Critical heat flux prediction method based on micro-nano hierarchical structure and machine learning

Abstract

The application belongs to the technical field of electronic equipment thermal management, and particularly relates to a critical heat flux density prediction method based on micro-nano hierarchical structure and machine learning, S1, a heat dissipation structure body with a micro-nano hierarchical structure surface is provided, which is used for two-phase heat exchange with a cooling working medium; S2, temperature data and visual image data of the micro-nano hierarchical structure surface are collected in real time through a multi-modal monitoring module; S3, the visual image data is processed through a data processing and prediction module, and a machine learning algorithm is used to predict the boiling critical heat flux, and the data processing and prediction module is in communication connection with the multi-modal monitoring module. The critical heat flux density prediction method based on the micro-nano hierarchical structure and the machine learning, through the optimized micro-nano hierarchical structure, synergistically enhances the capillary force and the liquid replenishment capacity, significantly delays the dry spot formation from the physical level, improves the boiling critical heat flux, and realizes early and accurate prediction of the boiling critical heat flux.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology for electronic devices, and in particular to a method for predicting critical heat flux density based on micro-nano hierarchical structures and machine learning. Background Technology

[0002] As integrated circuits and power devices evolve towards higher performance and smaller sizes, their heat flux density is increasing dramatically. Traditional single-phase heat dissipation methods, such as air cooling, are approaching their limits. Phase change heat transfer, especially boiling heat transfer, has become a key technology for addressing next-generation thermal challenges because it can utilize the latent heat of the working fluid and thus possesses extremely high heat dissipation capabilities. The performance upper limit of boiling heat transfer is determined by the critical heat flux. Decision, exceeding The heated surface will be covered by a vapor film, causing a rapid temperature rise and equipment failure. How can this be improved? And achieving accurate prediction is the current research focus.

[0003] In existing technologies, the heating surface is typically enhanced by constructing micron or nanostructures to increase nucleation sites, extend contact lines, and strengthen capillary forces, thereby improving… However, simple microstructures, in certain situations such as excessively small column spacing, have limited effect on improving capillary wicking capacity and may even inhibit liquid replenishment due to excessive flow resistance. Furthermore, the boiling process is highly random and nonlinear, with complex bubble dynamics, making traditional experience-based methods ineffective. The correlation-based approach has poor universality and cannot achieve real-time, accurate prediction. Therefore, there is an urgent need in this field for a method that can significantly improve... A comprehensive heat dissipation solution that can intelligently predict and avoid boiling over. Summary of the Invention

[0004] Based on the aforementioned technical problems, this invention proposes a critical heat flux density prediction method based on micro-nano hierarchical structures and machine learning.

[0005] This invention proposes a critical heat flux density prediction method based on micro / nano hierarchical structures and machine learning, comprising the following steps:

[0006] S1. Provide a heat dissipation structure body with a micro-nano hierarchical surface for two-phase heat exchange with the cooling working fluid.

[0007] S2. The temperature data and visual image data of the surface of the micro-nano hierarchical structure are collected in real time through the multimodal monitoring module.

[0008] S3. The visual image data is processed by the data processing and prediction module, and the boiling critical heat flux is predicted using a machine learning algorithm, wherein the physical descriptor of the boiling critical heat flux is denoted as... The data processing and prediction module is communicatively connected to the multimodal monitoring module.

[0009] S4. The signal receiving module is electrically connected to the prediction module to receive the early warning signal from the prediction module and transmit it to the control circuit. The control circuit executes the corresponding active control strategy according to the early warning signal.

[0010] Preferably, the surface of the micro / nano hierarchical structure includes:

[0011] An array of micropillars is disposed on a substrate, the diameter of the micropillars being... ,spacing ,high Meets the preset diameter / spacing ratio ,and The value is between 0.1 and 0.5 to optimize capillary wicking performance, which refers to the ability to absorb and conduct cooling fluids, including water and refrigerants, through capillary action.

