Finite element analysis-based method and system for predicting heat-conducting property of regenerated perforated brick

Abstract

The invention relates to the technical field of data analysis, in particular to a method and a system for predicting the heat-conducting property of a regenerated porous brick based on finite element analysis. The method comprises the steps that a heat flow load experiment is conducted on a to-be-tested brick type, so that a plurality of high heat flow areas are determined, and the high heat flow areas are the areas, with the heat flow intensity larger than an intensity threshold value, of the to-be-tested brick type in the heat flow load experiment; analyzing the heat flow intensity, the area distribution position and the area volume of the plurality of high heat flow areas to obtain a first performance prediction value of the to-be-tested brick type; and performing random hole adjustment on the to-be-tested brick type to obtain an adjusted brick type, and correcting the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a second performance prediction value of the to-be-tested brick type. According to the method, related data of the heat flow load experiment can be fully excavated, so that the thermal performance evaluation result of the perforated brick prepared based on the construction waste is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and in particular to a recycled porous brick heat conduction performance prediction method and system based on finite element analysis. BACKGROUND

[0002] With the rapid development of economy and the continuous improvement of urbanization level, a large number of buildings will face demolition, which will generate a large amount of construction waste. If the construction waste is not properly disposed of, it will cause land hardening, deep water pollution and other problems. By preparing the construction waste into porous bricks, not only the pollution of the construction waste to the environment is avoided, but also the resource recycling is fully realized.

[0003] After the construction waste is prepared into porous bricks, the thermal performance of the porous bricks needs to be analyzed to evaluate the heat preservation effect of the porous bricks as a house heat insulation structure. The existing method mainly evaluates the thermal performance of the porous bricks through finite element analysis, equivalent heat conduction system rapid calculation and other directions.

[0004] It is found in application that the existing technology lacks sufficient mining of thermal test data of the porous bricks, resulting in inaccurate evaluation of the thermal performance of the porous bricks. SUMMARY

[0005] The purpose of the present application is to provide a recycled porous brick heat conduction performance prediction method and system based on finite element analysis, which solves the technical problem of inaccurate evaluation of the thermal performance of the porous bricks in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a recycled porous brick heat conduction performance prediction method based on finite element analysis, which comprises: Performing a heat flow load experiment on a to-be-tested brick type to determine a plurality of high heat flow regions, wherein the high heat flow region is a region of the to-be-tested brick type in which the heat flow intensity is greater than the intensity threshold in the heat flow load experiment; Analyzing the heat flow intensity, region distribution position and region volume of the plurality of high heat flow regions to obtain a first performance prediction value of the to-be-tested brick type; Adjusting the random holes of the to-be-tested brick type to obtain an adjusted brick type, and correcting the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a second performance prediction value of the to-be-tested brick type.

[0007] In some embodiments, analyzing the heat flow intensity, region distribution position and region volume of the plurality of high heat flow regions to obtain the first performance prediction value of the to-be-tested brick type comprises: Analyzing the heat flow intensity difference and the region volume difference between the plurality of high heat flow regions to obtain a first factor; analyze a discrete degree of the region distribution positions of the plurality of high heat flow regions to obtain a second factor; analyze a total number of the plurality of high heat flow regions and a total amount of region volumes to obtain a third factor; obtain a first performance prediction value of the to-be-tested brick type according to the first factor, the second factor, and the third factor.

[0008] In some embodiments, the first factor is obtained by analyzing a heat flow intensity difference and a region volume difference between the plurality of high heat flow regions, including: In the plurality of high heat flow regions, a heat flow intensity difference between each high heat flow region and other high heat flow regions is analyzed to obtain a plurality of intensity difference values corresponding to each high heat flow region; In the plurality of high heat flow regions, a region volume difference between each high heat flow region and other high heat flow regions is analyzed to obtain a plurality of volume difference values corresponding to each high heat flow region; In the plurality of high heat flow regions, a characteristic difference value of each high heat flow region is determined according to the plurality of intensity difference values and the plurality of volume difference values corresponding to each high heat flow region; The first factor is determined according to the characteristic difference value of each high heat flow region.

[0009] In some embodiments, the first factor is determined according to the characteristic difference value of each high heat flow region, including: In the plurality of high heat flow regions, a weight coefficient of each high heat flow region is determined according to a near-hole limit distance of each high heat flow region, wherein the near-hole limit distance is used to represent a shortest distance between the corresponding high heat flow region and a hole in the to-be-tested brick type, and the near-hole limit distance and the weight coefficient are in a negative correlation relationship; The characteristic difference values of the plurality of high heat flow regions are weighted and calculated according to the weight coefficient of each high heat flow region to obtain the first factor.

[0010] In some embodiments, the second factor is obtained by analyzing a discrete degree of the region distribution positions of the plurality of high heat flow regions, including: In the plurality of high heat flow regions, a shortest interval distance between any two different high heat flow regions is analyzed to obtain a plurality of discrete factors, wherein the discrete factor and the shortest interval distance are in a positive correlation relationship; An average value of the plurality of discrete factors is calculated to obtain the second factor.

[0011] In some embodiments, the third factor is obtained by analyzing a total number of the plurality of high heat flow regions and a total amount of region volumes, including: Calculate the ratio of the total number of the plurality of high heat flow regions and the total volume of the regions to obtain the third factor.

[0012] In some embodiments, the method further comprises: Analyzing the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a prediction fluctuation coefficient; Determining a correction coefficient according to the prediction fluctuation coefficient, wherein the prediction fluctuation coefficient and the correction coefficient are in a negative correlation relationship; Correcting the first performance prediction value of the to-be-tested brick type according to the correction coefficient to obtain a second performance prediction value of the to-be-tested brick type.

