Flow characteristic detection method and system for radiation refrigeration coating

Abstract

The invention relates to the technical field of viscosity detection, and discloses a flow characteristic detection method and system for radiation refrigeration paints.The detection method comprises the steps that the radiation refrigeration paint is poured into a cylindrical container, and video data obtained by a high-speed camera are decomposed into image frames through a falling ball method; the method comprises the following steps of: preprocessing an image frame, acquiring a center coordinate and a radius of a small ball based on edge detection, extracting the center coordinate of the small ball in each frame of image, constructing a falling track of the small ball, calculating an instantaneous speed of the small ball according to detection time and center position change, and drawing an instantaneous speed-time change curve; acquiring the terminal speed according to the change curve; obtaining an initial viscosity coefficient of the small ball according to a Stokes formula; and calculating according to boundary effect correction, temperature correction and density correction to obtain a final viscosity coefficient. According to the method, the boundary effect, the temperature change and the density fluctuation in the movement process of the small ball are comprehensively corrected, so that the accuracy of viscosity coefficient detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flow characteristic detection, and in particular to a flow characteristic detection method and system for a radiation cooling coating. Background Art

[0002] Radiative cooling is a natural phenomenon that reduces the temperature of an object by radiating heat into outer space. The phenomenon is based on the ability of an object to emit heat through electromagnetic waves, especially infrared rays. When the surface of an object effectively radiates heat in the far-infrared band (usually in the range of 8 to 13 microns) while avoiding absorbing too much heat from the environment, its temperature can drop below the ambient temperature. Radiative cooling paint is a low-carbon building paint that can reduce the temperature of a building under sunlight by applying radiative cooling paint on the roof or exterior wall of a building, thereby reducing the use of air conditioning and saving energy.

[0003] When radiant cooling paint achieves cooling of the coated object during the day, the paint surface needs to have a high sunlight reflectivity to minimize the heat input from the sun. The stronger the viscosity of the radiant cooling paint, the greater the internal friction that hinders its flow, which manifests itself as a more viscous paint. When radiant cooling paint with high viscosity is applied to the surface of a building, it is very easy to leave brush marks or cause uneven coating, reducing the reflectivity of the paint. When the viscosity is weak, it is difficult to control the coating thickness of the paint, and it is easy to sag, resulting in uneven coating thickness. Therefore, it is particularly important to detect the viscosity of radiant cooling paint.

[0004] The falling ball method is one of the main methods for detecting fluid viscosity. However, during the falling ball method detection process, the data is inaccurate due to problems such as the actual container size being unable to achieve the ideal "infinite width and depth" condition and the fuzzy monitoring of the ball position. Summary of the invention

[0005] In view of this, the present invention proposes a method and system for detecting the flow characteristics of a radiation cooling coating, aiming to solve the problem of the lack of an intelligent method for detecting the viscosity of the radiation cooling coating in the current technology.

[0006] On the one hand, the present invention provides a method for detecting flow characteristics of a radiant cooling coating, comprising:

[0007] The radiation cooling coating is poured into a cylindrical container, and then a small ball of known density is placed in the cylindrical container. The timing starts when the small ball starts to fall in the cylindrical container, and the position of the small ball is recorded by a high-speed camera. The video data obtained by the high-speed camera is decomposed into image frames, and each image frame corresponds to a detection time to establish an image frame-detection time data set;

[0008] Preprocess the image frame, obtain the center coordinates and radius of the ball based on edge detection, extract the center coordinates of the ball in each frame of the image, construct the falling trajectory of the ball by comparing the changes in the center coordinates of the ball in multiple consecutive image frames, calculate the instantaneous speed of the ball according to the detection time and the change in the center position, and draw a change curve of the instantaneous speed-time; obtain the terminal speed according to the change curve; and obtain the initial viscosity coefficient of the ball according to the Stokes formula;

[0009] The initial viscosity coefficient is corrected for boundary effects according to the radius of the ball and the container wall; the temperature data on the falling trajectory of the ball is collected, and after weighted averaging all the temperature data, the initial viscosity coefficient is corrected for temperature; the effective density difference is obtained by integrating the density change of the radiation cooling coating on the falling trajectory of the ball, and the density correction is performed based on the effective density difference to obtain the final viscosity coefficient.

[0010] Furthermore, it also includes the ball qualification test:

[0011] Before the ball falls, 3D scanning is performed at several angles to generate a three-dimensional model of the ball, and the ellipticity of the ball is calculated by comparing the three-dimensional model with an ideal sphere;

[0012] The geometric outline of the ball is generated according to the three-dimensional model of the ball, and the maximum diameter and the minimum diameter of the ball are extracted through image processing, and recorded as the major axis length and the minor axis length respectively. When the ratio of the major axis length to the minor axis length is between 1.00 and 1.01, the ball is judged to be qualified; when the ratio of the major axis length to the minor axis length is not between 1.00 and 1.01, the ball is replaced and the ball qualification test is re-performed.

