Thermal management method for ka-band microstrip waveguide transition and power combining device

Abstract

The application discloses a heat management method of a Ka-band microstrip waveguide conversion and power synthesis device, and relates to the technical field of heat management.The method comprises the following steps: performing digital modeling, and generating a power simulation model; collecting waveguide synthesis test samples of a target integrated device; inputting the waveguide synthesis test samples into the power simulation model, performing power density testing, and generating a power density distribution network; performing temperature propagation analysis based on the power density distribution network, and generating a temperature distribution network; generating an optimal fluid channel network; and performing heat management control based on the optimal fluid channel network.The method solves the technical problem that the existing Ka-band microstrip waveguide conversion and power synthesis device cannot accurately simulate the power distribution and heat generation of the microstrip-waveguide conversion and power synthesis device under high-frequency and high-power conditions, and thus cannot meet the heat dissipation requirement, resulting in poor device stability and service life, and achieves the technical effect of improving the heat dissipation efficiency and stability of the device.

Description

Technical Field

[0001] The present application relates to the technical field related to thermal management, and in particular to a thermal management method for a Ka-band microstrip waveguide conversion and power synthesis device. Background Art

[0002] With the rapid development of modern communication technology, especially the increasing application of the Ka-band (26.5-40 GHz), the demand for high-performance, high-efficiency RF systems is growing. Due to its high frequency characteristics, the Ka-band enables longer communication distances and higher data rates, making it the preferred frequency band for applications such as satellite communications, radar detection, and high-speed wireless communications. However, the processing and transmission of high-frequency signals poses severe challenges to system thermal management, especially in microstrip-to-waveguide conversion and power combining devices. The high power density and compact design make thermal management a key factor in ensuring system stability and reliability. As a key component of the RF front-end, the performance of the microstrip-to-waveguide conversion part directly affects the signal transmission efficiency and quality of the entire system. The conversion process generates a certain amount of insertion loss and heat. Especially under high-frequency and high-power operating conditions, if this heat is not effectively dissipated, it will seriously affect the stability and service life of the system. At the same time, the waveguide combining part, as a key link in achieving signal power amplification, has extremely high internal power density, making thermal management a particularly prominent issue.

[0003] Therefore, in the current thermal management technologies for Ka-band microstrip waveguide conversion and power synthesis devices, there is a technical problem that it is difficult to accurately simulate the power distribution and heat generation of microstrip-waveguide conversion and power synthesis devices under high frequency and high power conditions, which leads to the inability to meet heat dissipation requirements and poor device stability and service life. Summary of the Invention

[0004] This application provides a thermal management method for a Ka-band microstrip waveguide conversion and power synthesis device, and adopts technical means such as digital modeling, power simulation, temperature propagation analysis and liquid cooling system optimization to solve the technical problem of the existing Ka-band microstrip waveguide conversion and power synthesis device, which is that it is difficult to accurately simulate the power distribution and heat generation of the microstrip-waveguide conversion and power synthesis device under high frequency and high power conditions, thereby resulting in the inability to meet the heat dissipation requirements and poor device stability and service life, thereby achieving the technical effect of improving the heat dissipation efficiency and stability of the device.

[0005] The application provides a thermal management method of a Ka-band microstrip waveguide conversion and power combination device, the method comprising: digitally modeling a target integrated device to generate a power simulation model, wherein the target integrated device comprises a microstrip-waveguide conversion part and a waveguide combination part; collecting a waveguide combination test sample of the target integrated device; inputting the waveguide combination test sample into the power simulation model to perform power density testing and generate a power density distribution network; performing temperature propagation analysis based on the power density distribution network to generate a temperature distribution network; performing layout optimization of a fluid channel network of a liquid cooling system based on the temperature distribution network to generate an optimal fluid channel network; and performing thermal management control based on the optimal fluid channel network.

[0006] In a possible implementation, the digitally modeling the target integrated device to generate the power simulation model comprises: constructing a three-dimensional model of the microstrip-waveguide conversion part and the waveguide combination part; and performing S parameter simulation based on the three-dimensional model to establish the power simulation model.

[0007] In a possible implementation, the temperature propagation analysis based on the power density distribution network to generate the temperature distribution network further comprises: performing heat source term conversion based on the power density distribution network to generate a plurality of heat source terms; collecting material structure features of the target integrated device to establish a heat propagation model, and combining the plurality of heat source terms to perform temperature propagation prediction to generate the temperature distribution network.

[0008] In a possible implementation, the collecting material structure features of the target integrated device to establish a heat propagation model, and combining the plurality of heat source terms to perform temperature propagation prediction to generate the temperature distribution network comprises: marking thermal conductivity coefficients at a plurality of positions on the target integrated device to generate the heat propagation model; loading the plurality of heat source terms into the heat propagation model to perform heat propagation simulation, generating temperature prediction values of a plurality of regions, and generating the temperature distribution network.

