Method for simulating temperature field of fabricated concrete tower based on multi-dimensional environment simulation

Abstract

The application relates to the technical field of data processing, and discloses a prefabricated concrete tower cylinder temperature field simulation method based on multi-dimensional environment simulation. The method comprises the following steps: a scale model is established, copper foil reinforcing sheets are pre-buried, LED and infrared plates are used to simulate solar radiation, and a fan is used to simulate a wind field; temperature measuring points are arranged in layers to collect data, and a three-dimensional temperature field is reconstructed through interpolation; heat stress distribution is calculated based on finite elements, and is mapped to a solid tower cylinder through a scale coefficient and a gap correction coefficient. The application improves the space-time similarity of prefabricated concrete tower cylinder temperature field simulation and the heat stress prediction accuracy.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation. Background Technology

[0002] As the supporting structure of large wind turbine generators, prefabricated concrete towers are subjected to the coupled effects of complex environmental factors during service, including surface temperature gradients caused by solar radiation, convective heat transfer caused by wind fields, and thermal stress cycles caused by diurnal temperature differences. To assess the structural safety of the tower under long-term thermal loads, existing technologies typically employ numerical simulation methods to establish finite element models, calculating temperature field distribution and thermal stress response by setting boundary conditions. However, due to the discrete nature of the thermal properties of concrete materials, the thermal conductivity of the annular adhesive layer is difficult to accurately characterize, and the dynamic changes of environmental parameters such as solar radiation and wind speed with time and space are complex. Pure numerical simulation methods cannot fully verify the reliability of the calculation results, while on-site measurements of physical towers are realistic but costly and time-consuming.

[0003] The main shortcomings of existing technologies are as follows: On the one hand, scaled-down physical model tests can reproduce the thermal response characteristics of the tower under controlled conditions, but traditional scaled-down tests only reduce the geometric dimensions proportionally, ignoring the similarity law requirement that the thermal diffusion time is proportional to the square of the size. This results in the temperature evolution rate of the scaled-down model being much faster than that of the solid structure, and the time scale distortion makes it impossible to directly map the test data to the actual working conditions. On the other hand, the fidelity of the environmental loading system is insufficient. Existing solar radiation simulation devices mostly use halogen lamps or a single light source, whose spectral distribution deviates significantly from the standard solar spectrum. Furthermore, they cannot simultaneously simulate the radiation components in the three directions of direct sunlight, sky scattering, and ground reflection. Wind field simulations also only provide constant wind speeds and lack the turbulent characteristics of the real atmospheric boundary layer. These simplifications result in significant differences between the boundary conditions and the actual working conditions.

[0004] A deeper technical problem lies in the lack of a systematic correction mechanism for the multiphysics mapping relationship between the scaled-down model and the solid tower. Since the rings of the prefabricated tower are connected by an adhesive layer only a few millimeters thick, the thermal resistance formed by this gap significantly affects the temperature field distribution. However, at a geometric scaling ratio of 1:15, proportionally reducing the adhesive layer thickness would make construction impossible, while directly ignoring the gap effect would distort the temperature gradients at the longitudinal and transverse seams, thus affecting the accuracy of thermal stress calculations. Furthermore, the limited number of temperature measurement nodes makes it difficult to capture the continuous temperature field distribution in the three-dimensional space inside the tower, especially the abrupt temperature changes in the gap region. Traditional interpolation methods do not consider the spatial heterogeneity of the gap location, resulting in insufficient reconstruction accuracy. When conducting finite element thermo-mechanical coupling analysis based on temperature measurement data, without systematic correction of the time scale of the scaled-down model, the gap thermal resistance, and the boundary convective heat transfer coefficient, the calculated thermal stress distribution will not accurately reflect the mechanical response of the solid tower under service conditions, ultimately leading to inaccurate structural safety assessments and failing to provide a reliable basis for the optimized design and operation and maintenance decisions of the prefabricated tower. Summary of the Invention

[0005] This application provides a method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation. This method solves the technical problems of heat diffusion time distortion, insufficient environmental loading fidelity, difficulty in equivalence of gap thermal resistance, and lack of multi-physics mapping correction in existing scaled-down tests. It improves the spatiotemporal similarity and thermal stress prediction accuracy of the temperature field simulation of prefabricated concrete towers.

[0006] This application provides a method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation. The method includes: Step S1: Determine the size ratio and thermal diffusivity correction value of the scaled model based on the parameters of the physical tower, pre-embed copper foil heat transfer enhancement sheets at the gaps of the ring sheet, and calculate the solar trajectory parameters of the target geographical location. Step S2: Compare the LED spectrum with the standard solar spectrum to calculate the spectral mismatch factor. When the spectral mismatch factor exceeds the threshold, a filter is superimposed for shaping. Drive the LED module according to the solar trajectory parameters and synchronously control the infrared radiation plate to form a three-dimensional coupled heat flow. Step S3: Calculate and correct the wind speed value according to the different height layers of the scaled model to drive the fan group and superimpose turbulent disturbance. Calculate the convective heat transfer coefficient according to the surface heat flux density and wall temperature under the three-dimensional coupled heat flow. When the deviation of the convective heat transfer coefficient exceeds the range, output the speed correction amount. Step S4: Arrange nested temperature measurement nodes in layers along the scaled model and densify them at the location of the copper foil heat transfer enhancement sheet. After collecting temperature signals, use a spatial interpolation algorithm to generate three-dimensional temperature field reconstruction data. Step S5: Use the reconstructed three-dimensional temperature field data as a load to solve the nodal temperature distribution and calculate the thermal stress distribution in the finite element model. Map the thermal stress distribution to the solid tower using the scaling correction coefficient corresponding to the size ratio and the gap correction coefficient corresponding to the copper foil heat transfer enhancement sheet.