[0013] In some embodiments, the method further comprises: Filling the to-be-tested insulation material in the holes of the to-be-tested brick type, and performing a heat flow load experiment on the to-be-tested brick type after the holes are filled to obtain a temperature change sequence of each hole in the to-be-tested brick type; Analyzing the data fluctuation degree of the temperature change sequence of each hole in the to-be-tested brick type to obtain a temperature change factor of each hole in the to-be-tested brick type; Determining an insulation performance prediction value of the to-be-tested brick type after the to-be-tested insulation material is filled according to the temperature change factor of each hole in the to-be-tested brick type.

[0014] In some embodiments, the method further comprises: Obtaining first isotherm data of the to-be-tested brick type obtained before the holes are filled and second isotherm data of the to-be-tested brick type obtained after the holes are filled with the to-be-tested insulation material; Analyzing the difference between the first isotherm data and the second isotherm data to obtain a heat conduction factor; Determining an insulation performance prediction value of the to-be-tested brick type after the to-be-tested insulation material is filled according to the temperature change factor of each hole in the to-be-tested brick type and the heat conduction factor.

[0015] In a second aspect, another embodiment of the present application further provides a regenerative porous brick heat conduction performance prediction system based on finite element analysis, the system comprising: The area identification module is used for carrying out a heat flow load experiment on the to-be-tested brick type to determine a plurality of high heat flow areas, wherein the high heat flow area is an area of the to-be-tested brick type in which the heat flow intensity is greater than an intensity threshold in the heat flow load experiment; The performance preliminary prediction module is used for analyzing the heat flow intensity, the area distribution position and the area volume of the plurality of high heat flow areas to obtain a first performance prediction value of the to-be-tested brick type. The prediction correction module is used for adjusting a random hole in the to-be-tested brick type to obtain an adjusted brick type, and correcting the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a second performance prediction value of the to-be-tested brick type.

[0016] In a third aspect, a further embodiment of the present application provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the method of the first aspect are implemented.

[0017] In a fourth aspect, a further embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method of the first aspect are implemented.

[0018] The present application has the following beneficial effects: The present application can improve the accuracy of the first performance prediction value of the to-be-tested brick type as much as possible by carrying out a heat flow load experiment on the to-be-tested brick type, identifying the high heat flow area in which the heat flow intensity is greater than the intensity threshold in the experiment, i.e., identifying the area in which the heat flow is prone to appear in the to-be-tested brick type, analyzing the heat flow intensity, the area distribution position and the area volume of the high heat flow area, and comprehensively evaluating the heat insulation performance of the to-be-tested brick type from multiple aspects. Finally, the to-be-tested brick type is adjusted with a random hole to simulate the random deformation of the to-be-tested brick type in actual application due to external factors, and the difference in the heat insulation performance of the to-be-tested brick type before and after the adjustment is compared to accurately predict the heat insulation performance of the to-be-tested brick type in actual application, so that the evaluation result of the thermal performance of the porous brick made of construction waste is more accurate and reliable. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1is a flow diagram of a regenerated porous brick heat conduction performance prediction method based on finite element analysis provided by an embodiment of the present application. Figure 2 is a schematic diagram of a regenerated porous brick sample provided by an embodiment of the present application. Figure 3 is a schematic diagram of a plurality of brick types of regenerated porous bricks provided by an embodiment of the present application. Figure 4 is Figure 3 the corresponding temperature distribution cloud map. Figure 5 is Figure 3 the corresponding heat flow distribution cloud map. Figure 6 is a structural diagram of a regenerated porous brick heat conduction performance prediction system based on finite element analysis provided by an embodiment of the present application. Figure 7 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object, the following describes the regenerated porous brick heat conduction performance prediction method and system based on finite element analysis provided by the present application, its specific implementation, structure, features and effects in detail in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0023] The specific scheme of the regenerated porous brick heat conduction performance prediction method and system based on finite element analysis provided by the present application is described in detail below in combination with the drawings.

[0024] In one embodiment, as shown in Figure 1 the regenerated porous brick heat conduction performance prediction method based on finite element analysis provided by the present application includes: Step S1, performing a heat flow load experiment on the brick type to be tested to determine a plurality of high heat flow regions.

[0025] The high heat flow region is a region of the brick type to be tested with a heat flow intensity greater than an intensity threshold in the heat flow load experiment.

[0026] In the present application, the heat flow load experiment on the to-be-tested brick type is specifically: based on the to-be-tested brick type, a recycled porous brick is constructed with construction waste as aggregate, a constant temperature difference is set on both sides of the constructed recycled porous brick to simulate the indoor and outdoor temperature difference, then a required heat flow density (such as 30 W / m² of building energy consumption standard) is applied, and the heat flow intensity of the recycled porous brick at each position is monitored. In the present application, the heat flow intensity is specifically the heat flow density. The intensity threshold can be set based on experience, and in the present application, the intensity threshold is set to 30 W / m².

[0027] In the present application, the recycled porous brick is constructed with construction waste as aggregate, supplemented by cementing materials such as P042.5 Portland cement, calcium aluminate cement, mineral powder, fly ash, additives such as sodium sulfate, water glass, water reducing agent, and water.

[0028] In the application, a small amount of recycled aggregate (i.e. construction waste) is placed in a drying oven and dried for 12 hours, then taken out and divided into 3 parts, then the bulk density of the recycled aggregate is obtained by the bulk density analysis method, and the apparent density and water absorption rate of the recycled aggregate are obtained by the apparent density analysis method, and then the ideal material ratio in the preparation process of the recycled porous brick is obtained according to the bulk density, apparent density and water absorption rate of the recycled aggregate, combined with the Dinger-Funk model (particle packing model).

