Radiator performance detection device and detection method

Abstract

The invention relates to the technical field of radiator performance detection, in particular to a radiator performance detection device which comprises a metal box. A round hole is formed in the upper end of the inner side face of the metal box body, the inner side face of the round hole is rotationally connected with a cylindrical outer cover in a sealed mode, a first electric control telescopic rod and a second electric control telescopic rod are arranged on the inner side face of the cylindrical outer cover, a programmable dynamic heat source module is installed at the telescopic end of the second electric control telescopic rod, and a base is magnetically attracted to the bottom of the inner side face of a pull-out type cover plate. Long screws are arranged at the left and right ends of the upper surface of the base; according to the radiator performance detection method, through integrated innovation of dynamic thermal excitation loading, multi-sensor fusion detection, an environment simulation cavity and a simulation-actual measurement collaborative algorithm, the core problems of lack of dynamic response, insufficient micro-scale detection precision, poor environment simulation capability and the like in the prior art are solved; the accuracy, the efficiency and the working condition coverage range of radiator performance detection are obviously improved.

Description

Technical Field

[0001] The present application relates to the technical field of radiator performance detection, and specifically to a radiator performance detection device and detection method. Background Art

[0002] As electronic devices rapidly develop toward higher power density and miniaturization, the performance of heat sinks, as core thermal management components, directly determines the reliability and lifespan of equipment. This is particularly true in areas such as new energy vehicles, 5G communication base stations, high-power chips, and high-computing servers, where the demand for efficient heat dissipation is becoming increasingly urgent. Chip heat dissipation, due to its high integration density, high instantaneous heat flux, and significant risk of thermal failure, presents a significant technical challenge.

[0003] Current radiator performance testing technologies mainly include the following three categories:

[0004] 1. Steady-state temperature test method: This method measures the thermal equilibrium temperature by fixing the heat source power. However, the actual operating conditions of the chip are often accompanied by transient power fluctuations. This method cannot capture dynamic response characteristics, and the test cycle is long, making it difficult to adapt to the rapid iteration requirements of the chip.

[0005] 2. Numerical simulation detection method: This method relies on CFD simulation modeling. However, chip heat sinks often use complex structures such as microchannels and nano-coatings. Meshing is difficult, and simplified boundary conditions can easily lead to simulation errors exceeding 20%. It also consumes a lot of computing power resources and cannot meet the low-cost verification needs of chip R&D.

[0006] 3. Infrared thermal imaging: Although surface temperature can be obtained non-contact, differences in the emissivity of chip packaging materials and internal heat source shielding result in a weak correlation between surface temperature and junction temperature, making it impossible to quantify key chip parameters. In addition, local hot spots in miniaturized heat sinks are easily masked by environmental radiation interference.

[0007] In summary, existing methods face challenges in chip cooling scenarios, including dynamic heat load mismatch, insufficient microstructure detection accuracy, and missing core thermal resistance parameters. This leads to a disconnect between heat sink design and actual chip operating conditions. This is particularly true for emerging technologies like 3D packaged chips, where traditional detection methods struggle to implement multi-physics coupled analysis, severely limiting the efficiency of iterative cooling solutions.

[0008] At the same time, existing devices for testing radiator performance have poor adjustability and cannot perform environmental simulation. Summary of the Invention

[0009] The present application provides a radiator performance detection device and detection method, which can effectively solve the problems in the background technology.

[0010] To achieve the above-mentioned objectives, the present application provides the following technical solutions: a radiator performance detection device, comprising a metal box; a circular hole is provided at the upper end of the inner side surface of the metal box, the inner side surface of the circular hole is sealed and rotatably connected to a cylindrical outer cover, the inner side surface of the cylindrical outer cover is provided with an electric telescopic rod 1 and an electric telescopic rod 2, the telescopic end of the electric telescopic rod 2 is installed with a programmable dynamic heat source module, the bottom of the inner side surface of the pull-out cover is magnetically adsorbed with a base, long screws are provided at both ends of the upper surface of the base, the outer side surface of the long screw is provided with a height-adjustable support plate, the upper surface of the support plate is provided with an insulation test bench, and the interior of the metal box is provided with multiple groups of distributed multi-sensor arrays.

[0011] The distributed multi-sensor array includes a permanent magnetic base, which is rotatably connected to a damping telescopic rod via a strong damping hinge arranged on its outer side, and a sensor is fixed to the telescopic end of the damping telescopic rod via a spring clip.

