the heat dissipation performance, the flow channel structure is optimized to reduce weight. 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.

3. The dynamic fractal microfluidic channel thermal control method according to claim 2, characterized in that: The step S1 also includes preprocessing the collected temperature distribution data, and the preprocessing includes cleaning, filtering, denoising and standardization.

4. The dynamic fractal microfluidic channel thermal control method according to claim 2, 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.

5. The dynamic fractal microfluidic channel thermal control method according to claim 2, 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°.

6. 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 by adopting 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; the bifurcation angle of the pipeline is bifurcated by adopting the Fibonacci sequence, including: Bifurcation angle θ n The dynamic adjustment can be expressed as: , where θ0 is the initial angle and Δθ represents the adjustment step size; 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; The three-layer composite structure includes, from the inside to the outside, a self-lubricating layer, a high thermal conductivity layer, and a thermal expansion regulating layer; 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; The multi-physics field collaborative optimization module is used to simulate the designed microchannel topology structure, adjust the channel structure, and obtain optimized channel topology parameters; the adjustment of the channel structure to obtain optimized channel topology parameters includes: Through thermal-mechanical-fluid coupling simulation, the flow channel topology, material properties and system parameters are optimized in a coordinated manner. Under the premise of meeting the heat dissipation performance, the flow channel structure is optimized to reduce weight. 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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