A method for testing heat transfer coefficient of elevator shaft structure on site

Abstract

The application discloses a kind of elevator shaft structure heat transfer coefficient field test methods, belong to elevator shaft heat transfer performance field detection technical field, including using finite element fluid simulation to establish three-dimensional heat transfer model containing block and mortar joint, determine optimization sensor arrangement scheme by parameterization scanning;Build virtual instrument test platform to synchronously collect temperature and heat flow data;Establish three-dimensional four thermal resistance transient heat transfer model, to solve internal temperature field by finite volume method;Adopt particle swarm algorithm to inverse identification the thermal conductivity of block and mortar joint;Utilize multilayer series thermal resistance formula to calculate comprehensive heat transfer coefficient and generate report, solve the test misalignment problem caused by ignoring thermal bridge effect and sensor installation deviation in prior art, can accurately determine the actual thermal insulation performance of elevator shaft wall in non-steady-state environment within a few hours.

Description

Technical Field

[0001] This invention belongs to the field of on-site testing technology for the heat transfer performance of elevator shafts, and specifically relates to an on-site testing method for the heat transfer coefficient of elevator shaft structures. Background Technology

[0002] As a vertical passageway connecting the upper and lower levels of a building, the elevator shaft generates heat primarily from three sources: first, the traction machine, frequency converter, and other drive and control components continuously dissipate heat due to energy loss during frequent start-stop cycles; second, high external temperatures in summer penetrate the shaft through heat conduction from the shaft walls, solar radiation, and gaps in landing doors; and third, the "chimney effect" caused by the shaft's high degree of enclosure, leading to a rapid rise of hot air from the lower levels and severe heat accumulation in the upper areas. Although the shaft itself does not actively generate heat, its extremely poor ventilation and heat dissipation conditions allow this heat to easily accumulate, creating a high-temperature environment far exceeding outdoor temperatures. This accelerates the aging of electrical components and increases the risk of equipment failure. Therefore, accurately measuring the heat transfer coefficient of the shaft's enclosure structure is a crucial prerequisite for assessing its insulation performance, guiding energy-saving renovations, and ensuring the safe operation of elevators.

[0003] However, existing on-site testing methods for the heat transfer coefficient of building envelopes, such as the heat flow meter method and the hot box method, have significant technical flaws when applied to elevator shafts, leading to seriously inaccurate test results. The fundamental reason for this is:

[0004] First, traditional methods are all based on the assumption of one-dimensional heat transfer for data analysis, ignoring the significant thermal bridging effect caused by the difference in thermal properties between the masonry blocks and the mortar joint materials in the shaft wall. This three-dimensional non-uniform heat flow transfer characteristic is incorrectly averaged, resulting in an essential deviation between the measured heat transfer coefficient and the actual insulation capacity of the shaft, and low data reliability.

[0005] Second, traditional methods lack specific settings for the non-uniform structure of mortar joints in masonry blocks when installing sensors. The installation positions and angles of thermocouples and heat flow meters mostly rely on experience, without considering the interference of flowing air caused by the chimney effect in the well shaft on the temperature measurement accuracy, or the impact of local heat flow distortion caused by thermal bridges in the mortar joints on heat flow measurement. The installation and measurement deviations introduced by these factors further seriously reduce the test accuracy.

[0006] Therefore, there is a need for an on-site testing method for the heat transfer coefficient of elevator shafts that can accurately reflect the thermal bridging effect of the mortar joint structure and eliminate measurement deviations caused by improper installation schemes, so as to accurately evaluate the actual thermal insulation performance of elevator shafts. Summary of the Invention

[0007] This invention provides a field testing method for the heat transfer coefficient of elevator shaft structures, which solves the technical problem in the prior art that the heat transfer coefficient test results have large deviations and cannot truly reflect the thermal insulation performance of the shaft due to neglecting the thermal bridging effect of the mortar joints of the masonry blocks and improper sensor installation schemes.

[0008] In view of the above problems, the technical solution proposed by the present invention is as follows:

[0009] This invention provides a method for on-site testing of the heat transfer coefficient of an elevator shaft structure, comprising the following steps:

[0010] S1. A three-dimensional mathematical and physical heat transfer model A of the shaft wall, including the block area and the mortar joint area, is established using finite element simulation. Wind speed boundary conditions simulating the chimney effect are applied to the inside of the shaft. Measurement deviations under different sensor installation schemes are analyzed by parametric scanning to determine the optimized sensor layout scheme.