[0029] In the present application, when the recycled porous brick is prepared based on the recycled aggregate, the water-cement ratio is 0.65, the curing temperature is 17℃, and the slag-fly ash ratio is 6:3:1.

[0030] The brick type specifically indicates the length-width ratio, hole row number, hole column number, hole arrangement mode, hole rate and other morphological characteristics of the recycled porous brick. Different brick types differ in at least one morphological characteristic. The to-be-tested brick type can be understood as any one of the pre-set multiple brick types.

[0031] It should be understood that the recycled porous brick with superior heat insulation performance can uniformly disperse the applied experimental heat flow to effectively block the heat flow between the two sides of the recycled porous brick, while the recycled porous brick with defective heat insulation performance cannot effectively disperse the applied experimental heat flow, so the experimental heat flow will be concentrated in the heat insulation defect area of the recycled porous brick and form a heat flow channel between the two sides of the recycled porous brick through the heat insulation defect area. The high heat flow area in the present application can be understood as the area suspected of having heat insulation defects in the heat flow load experiment of the recycled porous brick.

[0032] Step S2, analyze the heat flow intensity, area distribution position and area volume of the plurality of high heat flow areas to obtain the first performance prediction value of the to-be-tested brick type.

[0033] The first performance prediction value is used to represent the heat insulation performance of the recycled perforated brick constructed based on the to-be-tested brick type. The greater the first performance prediction value, the stronger the heat insulation performance of the recycled perforated brick constructed based on the to-be-tested brick type, that is, the better the heat insulation effect of the recycled perforated brick constructed based on the to-be-tested brick type.

[0034] Specifically, the heat flow intensity, the region distribution position, and the region volume of the plurality of high heat flow regions are analyzed to obtain the first performance prediction value of the to-be-tested brick type, including: The difference in heat flow intensity and the difference in region volume between the plurality of high heat flow regions are analyzed to obtain a first factor; The dispersion degree of the region distribution position of the plurality of high heat flow regions is analyzed to obtain a second factor; The total number and the total volume of the plurality of high heat flow regions are analyzed to obtain a third factor; The first performance prediction value of the to-be-tested brick type is obtained according to the first factor, the second factor, and the third factor.

[0035] The greater the difference in heat flow intensity and / or the greater the difference in region volume between the plurality of high heat flow regions, the higher the value of the first factor, which indicates that the to-be-tested brick type has a poorer effect on uniformly dispersing the experimental heat flow, the recycled perforated brick constructed based on the to-be-tested brick type has a poorer heat insulation performance in the high heat flow region, the risk of heat gradually gathering and rapidly flowing through the high heat flow region on both sides of the recycled perforated brick is higher, and the heat insulation performance of the recycled perforated brick constructed based on the to-be-tested brick type is poorer.

[0036] The higher the dispersion degree of the region distribution position of the plurality of high heat flow regions, the higher the value of the second factor, which indicates that the to-be-tested brick type has a better effect on uniformly dispersing the experimental heat flow, the risk of heat gradually gathering and rapidly flowing through the high heat flow region on both sides of the recycled perforated brick is lower, and the heat insulation performance of the recycled perforated brick constructed based on the to-be-tested brick type is better.

[0037] The more the total number of the plurality of high heat flow regions and the smaller the total volume, the higher the value of the third factor, which indicates that the to-be-tested brick type has a better effect on uniformly dispersing the experimental heat flow, the risk of heat gradually gathering and rapidly flowing through the high heat flow region on both sides of the recycled perforated brick is lower, and the heat insulation performance of the recycled perforated brick constructed based on the to-be-tested brick type is better.

[0038] It should be understood that the first factor is negatively correlated with the first performance prediction value, the second factor is positively correlated with the first performance prediction value, and the third factor is positively correlated with the first performance prediction value.

[0039] In some embodiments, the first factor is determined by analyzing the heat flow intensity difference and the volume difference between the plurality of high heat flow regions. In the plurality of high heat flow regions, a plurality of intensity difference values corresponding to each high heat flow region are determined by analyzing the heat flow intensity difference between each high heat flow region and other high heat flow regions. In the plurality of high heat flow regions, a plurality of volume difference values corresponding to each high heat flow region are determined by analyzing the volume difference between each high heat flow region and other high heat flow regions. In the plurality of high heat flow regions, a characteristic difference value of each high heat flow region is determined according to the plurality of intensity difference values and the plurality of volume difference values corresponding to the high heat flow region. The first factor is determined according to the characteristic difference value of each high heat flow region.

[0040] In this embodiment, each intensity difference value can be determined based on the absolute difference of heat intensity of the corresponding two different high heat flow regions, and each volume difference value can be determined based on the absolute difference of region volume of the corresponding two different high heat flow regions.

[0041] In applications, the average of the plurality of intensity difference values corresponding to each high heat flow region can be determined as the intensity difference characteristic value corresponding to the high heat flow region, and the average of the plurality of volume difference values corresponding to each high heat flow region can be determined as the volume difference characteristic value corresponding to the high heat flow region. Then, the product or sum of the intensity difference characteristic value and the volume difference characteristic value corresponding to each high heat flow region is calculated to obtain the characteristic difference value of each high heat flow region (after normalization processing, the normalization value interval is 0-1).