[0007] In this application, step S1 includes: Based on the height, outer diameter, and wall thickness of the solid tower, the height, outer diameter, and wall thickness of the scaled-down model are calculated according to a geometric scaling ratio of 1:15 to obtain the aforementioned size ratio; The thermal diffusivity of the solid tower is calculated based on the thermal conductivity, density, and specific heat capacity of the concrete. The thermal diffusivity of the solid tower is then compared with the square of the size ratio to obtain the target thermal diffusivity of the scaled-down model. By adjusting the material ratio of the scaled-down model, the actual thermal diffusivity of the scaled-down model is made to reach the target thermal diffusivity, thus obtaining the thermal diffusivity correction value. The gap thermal resistance of the solid tower is calculated based on the thickness of the adhesive layer of the solid tower and the thermal conductivity of the epoxy adhesive. The thickness of the copper foil heat transfer enhancement sheet is determined by finite element simulation inversion based on the size ratio and the gap thermal resistance, so that the ratio of the equivalent contact thermal resistance of the copper foil heat transfer enhancement sheet to the gap thermal resistance is equal to the size ratio. The solar declination angle is calculated based on the latitude of the target geographical location and the Julian day of the simulated date. The solar altitude angle and solar azimuth angle at different times are calculated based on the solar declination angle and the latitude to obtain the solar trajectory parameters.

[0008] In this application, step S2 includes: The spectral irradiance of a single LED bead at 200 wavelength sampling points is measured using a spectroradiometer. The difference between the spectral irradiance and the standard solar spectral irradiance is summed to obtain the spectral mismatch factor. When the spectral mismatch factor is greater than 0.15, a combination of narrow-band filters of different thicknesses is superimposed in front of the LED beads. The spectral irradiance is multiplied by the pre-calibrated filter transmittance function and then summed with the difference of the standard solar spectral irradiance to obtain the corrected spectral mismatch factor. Based on the solar azimuth and solar altitude angles in the solar trajectory parameters, the meridional and pitch angles of 14 LED modules are calculated using an inverse kinematics algorithm, and the servo motors are driven to move the LED modules to the target position. The ground reflected radiation intensity is calculated based on the ground albedo and the solar altitude angle. The surface temperature of the bottom infrared radiation plate is adjusted by the PID temperature control module to satisfy the Stefan-Boltzmann law. At the same time, the output power of the side wall auxiliary infrared plate is set according to the sky scattered radiation intensity to obtain the three-dimensional coupled heat flow.

[0009] In this application, step S3 includes: Based on the ground roughness length of the target simulation site and the reference wind speed at a height of 10m, combined with the ground clearance of different height layers of the scaled model, the wind speed profile value corresponding to each height layer is calculated. The wind speed profile value is then multiplied by the size ratio to the power of 0.5 to obtain the corrected wind speed value. The speed of the three-layer centrifugal fan group is adjusted by the frequency converter according to the corrected wind speed value. The turbulence intensity is multiplied by the Gaussian white noise signal to obtain the turbulence disturbance component. The turbulence disturbance component is superimposed on the corrected wind speed value to drive the fan group to output pulsating wind speed. Heat flux meters are arranged circumferentially on the surface of the scaled model to measure the surface heat flux density under the action of the three-dimensional coupled heat flux. Combined with the wall temperature measured by the surface temperature sensor and the ambient temperature in the test chamber, the surface heat flux density is calculated as a ratio of the temperature difference between the wall temperature and the ambient temperature to obtain the actual convective heat transfer coefficient. When the ratio of the deviation between the actual convective heat transfer coefficient and the theoretical convective heat transfer coefficient is greater than 0.1, the difference between the actual convective heat transfer coefficient and the theoretical convective heat transfer coefficient is input into the PID controller for proportional-integral-derivative calculation to obtain the speed correction amount.

[0010] In this application, step S4 includes: A ring-shaped measuring point layer is set every eighth of the model height along the height direction of the scaled model. Each layer has nested temperature measuring nodes containing K-type thermocouples, infrared temperature measuring modules and fiber optic grating sensors arranged every 45 degrees along the circumference. Thermocouple triple groups are densely arranged at the longitudinal and transverse seams of the annular sheet at the location of the copper foil heat transfer enhancement sheet, forming a fine measurement layout for the temperature field in the gap area. Temperature signals from the nested temperature measurement nodes and the thermocouple triplet are synchronously acquired at a sampling frequency of 10Hz using a hybrid architecture of RS485 bus and Ethernet to obtain 192 channels of temperature data. For any point inside the tower, the weighting coefficients are obtained by solving the Kriging equations using a semi-variogram model. The weighting coefficients are then weighted and summed with the known temperature at the measuring point to obtain the reconstructed three-dimensional temperature field data.

[0011] In this application, step S5 includes: The three-dimensional temperature field reconstruction data is imported into the finite element platform to establish a mesh model including the ring body, the longitudinal seam copper foil region and the transverse seam adhesive layer, and the thermal parameters of concrete, copper foil and epoxy adhesive are defined. The three-dimensional temperature field reconstruction data is used as the volume load input. Convective heat transfer boundary conditions and radiation absorption boundary conditions are applied to the outer surface, and the nodal temperature distribution at each time moment is obtained by solving. The thermal strain is calculated based on the difference between the nodal temperature distribution and the reference temperature, and the thermal stress tensor is calculated through constitutive relations to obtain the equivalent stress distribution. The square of the size ratio is used as the time correction factor. The gap correction factor is calculated based on the ratio of the thermal conductivity of the copper foil heat transfer enhancement sheet to the adhesive layer. The equivalent stress distribution is then mapped to the solid tower coordinate system.

[0012] In this application, the reconstructed three-dimensional temperature field data is used as the volume load input. Convective heat transfer boundary conditions and radiative absorption boundary conditions are applied to the outer surface, and the nodal temperature distribution at each time step is obtained by solving, including: The temperature values ​​of each spatial point in the reconstructed three-dimensional temperature field data are assigned to the corresponding grid cell nodes as the initial temperature field. A third type of boundary condition is applied to the nodes on the outer surface of the model, and the convective heat transfer coefficient, wall temperature, ambient temperature, and surface solar radiation absorptivity, along with the three-dimensional coupled heat flux intensity, are input into the outer surface boundary condition. Apply natural convection boundary conditions to the nodes on the inner surface of the model, and apply a fixed temperature boundary condition equal to the ambient temperature to the bottom surface. The transient heat conduction is solved by setting a time step, and the temperature evolution process within the scaled period is calculated iteratively to obtain the nodal temperature distribution at each time point.

[0013] In this application, natural convection boundary conditions are applied to the inner surface nodes of the model, and a fixed temperature boundary condition equal to the ambient temperature is applied to the bottom surface, including: The natural convection heat transfer coefficient is calculated based on the temperature difference between the wall temperature of the model's inner surface and the ambient temperature inside the test chamber. The natural convection heat transfer coefficient, the inner surface wall temperature, and the ambient temperature inside the test chamber are input into the boundary conditions of the inner surface nodes of the model. The ambient temperature inside the test chamber is assigned as a fixed temperature value to the boundary conditions of all nodes on the bottom surface of the model. The boundary conditions of the inner surface nodes and the boundary conditions of the bottom surface nodes of the model are combined with the boundary conditions of the outer surface to form a thermal boundary condition system.