[0012] Preferably, the number of the electrically controlled telescopic rods 1 is not less than one, and a silicone pressure plate is installed at the telescopic end of the electrically controlled telescopic rod 1.

[0013] Preferably, a wire entry port, an air inlet and an air outlet are provided at the rear end of the outer side surface of the metal box, and the air outlet is connected to the air inlet of the external variable frequency fan through a conduit.

[0014] Preferably, a pull-out cover is provided on the front side of the metal box, and the pull-out cover is a transparent cover.

[0015] Preferably, the support plate is fixed to the long screw by a nut.

[0016] A radiator performance detection method, comprising the above-mentioned radiator performance detection device, comprises the following steps:

[0017] S1. Dynamic thermal excitation loading: A programmable dynamic heat source module is used to apply a transient heat load simulating actual working conditions to the thermal contact surface of the heat sink under test. The transient heat load includes a step, pulse, or periodic power fluctuation waveform;

[0018] S2. Multi-dimensional Data Acquisition: A distributed multi-sensor array is used to synchronously collect the heat sink's temperature field, heat flux density, fluid flow rate, and pressure data. The sensor array includes microscale temperature sensors and heat flow meters located at the heat sink's microchannel inlet / outlet, fin gaps, and chip package hotspots.

[0019] S3. Data Fusion and Performance Modeling: Build a transient heat transfer model for the heat sink based on sensor data. Decouple the conduction, convection, and radiation heat dissipation paths using thermal resistance network analysis. Calculate the junction-to-heat sink thermal resistance and transient thermal response time constant.

[0020] S4. Dynamic performance evaluation: Generate a dynamic performance report for the heat sink based on thermal resistance, response time, and heat flux uniformity indicators, identify heat dissipation bottleneck areas, and output optimization suggestions.

[0021] Preferably, the thermal resistance R from the junction to the heat sink in step S3 is th-jc Calculated by the following formula:

[0022]

[0023] Where ΔT is the transient temperature difference between the chip junction temperature and the heat sink base temperature, P avg is the average power consumption, τ is the thermal response time constant, and P(t) is the time domain function of the dynamic heat source power.

[0024] Preferably, the thermal resistance network analysis method in step S3 includes:

[0025] Establish an equivalent thermal resistance model of chip junction temperature-heat sink base-fluid medium;

[0026] Combining transient heat load data with sensor feedback, the following inversion algorithm is used to solve the interface thermal resistance of each layer:

[0027]

[0028] Among them, T sensor is the sensor temperature vector, A is the heat conduction path coefficient matrix, R is the thermal resistance vector to be solved, and λ is the regularization factor.

[0029] Preferably, the method further comprises step S5: performing cross-modal comparison between the measured data and the infrared thermal imaging and CFD simulation results, locating the boundary condition errors of the simulation model through residual analysis, and generating a model correction parameter set.

[0030] Preferably, the transient thermal response time constant τ in step S3 is fitted to the sensor temperature data by the following formula:

[0031] T(t)=T0+ΔT max ·(1-e -t / τ )+β·sin(ωt)

[0032] Where T0 is the initial temperature, ΔT max is the steady-state temperature rise, β is the periodic heat load amplitude, and ω is the power fluctuation frequency.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] 1. This heat sink performance testing method addresses core issues in existing technologies, such as the lack of dynamic response, insufficient microscale detection accuracy, and poor environmental simulation capabilities, through the integrated innovation of dynamic thermal excitation loading, multi-sensor fusion detection, environmental simulation cavity, and simulation-measurement collaborative algorithm. This significantly improves the accuracy, efficiency, and operating condition coverage of heat sink performance testing, providing reliable testing tools and methodological support for cutting-edge fields such as high-power chips.

[0035] 2. This radiator performance detection device is suitable for many different types of radiators and can perform scene simulation, effectively improving the test accuracy. At the same time, this application adopts a distributed multi-sensor array, and different models and categories of test equipment can be installed according to specific needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the structure of this application;

[0037] Figure 2 This is the main view of this application;

[0038] Figure 3 This is the rear view of this application;

[0039] Figure 4 Schematic diagram of the structure of a distributed multi-sensor array.