[0009] In a possible implementation, the loading the plurality of heat source terms into the heat propagation model to perform heat propagation simulation, generating temperature prediction values of a plurality of regions, and generating the temperature distribution network further comprises: performing heat propagation simulation in the heat propagation model with the plurality of heat source terms as the center to generate a plurality of heat propagation regions; and performing heat superposition of repeated regions on the plurality of heat propagation regions to generate the temperature distribution network.

[0010] In a possible implementation, the layout of the fluid channel network of the liquid cooling system is optimized based on the temperature distribution network to generate an optimal fluid channel network, and the following processing is also performed: based on the temperature distribution network, the thermal balance area, the high-temperature thermal imbalance area and the low-temperature thermal imbalance area are located; wherein, the thermal balance area is the distribution area of ​​the concentrated temperature value in the temperature distribution network, the high-temperature thermal imbalance area is the distribution area greater than the concentrated temperature value, and the low-temperature thermal imbalance area is the distribution area less than the concentrated temperature value; based on the thermal balance area, the high-temperature thermal imbalance area and the low-temperature thermal imbalance area, uniform heat dissipation optimization is performed to generate the optimal fluid channel network.

[0011] In a possible implementation, heat dissipation uniformity optimization is performed based on the thermal balance region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region to generate the optimal fluid channel network, and the following processing is also performed: fluid channels are non-uniformly configured based on the thermal balance region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region to generate an initial fluid channel density distribution; wherein the initial fluid channel density distribution includes channel densities corresponding to the thermal balance region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region, respectively, and the channel density of the low-temperature thermal imbalance region, the channel density of the thermal balance region, and the high-temperature thermal imbalance region increase successively; heat dissipation uniformity is identified based on the initial fluid channel density distribution; and the initial fluid channel density distribution is iteratively optimized and tested based on the heat dissipation uniformity until the optimized heat dissipation uniformity meets the preset uniformity, thereby generating the optimal fluid channel network.

[0012] In a possible implementation, heat dissipation uniformity is identified based on the initial fluid channel density distribution, and the following processing is also performed: the heat dissipation properties of the liquid medium and the channel material properties of the liquid cooling system are obtained to establish a heat dissipation model; the initial fluid channel density distribution and the temperature distribution network are loaded into the heat dissipation model to identify heat dissipation uniformity.

[0013] The thermal management method for a Ka-band microstrip waveguide conversion and power synthesis device proposed in this application is intended to digitally model a target integrated device and generate a power simulation model, wherein the target integrated device includes a microstrip-waveguide conversion portion and a waveguide synthesis portion; collect waveguide synthesis test samples of the target integrated device; input the waveguide synthesis test samples into the power simulation model, perform power density testing, and generate a power density distribution network; perform temperature propagation analysis based on the power density distribution network to generate a temperature distribution network; optimize the layout of the fluid channel network of the liquid cooling system based on the temperature distribution network to generate an optimal fluid channel network; and perform thermal management control based on the optimal fluid channel network. This method solves the technical problem of existing Ka-band microstrip waveguide conversion and power synthesis devices, which is that it is difficult to accurately simulate the power distribution and heat generation of microstrip-waveguide conversion and power synthesis devices under high-frequency and high-power conditions, resulting in an inability to meet heat dissipation requirements and poor device stability and service life, thereby achieving the technical effect of improving the heat dissipation efficiency and stability of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic flow chart of a thermal management method for a Ka-band microstrip waveguide conversion and power combining device provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the process of generating a power simulation model in the thermal management method of the Ka-band microstrip waveguide conversion and power synthesis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0020] The embodiment of the present application provides a thermal management method for a Ka-band microstrip waveguide conversion and power synthesis device, such as Figure 1 As shown, the method includes:

[0021] Step S100, digitally modeling the target integrated device to generate a power simulation model, wherein the target integrated device includes a microstrip-waveguide conversion part and a waveguide synthesis part. The target integrated device consists of a microstrip-waveguide conversion part and a waveguide synthesis part, wherein the microstrip-waveguide conversion part plays the role of connecting different transmission lines (such as microstrip lines and waveguides) in the radio frequency system. Due to the differences in physical structure, transmission characteristics, etc. between microstrip lines and waveguides, direct connection will lead to a decrease in signal transmission efficiency or even distortion. The microstrip-waveguide conversion part is used to achieve effective conversion between microstrip lines and waveguides. The microstrip-waveguide conversion part may be implemented using a variety of technologies, such as ridge waveguide form transition, probe form transition, ridge fin line transition, etc. to achieve microstrip signals and waves. The waveguide synthesis part is used to synthesize or distribute multiple waveguide signals, and is usually used to merge multiple waveguide signals into a single waveguide signal to increase the output power of the system or implement specific signal processing methods. In high-frequency RF systems, the waveguide synthesis part needs to process high-power signals and ensure the stability and reliability of signal synthesis. For example, waveguide couplers, power dividers, synthesis networks, etc. are used to ensure that the signal can maintain low loss, high phase consistency and other performance characteristics during the synthesis process.