[0014] In this application, the natural convection heat transfer coefficient is calculated based on the temperature difference between the wall temperature of the model's inner surface and the ambient temperature inside the test chamber, including: Extract the wall temperature values ​​of the inner surface nodes of the model from the initial temperature field; The difference between the wall temperature value and the ambient temperature inside the test chamber is calculated to obtain the inner surface temperature difference. The absolute value of the temperature difference on the inner surface is raised to a fractional power to obtain the temperature difference correction factor; The natural convection heat transfer coefficient is obtained by multiplying the preset natural convection baseline coefficient by the temperature difference correction factor.

[0015] The technical solution provided in this application solves the similarity law distortion problem caused by the mismatch between thermal diffusion time and geometric dimensions in traditional scaled-down tests by establishing a scaled-down model and determining the size ratio and thermal diffusivity correction value based on the parameters of the actual tower. This ensures that the thermal response characteristics of the scaled-down model can accurately reflect the temperature evolution law of the actual tower. The technical solution of pre-embedding copper foil heat transfer enhancement sheets at the gaps in the ring plates cleverly utilizes the high thermal conductivity of copper foil to equivalently simulate the thermal resistance effect of the adhesive layer in the actual tower, avoiding the construction difficulties caused by proportionally reducing the thickness of the adhesive layer and ensuring accurate reproduction of the temperature gradient characteristics in the gap area. By calculating the solar trajectory parameters of the target geographical location and comparing the LED spectrum with the standard solar spectrum to calculate the spectral mismatch factor, a filter is superimposed to reshape the spectrum when it exceeds a threshold, achieving high-fidelity simulation of the solar radiation spectrum. Compared with traditional halogen lamps or single light sources, this significantly improves the accuracy of the wavelength distribution of radiant energy. This technique, which uses solar trajectory parameters to drive LED modules and synchronously controls infrared radiators to form a three-dimensional coupled heat flow, overcomes the limitations of existing solar radiation simulation devices that only provide unidirectional loading. By simulating direct sunlight with top LED modules, ground reflection with bottom infrared radiators, and sky scattering with side-wall auxiliary infrared panels, it collaboratively loads radiant energy from three spatial dimensions, realistically replicating the complex radiation environment experienced by the tower surface under actual operating conditions. The technique calculates and corrects wind speed values ​​based on different height layers of the scaled model to drive the fan assembly and superimposes turbulent disturbances. It not only considers the profile distribution of wind speed with height but also introduces a fluctuating wind speed component generated by multiplying turbulence intensity with Gaussian white noise, simulating the turbulent characteristics of the real atmospheric boundary layer. This makes the convective heat transfer boundary conditions closer to the actual service environment. The convective heat transfer coefficient is calculated based on the surface heat flux density and wall temperature under the action of the three-dimensional coupled heat flow, and a closed-loop feedback mechanism using a PID controller outputs speed correction values. This achieves dynamic optimization of the wind field simulation accuracy, ensuring that the convective heat transfer intensity on the scaled model surface remains consistent with the theoretical value.

[0016] Nested temperature measurement nodes are arranged in layers along the scaled model and densely arranged at the copper foil heat transfer enhancement sheet locations, forming a multi-layered temperature measurement network from the skin to the core and from the regular area to the gap area. After collecting temperature signals, a spatial interpolation algorithm is used to generate three-dimensional temperature field reconstruction data. The weight coefficients are solved based on the semi-variogram model using the Kriging interpolation method, so that the interpolation results not only consider the spatial distance between measurement points, but also make full use of the spatial correlation statistical law of the temperature field. Compared with the traditional inverse distance weighted or spline interpolation methods, it significantly improves the accuracy of temperature field reconstruction and the ability to capture local abrupt changes. In particular, the densely arranged measurement points in the gap area automatically obtain higher weights, ensuring the fine reconstruction of temperature gradients at the longitudinal and transverse seam locations. By using reconstructed three-dimensional temperature field data as the load to solve the nodal temperature distribution and calculate the thermal stress distribution in the finite element model, a seamless connection from experimental measurement to numerical calculation is achieved. The time scale is corrected by the scaling correction coefficient corresponding to the size ratio, and the temperature gradient and thermal stress amplitude in the gap region are corrected by the gap correction coefficient corresponding to the copper foil heat transfer enhancement sheet. This systematic correction mechanism maps the equivalent stress distribution to the coordinate system of the solid tower, solving the key technical bottleneck that scaled model test data is difficult to directly apply to the evaluation of solid structures. It enables the thermal stress distribution obtained based on scaled tests to truly reflect the mechanical response characteristics of the solid tower in the service environment, providing reliable technical support for the structural safety assessment, fatigue life prediction, and optimization design of prefabricated concrete towers. Compared with pure numerical simulation methods, it has the advantage of experimental verification credibility, and compared with on-site measurement of solid towers, it has significant advantages of low cost, short cycle, and controllable environment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an embodiment of the temperature field simulation method for prefabricated concrete towers based on multidimensional environment simulation in this application. Figure 2 This is a schematic diagram showing the comparison between the LED spectrum and the standard solar spectrum, as well as the effect of filter correction in the embodiments of this application; Figure 3 This is a schematic diagram of the daily variation curves of the temperature field at different heights in the scaled-down model of this application embodiment. Detailed Implementation

[0019] This application provides a method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the temperature field simulation method for prefabricated concrete towers based on multidimensional environmental simulation in this application includes: Step S1: Determine the size ratio and thermal diffusivity correction value of the scaled model based on the parameters of the physical tower, pre-embed copper foil heat transfer enhancement sheets at the gaps of the ring sheet, and calculate the solar trajectory parameters of the target geographical location. Step S2: Compare the LED spectrum with the standard solar spectrum to calculate the spectral mismatch factor. When the spectral mismatch factor exceeds the threshold, a filter is superimposed for shaping. Drive the LED module according to the solar trajectory parameters and simultaneously control the infrared radiation plate to form a three-dimensional coupled heat flow. S3 step: Calculate and correct the wind speed value according to the different height layers of the scaled model to drive the fan group and superimpose turbulent disturbance. Calculate the convective heat transfer coefficient according to the surface heat flux density and wall temperature under the action of three-dimensional coupled heat flow. When the deviation of the convective heat transfer coefficient exceeds the range, output the speed correction amount. Step S4: Nested temperature measurement nodes are arranged in layers along the scaled model and denser at the location of the copper foil heat transfer enhancement sheet. After collecting temperature signals, spatial interpolation algorithm is used to generate three-dimensional temperature field reconstruction data. Step S5: Use the reconstructed three-dimensional temperature field data as the load to solve the nodal temperature distribution and calculate the thermal stress distribution in the finite element model. Map the thermal stress distribution to the solid tower using the scaling correction factor corresponding to the size ratio and the gap correction factor corresponding to the copper foil heat transfer enhancement sheet.