[0012] Both the surface of the micron-shaped pillars and the substrate plane are covered with a nanostructure layer, which is any one of zinc oxide nanorods, carbon nanotubes, or metal oxide nanowires. The surface roughness of the nanostructure layer is... .

[0013] Through the above technical solution, the micro / nano hierarchical structure consists of a micron column array and nanostructures grown on it, with an optimized diameter / spacing ratio. < 0.3, such as =10μm, =50μm, forming a sparse arrangement, allowing the nanostructure to fully exert its capillary advantages and form a leading nano-wicking front. The nano-wicking front refers to the capillary action front provided by the nanostructure, which can rapidly transport liquid. In synergy with the capillary wicking performance of the microstructure, it enhances the overall liquid replenishment rate and greatly enhances the liquid replenishment rate between the micropillars. The nanostructure further increases the specific surface area and capillary force, endows the surface with superhydrophilicity, and synergistically enhances the capillary force and liquid replenishment ability from a physical level, significantly delaying the formation of dry spots.

[0014] Preferably, the multimodal monitoring module includes:

[0015] An infrared thermal imager, positioned on one side of a transparent heating substrate, is used to monitor the temperature distribution and dry spot formation on the surface of the micro / nano hierarchical structure.

[0016] A high-speed camera, configured on the transparent heating substrate and located on the same side as the infrared thermal imager, is used to acquire a sequence of bubble dynamics images during the boiling process.

[0017] Through the above technical solutions, a multimodal perception system combining bottom vision and infrared was constructed. The infrared thermal imager can accurately identify the formation of local dry spots and hot spots, and the high-speed camera records the entire dynamic process of bubble nucleation, growth, merging and detachment at a high frame rate of more than 1000fps, providing a complete, real-time, high-quality data source for subsequent intelligent prediction based on image sequences.

[0018] Preferably, the transparent heating substrate is one of sapphire, quartz, or transparent conductive oxide coated glass, specifically one of sapphire heater, quartz heater, or transparent conductive oxide coated glass heater.

[0019] The above technical solution utilizes a transparent heating substrate that combines excellent thermal conductivity, heat resistance, and optical transparency, ensuring that infrared thermal imagers and high-speed cameras can pass through the substrate without interference, enabling high-precision, in-situ monitoring of the surface state of micro-nano hierarchical structures.

[0020] Preferably, the data processing and prediction module performs the following operations:

[0021] Step 1: Perform principal feature analysis on the acquired image sequence, wherein the physical descriptor for the principal feature analysis is denoted as... Before extraction Individual main features To reduce data dimensionality;

[0022] Step 2: For the first primary feature Perform a Fast Fourier Transform on the time series to calculate its bubble detachment frequency. and the area fraction of the arid region As a key physical descriptor characterizing the boiling state, the physical descriptor of the Fourier transform is denoted as... .

[0023] Step 3: Utilize a bidirectional long short-term memory neural network, previously... Using millisecond-level main feature time series as input, predict the future. The main feature changes in milliseconds are used to reconstruct the predicted future bubble shape, where the physical descriptor of the bidirectional long short-term memory neural network is denoted as... Model.

[0024] Through the above technical solution, this module is the intelligent core of the system, and it first adopts unsupervised machine learning. Dimensionality reduction and feature extraction are performed on megapixel-level image sequences to simplify complex visual data. There are 1 main features, of which the first main feature is The footprint distribution of the bubble image was effectively captured, and then analyzed through the PC1 time series... Analysis revealed key descriptors with clear physical meaning, namely the bubble detachment frequency. and the area fraction of the arid region Finally, using By learning the long-term dependencies of the main feature time series, the network can predict the future evolution of bubble dynamics. This method overcomes the problem of inaccurate identification in high temperature difference regions by traditional image segmentation methods, and realizes a deep understanding and advanced prediction of boiling state.