[0013] Further, when the image frame is preprocessed, it includes:

[0014] Grayscale each of the image frames and perform denoising by Gaussian blurring;

[0015] A grayscale threshold is set in advance, the grayscale value in the image frame after grayscale processing is obtained, the grayscale value is compared with the grayscale threshold, when the grayscale value is greater than or equal to the grayscale threshold, the area corresponding to the grayscale value is recorded as the foreground, when the grayscale value is less than the grayscale threshold, the area corresponding to the grayscale value is recorded as the background; the foreground is set to white and the foreground area is a small ball, and the background is set to black.

[0016] Furthermore, when the position of the ball is recorded by a high-speed camera, the detection cycle of the high-speed camera is adjusted based on the movement of the ball:

[0017] The shortest frame interval of the high-speed camera is obtained and used as the shortest detection period, an initial detection period is preset, and the real-time detection period is adjusted based on the instantaneous speed of the ball. The real-time detection period satisfies the following relationship:

[0018]

[0019] Furthermore, the instantaneous speed of the ball is acquired in real time, and when the instantaneous speed of the ball does not change, the real-time detection period is restored to the initial detection period;

[0020] Among them, T is the real-time detection period, T0 is the initial detection period, v is the instantaneous speed of the ball, T max is the shortest detection cycle; when T>T max When T is T max .

[0021] Furthermore, when the center coordinates and radius of the ball are obtained based on edge detection, it includes:

[0022] The edge detection method based on the Canny algorithm detects the edge of the ball, and the strong and weak edges in the image are determined by the double threshold method. The contour-based Hough transform algorithm detects the edge points of the circular structure, connects the contours, extracts the complete closed edge, and selects the contours that meet the size range of the ball, excluding noise or other non-spherical targets;

[0023] For the closed circular contours screened out, the distribution of edge points is weighted and the circle is fitted using the least square method. After the fitting is completed, the coordinates of the center of the circle are the center coordinates of the small ball, and the radius of the fitted circle is the radius of the small ball;

[0024] The fitting accuracy is verified by calculating the average distance between the edge points of the fitted circle and the actual detected edge points. If the average distance exceeds a preset error threshold, contour extraction and fitting are performed again until it is below the error threshold.

[0025] Furthermore, when the initial viscosity coefficient is corrected for boundary effect according to the radius of the ball and the container wall, it includes:

[0026] The boundary effect correction factor is obtained through the following relationship:

[0027]

[0028] Among them, k1 is the boundary effect correction coefficient, R is the radius of the ball, and R c is the radius of the container, v is the instantaneous velocity of the ball, v max is the terminal velocity of the ball, and α is the correction coefficient.

[0029] Furthermore, when collecting the temperature data on the falling trajectory of the ball, weighted averaging all the temperature data, and performing temperature correction on the initial viscosity coefficient, it includes:

[0030] When the time satisfies a real-time detection cycle, the temperature data of the radiation cooling coating on the falling trajectory of the ball is collected, and a temperature data set is established, which is recorded as {T1, T2, T3...Tn}; the temperature is weighted averaged based on the instantaneous speed of the ball to obtain the average temperature value, and finally the temperature correction coefficient is obtained through the following relationship:

[0031]

[0032] Where k2 is the temperature correction coefficient; E a is the energy that needs to be overcome by the flow of the radiation cooling coating; R is the gas constant; T avg is the average temperature value; n is the total number of detections; w i is the weight of the temperature detected at the i-th time; v i is the instantaneous velocity of the ball detected for the i-th time; i = 1, 2, 3, ..., n.

[0033] Furthermore, when the effective density difference is obtained by integrating the density change of the radiation cooling coating on the falling trajectory of the ball, density correction is performed based on the effective density difference, including:

[0034] According to the change in coating density at different depths during the falling process of the ball, the density correction coefficient is calculated, and the density correction coefficient satisfies the following relationship:

[0035]

[0036] Among them, k3 is the density correction coefficient, h1 is the initial depth of the ball falling, h2 is the depth after the ball starts to fall at a uniform speed, ρ(h) is the density of the radiation cooling coating at a depth of h, and ρ0 is the density of the radiation cooling coating under standard conditions.

[0037] Furthermore, the final viscosity coefficient is obtained through the following relationship:

[0038] V = V0 × k1 × k2 × k3;

[0039] Among them, V is the final viscosity coefficient and V0 is the initial viscosity coefficient.