[0042] For example, the intensity difference characteristic value corresponding to the first high heat flow region in the plurality of high heat flow regions can be represented as: wherein, N represents the total number of the plurality of high heat flow regions, e represents the exponential function with the natural constant e as the base, n represents the first high heat flow region, and represents the absolute difference of heat flow intensity (after normalization processing) between the first high heat flow region and the nth high heat flow region in the plurality of high heat flow regions except the first high heat flow region. For example, the volume difference characteristic value corresponding to the first high heat flow region in the plurality of high heat flow regions can be represented as:

[0043] ​​​​​​ wherein, represents the volume difference between the first high heat flow area and the first high heat flow area among the multiple high heat flow areas. represents the absolute difference value of the volume (after normalization processing) between the first high heat flow area and the first high heat flow area among the multiple high heat flow areas. The characteristic difference value of the first high heat flow area among the multiple high heat flow areas can be represented as:

[0044] The characteristic difference value of the first high heat flow area among the multiple high heat flow areas can be represented as: The characteristic difference value of the first high heat flow area among the multiple high heat flow areas can be represented as: wherein, represents a normalization function.

[0045] Further, according to the characteristic difference value of each high heat flow area, the first factor is determined, comprising: In the multiple high heat flow areas, according to the near-hole limit distance of each high heat flow area, a weight coefficient of each high heat flow area is determined, wherein the near-hole limit distance is used to represent the shortest distance between the corresponding high heat flow area and the hole in the brick to be measured, and the near-hole limit distance and the weight coefficient are in a negative correlation. According to the weight coefficient of each high heat flow area, the characteristic difference values of the multiple high heat flow areas are weighted and calculated to obtain the first factor.

[0046] It is considered that the holes of the regenerated perforated brick are set to block the heat flow on both sides of the brick, and the non-linear design of the holes can effectively prolong the heat flow path, and then cooperate with the filled thermal insulation material of the holes to achieve better thermal insulation effect. Therefore, in the above setting, in addition to analyzing the volume difference and heat flow intensity difference between different high heat flow areas, the shortest distance between the high heat flow area and the hole in the brick to be measured is further analyzed, and the importance of each high heat flow area in the multiple high heat flow areas is determined, and the characteristic difference values of the multiple high heat flow areas are weighted and calculated, which can make the obtained first factor more accurate and reliable.

[0047] It should be understood that the smaller the near-hole limit distance of the high heat flow area, the closer the high heat flow area to the hole filled with thermal insulation material, the greater the narrowing of the heat flow channel affected by the hole shape, the higher the corresponding thermal stress of the high heat flow area, and the higher the probability of the high heat flow area becoming a real thermal insulation defect area, and therefore the higher the importance of the high heat flow area.

[0048] Exemplarily, the area key value of the first high heat flow area among the multiple high heat flow areas can be represented as: Exemplarily, the area key value of the first high heat flow area among the multiple high heat flow areas can be represented as: ​​​ wherein, represents the minimum Euclidean distance (after normalization) between the holes of the to-be-tested brick type and the high heat flow regions in the i-th high heat flow region.

[0049] After determining the region key value of each high heat flow region, the sum value of the region key values of the plurality of high heat flow regions is calculated, and the ratio of the region key value of each high heat flow region to the sum value is calculated, so as to obtain the weight coefficient of each high heat flow region.

[0050] In some embodiments, the dispersion degree of the region distribution positions of the plurality of high heat flow regions is analyzed to obtain a second factor, including: In the plurality of high heat flow regions, the shortest interval distance between any two different high heat flow regions is analyzed to obtain a plurality of dispersion factors, wherein the dispersion factor is in a positive correlation with the shortest interval distance; The average value of the plurality of dispersion factors is calculated to obtain the second factor.

[0051] The plurality of high heat flow regions are randomly combined in pairs to obtain a plurality of region combinations, the plurality of region combinations correspond one-to-one to the plurality of dispersion factors, and the dispersion factor is determined based on the shortest interval distance between the two different high heat flow regions included in the corresponding region combination.

[0052] In an example, the ratio between the shortest interval distance between the two different high heat flow regions included in each region combination and a reference interval distance can be calculated to obtain the dispersion factor corresponding to each region combination, wherein the reference interval distance can be determined based on experience (for example, set as the average value of the shortest interval distances between the plurality of high heat flow regions in the historical heat flow load experiment that performs best among a plurality of historical heat flow load experiments performed in the past).

[0053] In some embodiments, the total number of the plurality of high heat flow regions and the total volume of the region volume are analyzed to obtain a third factor, including: The ratio between the total number of the plurality of high heat flow regions and the total volume of the region volume is calculated to obtain the third factor.

[0054] The more the total number of high heat flow regions, the more sufficient the dispersion of the experimental heat flow based on the to-be-tested brick type to construct the regenerated perforated brick, the lower the risk of heat gradually gathering and rapidly flowing through the high heat flow regions on both sides of the regenerated perforated brick, and the better the heat insulation performance of the regenerated perforated brick based on the to-be-tested brick type.

[0055] ​On the contrary, the greater the total volume of the area, the more potential areas there are in the recycled perforated brick based on the brick type to be tested when facing the experimental heat flow, the higher the risk of heat flowing rapidly on both sides of the recycled perforated brick through the high heat flow area, and the worse the heat insulation performance of the recycled perforated brick based on the brick type to be tested.

[0056] In the application, in order to eliminate the numerical differences between different dimensions, the ratio of the total number of the plurality of high heat flow areas to the first reference value (i.e. the quantity feature ratio) and the ratio of the total volume of the plurality of high heat flow areas to the second reference value (i.e. the area feature ratio) are calculated first, and then the ratio of the quantity feature ratio to the area feature ratio is calculated to obtain the third factor.