[0040] In the figure: 1 metal box, 2 cylindrical outer cover, 3 electric control telescopic rod 1, 4 pull-out cover, 5 long screw, 6 base, 7 support plate, 8 silicone pressure plate, 9 thermal insulation test bench, 10 programmable dynamic heat source module, 11 electric control telescopic rod 2, 12 distributed multi-sensor array, 121 permanent magnetic base, 122 strong damping hinge, 123 damping telescopic rod, 124 spring clip, 125 sensor, 13 smoke sensor, 14 wire entry, 15 air inlet, 16 air outlet. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0042] In the description of this application, if the direction description is involved, for example, the direction or position relationship indicated by "up", "down", "front", "back", "left", "right", etc. is based on the attached Figure 2The orientations or positional relationships shown are for the purpose of facilitating the description of this application and simplifying the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting this application. When a feature is referred to as being "disposed," "fixed," or "connected" to another feature, it may be directly disposed, fixed, or connected to the other feature or indirectly disposed, fixed, or connected to the other feature.

[0043] See also Figure 1-4 , the present application provides the following technical solutions: a radiator performance detection device, comprising a metal box 1; a circular hole is provided at the upper end of the inner side surface of the metal box 1, and a cylindrical outer cover 2 is sealed and rotatably connected to the inner side surface of the circular hole, and an electric telescopic rod 1 3 and an electric telescopic rod 2 11 are provided on the inner side surface of the cylindrical outer cover 2, and a programmable dynamic heat source module 10 is installed on the telescopic end of the electric telescopic rod 2 11, and a base 6 is magnetically adsorbed on the bottom of the inner side surface of the pull-out cover 4, and long screws 5 are provided at the left and right ends of the upper surface of the base 6, and a height-adjustable support plate 7 is provided on the outer side surface of the long screw 5, and an insulation test bench 9 is provided on the upper surface of the support plate 7, and a plurality of distributed multi-sensor arrays 12 are provided inside the metal box 1.

[0044] Specifically, the radiator to be tested is placed on the upper surface of the thermal insulation test bench 9, thermal conductive silicone is applied to the bottom of the programmable dynamic heat source module 10, and then the programmable dynamic heat source module 10 is pressed tightly against the radiator CPU module installation position.

[0045] The distributed multi-sensor array 12 includes a permanent magnetic base 121 , which is rotatably connected to a damping telescopic rod 123 via a strong damping hinge 122 arranged on its outer side. A sensor 125 is fixed to the telescopic end of the damping telescopic rod 123 via a spring clip 124 .

[0046] Specifically, the sensor 125 includes a temperature sensor probe, an infrared imaging sensor, and the like.

[0047] More specifically, the installation position of the distributed multi-sensor array 12 can be adjusted at will, so that the sensors 125 can be close to the components under test.

[0048] Furthermore, the number of the electrically controlled telescopic rod 1 3 is not less than one, and a silicone pressure plate 8 is installed at the telescopic end of the electrically controlled telescopic rod 1 3 .

[0049] Specifically, the number of the electrically controlled telescopic rods 3 is two.

[0050] Furthermore, a wire entry port 14, an air inlet 15 and an air outlet 16 are provided at the rear end of the outer side surface of the metal box 1, and the air outlet 16 is connected to the air inlet of the external variable frequency fan through a conduit.

[0051] Furthermore, a pull-out cover 4 is provided on the front side of the metal box 1 , and the pull-out cover 4 is a transparent cover.

[0052] Furthermore, the support plate 7 is fixed on the long screw rod 5 through a nut.

[0053] A radiator performance detection method, comprising the above-mentioned radiator performance detection device, comprises the following steps:

[0054] S1. Dynamic thermal excitation loading: applying a transient heat load simulating actual working conditions to the thermal contact surface of the heat sink under test through the programmable dynamic heat source module 10, wherein the transient heat load includes a step, pulse or periodic power fluctuation waveform;

[0055] S2. Multi-dimensional Data Acquisition: A distributed multi-sensor array 12 is used to synchronously collect the heat sink's temperature field, heat flux density, fluid flow rate, and pressure data. The sensor array includes microscale temperature sensors and heat flow meters located at the heat sink's microchannel inlet / outlet, fin gaps, and chip package hotspots.

[0056] S3. Data Fusion and Performance Modeling: Build a transient heat transfer model for the heat sink based on sensor data. Decouple the conduction, convection, and radiation heat dissipation paths using thermal resistance network analysis. Calculate the junction-to-heat sink thermal resistance and transient thermal response time constant.

[0057] S4. Dynamic performance evaluation: Generate a dynamic performance report for the heat sink based on thermal resistance, response time, and heat flux uniformity indicators, identify heat dissipation bottleneck areas, and output optimization suggestions.

