Thermal performance detection method and equipment for insulating fiber sleeve

Abstract

The invention discloses a thermal performance detection method of an insulating fiber sleeve, which comprises the following steps: cutting the insulating fiber sleeve into a sleeve sample according to a preset size, pre-embedding a micro temperature sensor in the sleeve sample, placing the sleeve sample in a temperature gradient experiment device, and detecting the thermal performance of the sleeve sample through the micro temperature sensor and an external thermocouple. Continuously acquiring time-temperature data; establishing a heat conduction mathematical model of the sleeve according to the acquired time-temperature data in combination with thermophysical parameters of a sleeve material, calculating thermal characteristic parameters of the sleeve, and evaluating the thermal performance of the insulating fiber sleeve so as to determine whether the insulating fiber sleeve meets a preset protection thermal performance requirement or not; by setting a temperature gradient detection mode, the temperature difference environment of the casing pipe in actual use is simulated, the temperature change endurance capacity and the thermal performance of the casing pipe are accurately evaluated, mathematical operation is performed by establishing a heat conduction mathematical model of the casing pipe, thermal characteristic parameters of the casing pipe are calculated, and the accuracy and comprehensiveness of a detection result are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to a method and equipment for detecting the thermal performance of an insulating fiber sleeve. Background Art

[0002] With the rapid development of the new energy vehicle industry, the safety and reliability of electric drive systems have become the focus of attention. As an important protective element for cables and wires, insulating fiber sleeves are widely used in new energy vehicles to isolate electrical components, prevent current leakage, and protect cables from the external environment. In the actual operation of new energy vehicles, insulating fiber sleeves must be able to withstand complex and changeable thermal environments, such as high temperature, low temperature, and rapid temperature changes. These thermal environments may have a significant impact on the physical properties and insulation properties of the sleeves. Therefore, accurate and effective testing of the thermal properties of insulating fiber sleeves is of great significance to ensure the safe operation of vehicles.

[0003] At present, the thermal performance test of insulating fiber casing mainly adopts the test method under a single temperature condition, such as evaluating the heat resistance of the casing in a constant high temperature environment, or testing its cracking performance under low temperature conditions. This test method can only reflect the performance of the casing at a specific temperature, and cannot simulate the impact of temperature gradients and rapid temperature changes on the casing in actual use. In addition, traditional test methods usually only focus on the surface temperature changes of the casing, lack monitoring of the internal temperature changes of the casing, and cannot fully obtain the thermal conductivity characteristics data of the casing. The limitations of these test methods make it impossible to accurately evaluate the temperature change tolerance and thermal performance of the insulating fiber casing in a complex thermal environment, and it is difficult to meet the high requirements of new energy vehicles for the thermal performance testing of the casing.

[0004] In view of this, it is necessary to improve the temperature detection technology of the insulating sleeve in the prior art to solve the technical problems of single detection data and incomplete test results. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for detecting the thermal properties of an insulating fiber sleeve to solve the above technical problems.

[0006] To achieve this object, the present invention adopts the following technical solutions: A method for detecting thermal properties of an insulating fiber sleeve, comprising: Providing an insulating fiber sleeve to be tested, cutting the insulating fiber sleeve into a sleeve sample according to a preset size, and pre-embedding a micro temperature sensor inside the sleeve to monitor the internal temperature change in real time; Placing the casing sample in a temperature gradient experimental device, applying an adjustable high-temperature heat source to one end of the casing sample, and maintaining the other end at a low temperature to form a preset temperature gradient; Under the action of the temperature gradient, the temperature changes inside and on the surface of the casing sample are monitored simultaneously by the micro temperature sensor and the external thermocouple to continuously acquire time-temperature data; According to the acquired time-temperature data and the thermophysical parameters of the casing material, a heat conduction mathematical model of the casing is established, and the thermal characteristic parameters of the equivalent thermal conductivity and thermal diffusivity of the casing are calculated; The thermal characteristic parameters are compared with standard thermal performance indicators to evaluate the thermal performance of the insulating fiber sleeve to determine whether it meets the preset protective thermal performance requirements.

[0007] Optionally, the temperature gradient experimental device comprises: A high temperature detection assembly comprises an outer tube body and a heating plate arranged on the outer tube body, wherein the heating plate is used to heat the casing sample; The low temperature detection component comprises a placement tube body, the placement tube body is provided with a through hole for the sleeve sample to pass through, the inner wall of the through hole is provided with an annular liquid cooling pipeline, and the side wall of the placement tube body is provided with a plurality of thermocouples.