[0025] Preferably, in the data processing and prediction module, the main feature analysis extracts the first... The number of principal features ranges from 10 to 100.

[0026] The above technical solution effectively reduces the data dimension from millions of pixels to tens to hundreds of main features, while preserving key dynamic information of the boiling process and significantly reducing computational complexity, thus ensuring the real-time performance of the prediction algorithm.

[0027] Preferably, in the data processing and prediction module, when the area fraction of the arid region is detected... A sharp increase, judged as Precursors, triggering warning signals, and then utilizing Based on the model's predictions, adjust the heating power or cooling flow rate in advance.

[0028] Through the above technical solutions, when the system approaches hour, There will be a significant surge, which can be observed by monitoring the sharp changes in these two key descriptors in real time, such as... The rapid increase from 0.5 to over 0.6 is a pattern that has been verified through multiple experiments, enabling the application of [the technology / method / mechanism]. Early and accurate identification allows for a valuable time window for proactive control.

[0029] When the number of dry spots around the bubble reaches a critical limit, thus inhibiting liquid replenishment, it triggers... This will cause the surface temperature to rise, thereby increasing the number of nucleation sites and triggering drying. The critical heat flux density is calculated using the liquid flow rate driven by capillary action and the amount of liquid evaporation at the dry spot:

[0030]

[0031] - Critical heat flux density; C0 - A constant function determined by experimental conditions. -Latent heat of vaporization - Surface tension of the fluid, -Liquid film thickness, - Dry spot expansion rate, - Dry spot area fraction.

[0032] Preferably, the method for two-phase heat exchange with the cooling working fluid described in S1 includes the following steps:

[0033] S11. Prepare a heat dissipation surface with a specific micro-nano hierarchical structure.

[0034] S12. Start the system to cause the cooling medium to boil on the heat dissipation surface.

[0035] S13. The surface temperature field and bubble image sequence are collected in real time through the multimodal monitoring module.

[0036] S14. The data processing and prediction module processes image data and extracts the bubble detachment frequency. and the area fraction of the arid region .

[0037] S15, based on The model predicts the bubble dynamics behavior at future time steps.

[0038] S16. Based on the changing trend of the physical descriptor and the prediction results, determine whether it is close to the boiling critical heat flux. If it is close to the boiling critical heat flux, execute one of the early warning or active control strategies. If it is not close to the boiling critical heat flux, return to step S13 and continue monitoring.

[0039] The above technical solution provides a complete, closed-loop intelligent heat dissipation workflow. From surface preparation, condition monitoring, data processing to prediction and control, it seamlessly integrates advanced surface engineering, multimodal sensing, and artificial intelligence algorithms to form a comprehensive solution that can proactively predict and avoid boiling over, significantly improving the reliability and safety of heat dissipation for high heat density equipment.

[0040] Preferably, in the surface of the micro / nano hierarchical structure, the diameter / spacing ratio of the micropillars is... <0.3, and the height of the micrometer column >20μm.

[0041] Through the above technical solution, this optimized parameter combination ensures that the nano wicking front end always leads the bulk wicking front end, achieving the best liquid transport enhancement effect, which is the key to the synergistic advantages of micro-nano hierarchical structures.

[0042] Preferably, the active control strategy in step S16 includes one or more of the following: reducing heating power, activating auxiliary cooling, or issuing an audible and visual alarm.

[0043] The above technical solution provides a tiered and flexible active control method, which can be used to predict... To mitigate risks, the system can automatically take one or more combined measures, such as reducing power to prevent a crisis, activating auxiliary cooling to enhance heat dissipation, or issuing alarms to remind users to intervene, which greatly enhances the system's proactive safety protection capabilities.

[0044] The beneficial effects of this invention are as follows:

[0045] 1. Through optimized micro / nano hierarchical structures, capillary force and liquid replenishment capabilities are synergistically enhanced, significantly delaying dry spot formation at a physical level and improving performance. Innovatively employing unsupervised machine learning Extract descriptors with clear physical meaning from the image: bubble detachment frequency. and the area fraction of the arid region This overcomes the problem of traditional image segmentation methods failing to accurately identify bubbles in areas of high temperature difference, and achieves [the ability to accurately identify bubbles]. Early and accurate predictions.