[0058] Furthermore, the thermal resistance R from the junction to the heat sink in step S3 is th-jc Calculated by the following formula:

[0059]

[0060] Where ΔT is the transient temperature difference between the chip junction temperature and the heat sink base temperature, P avg is the average power consumption, τ is the thermal response time constant, and P(t) is the time domain function of the dynamic heat source power.

[0061] Furthermore, it is characterized in that the thermal resistance network analysis method in step S3 includes:

[0062] Establish an equivalent thermal resistance model of chip junction temperature-heat sink base-fluid medium;

[0063] Combining transient heat load data with sensor feedback, the following inversion algorithm is used to solve the interface thermal resistance of each layer:

[0064]

[0065] Among them, T sensor is the sensor temperature vector, A is the heat conduction path coefficient matrix, R is the thermal resistance vector to be solved, and λ is the regularization factor.

[0066] Furthermore, it is characterized in that the method further comprises step S5: performing cross-modal comparison between the measured data and the infrared thermal imaging and CFD simulation results, locating the boundary condition errors of the simulation model through residual analysis, and generating a model correction parameter set.

[0067] Furthermore, it is characterized in that the transient thermal response time constant τ in step S3 is fitted to the sensor temperature data by the following formula:

[0068] T(t)=T0+ΔT max ·(1-e -t / τ )+β·sin(ωt)

[0069] Where T0 is the initial temperature, ΔT max is the steady-state temperature rise, β is the periodic heat load amplitude, and ω is the power fluctuation frequency.

[0070] During use: place the radiator to be tested on the upper surface of the thermal insulation test bench 9, then send the base 6 into the interior of the metal box 1, and control the extension of the electric telescopic rod 3 to press the silicone pressure plate 8 tightly against the outer surface of the radiator to fix its position.

[0071] Then, apply thermally conductive silicone to the bottom of the programmable dynamic heat source module 10. The electrically controlled telescopic rod 2 11 is extended to press the programmable dynamic heat source module 10 against the CPU mounting socket of the radiator. If the programmable dynamic heat source module 10 is not aligned with the CPU mounting socket at this point, the silicone pressure plate 8 can be loosened to fine-tune the radiator's position.

[0072] The test sensor 125 is fixed to the telescopic end of the damping telescopic rod 123 through the spring clip 124, and the permanent magnetic base 121 is adsorbed on the inner side of the metal box 1. Then, the sensor 125 is pressed against the test station by adjusting the rotation and extension of the damping telescopic rod 123.

[0073] Then you can close the drawer cover 4 and start testing the device.

[0074] During the test, set the transient heat load waveform:

[0075] Step waveform: The power consumption of the simulated chip increases from 0W to 200W instantly when it starts;

[0076] Pulse waveform: peak power 300W, pulse width 10ms, repetition frequency 1Hz;

[0077] Periodic fluctuation waveform: The power fluctuates between 50W and 250W according to a sine curve.

[0078] The electric heating device on the programmable dynamic heat source module 10 is started, and the heat source temperature is quickly switched within the range of 30°C to 100°C through bidirectional current control.

[0079] Multi-dimensional data collection:

[0080] Temperature field data: chip junction temperature, radiator base temperature, microchannel wall temperature;

[0081] Heat flux density: The heat flux distribution at the fin root is measured by a thin film heat flux meter.

[0082] Data Fusion and Performance Modeling:

[0083] 1. Thermal resistance calculation

[0084] Calculate the thermal resistance from junction to heat sink according to the formula in step S3:

[0085]

[0086] 2. Thermal resistance network inversion

[0087] Construct an equivalent thermal resistance model (chip junction temperature → radiator base → cooling medium) and use the regularized least squares algorithm to solve the interface thermal resistance:

[0088] min R ||T sensor -A·R|| 2 +λ||R|| 2

[0089] Input data: sensor temperature vector, coefficient matrix A determined by material thermal conductivity and geometric dimensions, regularization factor λ = 0.01.

[0090] Output result: interface thermal resistance R.

[0091] Transient response fitting:

[0092] Apply the composite fitting formula to the sensor temperature data:

[0093] T(t)=T0+ΔT max ·(1-e -t / τ )+β·sin(ωt)

[0094] Finally, dynamic performance evaluation is carried out.