[0022] Further, such as Figure 2As shown, step S100 also includes step S110 of constructing a three-dimensional model of the microstrip-waveguide conversion part and the waveguide synthesis part; step S120 performs S parameter simulation based on the three-dimensional model to establish the power simulation model. Digital modeling of the target integrated device includes constructing a three-dimensional model of the microstrip-waveguide conversion part and the waveguide synthesis part, then performing S parameter simulation, and finally generating a power simulation model. Specifically, a computer-aided design tool (such as SolidWorks, AutoCAD, etc.) is used to construct a three-dimensional model of the microstrip-waveguide conversion and waveguide synthesis part, including key components such as microstrip lines, waveguides, microstrip probes, and impedance transformation sections. According to design requirements and physical dimensions, the geometric parameters of each part are accurately defined, such as the width of the microstrip line, the gradient angle of the conversion structure, the cross-sectional dimensions of the waveguide, etc., and material properties such as dielectric constant, conductivity, and permeability are set for the model to accurately reflect the electromagnetic properties of the actual material. Then, electromagnetic simulation software (such as ANSYS HFSS, CST Microwave Studio) to perform S-parameter simulation. Specifically, boundary conditions and excitation sources are set for the model, usually including matching of input and output ports and excitation of electromagnetic fields. The model is meshed to ensure sufficient calculation accuracy and efficiency. The simulation is run to calculate S-parameters (scattering parameters), such as S11 (reflection coefficient), which describes the proportion of the signal reflected from the input port; S21 (transmission coefficient), which describes the proportion of the signal transmitted from the input port to the output port; S12 (reverse transmission coefficient), which describes the proportion of the signal transmitted from the output port to the input port; and S22 (output reflection coefficient), which describes the proportion of the signal reflected from the output port. This is used to evaluate the reflection loss, insertion loss, and isolation of the device. S-parameters are calculated based on the ratio of the incident wave to the reflected wave: Among them, a j is the amplitude of the wave incident on port j, b i is the amplitude of the wave reflected or transmitted from port i; the simulation results are then used to analyze the power transmission efficiency, evaluate the power loss of the signal during the conversion and synthesis process, adjust the model parameters, optimize the microstrip-waveguide conversion efficiency and waveguide synthesis effect, perform multiple sets of parameter simulations, analyze the impact of different design schemes on power transmission, find the optimal design configuration, and finally generate a power simulation model that can simulate the performance of the target integrated device under different input powers, frequencies, and environmental conditions.

[0023] Step S200, collecting waveguide synthesis test samples of the target integrated device. Collecting waveguide synthesis test samples of the target integrated device specifically refers to collecting and recording relevant parameters and data of the waveguide synthesis part during the actual operation of the target integrated device (including the microstrip-waveguide conversion part and the waveguide synthesis part). Waveguide synthesis test samples are crucial for evaluating and optimizing the performance of the device. Specifically, waveguide synthesis test samples may include signal input and output parameters, power distribution and synthesis efficiency, insertion loss and return loss, temperature and stability and other key parameters. The output signal parameters of the waveguide synthesis part under different input power, frequency and other conditions, such as output power, phase, waveform, etc., reflect the conversion and processing capabilities of the waveguide synthesis part for the input signal; measuring and recording the efficiency of the waveguide synthesis part in the power distribution and synthesis process, including The data include the power distribution ratio of each output port, the total power of the synthesized output, and the synthesis efficiency. These indicators are directly related to the overall performance and efficiency of the target integrated device. The insertion loss and return loss data of the waveguide synthesis part are collected. The insertion loss reflects the energy loss when the signal passes through the waveguide synthesis part, while the return loss reflects the reflection of the signal during the transmission process. These data are of great significance for evaluating the transmission performance and signal quality of the device. Under different operating conditions, the temperature changes of the waveguide synthesis part and its impact on performance stability are monitored and recorded. High temperature may cause problems such as material performance degradation and increased signal attenuation. Temperature data is crucial for evaluating the thermal management capability and long-term stability of the device. Other key parameters (such as noise figure, linearity, cross modulation, etc.) are also included.

[0024] Step S300, input the waveguide synthesis test sample into the power simulation model, perform power density test, and generate a power density distribution network. Input the collected waveguide synthesis test sample (including key parameters such as power distribution ratio, synthesis efficiency, insertion loss, return loss, etc.) into the constructed power simulation model. Specifically, in the power simulation model, the input test sample data is used to simulate the power flow and conversion of the device during high power synthesis and signal amplification, focusing on key components that are prone to high power density, such as power amplifier areas, transistors or semiconductor chips, and microstrip waveguide converters. Through simulation calculations, a power density distribution network for the entire device is generated. This network graphically displays the power density distribution of various areas inside the device, including high power density areas and Low power density areas reflect the power consumption pattern inside the device, specifically including hotspot identification, power density gradients, and thermal management recommendations. These areas, known as hotspots, mark the areas with the highest power density inside the device. These hotspots are key areas prone to local overheating. The power density gradient shows the gradual change from high to low power density inside the device, helping to understand the flow and distribution of power within the device. Thermal management recommendations are based on the analysis results of the power density distribution network and provide targeted thermal management recommendations. For example, heat dissipation measures such as adding heat sinks, fans, or liquid cooling systems to hotspot areas can be implemented. The layout and structure of the power combiner can also be optimized to reduce power loss and heat accumulation.