[0008] Optionally, the outer tube body is provided with a plurality of adjusting bolts along its circumferential direction, one end of the adjusting bolt extends into the outer tube body and is connected to an abutment block, an inner wall of the abutment block is provided with an arc surface, and a positioning space for positioning the casing sample is formed between the plurality of abutment blocks; A plurality of heating plates are arranged in the outer tube body along the circumferential direction thereof, and an elastic member is arranged between the heating plate and the inner wall of the outer tube body, and the elastic member is used for pushing the heating plate to move inward.

[0009] Optionally, a ring gear and a ring guide rail are provided at one port of the outer tube body, a rotating plate is rotatably connected to the ring guide rail via a pulley, a thermocouple is provided on the rotating plate, and the detection end of the thermocouple is arranged inwardly; a driving motor is provided on the rotating plate, and a pinion is connected to the output shaft of the driving motor, and the pinion is meshingly connected to the ring gear.

[0010] Optionally, under the action of the temperature gradient, the temperature changes inside and on the surface of the casing sample are simultaneously monitored by the micro temperature sensor and the external thermocouple to continuously acquire time-temperature data, specifically including: Under the action of the temperature gradient, a synchronous data acquisition system is started to simultaneously acquire the micro temperature sensor data inside the casing sample and the thermocouple temperature data on the surface at a preset sampling frequency to form an original time-temperature data set; Performing data preprocessing on the original time-temperature data set, wherein the data preprocessing includes noise filtering, outlier removal and data smoothing to obtain an optimized time-temperature data set; The optimized time-temperature data set was synchronized along the time axis, and the internal and surface temperatures were analyzed to preliminarily evaluate the change of temperature gradients over time.

[0011] Optionally, the optimized time-temperature data set is synchronized according to the time axis, the internal temperature and the surface temperature are analyzed, and the change of the temperature gradient over time is preliminarily evaluated, specifically including: By calculating the temperature difference between adjacent temperature measurement points, the temperature gradient inside the casing sample is obtained, and the heat conduction inside the casing is analyzed; Analyze the temperature data of each temperature measurement point on the surface, and plot the optimized time-temperature data set into an actual time-temperature curve to show the change of temperature over time; Extract key characteristic parameters from the actual time-temperature curve: Calculate the rate of temperature rise or fall to obtain the temperature change rate; Identify the highest temperature and the lowest temperature in the actual time-temperature curve to obtain a temperature peak value; The temperature response time is obtained by measuring the time required for the temperature to reach the preset interval value.

[0012] Optionally, the heat conduction mathematical model of the sleeve is established based on the acquired time-temperature data and the thermophysical parameters of the sleeve material to calculate the thermal characteristic parameters of the equivalent thermal conductivity and thermal diffusivity of the sleeve, specifically including: Based on the optimized time-temperature data set, a one-dimensional unsteady-state heat conduction model is selected as the heat conduction model, and the material properties and geometric structure of the casing are integrated to establish a heat conduction mathematical model; Input known thermophysical parameters of the casing material into the heat conduction mathematical model; the known thermophysical parameters include specific heat capacity, density and initial thermal conductivity; The mathematical model of heat conduction is solved by using the numerical analysis method of the finite difference method, and combined with the optimized time-temperature data set, the model parameters are adjusted so that the output curve of the temperature changing with time output by the model is close to the actual time-temperature curve.

[0013] Optionally, the step of establishing a heat conduction mathematical model of the sleeve based on the acquired time-temperature data and combining the thermophysical parameters of the sleeve material to calculate the thermal characteristic parameters of the equivalent thermal conductivity and thermal diffusivity of the sleeve also includes: Through iterative optimization, the thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity in the model are adjusted until the fluctuation error between the output curve of the heat conduction mathematical model and the actual time-temperature curve is within a preset error range, thereby obtaining the thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity of the casing sample; The calculated thermal characteristic parameters are verified. During the verification process, the calculated thermal characteristic parameters are substituted into the heat conduction mathematical model, the temperature change process is simulated again, and compared with the actual time-temperature curve to verify the consistency of the calculated results.