[0011] S2, according to the optimized sensor arrangement scheme, temperature sensors and heat flow sensors are installed on the indoor side and the shaft side of the shaft wall, respectively, and temperature data B and heat flow data C are collected synchronously through the test platform;

[0012] S3, establish a three-dimensional four-thermal-resistance transient heat transfer analysis model H of the well wall, at least take the block thermal conductivity D1 and the mortar joint thermal conductivity D2 as parameters to be identified, and use the temperature data B as the time-varying boundary condition, and use the finite volume method to solve the internal temperature field E of the wall.

[0013] S4. Using the particle swarm optimization algorithm, the thermal conductivity of the block D1 and the thermal conductivity of the mortar joint D2 are used as optimization variables. The objective function is constructed by the deviation between the measured temperature and the calculated temperature at the corresponding position in the internal temperature field E. Iterative inversion is performed to obtain the identification value of the thermal conductivity of the block D10 and the thermal conductivity of the mortar joint D20.

[0014] S5. Based on the identification value D10 of the thermal conductivity of the masonry block and the identification value D20 of the thermal conductivity of the mortar joint, and combined with the thickness of each layer of the wall, the comprehensive heat transfer coefficient G of the elevator shaft wall is calculated using the thermal resistance series formula.

[0015] As a preferred technical solution of the present invention, the optimized sensor arrangement scheme in step S1 includes: installing the heat flow sensor at the geometric center of the block in a direction perpendicular to the mortar joint, and installing the temperature sensor at the center of the block surface and the surface of the mortar joint respectively.

[0016] As a preferred embodiment of the present invention, in step S2, the temperature sensor is a pre-embedded armored thermocouple, which is installed by drilling and filling with high thermal conductivity mortar, and the heat flow sensor is a thermopile heat flow meter, which is attached to the wall by thermally conductive silicone grease and a mechanical clamping device.

[0017] As a preferred technical solution of the present invention, the three-dimensional four-thermal-resistance transient heat transfer analysis model H established in step S3 is constructed based on the four-thermal-resistance structure of the inner plaster layer, block layer, mortar joint layer and outer plaster layer on the elevator shaft wall. The three-dimensional four-thermal-resistance transient heat transfer analysis model H sets thermal resistance nodes along the wall thickness direction and the horizontal and vertical directions of the plane, and introduces the temperature derivative with respect to time into the heat balance equation of each thermal resistance node to describe the three-dimensional heat flow transfer process inside the shaft wall under unsteady boundary conditions.

[0018] As a preferred technical solution of the present invention, the parameters to be identified in step S3 also include the well-side convective heat transfer coefficient D3 and the indoor-side convective heat transfer coefficient D4. The particle swarm algorithm uses the block thermal conductivity D1, the mortar joint thermal conductivity D2, the well-side convective heat transfer coefficient D3, and the indoor-side convective heat transfer coefficient D4 as optimization variables for joint identification.

[0019] As a preferred technical solution of the present invention, when solving step S3 using the finite volume method, the measured temperatures of the indoor side and shaft side surfaces in temperature data B are used as time-varying temperature boundary conditions, and the initial temperature field of each thermal resistance node is determined by interpolation based on the measured temperature at the start of the test.

[0020] As a preferred technical solution of the present invention, the objective function of the particle swarm algorithm in step S4 is the root mean square error between the measured temperatures collected from the measuring points on the block surface and the mortar joint surface and the corresponding temperatures on the block surface and the mortar joint surface calculated by the three-dimensional four-thermal-resistance transient heat transfer analysis model H.

[0021] As a preferred embodiment of the present invention, step S5, in calculating the overall heat transfer coefficient G, is as follows:

[0022] S51, based on the area ratio of blocks and mortar joints on the wall surface, the identification value of thermal conductivity of blocks D10 and the identification value of thermal conductivity of mortar joints D20 are converted into the equivalent thermal resistance of the block-mortar joint composite layer according to the parallel thermal resistance relationship.

[0023] S52, then summed in series with the thermal resistance of the remaining material layers and the surface heat transfer resistance to obtain the total thermal resistance, and the reciprocal of the total thermal resistance is used as the comprehensive heat transfer coefficient G.

[0024] As a preferred embodiment of the present invention, the hardware of the test platform includes data acquisition and signal conditioning hardware, which is used to condition and synchronously acquire the signals output by the temperature sensor and the heat flow sensor. The hardware is placed in an industrial control box outside the well.