[0021] It is understood that the executing entity of this application can be a prefabricated concrete tower temperature field simulation system based on multi-dimensional environment simulation, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0022] Specifically, based on the height, outer diameter, and wall thickness of the actual tower, the dimensional parameters corresponding to the scaled-down model are calculated according to a preset geometric scaling ratio. The scaling ratio is determined as the ratio of the actual size to the scaled-down size. Since scaling causes distortion in heat propagation time, the thermal diffusivity of the material needs to be corrected. The thermal diffusivity reflects the rate of heat diffusion and is calculated by dividing the material's thermal conductivity by the product of its density and specific heat capacity. For the concrete of the actual tower, the actual thermal diffusivity is calculated based on its thermal conductivity, density, and specific heat capacity. According to similarity theory, the thermal diffusivity of the scaled-down model must satisfy a specific proportional relationship with the actual thermal diffusivity; this ratio is equal to the square of the dimensional scaling ratio. By adjusting the concrete mix proportions of the scaled-down model to change its thermal conductivity, density, or specific heat capacity, the actual thermal diffusivity of the scaled-down model reaches the target value. The correction parameter obtained in this process is the thermal diffusivity correction value. The annular plates of the assembled tower are connected by an adhesive layer. This adhesive layer has poor thermal conductivity, creating thermal resistance. To effectively simulate the thermal resistance characteristics of the adhesive layer in the solid tower in a scaled-down model, copper foil heat transfer enhancement sheets are pre-embedded at the longitudinal and transverse seams of the annular plates in the scaled-down model. Thermal resistance is defined as the material thickness divided by the thermal conductivity. The thermal resistance of the adhesive layer in the solid tower is equal to the adhesive layer thickness divided by the thermal conductivity of the epoxy resin. The thermal conductivity of copper foil is much higher than that of epoxy resin. The thickness of the copper foil is determined through finite element simulation inversion, ensuring that the ratio of the equivalent contact thermal resistance obtained by dividing the copper foil thickness by its thermal conductivity to the thermal resistance of the solid adhesive layer equals the dimensional scale. The solar declination angle is calculated based on the latitude of the target's simulated geographical location and the Julian day of the simulated date. The Julian day is a series of consecutive days starting from a fixed date that reflects the Earth's orbital position. The solar declination angle represents the angle between the subsolar point and the equator, which changes periodically with the Earth's revolution. The solar altitude angle and azimuth angle at different times are calculated based on the declination angle and latitude, combined with the Earth's rotation angle. These angular parameters change continuously over time, forming the solar trajectory parameters.

[0023] A spectroradiometer is used to measure the spectral irradiance of a single LED at multiple wavelength sampling points. The light intensity value is recorded at each wavelength. Standard solar spectral irradiance is an internationally recognized reference for solar spectral energy distribution. The difference between the measured LED spectral irradiance and the standard solar spectral irradiance is calculated at each wavelength. The absolute value is then summed over all wavelengths and divided by the sum of the standard solar spectral irradiance to obtain the spectral mismatch factor. The smaller the factor, the closer the LED spectrum is to the solar spectrum. When the spectral mismatch factor exceeds a preset threshold, it indicates a significant deviation between the LED spectrum and the solar spectrum, requiring spectral correction. A combination of narrowband filters of varying thicknesses is stacked in front of the LED. These filters have selective transmittance for specific wavelengths. The measured LED spectral irradiance is multiplied by a pre-calibrated filter transmittance function at each wavelength to obtain the filter-corrected spectral irradiance. The difference between the corrected spectrum and the standard solar spectrum is then summed to obtain the corrected spectral mismatch factor. The target position angle of the LED module is calculated using an inverse kinematics algorithm based on the solar azimuth and altitude angles from the solar trajectory parameters. The inverse kinematics algorithm then infers the angles of each joint based on the desired end position. Here, the meridional rotation angle and pitch angle of the LED module are inferred from the solar position. Multiple LED modules are distributed along a circular track. The meridional angle of each module is equal to the solar azimuth angle plus the module's inherent offset angle on the track, and the pitch angle is equal to 90 degrees minus the solar altitude angle. The servo motor drives the LED module to move to the target position based on the calculated target angle. The ground reflected radiation intensity is calculated based on the ground albedo and solar altitude angle. The ground albedo is the ratio of reflected solar radiation to incident radiation. The surface temperature of the bottom infrared radiating plate is adjusted by a PID temperature control module to match the thermal radiation intensity emitted by the infrared radiating plate with the ground reflected radiation intensity. The output power of the side wall auxiliary infrared plate is set according to the sky scattered radiation intensity. The three radiation sources work together to form a three-dimensional coupled heat flow.

[0024] Based on the ground roughness length of the target simulation site and the reference wind speed at the reference height, combined with the ground clearance of different height layers in the scaled model, the wind speed profile values ​​corresponding to each height layer are calculated. The ground roughness length reflects the influence of surface roughness on wind speed, and the wind speed profile values ​​describe the variation of wind speed with height. The wind speed profile value is multiplied by the zero-fifth power of the size ratio to obtain the corrected wind speed value. The rotational speed of the centrifugal fan group is adjusted by the frequency converter according to the corrected wind speed value. The turbulence intensity is multiplied by the Gaussian white noise signal to obtain the turbulence disturbance component. The turbulence intensity represents the ratio of the wind speed fluctuation amplitude to the average wind speed. The Gaussian white noise signal is a random signal with zero mean and follows a Gaussian distribution. The multiplication of the two generates a fluctuating wind speed component with real turbulence statistical characteristics. The turbulence disturbance component is superimposed on the corrected wind speed value to drive the fan group to output a time-varying wind speed. Heat flux meters are arranged circumferentially on the scaled model surface to measure the surface heat flux density under three-dimensional coupled heat flux. The surface temperature sensor measures the wall temperature. The actual convective heat transfer coefficient is obtained by dividing the surface heat flux density by the temperature difference between the wall temperature and the ambient temperature. The theoretical convective heat transfer coefficient is predicted according to empirical formulas. When the deviation ratio between the actual convective heat transfer coefficient and the theoretical value exceeds the set range, the difference is input to the PID controller. The PID controller contains three parts: proportional, integral, and derivative. The outputs of the three parts are added together to obtain the speed correction amount to adjust the fan speed.