[0014] Optionally, comparing the thermal characteristic parameter with a standard thermal performance index to evaluate the thermal performance of the insulating fiber sleeve to determine whether it meets a preset protective thermal performance requirement specifically includes: The calculated thermal characteristic parameters are matched with the pre-established standard thermal performance index database; According to the application scenario of the casing sample, determine the target thermal performance level to be achieved, and extract the corresponding standard thermal performance index from the standard thermal performance index database; Numerically comparing the thermal characteristic parameters of the casing sample with the extracted standard parameters to calculate the deviation value; Based on the calculated deviation value, evaluate whether the sample meets the requirements, and classify the casing samples according to the evaluation results.

[0015] Compared with the prior art, the present invention has the following beneficial effects: providing an insulating fiber sleeve to be tested, cutting it into sleeve samples according to preset sizes, and embedding a micro temperature sensor inside, placing the sleeve sample in a temperature gradient experimental device, applying an adjustable high-temperature heat source at one end, and maintaining the other end at a low temperature to form a preset temperature gradient environment, under the action of the temperature gradient, synchronously monitoring the temperature changes inside and on the surface of the sleeve sample through the internal micro temperature sensor and the external thermocouple, continuously acquiring time-temperature data, combining the thermal physical parameters of the sleeve material, establishing a thermal conduction mathematical model of the sleeve, calculating the thermal characteristic parameters, comparing with the standard thermal performance indicators, evaluating the thermal performance of the sleeve, and completing the detection; the method simulates the temperature difference environment of the sleeve in actual use by setting a temperature gradient detection mode, accurately evaluates its temperature change tolerance and thermal performance, and calculates the thermal characteristic parameters of the sleeve by establishing a thermal conduction mathematical model of the sleeve for mathematical operations, greatly improving the accuracy of the detection result, and providing a comprehensive and accurate thermal performance detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0018] Figure 1 Schematic diagram of the overall structural layout of the temperature gradient experimental device of this embodiment; Figure 2 It is a front view structural schematic diagram of the high temperature detection component of the temperature gradient experimental device of this embodiment; Figure 3 It is a side view structural schematic diagram of the high temperature detection component of the temperature gradient experimental device of this embodiment; Figure 4 This is a schematic structural diagram of a low-temperature detection assembly of a temperature gradient experimental device of this embodiment; Figure 5 This is one of the flow charts of the thermal performance detection method of this embodiment; Figure 6 This is the second flow chart of the thermal performance detection method of this embodiment.

[0019] Illustrations: high temperature detection component 100, outer tube body 101, heating plate 102, low temperature detection component 200, placement tube body 201, through hole 202, liquid cooling pipe 203, thermocouple 300, adjustment bolt 103, abutment block 104, arc surface 105, ring gear 106, ring guide rail 107, rotating plate 108, drive motor 109, pinion 110, elastic member 111. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be understood that the terms "upper", "lower", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. It should be noted that when a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a centrally arranged component at the same time.

[0022] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0023] Combination Figures 1 to 6 As shown, an embodiment of the present invention provides a method for detecting thermal properties of an insulating fiber sleeve, comprising: S1, providing an insulating fiber sleeve to be tested, cutting the insulating fiber sleeve into sleeve samples according to a preset size, and pre-embedding a micro temperature sensor inside the sleeve to monitor the internal temperature change in real time; Provide the insulating fiber sleeve to be tested and cut it into sleeve samples according to the preset size. In order to monitor the temperature changes inside the sleeve in real time, micro temperature sensors are pre-embedded inside the sleeve sample. These sensors can accurately capture the temperature dynamics inside the sleeve and provide reliable data support for subsequent temperature data collection. During the sample preparation process, it is necessary to ensure the accuracy of the cutting size and the correct installation position of the sensor to avoid affecting the thermal conductivity characteristics of the sleeve and the accuracy of the test results.

[0024] S2, placing the casing sample in a temperature gradient experimental device, applying an adjustable high-temperature heat source to one end of the casing sample, and keeping the other end at a low temperature to form a preset temperature gradient; The prepared casing sample is placed in a temperature gradient experimental device, and an adjustable high-temperature heat source is applied to one end of the casing sample to simulate a high-temperature environment; the other end is kept at a low temperature to simulate a low-temperature environment, forming a preset temperature gradient. By adjusting the temperature of the high-temperature heat source and controlling the conditions at the low-temperature end, temperature gradients of different magnitudes can be achieved to simulate extreme temperature conditions in special use scenarios of new energy vehicles. The importance of this step lies in simulating the temperature difference under actual working conditions and examining the thermal performance of the casing under a temperature gradient environment.