[0040] On the other hand, the present invention also proposes a flow characteristic detection system applied to the above-mentioned radiation cooling coating, the detection system comprising:

[0041] The detection module includes: a cylindrical container, a small ball and a high-speed camera, wherein the high-speed camera is used to record the movement of the small ball, obtain the falling image data of the small ball, and capture the movement trajectory of the small ball;

[0042] An image processing module is configured to decompose the video data acquired by the high-speed camera into image frames, extract the edge of the ball, determine the center coordinates and radius of the ball, and calculate the terminal speed;

[0043] A data processing module is configured to calculate the instantaneous velocity of the ball based on the extracted center coordinates and radius, and draw an instantaneous velocity-time curve;

[0044] A temperature measurement module is configured to collect temperature data of the radiant cooling coating, establish a temperature data set and;

[0045] A weighted processing module is configured to perform weighted averaging on the collected temperature data and calculate a temperature correction coefficient;

[0046] A density measurement module is configured to collect the density of the radiation cooling coating;

[0047] A correction calculation module is configured to calculate a final viscosity coefficient based on a boundary effect correction coefficient, a temperature correction coefficient, and a density correction coefficient;

[0048] The control unit is configured to control the detection cycle adjustment of the high-speed camera and the synchronization of data acquisition.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] By adopting high-speed cameras and automated data processing, the reliance on manual operation is reduced and the intelligent level of detection is improved. The image processing and data analysis modules can automatically process and analyze a large amount of data, significantly improving the detection efficiency. Combined with edge detection, temperature correction and density correction technology, the falling speed and viscosity coefficient of the ball in the paint can be accurately measured. The correction of boundary effect, temperature change and density fluctuation ensures the high accuracy of the detection results. The reliability of the viscosity coefficient measurement results under various environmental conditions is ensured by boundary effect correction, temperature correction and density correction. Comprehensive correction can effectively solve the error problem in traditional detection methods. The center coordinates and radius of the ball are accurately extracted by edge detection technology, which improves the accuracy of data extraction. Gray processing and Gaussian blur denoising steps further improve the effect of image processing. According to the instantaneous speed of the ball, the detection cycle is dynamically adjusted, the data acquisition frequency is optimized, and the changes in the ball speed are effectively responded to, which improves the precision and accuracy of data acquisition and reduces the amount of data that the computer needs to process. The qualification test of the ball is carried out by three-dimensional scanning technology, which ensures that the ball used for testing has ideal spherical characteristics and further improves the reliability of the test. Finally, the detection system includes multiple functional modules, which improves the comprehensive performance and automation level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0052] Figure 1 This is a flow chart of a method for detecting flow characteristics of a radiant cooling coating according to an embodiment of the present invention;

[0053] Figure 2 This is a functional block diagram of a flow characteristic detection system for a radiant cooling coating according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] See also Figure 1As shown, an embodiment of the present invention provides a method for detecting flow characteristics of a radiation cooling coating, comprising:

[0056] S1: Pour the radiation cooling paint into a cylindrical container, then place a small ball of known density in the cylindrical container, start timing when the small ball starts to fall in the cylindrical container, and record the position of the small ball through a high-speed camera, decompose the video data obtained by the high-speed camera into image frames, and establish an image frame-detection time data set corresponding to the detection time of each image frame;

[0057] S2: Preprocess the image frames, obtain the center coordinates and radius of the ball based on edge detection, extract the center coordinates of the ball in each frame, and construct the falling trajectory of the ball by comparing the changes in the center coordinates of the ball in multiple consecutive image frames. Calculate the instantaneous speed of the ball according to the detection time and the change in the center position, and draw a curve of the instantaneous speed-time change; obtain the terminal speed according to the change curve; and obtain the initial viscosity coefficient of the ball according to the Stokes formula;

[0058] S3: Perform boundary effect correction on the initial viscosity coefficient according to the radius of the ball and the container wall; collect temperature data on the falling trajectory of the ball, perform temperature correction on the initial viscosity coefficient after weighted averaging all temperature data; integrate the density change of the radiation cooling coating on the falling trajectory of the ball to obtain the effective density difference, perform density correction based on the effective density difference, and obtain the final viscosity coefficient.

[0059] It can be understood that the falling speed and viscosity coefficient of the ball are accurately measured through edge detection, temperature correction and density correction technology, overcoming the error problem in the traditional method. At the same time, combined with high-speed cameras, image processing and data analysis modules, intelligent data processing and analysis are realized, manual operations are reduced, and detection efficiency and consistency are improved. The detection cycle is adjusted in real time to adapt to the instantaneous speed changes of the ball, the data acquisition frequency is optimized, and the flexibility and accuracy of the test are improved. In addition, through multiple corrections (boundary effect, temperature and density correction), the reliability of the test results under various environmental conditions is ensured, and the adaptability and accuracy of the test are improved.

[0060] In some embodiments of the present invention, a ball qualification test is also included:

[0061] Before the ball falls, 3D scans are performed at several angles to generate a 3D model of the ball. The ellipticity of the ball is calculated by comparing the 3D model with the ideal sphere.

[0062] The geometric outline of the ball is generated according to the three-dimensional model of the ball, and the maximum diameter and the minimum diameter of the ball are extracted through image processing, and recorded as the major axis length and the minor axis length respectively. When the ratio of the major axis length to the minor axis length is between 1.00 and 1.01, the ball is judged to be qualified; when the ratio of the major axis length to the minor axis length is not between 1.00 and 1.01, the ball is replaced and the ball qualification test is re-performed.

[0063] It should be noted that the software or device used in this embodiment is preferably:

[0064] 3D scanning: Artec Eva, Faro Focus;

[0065] 3D modeling and analysis: MeshLab, Geomagic Design X, SolidWorks;

[0066] Image acquisition: Canon EOS 5D Mark IV, Nikon D850;

[0067] Image processing: Adobe Photoshop, OpenCV.