[0025] Multiple annular measuring point layers are set at equal intervals along the height of the scaled model. Each layer has nested temperature measuring nodes containing K-type thermocouples, infrared temperature measurement modules, and fiber optic grating sensors arranged at equal angles along the circumference. K-type thermocouples are embedded at a fixed depth below the outer surface to measure the skin temperature. The infrared temperature measurement module is suspended at a fixed distance from the outer surface, pointing towards the wall along the normal direction to measure the transition layer temperature. The fiber optic grating sensor is embedded at the centerline of the wall thickness to measure the core temperature. These three types of sensors form a temperature gradient measurement from the skin to the core at the same measuring point. Three thermocouple triplets are densely arranged at the longitudinal and transverse seams of the annular sheet at the copper foil heat transfer enhancement plate location. Three thermocouples are embedded at different distances from the centerline of the longitudinal seam on both sides, and three thermocouples are embedded at different distances from the centerline of the transverse seam above and below, forming a fine measurement layout for the temperature field in the seam region. Multiple temperature data are obtained by synchronously acquiring temperature signals from the nested measuring nodes and thermocouple triplets at a fixed sampling frequency using a hybrid RS485 bus and Ethernet architecture. A timestamp synchronization protocol is used to ensure time alignment of all measuring point signals. For any spatial point inside the tower, the weighting coefficients are obtained by solving the Kriging equations using a semi-variogram model. The semi-variogram describes the statistical law of temperature difference between two points in space as a function of distance. The Kriging equations calculate the optimal weight of the unknown point temperature based on the semi-variogram and the known measurement point location. The weighting coefficients are then weighted and summed with the known measurement point temperatures to obtain the unknown point temperature. By traversing all spatial points inside the tower, the three-dimensional temperature field reconstruction data is generated.

[0026] The three-dimensional temperature field reconstruction data was imported into the finite element platform to establish a mesh model including the ring sheet body, the longitudinal seam copper foil region, and the transverse seam adhesive layer. The mesh was divided using hexahedral elements. The ring sheet body used a conventional mesh size, while the longitudinal seam copper foil region and the transverse seam adhesive layer used a refined mesh size to capture abrupt changes in temperature gradient. The thermal parameters of concrete, copper foil, and epoxy adhesive were defined, including thermal conductivity, density, specific heat capacity, coefficient of linear expansion, elastic modulus, and Poisson's ratio. The temperature values ​​of each spatial point in the reconstructed 3D temperature field data are assigned to the corresponding mesh element nodes as the initial temperature field. A third type of boundary condition is applied to the nodes on the outer surface of the model. The third type of boundary condition considers both convective heat transfer and thermal radiation. The convective heat transfer coefficient, wall temperature, ambient temperature, surface solar radiation absorptivity, and 3D coupled heat flux intensity are input into the outer surface boundary condition. Natural convection boundary conditions are applied to the nodes on the inner surface of the model. The wall temperature values ​​of the nodes on the inner surface of the model are extracted from the initial temperature field. The difference between the wall temperature value and the ambient temperature inside the test chamber is calculated to obtain the inner surface temperature difference. The absolute value of the inner surface temperature difference is raised to a fractional power to obtain the temperature difference correction factor. The preset natural convection base coefficient is multiplied by the temperature difference correction factor to obtain the natural convection heat transfer coefficient. A fixed temperature boundary condition equal to the ambient temperature is applied to the bottom surface. The transient heat conduction cycle is calculated by setting a time step to obtain the temperature evolution process within the scaled period and the node temperature distribution at each time point. Thermal strain is calculated based on the difference between the nodal temperature distribution and the reference temperature. Thermal strain represents the material's expansion or contraction deformation caused by temperature changes. The thermal stress tensor is calculated using constitutive relations, which describe the physical relationship between stress and strain. For linear elastic materials, stress equals the elastic modulus multiplied by the total strain minus the thermal strain plus a Poisson's ratio correction term. The equivalent stress distribution is then calculated from the thermal stress tensor. The square of the size ratio is used as the time correction factor, since the heat diffusion time is proportional to the square of the size. The time corresponding to the actual tower at a certain moment in the scaled model is equal to the scaled time multiplied by the time correction factor. The gap correction factor is calculated based on the ratio of the thermal conductivity of the copper foil heat transfer enhancement sheet to the adhesive layer. This correction factor is used to correct the temperature gradient at the longitudinal and transverse seam locations. The time coordinates of each node in the equivalent stress distribution are multiplied by the time correction factor, and the temperature gradients at the longitudinal and transverse seam locations are multiplied by the gap correction factor, completing the mapping of the equivalent stress distribution to the actual tower coordinate system.

[0027] In one specific embodiment, step S1 includes: Based on the height, outer diameter, and wall thickness of the solid tower, the height, outer diameter, and wall thickness of the scaled-down model are calculated according to a geometric scaling ratio of 1:15 to obtain the dimensional scale. The thermal diffusivity of the solid tower is calculated based on the thermal conductivity, density, and specific heat capacity of the concrete. The thermal diffusivity of the solid tower is then calculated by ratioing it to the square of the size ratio to obtain the target thermal diffusivity of the scaled-down model. By adjusting the material ratio of the scaled-down model, the actual thermal diffusivity of the scaled-down model is made to reach the target thermal diffusivity, thus obtaining the thermal diffusivity correction value. The gap thermal resistance of the solid tower is calculated based on the thickness of the adhesive layer of the solid tower and the thermal conductivity of the epoxy adhesive. The thickness of the copper foil heat transfer enhancement sheet is determined by finite element simulation inversion based on the size ratio and the gap thermal resistance, so that the ratio of the equivalent contact thermal resistance to the gap thermal resistance of the copper foil heat transfer enhancement sheet is equal to the size ratio. The solar declination angle is calculated based on the latitude of the target's geographical location and the Julian day of the simulated date. The solar altitude angle and solar azimuth angle at different times are then calculated based on the solar declination angle and latitude to obtain the solar trajectory parameters.