[0025] S3, under the action of temperature gradient, the temperature changes inside and on the surface of the casing sample are monitored simultaneously through the micro temperature sensor and the external thermocouple, and the time-temperature data are continuously acquired; Under the action of temperature gradient, the internal and surface temperature changes of the casing sample are monitored simultaneously by using the internal embedded micro temperature sensor and the externally arranged thermocouple. The time-temperature data is continuously acquired at a preset sampling frequency (such as 100 times per second) to form a synchronized original time-temperature data set. After preprocessing, an optimized time-temperature data set is obtained. Then, the optimized data set is analyzed and feature extracted, the internal temperature gradient is calculated, the surface temperature distribution is analyzed, the time-temperature change curve is drawn, and key characteristic parameters such as temperature change rate, temperature peak value and temperature response time are extracted.

[0026] S4, based on the acquired time-temperature data and in combination with the thermophysical parameters of the casing material, a heat conduction mathematical model of the casing is established, and thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity of the casing are calculated; Based on the optimized time-temperature data set obtained in step S3, a suitable heat conduction model (such as a one-dimensional non-steady-state heat conduction model) is selected, and the heat conduction mathematical model of the sleeve is established in combination with the material properties and geometric structure of the sleeve. The known thermophysical parameters of the sleeve material (including specific heat capacity, density and initial thermal conductivity) are input into the model. The heat conduction mathematical model is solved by a numerical analysis method (such as a finite difference method). By adjusting the model parameters, the temperature curve calculated by the model over time is consistent with the experimentally measured time-temperature curve. Iterative optimization is performed to adjust thermal characteristic parameters such as equivalent thermal conductivity and thermal diffusivity until the error between the model output and the actual data is within the preset error range. Finally, key thermal characteristic parameters such as equivalent thermal conductivity and thermal diffusivity of the sleeve sample are obtained. These parameters accurately reflect the thermal conductivity performance of the sleeve and provide a basis for thermal performance evaluation.

[0027] S5, compare the thermal characteristic parameters with standard thermal performance indicators to evaluate the thermal performance of the insulating fiber sleeve to determine whether it meets the preset protective thermal performance requirements.

[0028] Compare the calculated thermal characteristic parameters of the sleeve with the preset standard thermal performance indicators. Standard thermal performance indicators are formulated according to industry standards or specific application requirements, and include key parameters such as the upper limit of thermal conductivity and the lower limit of thermal diffusivity. During the comparison process, evaluate whether the equivalent thermal conductivity of the sleeve is lower than the upper limit of the standard (to ensure that the material has good thermal insulation properties), and whether the thermal diffusivity is higher than the lower limit of the standard (to ensure that the material can quickly and evenly disperse heat). If all thermal characteristic parameters meet or exceed the standard indicators, the thermal performance of the sleeve is judged to be qualified and meet the preset protective thermal performance requirements, and can be applied to actual production and use. If there are parameters that do not meet the standard requirements, the thermal performance is judged to be unqualified, and the reasons need to be analyzed and improvement suggestions are made.

[0029] The working principle of the present invention is as follows: providing an insulating fiber sleeve to be tested, cutting it into sleeve samples according to a preset size, and embedding a micro temperature sensor inside, placing the sleeve sample in a temperature gradient experimental device, applying an adjustable high-temperature heat source at one end, and maintaining the other end at a low temperature to form a preset temperature gradient environment, under the action of the temperature gradient, synchronously monitoring the temperature changes inside and on the surface of the sleeve sample through the internal micro temperature sensor and the external thermocouple, continuously acquiring time-temperature data, and establishing a thermal conduction mathematical model of the sleeve in combination with the thermal physical parameters of the sleeve material, calculating the thermal characteristic parameters, comparing with the standard thermal performance indicators, evaluating the thermal performance of the sleeve, and completing the detection; the method simulates the temperature difference environment of the sleeve in actual use by setting a temperature gradient detection mode, accurately evaluating its temperature change tolerance and thermal performance, and calculating the thermal characteristic parameters of the sleeve by establishing a thermal conduction mathematical model of the sleeve for mathematical operations, thereby greatly improving the accuracy of the detection result and providing a comprehensive and accurate thermal performance detection method.