[0025] Step S400: Perform temperature propagation analysis based on the power density distribution network to generate a temperature distribution network. Temperature propagation analysis involves predicting the temperature distribution within the target integrated device based on the generated power density distribution network. This includes heat conduction analysis, which uses heat conduction equations (such as Fourier's law) to analyze heat transfer between components within the device, while optimizing the heat conduction path by taking into account differences in material thermal conductivity. Temperature propagation is described using the Fourier heat conduction equation:

[0026]

[0027] Where k represents thermal conductivity, T represents temperature, q represents power density, ρ represents material density, and c p represents specific heat capacity. Thermal boundary conditions at the device surface or interface, such as adiabatic, convection, or radiation, are then defined. This involves analyzing the impact of gas or liquid flow within the device on heat transfer, considering the convective heat transfer coefficient, evaluating the convective heat dissipation effect, and, for high-temperature components, considering their radiative heat dissipation capacity. The temperature values ​​at each point within the target integrated device are obtained by solving the heat conduction equation, ultimately generating a temperature distribution network.

[0028] In one possible implementation, step S400 further includes step S410, performing heat source term conversion based on the power density distribution network to generate multiple heat source terms. Performing heat source term conversion based on the power density distribution network specifically involves identifying regions or points with higher power density, which are generally referred to as hot spots. Hot spots are the focus of heat source term conversion. Then, based on the characteristics of the power density distribution, the device or system is divided into multiple heat source regions. Each region may contain one or more hot spots, or may contain regions with relatively low power density but similar heating characteristics. For each heat source region, corresponding heat source term parameters are determined. These parameters include the location (coordinates) of the heat source, power (i.e., the total power of all hot spots in the region), heating mode (e.g., steady-state heating, transient heating), heating area or volume, etc. Based on the determined heat source term parameters, corresponding heat source terms are generated. These heat source terms may be point heat sources, surface heat sources, or volume heat sources, etc. The specific form depends on the shape and size of the heat source region. Each heat source term is considered an independent heat generating source, and its heating characteristics and parameters will directly affect the temperature distribution and heat transfer of the device or system.

[0029] Step S420, collect the material structure characteristics of the target integrated device, establish a heat propagation model, and combine the multiple heat source items to perform temperature propagation prediction to generate the temperature distribution network. The material structure characteristics of the target integrated device include the thermal conductivity, specific heat capacity, density and other thermal physical parameters of the various materials used in the device, as well as the geometric shape, size, hierarchical structure, etc. of the device. Specifically, the material structure characteristics of the target integrated device are obtained through experimental measurement, simulation analysis, etc., and the interface conditions between the device and other components or the environment, as well as possible boundary conditions, such as ambient temperature, convection heat transfer coefficient, etc., are also collected. Then, based on the collected geometric structure information, a geometric model of the device is established in the numerical simulation software, and the collected material property parameters are assigned to the corresponding parts in the geometric model. According to the interface and boundary condition information, Set the boundary conditions of the model and finally establish a heat propagation model. Load multiple heat source items obtained based on the power density distribution network conversion into the heat propagation model, including setting the heat source boundary conditions or heat source item parameters at the corresponding positions of the model, starting the numerical simulation software, and performing temperature propagation predictions. Based on heat transfer mechanisms such as heat conduction, convection, and radiation, as well as the heating characteristics of the heat source items, the heat propagation equation is solved by numerical methods, and the temperature distribution and heat transfer conditions of each point inside the device are calculated, and then a temperature distribution network is generated. Through the temperature distribution network, key information such as hot spots, cold spots, and temperature gradients inside the device can be clearly seen.

[0030] In a possible implementation, step S420 further includes step S421 , marking thermal conductivity coefficients at multiple locations on the target integrated device to generate the heat transfer model. Thermal conductivity is a physical quantity that describes a material's ability to conduct heat and reflects the rate of heat transfer within the material. Multiple locations where thermal conductivity measurements or estimates are required are identified within the target integrated device. These locations are typically near heat sources, heat-sensitive areas, or areas where significant temperature fluctuations are expected. Simulation tests are performed to obtain the thermal conductivity of the material at these locations. The acquired thermal conductivity data are then labeled at the corresponding locations to form a device thermal conductivity distribution. This provides basic data for the subsequent development of a heat propagation model, ensuring that the model accurately reflects heat transfer within the device. Based on heat transfer mechanisms such as conduction, convection, and radiation, mathematical equations and numerical methods are then used to simulate the transfer and distribution of heat within the device. Specifically, a heat propagation model is established using numerical simulation software (such as ANSYS or COMSOL) based on the device's geometry, material properties (including thermal conductivity), boundary conditions (such as ambient temperature and heat source distribution), and heat transfer mechanisms. The labeled thermal conductivity data and other relevant parameters are then input into the model as input conditions to ultimately generate the heat propagation model.