[0028] Specifically, the dimensions of the scaled-down model are calculated based on the height, outer diameter, and wall thickness of the actual tower cylinder according to a geometric scaling ratio. The scaling ratio is the ratio of the actual size to the scaled-down size, which is the size scale. Since scaling causes distortion in heat propagation time, the thermal diffusivity of the material needs to be corrected. The thermal diffusivity is calculated by dividing the material's thermal conductivity by the product of its density and specific heat capacity. For the concrete of the actual tower cylinder, the actual thermal diffusivity is calculated based on its thermal conductivity, density, and specific heat capacity. According to similarity theory, the thermal diffusivity of the scaled-down model must satisfy a proportional relationship with the actual thermal diffusivity equal to the square of the size scale. By adjusting the concrete material mix of the scaled-down model to change its thermal conductivity, density, or specific heat capacity, the actual thermal diffusivity of the scaled-down model reaches the target value, thus obtaining the corrected thermal diffusivity value.

[0029] The annular plates of the assembled tower are connected by an adhesive layer to form thermal resistance. The thermal resistance is the material thickness divided by the thermal conductivity. The thermal resistance of the adhesive layer of the solid tower is equal to the adhesive layer thickness divided by the thermal conductivity of the epoxy resin. Copper foil heat transfer enhancement plates are pre-embedded at the longitudinal and transverse seams of the annular plates in the scaled model. The thermal conductivity of copper foil is much higher than that of epoxy resin. The thickness of copper foil is determined by finite element simulation inversion so that the ratio of the equivalent contact thermal resistance obtained by dividing the copper foil thickness by the thermal conductivity of copper foil to the thermal resistance of the solid adhesive layer is equal to the size ratio. The solar declination angle is calculated based on the latitude of the target's simulated geographical location and the Julian day of the simulated date. The Julian day is a series of consecutive days starting from a fixed date that reflects the Earth's orbital position. The solar declination angle represents the angle between the subsolar point and the equator, which changes periodically with the Earth's revolution. Based on the declination angle and latitude, combined with the Earth's rotation angle, the solar altitude angle and azimuth angle at different times are calculated. The solar altitude angle represents the angle between the sunlight and the horizon, and the solar azimuth angle represents the angle between the sun's projection on the horizon and true north. These angular parameters change continuously over time, forming the solar trajectory parameters.

[0030] In one specific embodiment, step S2 includes: The spectral irradiance of a single LED bead at 200 wavelength sampling points was measured using a spectroradiometer. The difference between the spectral irradiance and the standard solar spectral irradiance was summed to obtain the spectral mismatch factor. When the spectral mismatch factor is greater than 0.15, a combination of narrow-band filters of different thicknesses is superimposed in front of the LED beads. The spectral irradiance is multiplied by the pre-calibrated filter transmittance function and then summed with the difference of the standard solar spectral irradiance to obtain the corrected spectral mismatch factor. Based on the solar azimuth and solar altitude angles in the solar trajectory parameters, the meridional and pitch angles of 14 LED modules are calculated using an inverse kinematics algorithm, and the servo motors are driven to move the LED modules to the target position. The ground reflected radiation intensity is calculated based on the ground albedo and solar altitude angle. The surface temperature of the bottom infrared radiation plate is adjusted by the PID temperature control module to satisfy the Stefan-Boltzmann law. At the same time, the output power of the side wall auxiliary infrared plate is set according to the sky scattered radiation intensity to obtain the three-dimensional coupled heat flow.

[0031] Specifically, a spectroradiometer is used to measure the spectral irradiance of a single LED at multiple wavelength sampling points. The light intensity value is recorded at each wavelength. Standard solar spectral irradiance is an internationally recognized reference for the energy distribution of the solar spectrum. The difference between the measured LED spectral irradiance and the standard solar spectral irradiance is calculated at each wavelength. The absolute value is then summed over all wavelengths and divided by the sum of the standard solar spectral irradiance to obtain the spectral mismatch factor. The smaller the factor, the closer the LED spectrum is to the solar spectrum. When the spectral mismatch factor exceeds a preset threshold, it indicates a significant deviation between the LED spectrum and the solar spectrum, requiring spectral correction. A combination of narrowband filters of varying thicknesses is stacked in front of the LED. These filters have selective transmittance for specific wavelengths. The measured LED spectral irradiance is multiplied by a pre-calibrated filter transmittance function at each wavelength to obtain the filter-corrected spectral irradiance. The difference between the corrected spectrum and the standard solar spectrum is then summed to obtain the corrected spectral mismatch factor.

[0032] The target position angle of the LED module is calculated using the solar azimuth and elevation angles from the solar trajectory parameters through an inverse kinematics algorithm. The inverse kinematics algorithm then back-calculates the angles of each joint based on the desired end position. Here, the meridional rotation angle and pitch tilt angle of the LED module are back-calculated based on the solar position. Multiple LED modules are distributed along a circular track. The meridional angle of each module is equal to the solar azimuth angle plus the inherent offset angle of the module on the track, and the pitch angle is equal to 90 degrees minus the solar elevation angle. The servo motor drives the LED module to move to the target position according to the calculated target angle to achieve dynamic tracking simulation of the solar trajectory.

[0033] The ground reflected radiation intensity is calculated based on the ground albedo and solar altitude angle. Ground albedo is the ratio of reflected solar radiation to incident radiation. The ground reflected radiation intensity equals the ground albedo multiplied by the direct solar radiation intensity and then multiplied by the sine of the solar altitude angle. The surface temperature of the bottom infrared radiating plate is adjusted by a PID temperature control module. According to the law of thermal radiation, the thermal radiation intensity emitted by the radiating plate is proportional to the fourth power of its surface temperature. The surface temperature is adjusted to match the thermal radiation intensity emitted by the infrared radiating plate with the calculated ground reflected radiation intensity. The output power of the side wall auxiliary infrared plate is set according to the sky diffuse radiation intensity. Sky diffuse radiation is diffuse light formed by the scattering of sunlight by the atmosphere. The side wall auxiliary infrared plate is tilted upward to simulate the sky scattering direction. The top LED module provides direct solar radiation, the bottom infrared radiating plate provides ground reflected radiation, and the side wall auxiliary infrared plate provides sky diffuse radiation. The three radiation sources are spatially distributed at different locations, loading radiant energy from different directions to form a three-dimensional coupled heat flow.