[0030] In this embodiment, it is specifically described that the temperature gradient experimental device includes: The high temperature detection assembly 100 includes an outer tube body 101 and a heating plate 102 disposed on the outer tube body 101 . The heating plate 102 is used to heat the sleeve sample.

[0031] The low temperature detection assembly 200 includes a placement tube body 201 . The placement tube body 201 is provided with a through hole 202 for the sleeve sample to pass through. The inner wall of the through hole 202 is provided with an annular liquid cooling pipe 203 . A plurality of thermocouples 300 are provided on the side wall of the placement tube body 201 .

[0032] It should be noted that the temperature gradient experimental device includes a high temperature detection component 100 and a low temperature detection component 200, which work together to form a preset temperature gradient. The high temperature detection component 100 is composed of an outer tube body 101 and a heating plate 102 arranged on the outer tube body 101. The heating plate 102 is used to apply an adjustable high temperature heat source to one end of the sleeve sample to accurately control the temperature of the high temperature end. The low temperature detection component 200 is composed of a placement tube body 201. The placement tube body 201 is provided with a through hole 202 for the sleeve sample to pass through. The inner wall of the through hole 202 is provided with an annular liquid cooling pipe 203, and the temperature of the low temperature end is maintained stable by circulating coolant. Several thermocouples 300 are arranged on the side wall of the placement tube body 201 for real-time monitoring of the temperature change of the low temperature end.

[0033] In this embodiment, it is further explained that the outer tube body 101 is provided with a plurality of adjusting bolts 103 along its circumferential direction, one end of the adjusting bolt 103 extends into the outer tube body 101 and is connected with an abutment block 104, the inner wall of the abutment block 104 is provided with an arc surface 105, and a positioning space for positioning the casing sample is formed between the plurality of abutment blocks 104; a plurality of heating plates 102 are provided in the outer tube body 101 along its circumferential direction, and an elastic member 111 is provided between the heating plate 102 and the inner wall of the outer tube body 101, and the elastic member 111 is used to push the heating plate 102 to move inward. When working, the casing sample is fixed by the joint action of the plurality of abutment blocks 104, and the heating plate 102 is in close contact with the outer surface of the casing sample under the action of the elastic member 111.

[0034] In the present embodiment, it is further described that a ring gear 106 and a ring guide rail 107 are provided at one end of the outer tube body 101, a rotating plate 108 is rotatably connected to the ring guide rail 107 via a pulley, the rotating plate 108 is provided with a thermocouple 300, and the detection end of the thermocouple 300 is arranged inwardly; a driving motor 109 is provided on the rotating plate 108, and the output shaft of the driving motor 109 is connected to a pinion 110, and the pinion 110 is meshingly connected to the ring gear 106.

[0035] It should be noted that, in this embodiment, the method of collecting temperature data is innovated, and the thermocouple 300 is designed to be annular and rotatable. When working, the driving motor 109 drives the pinion 110 to rotate, thereby driving the rotating plate 108 to rotate around the casing sample along the annular guide rail 107, so as to realize three-dimensional temperature sampling of the casing sample. This design can obtain comprehensive surface temperature data in the circumferential direction of the casing sample, capture the temperature distribution difference caused by material anisotropy or structural unevenness, and provide more comprehensive data support for the subsequent establishment of the heat conduction mathematical model and the accurate calculation of the thermal characteristic parameters.

[0036] In this embodiment, it is specifically described that step S3 specifically includes: S31, under the action of the temperature gradient, start the synchronous data acquisition system to simultaneously obtain the micro temperature sensor data inside the casing sample and the thermocouple temperature data on the surface at a preset sampling frequency to form an original time-temperature data set; synchronous acquisition ensures that the internal and surface temperature data correspond on the same time axis, making subsequent data analysis more accurate.

[0037] S32, performing data preprocessing on the original time-temperature data set, the data preprocessing includes noise filtering, outlier removal and data smoothing, to obtain an optimized time-temperature data set; thereby helping to eliminate data acquisition errors.

[0038] S33, synchronizing the optimized time-temperature data set along the time axis, analyzing the internal temperature and the surface temperature, and preliminarily evaluating the change of the temperature gradient over time.