[0025] As a preferred embodiment of the present invention, the software modules of the test platform include a data acquisition module, a report generation module, and a calculation engine module. The data acquisition module is used to set sampling parameters and store data, the report generation module is used to generate test reports, and the calculation engine module is used to perform finite element simulation, finite volume method solution, and particle swarm optimization algorithm inversion.

[0026] Compared with the prior art, the beneficial effects of the above-mentioned technical solutions provided by the embodiments of the present invention include at least the following:

[0027] (1) This invention utilizes finite element fluid simulation technology to establish a three-dimensional mathematical and physical heat transfer model that includes the block area and the mortar joint area, and analyzes the measurement deviation under different installation positions and angles of thermocouples and heat flow meters. This allows for the determination of an optimized sensor arrangement scheme. This scheme eliminates the local heat flow distortion error caused by the thermal bridge effect of the block mortar joint structure, as well as the measurement deviation introduced by the random installation of sensors, ensuring the spatial representativeness of the collected data and providing a reliable data foundation for the solution.

[0028] (2) This invention establishes a transient thermal analysis model that includes the block area and the mortar joint area, and identifies the thermal conductivity of the block and the mortar joint as independent variables. It abandons the traditional method of treating the entire wall as a homogeneous material in a one-dimensional simplification. This method can accurately quantify the real contribution of the block and the mortar joint to heat transfer, eliminate the fundamental error caused by the averaging of the thermal bridge effect, and make the final calculated comprehensive heat transfer coefficient truly reflect the three-dimensional non-uniform thermal insulation performance of the elevator shaft structure.

[0029] The installation scheme, which uses pre-embedded armored thermocouples with thermally conductive putty for sealing and embedded heat flow meters with mechanical clamping devices, eliminates the interference of flowing air in the wellbore on temperature measurement accuracy and the contact thermal resistance error caused by rough wall surfaces, thereby further reducing the measurement uncertainty introduced by installation deviations.

[0030] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating a field testing method for the heat transfer coefficient of an elevator shaft structure disclosed in this invention.

[0032] Figure 2 This is a test platform block diagram of an on-site test method for the heat transfer coefficient of an elevator shaft structure disclosed in this invention;

[0033] Figure reference numerals: 100, Test platform; 11, Data acquisition module; 12, Waveform display module; 13, Report generation module; 14, Calculation engine module; 14a, Finite element parameterized scanning unit; 14b, Finite volume method transient solver unit; 14c, Particle swarm optimization algorithm; 21, Sensor network; 22, PXI bus chassis; 23, Data acquisition card; 24, Embedded controller; 25, Signal transmission and conditioning module; 251, Shielded compensation wire; 252, Thermocouple signal conditioning unit; 253, General strain / voltage signal conditioning unit; 254, Heat flow meter signal conditioning unit;

[0034] A. Three-dimensional mathematical physics heat transfer model; B. Temperature data; C. Heat flow data; D1. Thermal conductivity of the block; D2. Thermal conductivity of the mortar joint; D3. Convective heat transfer coefficient on the shaft side; D4. Convective heat transfer coefficient on the indoor side; D10. Identified value of thermal conductivity of the block; D20. Identified value of thermal conductivity of the mortar joint; E. Internal temperature field; G. Comprehensive heat transfer coefficient; H. Three-dimensional four-thermal-resistance transient heat transfer analysis model. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] The core objective of this invention is to solve the technical problems of long test cycles, averaged thermal bridge effects, and inability to quantify sensor installation deviations caused by the traditional heat flow meter method and hot box method in elevator shaft scenarios due to the assumptions of homogeneous one-dimensional heat transfer and steady-state premise.

[0037] Specifically, this invention quantifies sensor installation deviation by establishing a three-dimensional simulation model including the block area and the mortar joint area, eliminates contact thermal resistance and air interference by using pre-embedded sensors with thermally conductive mortar and mechanical clamping devices, constructs a three-dimensional four-thermal-resistance transient heat transfer analysis model H to describe unsteady heat flow transfer, and uses particle swarm optimization algorithm to independently invert and identify the thermal conductivity of the block and mortar joint, ultimately achieving accurate on-site measurement of the heat transfer coefficient of the elevator shaft structure under the unsteady thermal environment caused by the chimney effect.

[0038] Next, specific embodiments will be used to illustrate the solution in detail:

[0039] In this embodiment of the invention, a method for on-site testing of the heat transfer coefficient of an elevator shaft structure is provided, such as... Figure 2As shown, the test platform 100 includes a sensor network 21, a signal transmission and conditioning module 25, and a software module integrated inside the test platform 100. The sensor network 21 consists of multiple sets of pre-embedded armored thermocouples and embedded thermopile heat flow meters, which are arranged on the indoor side of the shaft wall and the side surface of the shaft according to the optimized scheme.