[0031] Step S422 , loading the multiple heat source items into the heat propagation model, performing heat propagation simulation, generating temperature prediction values ​​of multiple regions, and generating the temperature distribution network. Clarify the specific parameters of each heat source item, including its position, power, heating mode (such as steady-state heating, transient heating), etc., which may come from various factors such as electronic equipment, chemical reactions, mechanical friction, etc. inside the device. Input the parameters of each heat source item into the heat propagation model, set the heat source boundary conditions at the corresponding position of the model, such as temperature, heat flux density, etc., set the heat source node, apply the heat source load, etc. Before loading the heat source item, ensure that the heat propagation model has been initialized, including setting the initial temperature, boundary conditions, material properties, etc. After loading the heat source item, start the heat propagation simulation. During the simulation, the computer will solve the heat propagation equation through numerical methods based on heat transfer mechanisms such as heat conduction, convection and radiation, as well as the heating characteristics of the heat source, and calculate the temperature distribution and heat transfer of each point inside the device. Finally, the temperature prediction value of each point inside the device at a specific time point is obtained, which reflects the influence of the heat source item on the temperature distribution of the target integrated device, that is, generate a temperature distribution network, and you can clearly see the temperature difference and heat transfer path of different areas.

[0032] In one possible implementation, step S422 further includes step S4221, in which a heat propagation simulation is performed in the heat propagation model with the multiple heat source items as the center to generate multiple heat propagation regions. In the heat propagation model, a heat propagation simulation is performed with these heat source items as the center. During the simulation, the heat transfer within the device or system is calculated based on heat transfer mechanisms such as heat conduction, convection, and radiation, as well as the heat generation characteristics of the heat source items. As the simulation progresses, heat propagates from the heat source items along the interior or surface of the material to the surrounding area, forming multiple heat propagation regions. These regions represent the distribution range of different temperature levels. Step S4222, the heat of repeated regions of the multiple heat propagation regions is superimposed to generate the temperature distribution network. In the heat propagation simulation results, there may be multiple overlapping heat propagation areas. These overlapping areas indicate that heat is simultaneously propagated to the area from multiple heat source items, resulting in a higher temperature in the area. For the overlapping areas, heat superposition processing is required, that is, the sum of the heat contributed by each heat source item in the overlapping area is calculated. Specifically, the degree of influence of each heat source item on the temperature of the area can be calculated based on factors such as the power distribution and distance of each heat source item in the overlapping area, and these influence degrees can be superimposed. The superimposed result reflects the actual temperature level of the overlapping area, that is, the temperature distribution that takes into account the joint action of multiple heat source items. After completing the heat superposition, a temperature distribution network is generated according to the simulation results.

[0033] Step S500, based on the temperature distribution network, the layout of the fluid channel network of the liquid cooling system is optimized to generate an optimal fluid channel network. Based on the temperature distribution network, the layout of the fluid channel network of the liquid cooling system is optimized to generate an optimal fluid channel network to ensure the efficient operation of the heat dissipation system. Since different positions generate different amounts of heat in the target integrated device, if the layout of the liquid pipeline is unreasonable, even if the same liquid cooling parameters are used, it may cause uneven heat dissipation, thereby affecting the overall performance and stability of the device. Therefore, by optimizing the pipeline layout and optimizing the pipeline distribution density in different areas, a more balanced and more effective heat dissipation effect is achieved. Specifically, the goal of optimizing the fluid channel network layout is determined, including improving the heat dissipation uniformity and ensuring that the coolant can flow evenly through each area, especially the hot spot area, to achieve a more balanced heat dissipation effect and improve the heat dissipation efficiency. By optimizing the pipeline layout, the flow efficiency and heat exchange efficiency of the coolant are improved, the overall temperature of the device is reduced, and the energy consumption of the liquid cooling system is minimized while ensuring the heat dissipation effect. Based on the equipment structure and temperature distribution, the layout of the fluid channel network is optimized to ensure that the coolant can flow evenly through each area, especially the hot spot area, to achieve a more balanced heat dissipation effect and improve the heat dissipation efficiency. The initial fluid channel layout is designed, and the initial design is used to simulate heat conduction and fluid flow to obtain the cooling effect and performance indicators of the fluid channel. Through the temperature distribution network, the high-temperature areas in the equipment are identified, and the areas that need to be cooled are determined. The heat transfer path from the hot spot area to the cooling channel is analyzed, and the thermal resistance of the existing design is evaluated. Computational fluid dynamics (CFD) tools are used to simulate the coolant flow in the fluid channel, evaluate the pressure drop and flow velocity distribution, adjust the shape and layout of the fluid channel, optimize the fluid flow path, reduce pressure drop, and improve cooling efficiency. Multi-objective optimization algorithms (such as genetic algorithms and particle swarm optimization) are used to simultaneously optimize the cooling effect and fluid mechanics performance, continuously adjust the channel's geometric parameters, fluid inlet and outlet positions, flow channel shape, etc., iteratively optimize the design, comprehensively evaluate different optimization schemes, and select the fluid channel layout with the best cooling effect and the smallest pressure drop, which is the optimal fluid channel network.