[0039] In this embodiment, it is specifically explained that step S33 specifically includes: S331, obtaining the temperature gradient inside the casing sample by calculating the temperature difference between adjacent temperature measurement points, and analyzing the heat conduction inside the casing; The temperature gradient inside the casing sample is obtained by calculating the temperature difference between adjacent internal temperature measurement points. Specifically, the temperature difference between each micro temperature sensor and its adjacent sensor is calculated using the optimized time-temperature data set to obtain the temperature gradient at each position. These temperature gradients reflect the conduction of heat inside the casing, and can analyze the thermal conductivity characteristics of the casing material and the uniformity of internal heat conduction, which helps to evaluate the thermal performance of the casing and identify possible thermal resistance locations.

[0040] S332, analyze the temperature data of each temperature measuring point on the surface, and plot the optimized time-temperature data set into an actual time-temperature curve to show the change of temperature over time; through the curve, the trend of temperature change, fluctuation and uniformity of temperature distribution can be intuitively observed.

[0041] S333, extract key characteristic parameters from the actual time-temperature curve: Calculate the rate of temperature rise or fall to obtain the temperature change rate; this parameter reflects the response speed of the casing material to temperature changes and embodies the thermal inertia characteristics of the material.

[0042] Identify the highest and lowest temperatures in the actual time-temperature curve to obtain temperature peaks; these peak temperatures help evaluate the bushing's ability to withstand extreme temperature conditions and determine whether it is at risk of overheating or overcooling.

[0043] The temperature response time is obtained by measuring the time required for the temperature to reach the preset interval value. This parameter describes the time required for the sleeve material to reach a specific temperature from the initial temperature, reflecting the efficiency of the material in absorbing or releasing heat. In this embodiment, it is specifically described that step S4 specifically includes: S41, based on the optimized time-temperature data set, a one-dimensional unsteady-state heat conduction model is selected as the heat conduction model, and the material properties and geometric structure of the casing are integrated to establish a heat conduction mathematical model; Based on the optimized time-temperature data set obtained above, a one-dimensional non-steady-state heat conduction model is selected as the heat conduction model. This model is suitable for describing the heat conduction process along a single direction, which is consistent with the aspect ratio characteristics of the casing sample and simplifies the complex three-dimensional heat conduction problem. Considering the material properties (such as thermal conductivity, specific heat capacity, density, etc.) and geometric structure (such as wall thickness, length, etc.) of the casing, a corresponding heat conduction mathematical model is established, which describes the heat transfer law in the casing material in the form of partial differential equations.

[0044] S42, inputting known thermophysical parameters of the casing material into a heat conduction mathematical model; the known thermophysical parameters include specific heat capacity, density and initial thermal conductivity; The known thermophysical parameters of the casing material are input into the heat conduction mathematical model. These parameters include specific heat capacity (the ability of the material to absorb heat), density (the mass per unit volume of the material) and initial thermal conductivity (the ability of the material to conduct heat). Inputting these known parameters can make the heat conduction model more accurately reflect the actual performance of the casing material and provide the necessary basic data for subsequent numerical solutions.

[0045] S43, the numerical analysis method of finite difference method is used to solve the mathematical model of heat conduction, and combined with the optimized time-temperature data set, the model parameters are adjusted to make the output curve of the temperature changing with time of the model output close to the actual time-temperature curve.

[0046] S44, adjusting the thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity in the model through iterative optimization until the fluctuation error between the output curve of the heat conduction mathematical model and the actual time-temperature curve is within a preset error range, thereby obtaining the thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity of the casing sample; Further iterative optimization is performed, focusing on adjusting the thermal characteristic parameters such as equivalent thermal conductivity and thermal diffusivity in the model. By continuously adjusting these parameters and calculating the error between the model output and the actual data in each iteration, the gap between the two is gradually narrowed. The iterative process continues until the fluctuation error between the output curve of the heat conduction mathematical model and the actual time-temperature curve is within the preset error range. At this point, it is considered that the model parameters have converged, and the equivalent thermal conductivity and thermal diffusivity obtained accurately reflect the thermal conduction characteristics of the casing sample.

[0047] S45, verifying the calculated thermal characteristic parameters. During the verification process, the calculated thermal characteristic parameters are substituted into the heat conduction mathematical model, the temperature change process is simulated again, and compared with the actual time-temperature curve to verify the consistency of the calculated results.