[0068] Specifically, in the preparation phase: set up the 3D scanner in an environment without light interference and vibration.

[0069] Scanning: Place the ball within the scanner's scanning range and scan from multiple angles to ensure that the entire surface of the ball is covered. An automatic rotating stage can be used to ensure comprehensive scanning angles.

[0070] Data processing: Use Artec Studio software to process and stitch the scanned data to generate a complete 3D model.

[0071] Load model: Import the generated 3D model and an ideal spherical model into the modeling software.

[0072] Model comparison: Use the software's comparison function to compare the actual model with the ideal spherical model. The software will calculate the difference between the two.

[0073] Calculate the ellipticity:

[0074] Ellipticity: Calculate the major and minor axes of the actual model and divide the major axis length by the minor axis length to get the ellipticity.

[0075] Comparison judgment:

[0076] Place the ball against a standardized background that contrasts well with the ball. Use a high-resolution camera to take images of the ball from multiple angles.

[0077] Grayscale the image and apply Gaussian blur to remove noise. Use Canny algorithm or Sobel algorithm for edge detection. Use Hough transform to extract the circular outline of the ball.

[0078] Geometric measurement: The lengths of the major and minor axes of the spheres were measured by fitting a circle using the least squares method.

[0079] Calculate the ratio by dividing the major axis length by the minor axis length. If the ratio is between 1.00 and 1.01, the ball is considered acceptable. If the ratio is not within this range, the ball fails and needs to be replaced and retested.

[0080] In some embodiments of the present invention, when preprocessing the image frame, it includes:

[0081] Each image frame is grayscaled and denoised by Gaussian blur;

[0082] A grayscale threshold is set in advance, the grayscale value in the image frame after grayscale processing is obtained, and the grayscale value is compared with the grayscale threshold. When the grayscale value is greater than or equal to the grayscale threshold, the area corresponding to the grayscale value is recorded as the foreground, and when the grayscale value is less than the grayscale threshold, the area corresponding to the grayscale value is recorded as the background; the foreground is set to white and the area of ​​the foreground is a small ball, and the background is set to black.

[0083] It is understandable that the purpose of grayscale processing is to convert a color image into a grayscale image, simplify the processing process and improve processing efficiency. Each pixel in a color image contains multiple color channels (such as RGB), while each pixel in a grayscale image contains only one grayscale value. Separate the foreground (i.e., the target object, the ball) and the background in the image. This step is the core of the binarization process, which is used to divide the image into two categories: foreground and background. The method is to set a grayscale threshold, which is preferably 128 in this embodiment, and compare the grayscale value of each pixel with the threshold.

[0084] Foreground: When the grayscale value is greater than or equal to the threshold, the pixel is marked as foreground.

[0085] Background: When the grayscale value is less than the threshold, the pixel is marked as background.

[0086] In the binary image, the foreground area is set to white (value 255) and the background area is set to black (value 0), making the target object in the image more prominent and facilitating subsequent processing and analysis.

[0087] In some embodiments of the present invention, when the position of the ball is recorded by a high-speed camera, the detection period of the high-speed camera is adjusted based on the movement of the ball:

[0088] The shortest frame interval of the high-speed camera is obtained and used as the shortest detection period. The initial detection period is preset and the real-time detection period is adjusted based on the instantaneous speed of the ball. The real-time detection period satisfies the following relationship:

[0089]

[0090] Furthermore, the instantaneous speed of the ball is obtained in real time, and when the instantaneous speed of the ball does not change, the real-time detection period is restored to the initial detection period;

[0091] Among them, T is the real-time detection period, T0 is the initial detection period, v is the instantaneous speed of the ball, T max is the shortest detection cycle; when T>T max When T is T max .

[0092] It is understandable that the ball may have different speeds at different falling stages. In order to accurately record the motion trajectory and instantaneous speed of the ball, the detection cycle of the camera needs to match the speed of the ball.

[0093] When the instantaneous speed of the ball is high, increasing the detection frequency (i.e. reducing the detection period) can capture more detailed motion information. This can prevent the ball from moving too much in the frame interval, resulting in loss of motion trajectory or blur. When the instantaneous speed of the ball is low, reducing the detection frequency (i.e. increasing the detection period) can still ensure sufficient image quality while reducing unnecessary data processing and reducing the computational burden.

[0094] The high frame rate of high-speed cameras generates a large amount of data, which requires more storage space and processing power. During the processing, if data is collected too frequently, it may lead to a waste of computing resources. Therefore, by dynamically adjusting the detection cycle according to the instantaneous speed of the ball, the data collection frequency can be optimized according to actual needs. This optimization reduces the burden of storage and processing and improves the overall efficiency of the system. It reduces the redundant part of the data. This helps save storage space and computing time.

[0095] Adapt to changes: When the instantaneous speed of the ball no longer changes (i.e. the ball reaches a constant speed state), the detection cycle will return to the initial setting value. This means that data collection will return to normal levels, avoiding unnecessary high-frequency collection.