[0034] Figure 2 This is a schematic diagram showing the comparison between the LED spectrum and the standard solar spectrum, as well as the effect of filter correction in the embodiments of this application; Figure 2 This illustrates a comparison between LED spectral irradiance and standard solar spectral irradiance, as well as the correction effect after spectral shaping using superimposed filters. Figure 2 As shown, the thick solid line represents the relative spectral irradiance distribution of the standard solar spectrum (AM1.5 reference spectrum) in the wavelength range of 300-1100 nm, the long dashed line represents the measured spectral irradiance of the original LED chip at 200 wavelength sampling points, and the short dashed line represents the corrected spectral irradiance after the addition of a narrowband filter combination. The original LED spectrum exhibits a significant blue light peak near 450 nm, and insufficient irradiance in the red and near-infrared bands above 650 nm, resulting in a spectral mismatch factor of 0.287, exceeding the threshold requirement of 0.15. By adding a combination of narrowband filters of different thicknesses in front of the LED chip, the blue light peak in the 400-500 nm band is selectively attenuated, while the red and near-infrared radiation in the 600-1100 nm band is enhanced. This reduces the corrected spectral mismatch factor to 0.098, meeting the accuracy requirements of solar radiation simulation and ensuring the spectral fidelity of the direct solar radiation portion in the three-dimensional coupled heat flow.

[0035] In one specific embodiment, step S3 includes: Based on the ground roughness length of the target simulation site and the reference wind speed at a height of 10m, combined with the ground clearance of different height layers of the scaled model, the wind speed profile value corresponding to each height layer is calculated. The wind speed profile value is multiplied by the size scale to the power of 0.5 to obtain the corrected wind speed value. The speed of the three-layer centrifugal fan group is adjusted by the frequency converter according to the corrected wind speed value. The turbulence intensity is multiplied by the Gaussian white noise signal to obtain the turbulence disturbance component. The turbulence disturbance component is superimposed on the corrected wind speed value to drive the fan group to output pulsating wind speed. Heat flux meters are arranged circumferentially on the scaled model to measure the surface heat flux density under three-dimensional coupled heat flow. Combined with the wall temperature measured by the surface temperature sensor and the ambient temperature in the test chamber, the surface heat flux density is calculated as a ratio of the temperature difference between the wall temperature and the ambient temperature to obtain the actual convective heat transfer coefficient. When the ratio of the deviation between the actual and theoretical convective heat transfer coefficients is greater than 0.1, the difference between the actual and theoretical convective heat transfer coefficients is input into the PID controller for proportional-integral-derivative calculation to obtain the speed correction.

[0036] Specifically, based on the ground roughness length of the target simulation site and the reference wind speed at the reference height, combined with the ground clearance of different height layers of the scaled model, the wind speed profile values ​​corresponding to each height layer are calculated. The ground roughness length reflects the influence of surface roughness on wind speed, while the wind speed profile values ​​describe the variation of wind speed with height; generally, wind speeds are lower at lower levels and higher at higher levels. In the calculation formula, the wind speed profile value equals the reference wind speed multiplied by the natural logarithm of the ratio of the current height to the roughness length, divided by the natural logarithm of the ratio of the reference height to the roughness length. Since the Reynolds number of the scaled model and the solid tower are different, the wind speed needs to be corrected. The Reynolds number reflects the ratio of fluid inertial force to viscous force. To maintain the similarity of the Reynolds number between the scaled model and the solid tower and satisfy the trade-off condition of Froude number similarity, the wind speed profile value is multiplied by the zero-fifth power of the size ratio to obtain the corrected wind speed value. This correction ensures that the convective heat transfer characteristics of the scaled model surface are similar to those of the solid tower.

[0037] The rotational speed of the three-layer centrifugal fan group is adjusted via a frequency converter based on the corrected wind speed value. The fan speed and output wind speed are linearly related. The target rotational speed is calculated based on the experimentally calibrated relationship between rotational speed and wind speed, and then controlled by the frequency converter to achieve the target speed. The turbulence intensity is multiplied by a Gaussian white noise signal to obtain the turbulence disturbance component. The turbulence intensity represents the ratio of the wind speed fluctuation amplitude to the average wind speed, reflecting the severity of turbulence. The Gaussian white noise signal is a random signal with zero mean and a Gaussian distribution whose spectrum is uniformly distributed across all frequencies. Multiplying the two generates a fluctuating wind speed component with true turbulence statistical characteristics. The turbulence disturbance component is superimposed on the corrected wind speed value to drive the fan group to output a time-varying wind speed containing both average and fluctuating wind speeds, simulating the turbulence characteristics of the real atmospheric boundary layer.

[0038] Heat flux meters are arranged circumferentially on the surface of the scaled model to measure the surface heat flux density under three-dimensional coupled heat flux. The heat flux density represents the heat passing through a unit area. The surface temperature sensor measures the wall temperature and the meteorological station inside the test chamber measures the ambient temperature. The surface heat flux density is divided by the temperature difference between the wall temperature and the ambient temperature to obtain the actual convective heat transfer coefficient, which represents the intensity of convective heat transfer. The theoretical convective heat transfer coefficient is predicted based on an empirical formula. This formula expresses the convective heat transfer coefficient as a function of wind speed, including a constant term and a zero-fifth power term of wind speed. When the ratio of the deviation between the actual and theoretical convective heat transfer coefficients exceeds the set range, it indicates that the wind speed control is deviating from the target value. The difference between the actual and theoretical convective heat transfer coefficients is input into a PID controller. The PID controller includes a proportional term that generates a control quantity based on the current deviation, an integral term that generates a control quantity based on the accumulation of historical deviations to eliminate steady-state errors, and a derivative term that generates a control quantity based on the rate of change of deviation to suppress oscillations. The outputs of the three terms are multiplied by their respective coefficients and then added together to obtain the speed correction quantity. This correction quantity is used by the frequency converter to adjust the fan speed so that the actual convective heat transfer coefficient approaches the theoretical value.