[0048] In this embodiment, it is specifically described that step S5 specifically includes: S51, matching the calculated thermal characteristic parameters with a pre-established standard thermal performance index database; The calculated thermal characteristic parameters of the bushing sample (such as equivalent thermal conductivity, thermal diffusivity, etc.) are matched with the pre-established standard thermal performance index database. This database contains standard thermal performance indicators of various materials in different application scenarios, which can be used as a reference for evaluating the thermal performance of bushing samples. The purpose of this step is to screen out the benchmark values ​​related to the sample from the massive standard data to ensure the accuracy of the evaluation results.

[0049] S52, according to the application scenario of the casing sample, such as the temperature range to be borne and the complexity of the thermal environment, determine the target thermal performance level to be achieved, and extract the corresponding standard thermal performance index from the standard thermal performance index database.

[0050] S53, numerically comparing the thermal characteristic parameters of the casing sample with the extracted standard parameters, and calculating the deviation value; The thermal characteristic parameters of the bushing sample are numerically compared with the extracted standard thermal performance indicators. Specifically, for each thermal characteristic parameter, the deviation between the sample parameter value and the standard indicator value is calculated. The deviation value can be calculated as an absolute difference or a relative error (such as a percentage error). Through numerical comparison, the gap between the thermal performance of the sample and the standard requirements is quantified. This step provides an objective quantitative basis for the evaluation and helps to accurately determine whether the sample has achieved the expected thermal performance level.

[0051] S54, evaluating whether the sample meets the requirements according to the calculated deviation value, and classifying the casing sample according to the evaluation result.

[0052] The process of evaluating whether the sample meets the requirements specifically includes: Allowable error range: Determined according to industry standards or design requirements. For example, a deviation within ±5% is considered acceptable.

[0053] Evaluation conclusion: Clearly indicate whether the sample meets the preset thermal protection performance requirements, and record the unqualified items for subsequent improvement.

[0054] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting thermal properties of an insulating fiber sleeve, characterized in that: include: Providing an insulating fiber sleeve to be tested, cutting the insulating fiber sleeve into a sleeve sample according to a preset size, and pre-embedding a micro temperature sensor inside the sleeve to monitor the internal temperature change in real time; The casing sample is placed in a temperature gradient experimental device, an adjustable high-temperature heat source is applied to one end of the casing sample, and the other end is kept at a low temperature condition to form a preset temperature gradient; Under the action of the temperature gradient, the temperature changes inside and on the surface of the casing sample are monitored simultaneously by the micro temperature sensor and the external thermocouple to continuously acquire time-temperature data; According to the acquired time-temperature data and the thermophysical parameters of the casing material, a heat conduction mathematical model of the casing is established, and the thermal characteristic parameters of the equivalent thermal conductivity and thermal diffusivity of the casing are calculated; The thermal characteristic parameters are compared with standard thermal performance indicators to evaluate the thermal performance of the insulating fiber sleeve to determine whether it meets the preset protective thermal performance requirements.

2. The thermal performance detection method of the insulating fiber sleeve according to claim 1 is characterized in that: The temperature gradient experimental device comprises: A high temperature detection assembly comprises an outer tube body and a heating plate arranged on the outer tube body, wherein the heating plate is used to heat the sleeve sample; The low temperature detection component comprises a placement tube body, the placement tube body is provided with a through hole for the sleeve sample to pass through, the inner wall of the through hole is provided with an annular liquid cooling pipeline, and the side wall of the placement tube body is provided with a plurality of thermocouples.

3. The thermal performance detection method of the insulating fiber sleeve according to claim 2 is characterized in that: The outer tube body is provided with a plurality of adjusting bolts along its circumferential direction, one end of the adjusting bolt extends into the outer tube body and is connected with an abutment block, the inner wall of the abutment block is provided with an arc surface, and a positioning space for positioning the casing sample is formed between the plurality of abutment blocks; A plurality of heating plates are arranged in the outer tube body along the circumferential direction thereof, and an elastic member is arranged between the heating plate and the inner wall of the outer tube body, and the elastic member is used for pushing the heating plate to move inward.

4. The thermal performance detection method of the insulating fiber sleeve according to claim 3 is characterized in that: A ring gear and a ring guide are provided at one end of the outer tube body, a rotating plate is rotatably connected to the ring guide rail via a pulley, a thermocouple is provided on the rotating plate, and the detection end of the thermocouple is arranged inwardly; a driving motor is provided on the rotating plate, and a pinion is connected to the output shaft of the driving motor, and the pinion is meshed with the ring gear.