[0096] In some embodiments of the present invention, when obtaining the center coordinates and radius of the ball based on edge detection, it includes:

[0097] The edge detection method based on the Canny algorithm detects the edge of the ball, and the strong and weak edges in the image are determined by the double threshold method. The contour-based Hough transform algorithm detects the edge points of the circular structure, connects the contours, extracts the complete closed edge, and selects the contours that meet the size range of the ball, excluding noise or other non-spherical targets;

[0098] For the closed circular contours screened out, the distribution of edge points is weighted and the circle is fitted using the least squares method. After fitting, the coordinates of the center of the circle are the center coordinates of the ball, and the radius of the fitted circle is the radius of the ball.

[0099] The fitting accuracy is verified by calculating the average distance between the edge points of the fitted circle and the actual detected edge points. If the average distance exceeds the preset error threshold, the contour extraction and fitting are performed again until it is below the error threshold.

[0100] It can be understood that edge detection: Use the Canny algorithm to detect the edge of the image. The Canny algorithm can effectively identify the edges in the image and has good noise suppression and edge detection capabilities.

[0101] Double Threshold Method: Double threshold method is used to determine strong and weak edges in the image, thereby distinguishing significant edges from noise. The separation of strong and weak edges helps to extract clearer edge information.

[0102] High-precision detection: The Canny algorithm can effectively detect the edge of the ball, reduce the impact of noise, and improve the accuracy of edge detection.

[0103] Detail preservation: The dual thresholding method can better preserve the actual edge information while removing unnecessary noise.

[0104] Hough transform algorithm steps:

[0105] Circle detection: Based on the Hough transform algorithm, detect the edge points of circular structures in the image. Hough transform is particularly suitable for detecting geometric shapes in images, such as circles.

[0106] Contour connection: Through contour connection, the complete closed circular edge is extracted, and the contours that meet the size range of the small ball are screened out, excluding other non-spherical targets and noise.

[0107] Accurate contour extraction: Hough transform can accurately detect circular contours and effectively filter out noise or irrelevant targets.

[0108] Robustness: It has good robustness to irregular contours or partially missing edges.

[0109] Circular fitting and accuracy verification steps:

[0110] Least squares fitting: For the closed circular contours selected, the distribution of edge points is weighted and the least squares method is used to fit the circle. The center coordinates of the fitted circle are the center coordinates of the ball, and the radius of the fitted circle is the radius of the ball.

[0111] Accuracy verification: Calculate the average distance between the edge points of the fitted circle and the actual detected edge points. If the average distance exceeds the preset error threshold, re-extract and re-fit the contour until the error is below the threshold.

[0112] High-precision fitting: The least squares method can accurately fit the circular contour of the ball and provide accurate center coordinates and radius.

[0113] Error control: Ensure the reliability and accuracy of the test results by verifying the fitting accuracy. The retry mechanism can handle inaccurate fitting and further improve the accuracy of the results.

[0114] Through the above steps, the embodiment of the present invention can efficiently and accurately obtain the center coordinates and radius of the ball, and provide a reliable image data processing method. These steps combine advanced edge detection technology, circle detection algorithm and fitting accuracy verification to ensure accurate extraction of ball shape features, thereby improving the accuracy of detection results and the overall performance of the system.

[0115] In some embodiments of the present invention, when the initial viscosity coefficient is corrected for boundary effect according to the radius of the ball and the container wall, it includes:

[0116] The boundary effect correction factor is obtained through the following relationship:

[0117]

[0118] Among them, k1 is the boundary effect correction coefficient, R is the radius of the ball, and R c is the radius of the container, v is the instantaneous velocity of the ball, v max is the terminal velocity of the ball, and α is the correction coefficient.

[0119] It can be understood that the above formula represents the influence of the fluid resistance generated by the container wall on the calculation of the viscosity coefficient during the falling process of the ball. Due to the existence of the container wall, the flow state of the fluid around the ball will be restricted, causing the falling speed of the ball to deviate from the ideal value of the theoretical calculation. Therefore, the calculation results must be corrected.

[0120] Influence of boundary effect: During the falling process of the ball, if the container wall is too close, the fluid will not be able to flow freely due to space restrictions, generating additional resistance, causing the ball to slow down. At this time, the calculated viscosity coefficient will be too large. Therefore, the boundary effect correction coefficient is to compensate for this error, making the calculation of the viscosity coefficient of the ball more accurate. The ratio of the instantaneous speed to the terminal speed is used to adjust the correction strength. When the instantaneous speed is close to the terminal speed, the ball has basically overcome the initial resistance and entered the uniform falling stage. The correction at this time is small. When the instantaneous speed of the ball is much lower than the terminal speed, the correction coefficient is larger because the influence of the boundary effect is more obvious at this time. The correction coefficient is a key variable in the formula and is an empirical value obtained through experiments. It is equivalent to the "adjustment knob" we use in the experiment to adjust the model results to be more consistent with the actual situation. The introduction of this coefficient enhances the flexibility of the formula and can be adjusted under different conditions, making the corrected results more accurate.