[0046] 2. By setting The model can not only analyze the current state but also predict future bubble dynamics evolution, providing a valuable time window for active control. The seamless integration of surface engineering, advanced sensing, and artificial intelligence algorithms forms a complete, intelligent, and highly reliable heat dissipation solution. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of a critical heat flux density prediction method based on micro-nano hierarchical structures and machine learning proposed in this invention.

[0048] Figure 2 This is a scanning electron microscope image of the surface of a micro-nano hierarchical structure, which is proposed in this invention as a critical heat flux density prediction method based on micro-nano hierarchical structure and machine learning.

[0049] Figure 3 This is a schematic diagram of boiling bubble image processing for a critical heat flux density prediction method based on micro-nano hierarchical structure and machine learning proposed in this invention.

[0050] Figure 4 This invention proposes a critical heat flux density prediction method based on micro / nano hierarchical structures and machine learning. Model prediction A flowchart.

[0051] In the diagram: 1. Sapphire heater; 2. Micro-nano hierarchical surface structure; 3. Infrared thermal imager; 4. High-speed camera; 5. Data processing and prediction module; 6. Control circuit; 7. Signal receiving module; 8. Model. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0053] Reference Figures 1-4 A critical heat flux density prediction method based on micro / nano hierarchical structures and machine learning includes the following steps:

[0054] S1. Provide a heat dissipation structure body with a micro-nano hierarchical structure surface 2 for two-phase heat exchange with the cooling working fluid.

[0055] S2. The temperature data and visual image data of the surface 2 of the micro-nano hierarchical structure are collected in real time through the multimodal monitoring module.

[0056] S3. The visual image data is processed by the data processing and prediction module 5, and the boiling critical heat flux is predicted using a machine learning algorithm. The physical descriptor of the boiling critical heat flux is denoted as... The data processing and prediction module 5 is connected to the multimodal monitoring module.

[0057] S4. The signal receiving module 7 is electrically connected to the prediction module 5 to receive the early warning signal from the prediction module 5 and transmit it to the control circuit 6. The control circuit 6 executes the corresponding active control strategy according to the early warning signal.

[0058] Micro-nano hierarchical structure surface 2 includes:

[0059] The array consists of micropillars arranged on a substrate, with the diameter of the micropillars being... ,spacing ,high Meets the preset diameter / spacing ratio ,and The value should be between 0.1 and 0.5 to optimize capillary wicking performance, which refers to the ability to absorb and conduct cooling fluids through capillary action. Cooling fluids include water and refrigerants.

[0060] Both the surface of the micron-sized pillars and the substrate plane are covered with a nanostructure layer, which can be any one of zinc oxide nanorods, carbon nanotubes, or metal oxide nanowires. The surface roughness of the nanostructure layer is... This micro / nano hierarchical structure consists of a micrometer-scale column array and nanostructures grown on it, with an optimized diameter / spacing ratio. < 0.3, such as =10μm, =50μm, forming a sparse arrangement, allowing the nanostructure to fully exert its capillary advantages and form a leading nano-wicking front. The nano-wicking front refers to the capillary action front provided by the nanostructure, which can rapidly transport liquid. In synergy with the capillary wicking performance of the microstructure, it enhances the overall liquid replenishment rate and greatly enhances the liquid replenishment rate between the micropillars. The nanostructure further increases the specific surface area and capillary force, endows the surface with superhydrophilicity, and synergistically enhances the capillary force and liquid replenishment ability from a physical level, significantly delaying the formation of dry spots.

[0061] The multimodal monitoring module includes:

[0062] Infrared thermal imager 3, configured on one side of the transparent heating substrate, is used to monitor the temperature distribution and dry spot formation on the surface 2 of the micro-nano hierarchical structure.