[0034] In a possible implementation, step S500 further includes step S510 of locating a thermal equilibrium region, a high-temperature thermal imbalance region, and a low-temperature thermal imbalance region based on the temperature distribution network. Step S520, wherein the thermal equilibrium region is a region with a concentrated temperature value in the temperature distribution network, the high-temperature thermal imbalance region is a region with a temperature value greater than the concentrated temperature value, and the low-temperature thermal imbalance region is a region with a temperature value less than the concentrated temperature value. The thermal equilibrium region refers to a region with a relatively concentrated and stable temperature value in the temperature distribution network, and the temperature fluctuation in the region is small, indicating that the heat in the region reaches a relatively balanced state. By analyzing the temperature data in the temperature distribution network, the region with a relatively concentrated and small fluctuation in temperature value can be determined as the thermal equilibrium region. The high-temperature thermal imbalance region refers to a region with a temperature value significantly higher than the temperature of the thermal equilibrium region in the temperature distribution network. The temperature in these regions abnormally rises due to excessive heat accumulation, affecting the thermal stability and efficiency of the system. In the temperature distribution network, the region with a temperature value greater than the concentrated temperature value of the thermal equilibrium region can be determined as the high-temperature thermal imbalance region. The low-temperature thermal imbalance region refers to a region with a temperature value significantly lower than the temperature of the thermal equilibrium region in the temperature distribution network. The temperature in these regions abnormally decreases due to insufficient heat transfer or excessive heat dissipation. Similarly, in the temperature distribution network, the region with a temperature value less than the concentrated temperature value of the thermal equilibrium region can be determined as the low-temperature thermal imbalance region.

[0035] Step S530, based on the thermal equilibrium region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region, performs uniform heat dissipation optimization to generate the optimal fluid channel network. By adjusting the layout and parameters of the fluid channel, the uniform distribution and effective dissipation of heat inside the system are achieved, avoiding the occurrence of high-temperature thermal imbalance and low-temperature thermal imbalance, improving the thermal stability and efficiency of the system. Specifically, the high-temperature thermal imbalance region and the low-temperature thermal imbalance region are analyzed in depth to find out the specific reasons for thermal imbalance, which may include uneven distribution of heat source terms, insufficient performance of heat dissipation materials, unreasonable design of fluid channels, etc. According to the analysis results, the fluid channel network is designed to optimize the heat dissipation effect. The design of the fluid channel network should consider factors such as fluid flow path, flow distribution, pressure loss, etc. to ensure efficient heat transfer and dissipation. In the design process, numerical simulation and experimental verification can be combined to predict and evaluate the performance of the fluid channel network. By continuously optimizing design parameters such as channel size, shape, layout, etc., the best heat dissipation effect is achieved. In addition to the design of the fluid channel network, the heat dissipation structure is optimized, which may include increasing the heat dissipation area, improving the thermal conductivity of the heat dissipation material, introducing heat pipe technology, etc. to further improve the heat dissipation efficiency and reduce the degree of thermal imbalance. Finally, the optimal fluid channel network is generated, which can meet the heat dissipation demand to the greatest extent, making the temperature distribution inside the device or system more uniform, thereby improving the operating efficiency and stability of the equipment.

[0036] In one possible implementation, step S530 further includes step S531, performing a non-uniform configuration of fluid channels based on the thermal equilibrium region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region to generate an initial fluid channel density distribution. Step S532, wherein the initial fluid channel density distribution includes channel densities corresponding to the thermal equilibrium region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region, respectively, with the channel density of the low-temperature thermal imbalance region increasing in sequence with the channel density of the thermal equilibrium region and the high-temperature thermal imbalance region. According to the temperature characteristics and heat dissipation requirements of the thermal equilibrium area, high-temperature thermal imbalance area and low-temperature thermal imbalance area, the fluid channels are non-uniformly configured. By adjusting the channel density of different areas, the heat dissipation requirements of each area can be met more efficiently, and an initial fluid channel density distribution is generated, including the channel densities corresponding to the thermal equilibrium area, high-temperature thermal imbalance area and low-temperature thermal imbalance area. Specifically, the temperature in the low-temperature thermal imbalance area is relatively low and the heat dissipation demand is relatively small, so the configured fluid channel density is also relatively low; the temperature distribution in the thermal equilibrium area is relatively uniform, the heat dissipation demand is moderate, and the configured fluid channel density is also at a medium level; the temperature in the high-temperature thermal imbalance area is high and the heat dissipation demand is the greatest, so the configured fluid channel density is the highest to ensure that heat can be dissipated in a timely and effective manner.