[0057] The first reference value and the second reference value can be set based on experience (for example, the total number of the plurality of high heat flow areas in the historical heat flow load experiment with the best performance among a plurality of historical heat flow load experiments performed in the past is determined as the first reference value, and the total volume of the plurality of high heat flow areas is determined as the second reference value).

[0058] Exemplarily, the first performance prediction value of the adjusted brick type is which can be expressed as: wherein, indicates the first factor, indicates the second factor, indicates the third factor.

[0059] Step S3, adjusting the random holes of the brick type to be tested to obtain an adjusted brick type, and correcting the first performance prediction value of the brick type to be tested according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the brick type to be tested, to obtain a second performance prediction value of the brick type to be tested.

[0060] The correcting the first performance prediction value of the brick type to be tested according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the brick type to be tested, to obtain a second performance prediction value of the brick type to be tested, includes: analyzing the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the brick type to be tested to obtain a prediction fluctuation coefficient; determining a correction coefficient according to the prediction fluctuation coefficient, wherein the prediction fluctuation coefficient and the correction coefficient are in a negative correlation relationship; correcting the first performance prediction value of the brick type to be tested according to the correction coefficient to obtain a second performance prediction value of the brick type to be tested.

[0061] Considering that the regularity of the internal pores of recycled porous bricks may be disrupted during actual application or production (e.g., defects or substandard regularity of pores due to process errors during production, or defects or deformation of pores due to external forces during application), this invention selects to randomly adjust the pores of the brick type under test to simulate the disruption of the regularity of the internal pores of the recycled porous bricks. The performance differences of the recycled porous bricks produced based on the brick type under test before and after adjustment are analyzed to evaluate the performance stability of the recycled porous bricks produced based on the brick type under test in actual applications. Based on this, the original predicted thermal performance is corrected, achieving accurate prediction of the thermal performance of the brick type under test under actual working conditions.

[0062] The aforementioned random pore adjustment includes, but is not limited to: randomly disrupting the structural integrity and shape regularity of the pores in recycled porous bricks produced based on the tested brick type by applying pressure or corrosion.

[0063] The process for obtaining the first performance prediction value of the adjusted brick type can refer to the process for obtaining the first performance prediction value of the brick type to be tested. To avoid repetition, it will not be described again.

[0064] It should be understood that the greater the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the brick type to be tested, the greater the prediction fluctuation coefficient. The greater the prediction fluctuation coefficient, the worse the stability of the thermal performance of the brick type to be tested under actual complex working conditions, and the worse the thermal performance of the brick type to be tested under actual complex working conditions, and vice versa.

[0065] Considering that the thermal performance of the brick under test in an ideal test environment (i.e., the first performance prediction value) is usually weaker than that in a real environment, this invention uses a correction coefficient to adaptively reduce the thermal performance of the brick under test in an ideal test environment in order to more accurately predict the thermal performance of the brick under test in a real environment. The greater the difference between the first performance prediction value of the brick and the first performance prediction value of the brick under test, the greater the performance reduction.

[0066] For example, the second performance prediction value of the brick type under test It can be represented as: in, Represents the normalization function. This represents the first predicted performance value of the brick type to be tested. This indicates the first of several adjusted brick types obtained after random hole adjustments to the tested brick type. The second performance prediction value for the adjusted brick type. This represents the total number of adjusted brick types obtained after random hole adjustments to the brick type under test.

[0067] In some embodiments, after the first performance prediction value of the to-be-tested brick type is corrected according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain the second performance prediction value of the to-be-tested brick type, the method further comprises: filling the to-be-tested thermal insulation material in the holes of the to-be-tested brick type, and performing a heat flow load experiment on the to-be-tested brick type after the holes are filled to obtain a temperature change sequence of each hole in the to-be-tested brick type; analyzing the data fluctuation degree of the temperature change sequence of each hole in the to-be-tested brick type to obtain a temperature change factor of each hole in the to-be-tested brick type; determining a thermal insulation performance prediction value of the to-be-tested brick type after the to-be-tested thermal insulation material is filled according to the temperature change factor of each hole in the to-be-tested brick type.

[0068] The present application further fills the to-be-tested thermal insulation material in the produced recycled perforated brick of each to-be-tested brick type after independently predicting the thermal performance of the recycled perforated brick, and further analyzes the temperature change of the recycled perforated brick produced by the to-be-tested brick type after the holes are filled to comprehensively determine the thermal insulation performance of the to-be-tested brick type after the to-be-tested thermal insulation material is filled.

[0069] In actual application, after the second performance prediction values of a plurality of to-be-tested brick types are determined, the recycled perforated bricks produced by a plurality of to-be-tested brick types ranked in the front of the second performance prediction values can be filled with the to-be-tested thermal insulation material, so as to reduce the number of brick types required for subsequent heat flow load experiments, thereby reducing the overall experimental cost and improving the test efficiency while ensuring that the target thermal insulation material of the target brick type tested and matched with the target brick type can provide superior thermal performance.

[0070] It should be noted that the more serious the data fluctuation degree of the temperature change sequence of the hole, the worse the thermal insulation effect of the corresponding hole, and the greater the risk of thermal insulation defects of the corresponding brick type after the corresponding thermal insulation material is filled.

[0071] The temperature change sequence of the hole can be understood as a data sequence of the extreme temperature (usually the maximum temperature value) of the corresponding hole changing with time within a set time period.