[0095] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A radiator performance detection device, characterized in that: The invention comprises a metal box (1); a circular hole is provided at the upper end of the inner side surface of the metal box (1); a cylindrical outer cover (2) is connected to the inner side surface of the circular hole in a sealed and rotatable manner; an electric-controlled telescopic rod (1) and an electric-controlled telescopic rod (11) are provided on the inner side surface of the cylindrical outer cover (2); a programmable dynamic heat source module (10) is installed at the telescopic end of the electric-controlled telescopic rod (11); a base (6) is magnetically adsorbed on the bottom of the inner side surface of the pull-out cover (4); long screws (5) are provided at both left and right ends of the upper surface of the base (6); a height-adjustable support plate (7) is provided on the outer side surface of the long screw (5); a heat-insulating test bench (9) is provided on the upper surface of the support plate (7); and a plurality of distributed multi-sensor arrays (12) are provided inside the metal box (1); The distributed multi-sensor array (12) comprises a permanent magnetic base (121), the permanent magnetic base (121) being rotatably connected to a damping telescopic rod (123) via a strong damping hinge (122) arranged on an outer side thereof, and a sensor (125) being fixed to the telescopic end of the damping telescopic rod (123) via a spring clip (124).

2. The radiator performance detection device according to claim 1, characterized in that: The number of the electric-controlled telescopic rod (3) is not less than one, and a silicone pressure plate (8) is installed at the telescopic end of the electric-controlled telescopic rod (3).

3. The radiator performance detection device according to claim 1, characterized in that: The rear end of the outer side of the metal box (1) is provided with a wire insertion port (14), an air inlet (15) and an air outlet (16), and the air outlet (16) is connected to the air inlet of the external variable frequency fan through a conduit.

4. The radiator performance detection device according to claim 1, characterized in that: The front side of the metal box (1) is provided with a pull-out cover plate (4), which is a transparent cover plate; the support plate (7) is fixed on the long screw rod (5) via a nut.

5. A radiator performance detection method, comprising the radiator performance detection device according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Dynamic thermal excitation loading: applying a transient heat load simulating actual working conditions to the thermal contact surface of the heat sink to be tested through a programmable dynamic heat source module (10), wherein the transient heat load includes a step, pulse or periodic power fluctuation waveform; S2. Multi-dimensional data acquisition: A distributed multi-sensor array (12) is used to synchronously collect the temperature field, heat flux density, fluid flow rate and pressure data of the radiator, wherein the sensor array includes micro-scale temperature sensors and heat flow meters, which are arranged at the radiator microchannel inlet / outlet, fin gap and chip packaging hot spot corresponding areas; S3. Data Fusion and Performance Modeling: Build a transient heat transfer model for the heat sink based on sensor data. Decouple the conduction, convection, and radiation heat dissipation paths using thermal resistance network analysis. Calculate the junction-to-heat sink thermal resistance and transient thermal response time constant. S4. Dynamic performance evaluation: Generate a dynamic performance report for the heat sink based on thermal resistance, response time, and heat flux uniformity indicators, identify heat dissipation bottleneck areas, and output optimization suggestions.

6. The radiator performance detection method according to claim 5, characterized in that: The thermal resistance R from the junction to the heat sink in step S3 th-jc Calculated by the following formula: Where ΔT is the transient temperature difference between the chip junction temperature and the heat sink base temperature, P avg is the average power consumption, τ is the thermal response time constant, and P(t) is the time domain function of the dynamic heat source power.

7. The radiator performance detection method according to claim 5, characterized in that: The thermal resistance network analysis method in step S3 includes: Establish an equivalent thermal resistance model of chip junction temperature-heat sink base-fluid medium; Combining transient heat load data with sensor feedback, the following inversion algorithm is used to solve the interface thermal resistance of each layer: Among them, T sensor is the sensor temperature vector, A is the heat conduction path coefficient matrix, R is the thermal resistance vector to be solved, and λ is the regularization factor.

8. The radiator performance detection method according to claim 5, characterized in that: The method further comprises step S5: performing cross-modal comparison of measured data with infrared thermal imaging and CFD simulation results, locating boundary condition errors of the simulation model through residual analysis, and generating a model correction parameter set.

9. The radiator performance detection method according to claim 5, characterized in that: In step S3, the transient thermal response time constant τ is fitted to the sensor temperature data by the following formula: T(t)=T0++ΔT max ·(1-e -t / τ )+β·sin(ωt) Where T0 is the initial temperature, ΔT max is the steady-state temperature rise, β is the periodic heat load amplitude, and ω is the power fluctuation frequency.

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