[0040] The signal transmission and conditioning module 25 includes a shielded compensation wire 251, a thermocouple signal conditioning unit 252, and a heat flow meter signal conditioning unit 254. The shielded compensation wire 251 is used to transmit thermocouple signals and suppress electromagnetic interference. The thermocouple signal conditioning unit 252 is used to perform cold junction compensation and amplification on the thermocouple signals. The heat flow meter signal conditioning unit 254 is used to perform low-pass filtering and amplification on the microvolt signal output by the heat flow meter.

[0041] The hardware of the test platform 100 is placed in an industrial control box outside the well. The industrial control box integrates a PXI bus chassis 22, an embedded controller 24, a data acquisition card 23, a thermocouple signal conditioning unit 252, and a general strain / voltage signal conditioning unit 253. The PXI bus chassis 22 provides a bus for modular instruments, the embedded controller 24 is responsible for running software modules and controlling the data acquisition process, the data acquisition card 23 performs multi-channel synchronous analog-to-digital conversion, and the thermocouple signal conditioning unit 252 and the general strain / voltage signal conditioning unit 253 are used for conditioning thermocouple and heat flow meter signals, respectively.

[0042] The software portion of the test platform 100 is written based on a virtual instrument development environment, and communicates and controls with the PXI bus chassis 22 through an interface. The test platform 100 has the following functional modules:

[0043] Data acquisition module 11 is used to set sampling frequency, channel configuration, range selection and binary data storage path, and provides calibration and channel status alarm functions;

[0044] The waveform display module 12 is used to draw the temperature time history curve and heat flow time history curve of each measuring point in real time. It supports curve scaling, cursor reading and outlier marking, so that testers can monitor data quality.

[0045] The report generation module 13 is used to automatically draw a schematic diagram of the test point layout, a temperature time history curve of each test point, calculate the comprehensive heat transfer coefficient G and its confidence interval after the test and identification process is completed, and output all charts and data in the form of a structured document as a test report.

[0046] The computation engine module 14 integrates three independently callable units, including a finite element parameterized scanning unit 14a, a finite volume method transient solver unit 14b, and a particle swarm optimization algorithm unit 14c. The three units communicate with each other through a standardized data interface, and the input and output adopt a unified format for easy post-processing.

[0047] This embodiment uses an elevator shaft in a high-rise residential building as the target test object. The shaft wall is constructed of a combination of hollow concrete blocks and cement mortar joints. Both the interior side and the shaft side have cement mortar plaster layers. The concrete block dimensions are 390mm long × 190mm wide × 190mm thick. The horizontal mortar joint thickness is 10mm with a spacing of 200mm, the vertical mortar joint thickness is 10mm with a spacing of approximately 400mm, the inner plaster layer thickness is 15mm, and the outer plaster layer thickness is 20mm. The test objective is to obtain the on-site comprehensive heat transfer coefficient of the elevator shaft wall.

[0048] Step S1: Determine the optimized sensor layout scheme using finite element fluid simulation.

[0049] Before the test, a laser rangefinder and vernier calipers were used on-site to obtain the geometric parameters of the wall under test, including the size of the blocks, the width and spacing of the mortar joints, and the thickness of the inner and outer plaster layers. The estimated thermal conductivity, density, and specific heat capacity of each layer of material were also obtained. The thermal conductivity of the concrete blocks was 1.0 W / (m·K), the density was 2200 (kg / m³), and the specific heat capacity was 880 J / (kg·K). The estimated thermal conductivity of the cement mortar joints was 1.5 W / (m·K), the density was 1800 (kg / m³), and the specific heat capacity was 1050 J / (kg·K). The estimated thermal conductivity of the cement mortar plaster layer was 1.2 W / (m·K), the density was 2000 (kg / m³), and the specific heat capacity was 1000 J / (kg·K).