[0072] In one example, the step of obtaining the temperature change factor of the hole comprises: performing curve fitting based on the temperature change sequence of the corresponding hole to obtain a fitting curve of the corresponding hole; obtaining the absolute value of the slope of each data point in the temperature change sequence of the corresponding hole in the corresponding fitting curve to obtain a plurality of slope characteristic values of the corresponding hole; and then calculating the average value of the plurality of slope characteristic values of the corresponding hole to obtain a first temperature change parameter of the corresponding hole; calculating the absolute difference value of two extreme data points (the data point corresponding to the maximum temperature and the data point corresponding to the minimum temperature) in the temperature change sequence of the corresponding hole to obtain a second temperature change parameter of the corresponding hole; Finally, the product of the first temperature change parameter and the second temperature change parameter of the corresponding hole is calculated and normalized to obtain a temperature change factor of the corresponding hole.

[0073] The greater the first temperature change parameter, the greater the amplitude of the extreme temperature change over time monitored by the corresponding hole within the set time period, that is, the worse the heat preservation and insulation effect of the corresponding hole; similarly, the greater the second temperature change parameter, the greater the intensity of the extreme temperature change over time monitored by the corresponding hole within the set time period, which also indicates that the heat preservation and insulation effect of the corresponding hole is worse.

[0074] Exemplarily, the to-be-tested thermal insulation material can be any thermal insulation material in a plurality of preset thermal insulation materials, and the plurality of preset thermal insulation materials include polyurethane foam, foaming glue, rubber sponge, cement mortar, glass cotton, etc.

[0075] Specifically, the method comprises: obtaining first isothermal line data of a heat flow load experiment performed by the to-be-tested brick type before hole filling and second isothermal line data of a heat flow load experiment performed by the to-be-tested brick type after hole filling using the to-be-tested thermal insulation material; analyzing the difference between the first isothermal line data and the second isothermal line data to obtain a heat conduction factor; determining the thermal insulation performance prediction value of the to-be-tested brick type after filling the to-be-tested thermal insulation material according to the temperature change factor of each hole in the to-be-tested brick type and the heat conduction factor.

[0076] In the heat flow load experiment, the isothermal line can indirectly reflect the transmission path of the heat flow, and therefore, by analyzing the difference in isothermal line distribution of the to-be-tested brick type after filling the to-be-tested thermal insulation material, the thermal insulation performance of the to-be-tested thermal insulation material after filling can be accurately evaluated, and the thermal insulation performance of the to-be-tested brick type after filling the to-be-tested thermal insulation material can be comprehensively and accurately predicted in combination with the aforementioned analyzed temperature change.

[0077] The greater the difference between the first isothermal line data and the second isothermal line data, the more significant the heat flow bypassing the hole after the hole is filled with the to-be-tested thermal insulation material, and the better the thermal insulation performance of the recycled porous brick produced therefrom.

[0078] Exemplarily, based on the first to-be-tested brick type, the thermal insulation performance prediction value of the recycled porous brick produced therefrom after filling the first to-be-tested thermal insulation material can be represented as: Exemplarily, based on the first to-be-tested brick type, the thermal insulation performance prediction value of the recycled porous brick produced therefrom after filling the first to-be-tested thermal insulation material can be represented as:​​ in, Represents the normalization function. This represents an exponential function with base e. Indicates based on the first The recycled porous bricks produced from the tested brick type were filled with the first... The average value of the temperature change factor of multiple pores after the thermal insulation material to be tested is obtained; Indicates based on the first The total number of multi-level temperatures measured for recycled porous bricks produced from the tested brick type. Indicates based on the first The recycled porous bricks produced from the tested brick type were filled with the first... The corresponding measurements taken before and after testing the thermal insulation material. The dynamic time-normalized distance between isotherms at different temperature levels Instructions based on the first The measurement was taken when the recycled porous bricks produced by the tested brick type were not filled with pores. The sequence of isotherm positions for each temperature level (each data point in the sequence indicates the corresponding isotherm position). (Location of a monitoring point for the temperature level) Instructions based on the first The recycled porous bricks produced by the tested brick type were filled with the first... The corresponding measurement after the type of thermal insulation material to be tested The sequence of isotherm positions for each temperature level.

[0079] In practical applications, the above sampling method can be used to obtain the predicted thermal insulation performance of each brick type after filling each thermal insulation material. Then, the combination with the largest predicted thermal insulation performance is determined as the target combination, so that recycled porous bricks with better thermal performance can be produced according to the brick type and thermal insulation material indicated by the target combination.

[0080] In summary, this study conducts heat flux load experiments on the tested bricks and identifies high heat flux regions where the heat flux intensity exceeds the intensity threshold, i.e., areas prone to insulation defects. Then, it analyzes the heat flux intensity, distribution location, and volume of these high heat flux regions to comprehensively evaluate the insulation performance of the tested bricks from multiple perspectives. This approach aims to maximize the accuracy of the initial performance prediction values ​​for the tested bricks. Finally, random hole adjustments are made to the tested bricks to simulate random deformation caused by external factors in practical applications. The differences in insulation performance before and after the adjustments are compared to accurately predict the insulation performance of the tested bricks in practical applications. This makes the thermal performance evaluation results of porous bricks made from construction waste more accurate and reliable.

[0081] In one example, the recycled porous brick is made of construction waste as aggregate, supplemented by cementing materials PO42.5 Portland cement, calcium aluminate cement, mineral powder (grade S105), fly ash (fineness 9.5%, loss on ignition 3.1%, water requirement 89%), additives sodium sulfate (molecular weight 284.22, content (calculated by weight) 19.3%-22.8%), water glass (sodium silicate), water reducing agent (polycarboxylic acid type high performance water reducing agent, water reducing rate ≥25%), and water (25°C tap water).