[0063] A high-speed camera 4, configured on the transparent heating substrate and located on the same side as the infrared thermal imager 3, is used to acquire image sequences of bubble dynamics during the boiling process, thus constructing a multimodal perception system that combines bottom vision and infrared. The infrared thermal imager 3 can accurately identify the formation of local dry spots and hot spots. The high-speed camera 4 records the entire dynamic process of bubble nucleation, growth, merging, and detachment at a high frame rate of more than 1000fps, providing a complete, real-time, high-quality data source for subsequent intelligent prediction based on image sequences.

[0064] The transparent heating substrate is one of sapphire, quartz, or transparent conductive oxide coated glass, specifically one of sapphire heater 1, quartz heater, or transparent conductive oxide coated glass heater. The selected transparent heating substrate has good thermal conductivity, heat resistance, and optical transparency, ensuring that the infrared thermal imager 3 and high-speed camera 4 can pass through the substrate without interference, and realize high-precision, in-situ monitoring of the state of the micro-nano hierarchical structure surface 2.

[0065] Data processing and prediction module 5 performs the following operations:

[0066] Step 1: Perform principal feature analysis on the acquired image sequence, where the physical descriptor for principal feature analysis is denoted as... Before extraction Individual main features To reduce data dimensionality;

[0067] Step 2: For the first primary feature Perform a Fast Fourier Transform on the time series to calculate its bubble detachment frequency. and the area fraction of the arid region As a key physical descriptor characterizing the boiling state, the physical descriptor of the Fourier transform is denoted as... .

[0068] Step 3: Utilize a bidirectional long short-term memory neural network, previously... Using millisecond-level main feature time series as input, predict the future. The main feature changes in milliseconds are used to reconstruct the predicted future bubble shape, where the physical descriptor of the bidirectional long short-term memory neural network is denoted as... Model 8, this module is the intelligent core of the system, and it first employs unsupervised machine learning. Dimensionality reduction and feature extraction are performed on megapixel-level image sequences to simplify complex visual data. There are 1 main features, of which the first main feature is The footprint distribution of the bubble image was effectively captured, and then analyzed through the PC1 time series... Analysis revealed key descriptors with clear physical meaning, namely the bubble detachment frequency. and the area fraction of the arid region Finally, using Model 8 network learns the long-term dependencies of the main feature time series to predict the future evolution of bubble dynamics. This method overcomes the problem of inaccurate identification of high temperature difference regions by traditional image segmentation methods and achieves a deep understanding and advanced prediction of boiling state.

[0069] In the data processing and prediction module 5, the main feature analysis extracts the first... The number of principal features ranges from 10 to 100, effectively reducing the data dimension from millions of pixels to tens to hundreds of principal features. While retaining key dynamic information of the boiling process, this significantly reduces computational complexity and ensures the real-time performance of the prediction algorithm.

[0070] In the data processing and prediction module 5, when the area fraction of the arid region is monitored... A sharp increase, judged as Precursors, triggering warning signals, and then utilizing Based on the predictions of Model 8, heating power or cooling flow rate can be adjusted in advance when the system approaches... hour, There will be a significant surge, which can be observed by monitoring the sharp changes in these two key descriptors in real time, such as... A rapid increase from 0.5 to over 0.6 can achieve [the following]: Early and accurate identification allows for a valuable time window for proactive control.

[0071] When the number of dry spots around the bubble reaches a critical limit, thus inhibiting liquid replenishment, it triggers... This will cause the surface temperature to rise, thereby increasing the number of nucleation sites and triggering drying. The critical heat flux density is calculated using the liquid flow rate driven by capillary action and the amount of liquid evaporation at the dry spot:

[0072]

[0073] - Critical heat flux density; C0 - A constant function determined by experimental conditions. -Latent heat of vaporization - Surface tension of the fluid, -Liquid film thickness, - Dry spot expansion rate, - Dry spot area fraction.