[0050] The aforementioned geometric parameters and thermal conductivity are input into the finite element parameterized scanning unit 14a in the calculation engine module 14. The finite element parameterized scanning unit 14a calls the finite element simulation environment and implements parameterized calls to the simulation solver through the defined interface. Multiple sets of input parameters are generated by combining the variables to be scanned, and are submitted to the simulation solver in a loop for automatic solution and collection of calculation results. This enables batch automatic evaluation of different schemes. The established three-dimensional mathematical physics heat transfer model A includes two parts: solid heat transfer control equation and air domain turbulence control equation. The solid heat transfer control equation is solved in steady state using a direct solver to improve efficiency. The air domain turbulence control equation is solved using a turbulence model to simulate airflow caused by the chimney effect. The geometric model adopts an explicit modeling method, constructing an independent geometric domain for each block and each mortar joint. Common node boundaries are defined between the blocks and mortar joints to ensure heat flow continuity, so that the model can truly reflect the thermal bridge effect caused by the difference between the block and mortar joint materials, rather than treating them as equivalent homogenization.

[0051] The boundary conditions are set as follows: On one side of the air domain inside the shaft, a velocity inlet boundary condition that varies with height is applied to simulate the chimney effect. The wind speed increases uniformly from the bottom to the top, and the specific distribution is determined by linear interpolation based on the measured wind speed of 0.4 m / s at the bottom and 1.8 m / s at the top. On the outdoor surface of the wall, the outdoor air temperature time history curve (discrete data at 1-minute intervals) and the natural convection heat transfer boundary are applied. The convection heat transfer coefficient is set to 23 W / (m·K) according to conventional values.

[0052] The simulation employs parametric scanning to systematically analyze the deviations of different sensor installation schemes. Since the placement and installation angle of the heat flux meter directly affect whether the captured heat flux density represents the true average heat flux of the wall, and the thermocouple's embedding location determines whether the measured temperature reflects the true surface temperature of the masonry blocks and mortar joints, the following three variables are selected for scanning:

[0053] Variable 1: The position of the heat flow meter on the surface of the block. With the geometric center of the block as the origin, the position is offset by 0mm, 30mm and 60mm in the horizontal direction, and by 0mm, 40mm and 80mm in the vertical direction, for a total of 9 position combinations.

[0054] Variable 2: The installation angle of the heat flow meter relative to the adjacent vertical mortar joint is set to 0° (parallel to the mortar joint direction), 45°, and 90° (perpendicular to the mortar joint direction), respectively.

[0055] Variable 3: The location of the thermocouple installation points. Point 1 is the center of the block surface (adjacent to the heat flow meter installation location), point 2 is the center of the vertical mortar joint surface, point 3 is 5cm horizontally from the edge of the heat flow meter, and point 4 is 10cm horizontally from the edge of the heat flow meter.

[0056] After cross-scanning all combinations of the above three variables, a total of 108 sensor installation schemes to be evaluated are generated. For each scheme, the simulation solver calculates and outputs the integrated heat flux density value on the heat flux meter sensing surface, as well as the local surface temperature value at each thermocouple point, and compares it with the theoretical true average heat flux density obtained by integrating the surface of the block and mortar joint area to calculate the relative deviation.

[0057] The analysis results show that when the heat flux meter is installed at 90° perpendicular to the mortar joint direction at the geometric center of the block, the heat flux density measurement deviation is the smallest, at 1.8%. When the installation angle deviates from the vertical direction by more than 15 degrees or the pasting position deviates from the center of the block by more than 30 mm, the measurement deviation increases sharply to more than 15%, and can reach 42% in extreme cases. The deviations between the temperatures measured at thermocouple points 1 and 2 and the average surface temperature are both less than 0.3℃, while the deviations at points 3 and 4 are 0.8 to 1.5℃.

[0058] Based on the above quantitative analysis, the optimized sensor arrangement scheme of this embodiment is determined as follows: In each test unit, an embedded thermopile heat flow meter is installed at 90° perpendicular to the mortar joint direction at the geometric center of the block, and three pre-embedded armored thermocouples are installed at point 1 (the surface center of the same block), point 2 (the midpoint of the adjacent vertical mortar joint surface), and point 3 (5cm horizontally from the edge of the heat flow meter, as an auxiliary verification point).

[0059] Step S2: Build a virtual instrument-based test platform 100 and perform on-site data acquisition;

[0060] According to the sensor arrangement scheme determined in step S1, mark the positions for attaching heat flow meters and the drilling points for each thermocouple on the indoor side of the target floor and the side surface of the shaft wall.

[0061] Armored thermocouples are selected, and holes are drilled at the marked points to ensure that the probe is embedded in the wall material. After drilling, the holes are filled with high thermal conductivity putty to form a tight thermal contact between the probe and the wall without air gaps, thus eliminating the temperature measurement error caused by the flow of air washing the probe along the wall surface.