[0121] Without correction, the presence of the container wall will interfere with the falling of the ball. If such interference is not eliminated, the measured viscosity coefficient will have errors, and the deviation may be very significant. Through boundary effect correction, the actual motion state of the ball without boundary interference can be restored as much as possible.

[0122] Improve the accuracy of viscosity coefficient measurement: The boundary effect is not just a kind of interference, it is a "dynamic interference" that changes with many factors such as the falling speed of the ball and the distance from the container wall. If no correction is made, the viscosity coefficient finally measured will fluctuate greatly under different experimental conditions. This is extremely detrimental to the accuracy of the experimental results. After correction, a stable and reliable viscosity coefficient can be obtained. In summary, boundary effect correction is not only a technological innovation to improve the accuracy of viscosity coefficient measurement in experiments, but also an indispensable correction method. It fully considers the complex interaction between the ball and the container wall, and eliminates this interference in a scientific way, making the measurement results closer to reality. The proposal of this correction provides more solid data support for the study and application of the flow characteristics of radiant cooling coatings.

[0123] In some embodiments of the present invention, when collecting temperature data on the falling trajectory of the ball, weighted averaging all temperature data, and performing temperature correction on the initial viscosity coefficient, the method includes:

[0124] When the time satisfies a real-time detection cycle, the temperature data of the radiation cooling coating on the falling trajectory of the ball is collected, and a temperature data set is established, which is recorded as {T1, T2, T3...Tn}; the temperature is weighted averaged based on the instantaneous speed of the ball to obtain the average temperature value, and finally the temperature correction coefficient is obtained through the following relationship:

[0125]

[0126] Where k2 is the temperature correction coefficient; E a is the energy that needs to be overcome by the flow of the radiation cooling coating; R is the gas constant; T avg is the average temperature value; n is the total number of detections; w i is the weight of the temperature detected at the i-th time; v i is the instantaneous velocity of the ball detected for the i-th time; i = 1, 2, 3, ..., n.

[0127] It is understood that regarding the establishment of the temperature data set: the temperature data collected at the end of each real-time detection cycle forms a data set, which is used for subsequent processing and analysis. The timely collection of temperature data can reflect the actual environmental conditions of the ball at different time points.

[0128] Weighted averaging: By weighted averaging the temperature data, the impact of the instantaneous speed of each detection point on the temperature can be comprehensively considered to obtain a more accurate average temperature value. This method can eliminate the error caused by a single temperature measurement to a certain extent and provide a more stable correction result.

[0129] Calculation of temperature correction coefficient: By calculating the temperature correction coefficient, the initial viscosity coefficient can be appropriately corrected for temperature. This step is to adjust the viscosity coefficient so that it can reflect the actual impact of temperature changes on material fluidity. In this way, the final viscosity coefficient can more truly reflect the performance of the material under actual use conditions.

[0130] Temperature is an important factor affecting material viscosity. The viscosity of radiant cooling coatings usually changes with temperature. By monitoring temperature data in real time, the initial viscosity can be accurately corrected to reflect the actual material behavior under different temperature conditions. Increasing temperature usually reduces the viscosity, making the coating more fluid. Conversely, decreasing temperature increases viscosity. Accurate temperature measurement can help understand the flow characteristics of the coating at different temperatures.

[0131] The calculation of the temperature correction coefficient helps to accurately reflect the effect of ambient temperature on the fluidity of the material, making the final viscosity coefficient measurement more accurate. The weighted averaging method can adapt to temperature changes during the test process, ensuring that the temperature correction coefficient can be dynamically adjusted to adapt to different experimental environments. Through accurate temperature correction, the reliability of the test results is improved, which helps to better predict and control the fluidity of the material in practical applications.

[0132] In some embodiments of the present invention, when the effective density difference is obtained by integrating the density change of the radiation cooling coating on the falling trajectory of the ball, the density correction is performed based on the effective density difference, including:

[0133] According to the change in coating density at different depths during the falling process of the ball, the density correction coefficient is calculated. The density correction coefficient satisfies the following relationship:

[0134]

[0135] Among them, k3 is the density correction coefficient, h1 is the initial depth of the ball falling, h2 is the depth after the ball starts to fall at a uniform speed, ρ(h) is the density of the radiation cooling coating at a depth of h, and ρ0 is the density of the radiation cooling coating under standard conditions.

[0136] It should be noted that the density correction factor is used to correct the quantitative value of the density change of the radiant cooling coating at different depths. This factor is calculated based on the density data recorded during the falling process and is used to adjust the initial viscosity coefficient to make it more accurately reflect the actual flow properties.

[0137] By calculating the density correction factor, the density changes at different depths can be taken into account and the initial viscosity coefficient can be adjusted to more accurately reflect the actual coating performance. By adjusting the initial viscosity coefficient through the correction factor, the error caused by density changes can be compensated, making the test results more accurate and reliable. This correction process enables the test data to better simulate the coating performance in the actual application environment and improve the practical application value of the test results.