[0074] The method for two-phase heat exchange with the cooling working fluid in S1 includes the following steps:

[0075] S11. Prepare a heat dissipation surface with a specific micro-nano hierarchical structure.

[0076] S12. Start the system to cause the cooling medium to boil on the heat dissipation surface.

[0077] S13. The surface temperature field and bubble image sequence are collected in real time through the multimodal monitoring module.

[0078] S14, Data Processing and Prediction Module 5 processes image data and extracts bubble detachment frequency. and the area fraction of the arid region .

[0079] S15, based on Model 8 predicts the bubble dynamics behavior at future time steps.

[0080] S16. Based on the changing trend of the physical descriptor and the prediction results, determine whether it is close to the boiling critical heat flux. If it is close to the boiling critical heat flux, execute one of the early warning or active control strategies. If it is not close to the boiling critical heat flux, return to step S13 and continue monitoring. It provides a complete and closed-loop intelligent heat dissipation workflow. From surface preparation, state monitoring, data processing to prediction and control, it seamlessly integrates advanced surface engineering, multimodal sensing and artificial intelligence algorithms to form a comprehensive solution that can actively predict and avoid boiling crises, significantly improving the reliability and safety of heat dissipation for high heat density equipment.

[0081] In the micro / nano hierarchical surface 2, the diameter / spacing ratio of the micron pillars <0.3, and the height of the micrometer column With a diameter of >20μm, this optimized parameter combination ensures that the nano wicking front always leads the bulk wicking front, achieving the best liquid transport enhancement effect, which is the key to the synergistic advantages of micro-nano hierarchical structures.

[0082] The active control strategy in step S16 includes one or more of the following: reducing heating power, activating auxiliary cooling, or issuing audible and visual alarms. This provides a tiered and flexible active control mechanism, which can be activated once a warning is issued. To mitigate risks, the system can automatically take one or more combined measures, such as reducing power to prevent a crisis, activating auxiliary cooling to enhance heat dissipation, or issuing alarms to remind users to intervene, which greatly enhances the system's proactive safety protection capabilities.

[0083] Through optimized micro / nano hierarchical structures, capillary forces and liquid replenishment capabilities are synergistically enhanced, significantly delaying dry spot formation at a physical level and improving performance. Innovatively employing unsupervised machine learning Extract descriptors with clear physical meaning from the image: bubble detachment frequency. and the area fraction of the arid region This overcomes the problem of traditional image segmentation methods failing to accurately identify bubbles in areas of high temperature difference, and achieves [the ability to accurately identify bubbles]. Early and accurate predictions.

[0084] Working principle: The system operation process of this invention is as follows: Cooling water wets the heat dissipation surface, heating begins, the water begins to boil, high-speed camera 4 acquires images at 2000 fps, and transmits them to data processing and prediction module 5. Data processing and prediction module 5 processes the image sequence. Processing and calculating the real-time area fraction of the dried-up region. and bubble detachment frequency Meanwhile, pre-trained with historical data Model 8 is based on the first 200ms Sequence, predicting the next 60ms When the heat flux rises to near At that time, the data processing and prediction module 5 detected... The voltage rapidly increased from 0.5 to above 0.6, prompting the data processing and prediction module 5 to immediately issue a warning signal and reduce the heating power by 20% via the control circuit 6, successfully preventing further damage. After the occurrence of this, the surface temperature returned to normal.