[0062] A thermopile-type thin-film heat flow meter was selected, with its surface coating similar in color to the wall to reduce interference from radiative heat transfer. A thin layer of high thermal conductivity silicone grease was applied to the back of the heat flow meter, which was then attached to the geometric center of the block at a 90° angle perpendicular to the mortar joint. Uniform pressure was applied using an adjustable mechanical clamping device to ensure no air gaps between the back and the wall, thus eliminating contact thermal resistance errors.

[0063] On the opposite side of the wall, at the corresponding location, a symmetrical set of sensors is installed using the same process to achieve synchronous monitoring of the surface temperature and heat flux density on both the inner and outer sides.

[0064] After all the shielded compensation wires 251 of the sensors are sorted and collected, they are led out through the gaps in each layer to the industrial control box outside the well. After the connection is completed, the test platform 100 is started, the sampling frequency is set to 1Hz per channel, continuous sampling and storage are performed, and the waveform display module 12 refreshes the curves of each measuring point in real time. When the temperature fluctuation tends to be relatively stable (the temperature difference within adjacent 30 minutes is less than 0.5℃) and the data recording time is at least 3 hours, the data acquisition is stopped.

[0065] Step S3: Establish a three-dimensional four-thermal-resistance transient heat transfer analysis model H and solve for the internal temperature field;

[0066] After data acquisition is completed, the finite volume method transient solver unit 14b in the computing engine module 14 is started. The finite volume method transient solver unit 14b is written based on the numerical calculation program and includes mesh discretization, thermal balance equation assembly, time step iterative solution and result output.

[0067] Based on the actual geometric structure and material layering information of the tested wall recorded in step S1, a three-dimensional four-thermal-resistance transient heat transfer analysis model H is established. The wall is divided into an inner plaster layer, a block-mortar composite layer, and an outer plaster layer along the thickness direction. The block area and the mortar joint area are meshed in the horizontal and vertical directions of the plane, respectively. The node spacing in the mortar joint area is smaller to accurately capture the temperature gradient change caused by thermal bridges. Each discrete element is assigned a lumped heat capacity node, and the thermal resistance between adjacent nodes is defined. The thermal conductivity of the nodes in the block area in the three-dimensional four-thermal-resistance transient heat transfer analysis model H is temporarily denoted as the block thermal conductivity D1, the thermal conductivity of the nodes in the mortar joint area is temporarily denoted as the mortar joint thermal conductivity D2, and the convective heat transfer coefficients of the boundary nodes on the indoor side and the shaft side are temporarily denoted as the shaft side convective heat transfer coefficient D3 and the indoor side convective heat transfer coefficient D4, respectively. These coefficients are all parameters to be identified.

[0068] For each heat capacity node, a transient heat balance equation (i.e., the rate of change of internal energy of the node + the net heat flow into the node) is established based on the principle of energy conservation. All node equations are combined to obtain a set of ordinary differential equations, which are solved using a fully implicit time discretization scheme to ensure the numerical stability of the solution under a large time step. The time step is set to 10 seconds, and the iterative method is used to solve until convergence within each time step.

[0069] During the solution process, the measured temperature sequences of the indoor and shaft side surfaces collected in step S2 are used as time-varying temperature boundary conditions for direct loading. The initial temperature field inside the wall is determined by three-dimensional linear interpolation based on the measured values ​​of all embedded thermocouples at the start of the test. This ensures that the initial state of the three-dimensional four-thermal-resistance transient heat transfer analysis model H is consistent with the actual situation on site, thus avoiding the impact of initial condition deviation on the accuracy of subsequent parameter identification.

[0070] After the finite volume method transient solver unit 14b completes the calculation along the time axis, it outputs the temperature change sequence of all nodes inside the wall, i.e., the internal temperature field E. The finite volume method transient solver unit 14b is encapsulated as a callable forward problem solving function. Its input is a parameter set containing D1, D2, D3, and D4, and its output is the calculated temperature sequence at the corresponding measurement point location, which can be repeatedly called by the particle swarm optimization algorithm unit 14c in step S4.

[0071] Step S4: Based on the particle swarm optimization algorithm, the thermal conductivity of the blocks and mortar joints is intelligently inverted and identified.

[0072] The particle swarm optimization algorithm unit 14c adopts the standard particle swarm optimization algorithm framework, with four-dimensional optimization variables: block thermal conductivity D1, mortar joint thermal conductivity D2, shaft side convective heat transfer coefficient D3, and indoor side convective heat transfer coefficient D4. The search boundaries for each dimension are preset according to the material type and shaft environment. For example, the search range for block thermal conductivity is 0.2~2.0 W / (m·K), mortar joint thermal conductivity is 0.5~3.0 W / (m·K), shaft side convective heat transfer coefficient is 5~25 (W / (m·K)), and indoor side convective heat transfer coefficient is 1~10 (W / (m·K)).