[0138] It is understandable that in actual applications, the density of the coating may vary with depth. By correcting for density changes, the performance of the coating in the actual use environment can be accurately simulated, providing more realistic performance data. The density correction process helps to eliminate measurement errors caused by density changes, thereby improving the test accuracy of the viscosity coefficient. This is important for ensuring the quality and performance of the coating. By integrating the density at different depths and calculating the density correction factor, the consistency of the test data can be ensured. This is essential for quality control and performance evaluation of materials. The coating may encounter different depth conditions in actual application. Through density correction, the experimental results can be made more consistent with the actual application conditions, thereby enhancing the practical significance and application value of the test results.

[0139] In some embodiments of the present invention, the final viscosity coefficient is obtained by the following relationship:

[0140] V = V0 × k1 × k2 × k3;

[0141] Among them, V is the final viscosity coefficient and V0 is the initial viscosity coefficient.

[0142] See also Figure 2As shown, an embodiment of the present invention further provides a flow characteristic detection system for a radiation cooling coating, comprising:

[0143] The detection module includes: a cylindrical container, a small ball and a high-speed camera, wherein the high-speed camera is used to record the movement of the small ball, obtain the falling image data of the small ball, and capture the movement trajectory of the small ball;

[0144] An image processing module is configured to decompose the video data acquired by the high-speed camera into image frames, extract the edge of the ball, determine the center coordinates and radius of the ball, and calculate the terminal speed;

[0145] A data processing module is configured to calculate the instantaneous velocity of the ball based on the extracted center coordinates and radius, and draw an instantaneous velocity-time curve;

[0146] A temperature measurement module is configured to collect temperature data of the radiant cooling coating, establish a temperature data set and;

[0147] A weighted processing module is configured to perform weighted averaging on the collected temperature data and calculate a temperature correction coefficient;

[0148] A density measurement module is configured to collect the density of the radiation cooling coating;

[0149] A correction calculation module is configured to calculate a final viscosity coefficient based on a boundary effect correction coefficient, a temperature correction coefficient, and a density correction coefficient;

[0150] The control unit is configured to control the detection cycle adjustment of the high-speed camera and the synchronization of data acquisition.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting flow characteristics of a radiation cooling coating, characterized in that: include: The radiation cooling coating is poured into a cylindrical container, and then a small ball of known density is placed in the cylindrical container. The timing starts when the small ball starts to fall in the cylindrical container, and the position of the small ball is recorded by a high-speed camera. The video data obtained by the high-speed camera is decomposed into image frames, and each image frame corresponds to a detection time to establish an image frame-detection time data set; Preprocess the image frame, obtain the center coordinates and radius of the ball based on edge detection, extract the center coordinates of the ball in each frame of the image, construct the falling trajectory of the ball by comparing the changes in the center coordinates of the ball in multiple consecutive image frames, calculate the instantaneous speed of the ball according to the detection time and the change in the center position, and draw a change curve of the instantaneous speed-time; obtain the terminal speed according to the change curve; and obtain the initial viscosity coefficient of the ball according to the Stokes formula; The initial viscosity coefficient is corrected for boundary effects according to the radius of the ball and the container wall; the temperature data on the falling trajectory of the ball is collected, and after weighted averaging all the temperature data, the initial viscosity coefficient is corrected for temperature; the effective density difference is obtained by integrating the density change of the radiation cooling coating on the falling trajectory of the ball, and the density correction is performed based on the effective density difference to obtain the final viscosity coefficient.

2. The method for detecting flow characteristics of the radiation cooling coating according to claim 1, characterized in that: Also includes small ball qualification testing: Before the ball falls, 3D scanning is performed at several angles to generate a three-dimensional model of the ball, and the ellipticity of the ball is calculated by comparing the three-dimensional model with an ideal sphere; The geometric outline of the ball is generated according to the three-dimensional model of the ball, and the maximum diameter and the minimum diameter of the ball are extracted through image processing, and recorded as the major axis length and the minor axis length respectively. When the ratio of the major axis length to the minor axis length is between 1.00 and 1.01, the ball is judged to be qualified; when the ratio of the major axis length to the minor axis length is not between 1.00 and 1.01, the ball is replaced and the ball qualification test is re-performed.

3. The method for detecting flow characteristics of the radiation cooling coating according to claim 2, characterized in that: When the image frame is preprocessed, it includes: Grayscale each of the image frames and perform denoising by Gaussian blurring; A grayscale threshold is set in advance, the grayscale value in the image frame after grayscale processing is obtained, the grayscale value is compared with the grayscale threshold, when the grayscale value is greater than or equal to the grayscale threshold, the area corresponding to the grayscale value is recorded as the foreground, when the grayscale value is less than the grayscale threshold, the area corresponding to the grayscale value is recorded as the background; the foreground is set to white and the foreground area is a small ball, and the background is set to black.