[0085] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting critical heat flux density based on micro / nano hierarchical structures and machine learning, characterized in that: Includes the following steps: S1. Provide a heat dissipation structure body with a micro-nano hierarchical structure surface (2) for two-phase heat exchange with the cooling working fluid; The method for two-phase heat exchange with a cooling working fluid as described in S1 includes the following steps; S11. Prepare a heat dissipation surface with a micro-nano hierarchical structure; S12. Start the system to cause the cooling medium to boil on the heat dissipation surface; S13. Real-time acquisition of surface temperature field and bubble image sequence through multimodal monitoring module; S14. Data Processing and Prediction Module (5) processes image data and extracts bubble detachment frequency. and the area fraction of the arid region ; S15, based on Model (8) predicts the bubble dynamics behavior at future time steps; S16. Based on the changing trend of the physical descriptor and the prediction results, determine whether it is close to the boiling critical heat flux. If it is close to the boiling critical heat flux, execute one of the early warning or active control strategies. If it is not close to the boiling critical heat flux, return to step S13 and continue monitoring. The active control strategy described in step S16 includes one or more of the following: reducing heating power, activating auxiliary cooling, or issuing an audible and visual alarm. S2. The temperature data and visual image data of the surface (2) of the micro-nano hierarchical structure are collected in real time through the multimodal monitoring module; The micro / nano hierarchical structure surface (2) includes; An array of micropillars is disposed on a substrate, the diameter of the micropillars being... ,spacing ,high Meets the preset diameter / spacing ratio ,and The value should be between 0.1 and 0.5 to optimize capillary wicking performance; Both the surface of the micron-shaped pillars and the substrate plane are covered with a nanostructure layer, which is any one of zinc oxide nanorods, carbon nanotubes, or metal oxide nanowires. The surface roughness of the nanostructure layer is... ; In the micro / nano hierarchical structure surface (2), the diameter / spacing ratio of the micropillars is... <0.3, and the height of the micrometer column >20μm; S3. The visual image data is processed by the data processing and prediction module (5), and the boiling critical heat flux is predicted using a machine learning algorithm, wherein the physical descriptor of the boiling critical heat flux is denoted as... The data processing and prediction module (5) is communicatively connected to the multimodal monitoring module; The data processing and prediction module (5) performs the following operations; Step 1: Preprocess the bubble images using the U-Net segmentation network to accurately identify bubble boundaries and dried-up areas. Perform principal feature analysis on the acquired image sequence, where the physical descriptor for the principal feature analysis is denoted as... Before extraction Individual main features To reduce data dimensionality; Step 2: For the first primary feature Perform a Fast Fourier Transform on the time series to calculate its bubble detachment frequency. and the area fraction of the arid region As a key physical descriptor characterizing the boiling state, the physical descriptor of the Fourier transform is denoted as... ; Step 3: Utilize a bidirectional long short-term memory neural network, previously... Using millisecond-level principal feature time series as input, predict the future. The main feature changes in milliseconds are used to reconstruct the predicted future bubble shape, where the physical descriptor of the bidirectional long short-term memory neural network is denoted as... Model (8); In the data processing and prediction module (5), the main feature analysis extracts the first... The number of principal features ranges from 10 to 100; In the data processing and prediction module (5), when the area fraction of the arid region is detected... A sharp increase, judged as Precursors, triggering warning signals, and then utilizing Based on the prediction results of model (8), adjust the heating power or cooling flow rate in advance; S4. The signal receiving module (7) is electrically connected to the prediction module (5) to receive the early warning signal from the prediction module (5) and transmit it to the control circuit (6). The control circuit (6) executes the corresponding active control strategy according to the early warning signal.

2. The critical heat flux density prediction method based on micro / nano hierarchical structures and machine learning according to claim 1, characterized in that: The multimodal monitoring module includes: An infrared thermal imager (3) is disposed on one side of a transparent heating substrate to monitor the temperature distribution and dry spot formation on the surface (2) of the micro-nano hierarchical structure. A high-speed camera (4) is disposed on the transparent heating substrate and located on the same side as the infrared thermal imager (3) for acquiring a sequence of bubble dynamics images during the boiling process.

3. The critical heat flux density prediction method based on micro / nano hierarchical structures and machine learning according to claim 2, characterized in that: The transparent heating substrate is one of sapphire, quartz, or transparent conductive oxide coated glass.

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