[0073] Algorithm parameter settings: population size 40, inertia weight linearly decreasing from 0.9 to 0.4, learning factor 2.0, maximum number of iterations 200, early convergence condition is that the relative change of the global optimal fitness value is less than 1×10⁻ over 30 consecutive iterations. 6 ;

[0074] The fitness function is determined by the root mean square error between the measured temperature sequences of the center measuring point (point 1) on the surface of the block and the center measuring point (point 2) on the surface of the mortar joint and the corresponding theoretical temperature sequence calculated by the forward problem solution function. The root mean square error is used to quantitatively evaluate the degree of agreement between the model prediction results corresponding to the identified parameters and the measured data. The smaller the root mean square error, the closer the set of parameters are to the true values.

[0075] Iterative process: After population initialization, the particle velocity and position are updated in each generation, boundary constraints are applied, fitness is evaluated, and individual optimal and global optimal values ​​are updated. The process terminates when the convergence condition is met, and the thermal conductivity identification values ​​D10 and D20 of the mortar joint corresponding to the global optimal position are output. In this embodiment, the algorithm converges after 178 iterations, and D10 is 1.15 W / (m·K) and D20 is 1.62 W / (m·K).

[0076] Step S5: Calculate the overall heat transfer coefficient and automatically generate a test report;

[0077] The report generation module 13 automatically reads the identification results of the block thermal conductivity identification value D10 and the mortar joint thermal conductivity identification value D20. Combined with the thickness of each layer of material (inner plaster layer 0.015m, block layer 0.190m, outer plaster layer 0.020m) and the area ratio of the block to the mortar joint (block 0.82, mortar joint 0.18), the comprehensive heat transfer coefficient G of the wall is calculated using the multi-layer series thermal resistance combination calculation method.

[0078] The calculation process is as follows: determine the heat transfer resistance of the indoor side and the shaft side surface (take 0.11 and 0.04 m²·K / W respectively), calculate the thermal conductivity resistance of the inner and outer plaster layers, calculate the equivalent thermal resistance of the block mortar joint composite layer, and obtain the thermal resistance of the block area (thickness divided by D10) and the mortar joint area (thickness divided by D20) by area weighting parallel rule, add the thermal resistance of each part to obtain the total thermal resistance, and its reciprocal is the comprehensive heat transfer coefficient G;

[0079] Meanwhile, based on the particle scattering and fitness surface gradient in the final iteration of the particle swarm, the approximate 95% confidence interval of each parameter to be identified is obtained, and the confidence interval of the comprehensive heat transfer coefficient G is calculated through error propagation.

[0080] The report generation module 13 automatically generates a test report, which includes: basic project information, a schematic diagram of the measurement point layout, temperature-time history curves of the measurement points on the surface of the block and the mortar joint (including a comparison of measured and model calculated curves), identification values ​​and 95% confidence intervals of each parameter to be identified, the comprehensive heat transfer coefficient G and its confidence interval, etc. This report can be used for elevator shaft thermal environment assessment, equipment heat dissipation safety verification and energy-saving renovation scheme design for building envelope.

[0081] Through the above steps, the method of the present invention does not require waiting for steady-state conditions and can complete the accurate on-site measurement of the heat transfer coefficient of the elevator shaft structure under temperature fluctuations caused by the chimney effect. The obtained comprehensive heat transfer coefficient truly reflects the three-dimensional non-uniform thermal insulation performance of the wall, including the thermal bridge effect of the masonry mortar joints, eliminating the principle error of the homogeneous one-dimensional assumption and the measurement deviation of the arbitrary installation of sensors.