4. The method for detecting flow characteristics of the radiation cooling coating according to claim 3, characterized in that: When the position of the ball is recorded by a high-speed camera, the detection cycle of the high-speed camera is adjusted based on the movement of the ball: The shortest frame interval of the high-speed camera is obtained and used as the shortest detection period, an initial detection period is preset, and the real-time detection period is adjusted based on the instantaneous speed of the ball. The real-time detection period satisfies the following relationship: Furthermore, the instantaneous speed of the ball is acquired in real time, and when the instantaneous speed of the ball does not change, the real-time detection period is restored to the initial detection period; Among them, T is the real-time detection period, T0 is the initial detection period, v is the instantaneous speed of the ball, T max is the shortest detection cycle; when T>T max When T is T max .

5. The method for detecting flow characteristics of the radiation cooling coating according to claim 4, characterized in that: When the center coordinates and radius of the ball are obtained based on edge detection, it includes: The edge detection method based on the Canny algorithm detects the edge of the ball, and the strong and weak edges in the image are determined by the double threshold method. The contour-based Hough transform algorithm detects the edge points of the circular structure, connects the contours, extracts the complete closed edge, and selects the contours that meet the size range of the ball, excluding noise or other non-spherical targets; For the closed circular contours screened out, the distribution of edge points is weighted and the circle is fitted using the least square method. After the fitting is completed, the coordinates of the center of the circle are the center coordinates of the small ball, and the radius of the fitted circle is the radius of the small ball; The fitting accuracy is verified by calculating the average distance between the edge points of the fitted circle and the actual detected edge points. If the average distance exceeds a preset error threshold, contour extraction and fitting are performed again until it is below the error threshold.

6. The method for detecting flow characteristics of the radiation cooling coating according to claim 5, characterized in that: When the boundary effect correction is performed on the initial viscosity coefficient according to the radius of the ball and the container wall, it includes: The boundary effect correction factor is obtained through the following relationship: Among them, k1 is the boundary effect correction coefficient, R is the radius of the ball, and R c is the radius of the container, v is the instantaneous velocity of the ball, v max is the terminal velocity of the ball, and α is the correction coefficient.

7. The method for detecting flow characteristics of the radiation cooling coating according to claim 6, characterized in that: When collecting the temperature data on the falling trajectory of the ball, weighted averaging all the temperature data, and performing temperature correction on the initial viscosity coefficient, the method includes: When the time satisfies a real-time detection cycle, the temperature data of the radiation cooling coating on the falling trajectory of the ball is collected, and a temperature data set is established, which is recorded as {T1, T2, T3...Tn}; the temperature is weighted averaged based on the instantaneous speed of the ball to obtain the average temperature value, and finally the temperature correction coefficient is obtained through the following relationship: Where k2 is the temperature correction coefficient; E a is the energy that needs to be overcome by the flow of the radiation cooling coating; R is the gas constant; T avg is the average temperature value; n is the total number of detections; w i is the weight of the temperature detected at the i-th time; v i is the instantaneous velocity of the ball detected for the i-th time; i = 1, 2, 3, ..., n.

8. The method for detecting flow characteristics of the radiation cooling coating according to claim 7, characterized in that: When the density change of the radiation cooling coating on the falling trajectory of the ball is integrated to obtain an effective density difference, and density correction is performed based on the effective density difference, it includes: According to the change in coating density at different depths during the falling process of the ball, the density correction coefficient is calculated, and the density correction coefficient satisfies the following relationship: Among them, k3 is the density correction coefficient, h1 is the initial depth of the ball falling, h2 is the depth after the ball starts to fall at a uniform speed, ρ(h) is the density of the radiation cooling coating at a depth of h, and ρ0 is the density of the radiation cooling coating under standard conditions.

9. The method for detecting flow characteristics of the radiation cooling coating according to claim 8, characterized in that: The final viscosity coefficient is obtained by the following relationship: V = V0 × k1 × k2 × k3; Among them, V is the final viscosity coefficient and V0 is the initial viscosity coefficient.

10. A flow characteristics detection system for a radiation cooling coating, used to implement the flow characteristics detection method for a radiation cooling coating according to any one of claims 1 to 9, characterized in that: The detection system comprises: The detection module includes: a cylindrical container, a small ball and a high-speed camera, wherein the high-speed camera is used to record the movement of the small ball, obtain the falling image data of the small ball, and capture the movement trajectory of the small ball; An image processing module is configured to decompose the video data acquired by the high-speed camera into image frames, extract the edge of the ball, determine the center coordinates and radius of the ball, and calculate the terminal speed; A data processing module is configured to calculate the instantaneous velocity of the ball based on the extracted center coordinates and radius, and draw an instantaneous velocity-time curve; A temperature measurement module is configured to collect temperature data of the radiant cooling coating, establish a temperature data set and; A weighted processing module is configured to perform weighted averaging on the collected temperature data and calculate a temperature correction coefficient; A density measurement module is configured to collect the density of the radiation cooling coating; A correction calculation module is configured to calculate a final viscosity coefficient based on a boundary effect correction coefficient, a temperature correction coefficient, and a density correction coefficient; The control unit is configured to control the detection cycle adjustment of the high-speed camera and the synchronization of data acquisition.

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