[0037] Step S533: Identify heat dissipation uniformity based on the initial fluid channel density distribution. Using numerical simulation or experimental testing, evaluate the system's heat dissipation performance under the initial fluid channel density distribution. Specifically, evaluate and identify the system's heat dissipation uniformity by calculating parameters such as the temperature distribution and temperature gradient in each region. Step S534: Iteratively optimize the initial fluid channel density distribution based on the heat dissipation uniformity until the optimized heat dissipation uniformity meets the preset uniformity, thereby generating the optimal fluid channel network. Based on the identification results of heat dissipation uniformity, the initial fluid channel density distribution is iteratively optimized and tested. Specifically, by adjusting parameters such as the channel density, shape, and layout of each area, the changes in heat dissipation uniformity are observed. During the iterative process, multiple adjustments and optimizations may be required to find the optimal fluid channel configuration scheme. The optimization goal is to make the heat dissipation uniformity of the system meet the preset uniformity requirements, which means that under the optimized fluid channel network, the temperature distribution of the system is more uniform, and the temperature difference between each area is controlled within a reasonable range. After multiple iterative optimization tests, when the heat dissipation uniformity of the system meets the preset uniformity requirements, the optimal fluid channel network is generated. This fluid channel network not only has efficient heat dissipation performance, but also ensures the stability and reliability of system operation.

[0038] In a possible implementation, the step S533 further includes a step S5331 of acquiring the heat dissipation properties of the liquid medium of the liquid cooling system and the material properties of the channel, and establishing a heat dissipation model. The heat dissipation properties of the liquid medium mainly include its specific heat capacity, thermal conductivity, viscosity, thermal stability and other physical properties, which directly affect the heat absorption, heat transfer and heat dissipation capacity of the liquid in the heat dissipation process. Common liquid media include water, mineral oil, ethylene glycol solution and the like. For example, water has high specific heat capacity and thermal conductivity, and can quickly absorb and transfer heat; mineral oil has good high-temperature stability and is not easy to burn, and is suitable for heat dissipation in high-temperature environments; the properties of the channel material mainly include its thermal conductivity, corrosion resistance, strength, processability and the like, which determine the heat transfer efficiency and durability of the channel in the heat dissipation process. Common channel materials include copper, aluminum, titanium alloy and the like, which all have good thermal conductivity, but have advantages and disadvantages. For example, copper has the best thermal conductivity, but is relatively expensive; aluminum has slightly lower thermal conductivity than copper, but is relatively inexpensive and easy to process. After the properties of the liquid medium and the channel material are acquired, a heat dissipation model can be established based on these properties to describe the heat transfer and heat dissipation process in the liquid cooling system. The model can consider various factors, such as the flow state of the liquid, the geometry of the channel, the distribution of heat sources and the like.

[0039] Step S5332, load the initial fluid channel density distribution and the temperature distribution network into the heat dissipation model to identify the heat dissipation uniformity. The initial fluid channel density distribution determined according to the thermal equilibrium region, the high-temperature thermal imbalance region and the low-temperature thermal imbalance region is loaded into the heat dissipation model, that is, the channel density parameters of different regions are input into the model so that the model can simulate the corresponding fluid flow and heat dissipation. The data of the temperature distribution network is also loaded into the heat dissipation model. By loading the temperature distribution network, the model can calculate the heat dissipation effect of the system under the given initial fluid channel density distribution. After loading the initial fluid channel density distribution and the temperature distribution network into the heat dissipation model, the heat dissipation uniformity can be identified. Specifically, the heat dissipation model is used for simulation calculation to obtain the heat dissipation effect of the system under the given initial fluid channel density distribution and the temperature distribution network, including the temperature distribution, temperature gradient, heat dissipation efficiency and other parameters of each region. According to the results obtained by simulation calculation, the temperature difference and temperature gradient of each region are calculated to evaluate the heat dissipation uniformity of the system. If the temperature difference and temperature gradient are small, it indicates that the heat dissipation uniformity is high; otherwise, it indicates that the heat dissipation uniformity is low. According to the evaluation result of the heat dissipation uniformity, corresponding optimization suggestions are given. For example, if the heat dissipation uniformity is low, the initial fluid channel density distribution or other heat dissipation parameters (such as the flow rate, temperature and the like of the liquid medium) are adjusted to improve the heat dissipation uniformity and the overall heat dissipation performance of the system.