[0082] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0083] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0084] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0085] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CDROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0086] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

Claims

1. A method for on-site testing of the heat transfer coefficient of an elevator shaft structure, characterized in that, Includes the following steps: S1. A three-dimensional mathematical and physical heat transfer model A of the shaft wall, including the block area and the mortar joint area, is established using finite element simulation. Wind speed boundary conditions simulating the chimney effect are applied to the inside of the shaft. Measurement deviations under different sensor installation schemes are analyzed by parametric scanning to determine the optimized sensor layout scheme. S2, according to the optimized sensor arrangement scheme, temperature sensors and heat flow sensors are installed on the indoor side and the shaft side of the shaft wall respectively, and temperature data B and heat flow data C are collected synchronously through the test platform (100); S3, establish a three-dimensional four-thermal-resistance transient heat transfer analysis model H of the well wall, at least take the block thermal conductivity D1 and the mortar joint thermal conductivity D2 as parameters to be identified, and use the temperature data B as the time-varying boundary condition, and use the finite volume method to solve the internal temperature field E of the wall. S4. Using the particle swarm optimization algorithm, the thermal conductivity of the block D1 and the thermal conductivity of the mortar joint D2 are used as optimization variables. The objective function is constructed by the deviation between the measured temperature and the calculated temperature at the corresponding position in the internal temperature field E. Iterative inversion is performed to obtain the identification value of the thermal conductivity of the block D10 and the thermal conductivity of the mortar joint D20. S5. Based on the identification value D10 of the thermal conductivity of the masonry block and the identification value D20 of the thermal conductivity of the mortar joint, and combined with the thickness of each layer of the wall, the comprehensive heat transfer coefficient G of the elevator shaft wall is calculated using the thermal resistance series formula.

2. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, The optimized sensor arrangement scheme in step S1 includes: installing the heat flow sensor at the geometric center of the block in a direction perpendicular to the mortar joint, and installing the temperature sensor at the center of the block surface and the surface of the mortar joint, respectively.

3. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, In step S2, the temperature sensor is a pre-embedded armored thermocouple, which is installed by drilling and filling with high thermal conductivity mortar. The heat flow sensor is a thermopile heat flow meter, which is attached to the wall by thermally conductive silicone grease and a mechanical clamping device.

4. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, In step S3, the three-dimensional four-thermal-resistance transient heat transfer analysis model H is constructed based on the four-thermal-resistance structure of the inner plaster layer, block layer, mortar joint layer and outer plaster layer on the elevator shaft wall. The three-dimensional four-thermal-resistance transient heat transfer analysis model H sets thermal resistance nodes along the wall thickness direction and the horizontal and vertical directions of the plane. The temperature derivative with respect to time is introduced into the heat balance equation of each thermal resistance node to describe the three-dimensional heat flow transfer process inside the shaft wall under unsteady boundary conditions.

5. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 4, characterized in that, The parameters to be identified in step S3 also include the well-side convective heat transfer coefficient D3 and the indoor-side convective heat transfer coefficient D4. The particle swarm algorithm uses the block thermal conductivity D1, the mortar joint thermal conductivity D2, the well-side convective heat transfer coefficient D3, and the indoor-side convective heat transfer coefficient D4 as optimization variables for joint identification.

6. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 5, characterized in that, When solving step S3 using the finite volume method, the measured temperatures of the indoor and shaft surfaces in temperature data B are used as time-varying temperature boundary conditions, and the initial temperature field of each thermal resistance node is determined by interpolation based on the measured temperature at the start of the test.

7. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, In step S4, the objective function of the particle swarm algorithm is the root mean square error between the measured temperatures collected from the measuring points on the block surface and the mortar joint surface and the temperatures at corresponding locations on the block surface and the mortar joint surface calculated by the three-dimensional four-thermal-resistance transient heat transfer analysis model H.

8. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, The step S5 in calculating the overall heat transfer coefficient G is as follows: S51, based on the area ratio of blocks and mortar joints on the wall surface, the identification value of thermal conductivity of blocks D10 and the identification value of thermal conductivity of mortar joints D20 are converted into the equivalent thermal resistance of the block-mortar joint composite layer according to the parallel thermal resistance relationship. S52, then summed in series with the thermal resistance of the remaining material layers and the surface heat transfer resistance to obtain the total thermal resistance, and the reciprocal of the total thermal resistance is used as the comprehensive heat transfer coefficient G.

9. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, The hardware of the test platform (100) includes data acquisition and signal conditioning hardware, which is used to condition and synchronously acquire the signals output by the temperature sensor and the heat flow sensor. The hardware is placed in the industrial control box outside the well.

10. The method for on-site testing of the heat transfer coefficient of an elevator shaft structure according to claim 1, characterized in that, The software modules of the test platform (100) include a data acquisition module (11), a report generation module (13), and a calculation engine module (14). The data acquisition module (11) is used to set sampling parameters and store data. The report generation module (13) is used to generate test reports. The calculation engine module (14) is used to perform finite element simulation, finite volume method solution, and particle swarm algorithm inversion.

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