[0082] Specific preparation process is that the construction waste aggregate, cementing materials and additional materials are mixed and stirred (not including water glass), dry mixing in a stirrer at a stirring speed of 55r / min for 2-3min to prepare a mixture; the water glass is dissolved in normal temperature water at 25°C, and stirred uniformly until completely melted; the obtained solution is poured into the mixture, and stirred for 3-4min to obtain a slurry, the prepared slurry is poured into 100mm×100mm×100mm molds with oil on the contact surface in two times, the thickness of each layer is consistent, and the middle is separated by 10s, it should be noted that the concrete should not be molded by using a vibration table, manual tamping or a vibrating rod method after molding; finally, the mold is placed in a curing box for curing and molding for 7-28 days, and the temperature and humidity are controlled to 20°C and 95% respectively. The prepared recycled porous brick sample is shown in Figure 2 .

[0083] The style corresponding to a batch of molds is shown in Figure 3 , specifically including 6 styles, and the hole type, hole number and hole distribution density of each style at least partially differ. The temperature distribution cloud chart and heat flow distribution cloud chart corresponding to the above 6 styles are shown in Figure 4 and Figure 5 .

[0084] In the actual test process, a plurality of recycled porous bricks can be prepared in batches based on the above process, and at least one of the hole type, hole number and hole distribution density of the recycled porous bricks of different batches is different, so as to fully test the practicability of the scheme of the application by testing different combinations of hole type, hole number and hole distribution density.

[0085] Specifically, 5 batches are tested in the present example, and the corresponding test data is shown in Table 1.

[0086] ​​​In the above table, the brick type of type 1 specifically indicates a pattern with 8 hole columns, 2 hole rows, a hole rate of 30%, a hole row spacing of 20 mm, a hole column spacing of 8 mm, and a hole type of a circle; the brick type of type 2 specifically indicates a pattern with 8 hole columns, 2 hole rows, a hole rate of 30%, a hole row spacing of 20 mm, a hole column spacing of 8 mm, and a hole type of a square. The average first performance is an average of first performance prediction values of a plurality of recycled perforated bricks of a corresponding type in a corresponding batch, the average second performance is an average of second performance prediction values of the plurality of recycled perforated bricks of the corresponding type in the corresponding batch, and the average thermal insulation performance is an average of thermal insulation performance prediction values of the plurality of recycled perforated bricks of the corresponding type in the corresponding batch.

[0087] As can be seen from the data in Table 1, the method described in the present application can be used to more accurately correct the first performance prediction value, so as to more accurately predict the actual thermal performance of the recycled perforated brick. In an embodiment, as shown in Figure 6 the provided finite element analysis-based recycled perforated brick thermal conductivity performance prediction system 200 includes: a region identification module 201 configured to perform a heat flow load experiment on a to-be-tested brick type to determine a plurality of high heat flow regions, wherein the high heat flow region is a region of the to-be-tested brick type in which the heat flow intensity is greater than an intensity threshold in the heat flow load experiment; a performance preliminary prediction module 202 configured to analyze the heat flow intensity, region distribution position, and region volume of the plurality of high heat flow regions to obtain a first performance prediction value of the to-be-tested brick type; a prediction correction module 203 configured to perform random hole adjustment on the to-be-tested brick type to obtain an adjusted brick type, and correct the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type, to obtain a second performance prediction value of the to-be-tested brick type.

[0088] It should be noted that the system provided in the above embodiments is only exemplified by the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the finite element analysis-based recycled perforated brick thermal conductivity performance prediction system and the finite element analysis-based recycled perforated brick thermal conductivity performance prediction method provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described here.

[0089] The embodiment of the present application also provides an electronic device. Please refer to Figure 7 The electronic device can include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and executable on the processor 301.

[0090] The program 3021, when executed by the processor 301, can implement the Figure 1 Any step in the corresponding method embodiment and achieving the same beneficial effects, hereinafter will not be repeated.

[0091] Those skilled in the art can understand that all or part of the steps of the method in the above embodiments can be completed by relevant hardware instructed by the program, and the program can be stored in a readable medium.

[0092] The embodiment of the present application also provides a readable storage medium, and the readable storage medium has a computer program stored thereon, and the computer program can implement the above-mentioned Figure 1 Any step in the corresponding method embodiment and achieving the same beneficial effects, hereinafter will not be repeated.

[0093] The computer readable storage medium of the embodiment of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, device or apparatus.

[0094] The computer readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which a computer readable program code is borne. Such a propagated data signal can take many forms, including but not limited to electro-magnetic, optical or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a storage medium and that can transmit, propagate or transport a program for use by or in connection with an instruction execution system, device or apparatus.

[0095] The program code contained in the storage medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0096] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0097] The embodiment of the present application further provides a computer program product, which, when running on a computer, enables the computer to execute the above related steps to realize the finite element analysis based regenerated perforated brick heat conduction performance prediction method and system provided by the above embodiment.

[0098] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0099] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A method for predicting the thermal conductivity of recycled perforated brick based on finite element analysis, characterized by, The method comprises: performing a heat flow load experiment on the to-be-tested brick type to determine a plurality of high heat flow regions, wherein the high heat flow region is a region of the to-be-tested brick type in which the heat flow intensity is greater than a threshold intensity in the heat flow load experiment; analyzing the heat flow intensity, region distribution position, and region volume of the plurality of high heat flow regions to obtain a first performance prediction value of the to-be-tested brick type; performing random hole adjustment on the to-be-tested brick type to obtain an adjusted brick type, and correcting the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a second performance prediction value of the to-be-tested brick type; the correcting the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a second performance prediction value of the to-be-tested brick type comprises: analyzing the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a prediction fluctuation coefficient; determining a correction coefficient according to the prediction fluctuation coefficient, wherein the prediction fluctuation coefficient and the correction coefficient are in a negative correlation relationship; correcting the first performance prediction value of the to-be-tested brick type according to the correction coefficient to obtain a second performance prediction value of the to-be-tested brick type, wherein the second performance prediction value is less than the first performance prediction value.

2. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 1, characterized by, analyzing the heat flow intensity, region distribution position, and region volume of the plurality of high heat flow regions to obtain a first performance prediction value of the to-be-tested brick type comprises: analyzing the heat flow intensity difference and the region volume difference between the plurality of high heat flow regions to obtain a first factor; analyzing the discrete degree of the region distribution position of the plurality of high heat flow regions to obtain a second factor; analyzing the total number and total volume of the plurality of high heat flow regions to obtain a third factor; obtaining the first performance prediction value of the to-be-tested brick type according to the first factor, the second factor, and the third factor.

3. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 2, characterized in that, analyzing the heat flow intensity difference and the region volume difference between the plurality of high heat flow regions to obtain a first factor comprises: in the plurality of high heat flow regions, analyzing the heat flow intensity difference between each high heat flow region and other high heat flow regions to obtain a plurality of intensity difference values corresponding to each high heat flow region; in the plurality of high heat flow regions, analyzing the region volume difference between each high heat flow region and other high heat flow regions to obtain a plurality of volume difference values corresponding to each high heat flow region; in the plurality of high heat flow regions, determining a characteristic difference value of each high heat flow region according to the plurality of intensity difference values and the plurality of volume difference values corresponding to each high heat flow region; determining the first factor according to the characteristic difference value of each high heat flow region.

4. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 3, characterized by, determining the first factor according to the characteristic difference value of each high heat flow region comprises: in the plurality of high heat flow regions, determining a weight coefficient of each high heat flow region according to a near-hole limit distance of each high heat flow region, wherein the near-hole limit distance is used to represent the shortest distance between the corresponding high heat flow region and a hole in the to-be-tested brick type, and the near-hole limit distance and the weight coefficient are in a negative correlation relationship; According to the weight coefficient of each high heat flow area, the characteristic difference values of the multiple high heat flow areas are weighted and calculated to obtain the first factor.

5. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 2, characterized by, The dispersion degree of the region distribution positions of the multiple high heat flow areas is analyzed to obtain a second factor, including: In the multiple high heat flow areas, the shortest interval distance between any two different high heat flow areas is analyzed to obtain multiple dispersion factors, wherein the dispersion factor is in a positive correlation with the shortest interval distance; The average value of the multiple dispersion factors is calculated to obtain the second factor.

6. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 2, characterized by, The total number and the total volume of the multiple high heat flow areas are analyzed to obtain a third factor, including: The ratio of the total number and the total volume of the multiple high heat flow areas is calculated to obtain the third factor.

7. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 1, characterized by, After the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type is used to correct the first performance prediction value of the to-be-tested brick type to obtain the second performance prediction value of the to-be-tested brick type, the method further includes: The to-be-tested insulation material is filled in the holes of the to-be-tested brick type, and the to-be-tested brick type after the hole filling is subjected to a heat flow load experiment to obtain a temperature change sequence of each hole in the to-be-tested brick type; The data fluctuation degree of the temperature change sequence of each hole in the to-be-tested brick type is analyzed to obtain a temperature change factor of each hole in the to-be-tested brick type; According to the temperature change factor of each hole in the to-be-tested brick type, a heat preservation performance prediction value of the to-be-tested brick type after the to-be-tested insulation material is filled is determined.

8. The finite element analysis-based prediction method of the heat transfer performance of a regenerative perforated brick according to claim 7, characterized in that, According to the temperature change factor of each hole in the to-be-tested brick type, a heat preservation performance prediction value of the to-be-tested brick type after the to-be-tested insulation material is filled is determined. The first isotherm data of the to-be-tested brick type obtained before the hole filling and the second isotherm data of the to-be-tested brick type obtained after the hole filling are acquired; The difference between the first isotherm data and the second isotherm data is analyzed to obtain a heat conduction factor; According to the temperature change factor of each hole in the to-be-tested brick type and the heat conduction factor, a heat preservation performance prediction value of the to-be-tested brick type after the to-be-tested insulation material is filled is determined.

9. A system for predicting the thermal conductivity of recycled perforated brick based on finite element analysis, characterized by, The system includes: A region identification module is configured to perform a heat flow load experiment on a to-be-tested brick type to determine multiple high heat flow areas, wherein the high heat flow area is a region of the to-be-tested brick type with a heat flow intensity greater than an intensity threshold value in the heat flow load experiment; A performance preliminary prediction module is configured to analyze the heat flow intensity, the region distribution position, and the region volume of the multiple high heat flow areas to obtain a first performance prediction value of the to-be-tested brick type; A prediction correction module is configured to adjust random holes of the to-be-tested brick type to obtain an adjusted brick type, and correct the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain a second performance prediction value of the to-be-tested brick type; The step of correcting the first performance prediction value of the to-be-tested brick type according to the difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type to obtain the second performance prediction value of the to-be-tested brick type includes: The difference between the first performance prediction value of the adjusted brick type and the first performance prediction value of the to-be-tested brick type is analyzed to obtain a prediction fluctuation coefficient; A correction coefficient is determined according to the prediction fluctuation coefficient, wherein the prediction fluctuation coefficient and the correction coefficient are in a negative correlation relationship; The first performance prediction value of the to-be-tested brick type is corrected according to the correction coefficient to obtain a second performance prediction value of the to-be-tested brick type, wherein the second performance prediction value is less than the first performance prediction value.

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