[0040] Step S600: Perform thermal management control based on the optimal fluid channel network. Specifically, thermal management control based on the optimal fluid channel network involves placing the optimized fluid channel network in the target device or system to ensure that the coolant can efficiently flow through each key area according to the predetermined path and flow rate. Suitable pipe materials and coolant are selected based on system requirements and the working environment to ensure good corrosion resistance, thermal conductivity, and flow properties. Temperature sensors are deployed at key locations in the target device or system to monitor the temperature of each area in real time and transmit the data to the control system. Based on the real-time monitored temperature data and preset temperature thresholds, the coolant flow rate, temperature, and other parameters are automatically adjusted to achieve precise temperature control. The fluid channel network, temperature sensors, control system, and other components are tightly integrated to form a complete thermal management system. By continuously optimizing the control system's algorithms and parameter settings, the system's response speed and stability are improved while reducing energy consumption and costs.

[0041] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A thermal management method for a Ka-band microstrip waveguide conversion and power combining device, characterized in that: include: Digitally modeling a target integrated device to generate a power simulation model, wherein the target integrated device includes a microstrip-waveguide conversion part and a waveguide synthesis part; collecting a waveguide synthesis test sample of the target integrated device; Inputting the waveguide synthesis test sample into the power simulation model to perform a power density test and generate a power density distribution network; Performing temperature propagation analysis based on the power density distribution network to generate a temperature distribution network; Optimizing the layout of the fluid channel network of the liquid cooling system based on the temperature distribution network to generate an optimal fluid channel network; performing thermal management control based on the optimal fluid channel network; Performing temperature propagation analysis based on the power density distribution network to generate a temperature distribution network includes: Performing heat source term conversion based on the power density distribution network to generate multiple heat source terms; Collecting the material structure characteristics of the target integrated device, establishing a heat propagation model, and combining the multiple heat source items to perform temperature propagation prediction to generate the temperature distribution network; Collecting the material structure characteristics of the target integrated device, establishing a heat propagation model, and combining the multiple heat source items to perform temperature propagation prediction to generate the temperature distribution network, including: marking thermal conductivity coefficients at multiple locations on the target integrated device to generate the heat propagation model; Loading the multiple heat source items into the heat propagation model, performing heat propagation simulation, generating temperature prediction values ​​for multiple regions, and generating the temperature distribution network; The method includes: loading the plurality of heat source items into the heat propagation model, performing heat propagation simulation, generating temperature prediction values ​​for a plurality of regions, and generating the temperature distribution network, including: In the heat propagation model, a heat propagation simulation is performed with the multiple heat source items as the center to generate multiple heat propagation areas; The heat distribution network is generated by performing heat superposition of repeated regions on the plurality of heat propagation regions.

2. The thermal management method for the Ka-band microstrip waveguide conversion and power combining device according to claim 1, wherein: Digitally model the target integrated device and generate a power simulation model, including: Constructing a three-dimensional model of the microstrip-waveguide transition portion and the waveguide synthesis portion; An S-parameter simulation is performed based on the three-dimensional model to establish the power simulation model.

3. The thermal management method for the Ka-band microstrip waveguide conversion and power combining device according to claim 1, wherein: Optimizing the layout of the fluid channel network of the liquid cooling system based on the temperature distribution network to generate an optimal fluid channel network includes: locating a thermal equilibrium region, a high-temperature thermal imbalance region, and a low-temperature thermal imbalance region based on the temperature distribution network; The thermal balance region is a distribution region of a concentrated temperature value in the temperature distribution network, the high-temperature thermal imbalance region is a distribution region with a temperature greater than the concentrated temperature value, and the low-temperature thermal imbalance region is a distribution region with a temperature less than the concentrated temperature value; The optimal fluid channel network is generated by performing uniform heat dissipation optimization based on the thermal balance area, the high-temperature thermal imbalance area, and the low-temperature thermal imbalance area.

4. The thermal management method for the Ka-band microstrip waveguide conversion and power combining device according to claim 3, wherein: Performing uniform heat dissipation optimization based on the thermally balanced region, the high-temperature thermally unbalanced region, and the low-temperature thermally unbalanced region to generate the optimal fluid channel network includes: Performing non-uniform configuration of fluid channels based on the thermal equilibrium region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region to generate an initial fluid channel density distribution; The initial fluid channel density distribution includes channel densities corresponding to the thermal equilibrium region, the high-temperature thermal imbalance region, and the low-temperature thermal imbalance region, respectively, and the channel density of the low-temperature thermal imbalance region, the thermal equilibrium region, and the high-temperature thermal imbalance region increase in sequence; Identifying heat dissipation uniformity based on the initial fluid channel density distribution; An iterative optimization test is performed on the initial fluid channel density distribution based on the heat dissipation uniformity until the optimized heat dissipation uniformity meets a preset uniformity, thereby generating the optimal fluid channel network.

5. The thermal management method for the Ka-band microstrip waveguide conversion and power combining device according to claim 4, wherein: Identifying heat dissipation uniformity based on the initial fluid channel density distribution includes: Obtaining the heat dissipation properties of the liquid medium and the channel material properties of the liquid cooling system, and establishing a heat dissipation model; The initial fluid channel density distribution and the temperature distribution network are loaded into the heat dissipation model to identify heat dissipation uniformity.

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