5. The thermal performance detection method of the insulating fiber sleeve according to claim 1, characterized in that: Under the action of the temperature gradient, the temperature changes inside and on the surface of the casing sample are monitored simultaneously by the micro temperature sensor and the external thermocouple, and time-temperature data are continuously acquired, specifically including: Under the action of the temperature gradient, a synchronous data acquisition system is started to simultaneously acquire the micro temperature sensor data inside the casing sample and the thermocouple temperature data on the surface at a preset sampling frequency to form an original time-temperature data set; Performing data preprocessing on the original time-temperature data set, wherein the data preprocessing includes noise filtering, outlier removal and data smoothing to obtain an optimized time-temperature data set; The optimized time-temperature data set was synchronized along the time axis, and the internal and surface temperatures were analyzed to preliminarily evaluate the change of temperature gradients over time.

6. The thermal performance detection method of the insulating fiber sleeve according to claim 5, characterized in that: The optimized time-temperature data set is synchronized according to the time axis, the internal temperature and the surface temperature are analyzed, and the change of the temperature gradient over time is preliminarily evaluated, including: By calculating the temperature difference between adjacent temperature measurement points, the temperature gradient inside the casing sample is obtained, and the heat conduction inside the casing is analyzed; Analyze the temperature data of each temperature measurement point on the surface, and plot the optimized time-temperature data set into an actual time-temperature curve to show the change of temperature over time; Extract key characteristic parameters from the actual time-temperature curve: Calculate the rate of temperature rise or fall to obtain the temperature change rate; Identify the highest temperature and the lowest temperature in the actual time-temperature curve to obtain a temperature peak value; The temperature response time is obtained by measuring the time required for the temperature to reach the preset interval value.

7. The thermal performance detection method of the insulating fiber sleeve according to claim 6, characterized in that: The method of establishing a heat conduction mathematical model of the sleeve based on the acquired time-temperature data and combining the thermophysical parameters of the sleeve material to calculate the thermal characteristic parameters of the equivalent thermal conductivity and thermal diffusivity of the sleeve specifically includes: Based on the optimized time-temperature data set, a one-dimensional unsteady-state heat conduction model is selected as the heat conduction model, and the material properties and geometric structure of the casing are integrated to establish a heat conduction mathematical model; Input known thermophysical parameters of the casing material into the heat conduction mathematical model; the known thermophysical parameters include specific heat capacity, density and initial thermal conductivity; The mathematical model of heat conduction is solved by using the numerical analysis method of the finite difference method, and combined with the optimized time-temperature data set, the model parameters are adjusted so that the output curve of the temperature changing with time output by the model is close to the actual time-temperature curve.

8. The thermal performance detection method of the insulating fiber sleeve according to claim 7, characterized in that: The method of establishing a heat conduction mathematical model of the sleeve based on the acquired time-temperature data and combining the thermophysical parameters of the sleeve material to calculate the thermal characteristic parameters of the equivalent thermal conductivity and thermal diffusivity of the sleeve also includes: Through iterative optimization, the thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity in the model are adjusted until the fluctuation error between the output curve of the heat conduction mathematical model and the actual time-temperature curve is within a preset error range, thereby obtaining the thermal characteristic parameters of equivalent thermal conductivity and thermal diffusivity of the casing sample; The calculated thermal characteristic parameters are verified. During the verification process, the calculated thermal characteristic parameters are substituted into the heat conduction mathematical model, the temperature change process is simulated again, and compared with the actual time-temperature curve to verify the consistency of the calculated results.

9. The thermal performance detection method of the insulating fiber sleeve according to claim 1, characterized in that: The step of comparing the thermal characteristic parameters with standard thermal performance indicators and evaluating the thermal performance of the insulating fiber sleeve to determine whether it meets the preset protective thermal performance requirements specifically includes: The calculated thermal characteristic parameters are matched with the pre-established standard thermal performance index database; According to the application scenario of the casing sample, determine the target thermal performance level to be achieved, and extract the corresponding standard thermal performance index from the standard thermal performance index database; Numerically comparing the thermal characteristic parameters of the casing sample with the extracted standard parameters to calculate the deviation value; Based on the calculated deviation value, evaluate whether the sample meets the requirements, and classify the casing samples according to the evaluation results.

Images