[0039] Figure 3 This is a schematic diagram of the daily variation curves of the temperature field at different heights in the scaled-down model of this application embodiment; Figure 3 This illustrates the evolution of the temperature field over time in different height layers (bottom, middle, and top) of a scaled model under three-dimensional coupled heat flow. Figure 3 As shown, the solid line represents the temperature curve of the top temperature measuring node, the short dashed line represents the temperature curve of the middle temperature measuring node, and the long dashed line represents the temperature curve of the bottom temperature measuring node. Due to the combined effects of direct solar radiation simulated by the LED module, ground reflected radiation simulated by the bottom infrared radiation plate, and sky scattered radiation simulated by the auxiliary infrared plate on the side wall, the top temperature is most significantly affected by direct solar radiation, reaching a peak of approximately 35°C between 12:00 and 14:00; the middle temperature is affected by both direct and scattered radiation, with a peak of approximately 32°C; and the bottom temperature is mainly affected by ground reflection, with a peak of approximately 28°C. During the nighttime hours (0:00-6:00 and 18:00-24:00), the temperatures at the three altitude levels tend to be consistent, all close to the ambient temperature of 20°C. This temperature distribution pattern verifies that the multi-dimensional environmental simulation system of this application can effectively reproduce the temperature field characteristics of the actual tower under real solar radiation and wind field conditions.

[0040] In one specific embodiment, step S4 includes: A ring-shaped layer of measuring points is set every eighth of the model height along the height direction of the scaled model. Each layer has nested temperature measuring nodes containing K-type thermocouples, infrared temperature measuring modules and fiber optic grating sensors arranged every 45 degrees along the circumference. Thermocouple triplets are densely arranged at the longitudinal and transverse seams of the annular plate at the location of the copper foil heat transfer enhancement sheet to form a fine measurement layout for the temperature field in the gap area. Temperature signals from nested temperature measurement nodes and thermocouple triple groups are synchronously acquired at a sampling frequency of 10Hz using a hybrid architecture of RS485 bus and Ethernet to obtain 192 channels of temperature data. For any point inside the tower, the weighting coefficients are obtained by solving the Kriging equations using a semi-variogram model. The weighting coefficients are then summed with the known temperature at the measuring point to obtain the reconstructed three-dimensional temperature field data.

[0041] Specifically, multiple annular measuring point layers are set at equal intervals along the height of the scaled model. Each layer has nested temperature measuring nodes containing K-type thermocouples, infrared temperature measuring modules, and fiber optic grating sensors arranged at equal angles along the circumference. The K-type thermocouples are embedded at a fixed depth below the outer surface to measure the skin temperature. The infrared temperature measuring module is suspended at a fixed distance from the outer surface and points towards the wall along the normal direction to measure the transition layer temperature. The fiber optic grating sensor is embedded at the center line of the wall thickness to measure the core temperature. The three types of sensors form a temperature gradient measurement from the skin to the core at the same measuring point. At the longitudinal and transverse seams of the copper foil heat transfer enhancement sheet, triple thermocouple groups are densely arranged. Three thermocouples are embedded at different distances from the center line of the seam on both sides of the longitudinal seam to form a longitudinal temperature gradient measurement. Three thermocouples are embedded at different distances from the center surface of the seam above and below the transverse seam to form a transverse temperature gradient measurement. The densely arranged triple thermocouple groups can capture the abrupt temperature field changes in the seam area caused by the difference in thermal conductivity of the copper foil heat transfer enhancement sheet.

[0042] Multiple temperature data streams are obtained by synchronously acquiring temperature signals from nested temperature measurement nodes and thermocouple triplets at a fixed sampling frequency using a hybrid architecture of RS485 bus and Ethernet. RS485 bus is a serial communication protocol suitable for long-distance multi-node communication, while Ethernet provides a high-speed data transmission channel. The hybrid architecture combines the advantages of both to achieve reliable data acquisition from a large number of sensors. A timestamp synchronization protocol is used to mark all measurement point data with the same time stamp during each sampling to ensure time alignment of signals from measurement points at different locations. The multiple temperature data streams include temperature values ​​from all ring measurement point layers and encrypted measurement points.

[0043] For any spatial point inside the tower, the weighting coefficients are obtained by solving the Kriging equations using a semi-variogram model. The semi-variogram describes the statistical law of the variation of the expected square of the temperature difference between two points in space with distance. Commonly used semi-variogram models include a nugget effect term, a sill value, and a range parameter. The nugget effect reflects measurement error or small-scale variation, the sill value represents the maximum amplitude of spatial variation, and the range represents the distance at which spatial correlation disappears. The Kriging equations establish a linear system of equations based on the semi-variogram and the known positional relationship of the measuring points. The unknowns in the equations are the weighting coefficients of the contributions of each known measuring point to the temperature of the unknown point. Solving the equations yields the optimal weighting coefficients that minimize the estimation variance. The weighting coefficients are then weighted and summed with the temperatures of the known measuring points to obtain the temperature of the unknown point. This process is repeated for all spatial points inside the tower to generate the three-dimensional temperature field reconstruction data. The reconstructed data automatically receives higher weights at the denser measuring points in the gap region, thereby improving the reconstruction accuracy of the temperature abrupt change zone.

[0044] In one specific embodiment, step S5 includes: Import the three-dimensional temperature field reconstruction data into the finite element platform to establish a mesh model including the ring sheet body, the longitudinal seam copper foil region and the transverse seam adhesive layer, and define the thermal parameters of concrete, copper foil and epoxy adhesive. The three-dimensional temperature field reconstruction data is used as the volume load input. Convective heat transfer boundary conditions and radiation absorption boundary conditions are applied to the outer surface, and the nodal temperature distribution at each time moment is obtained by solving. The thermal strain is calculated based on the difference between the nodal temperature distribution and the reference temperature, and the thermal stress tensor is calculated through constitutive relations to obtain the equivalent stress distribution. The square of the size ratio is used as the time correction factor. The gap correction factor is calculated based on the ratio of the thermal conductivity of the copper foil heat transfer enhancement sheet to the adhesive layer. The equivalent stress distribution is then mapped to the solid tower coordinate system.

[0045] Specifically, the reconstructed three-dimensional temperature field data was imported into a finite element platform to establish a mesh model containing the ring sheet body, the longitudinally seamed copper foil region, and the transversely seamed adhesive layer. Hexahedral elements were used for mesh generation; the ring sheet body used a standard mesh size, while the longitudinally seamed copper foil region and the transversely seamed adhesive layer used a refined mesh size to capture abrupt temperature gradient changes. The total number of mesh elements was determined based on computational accuracy and resource balance. The thermal parameters of the concrete, copper foil, and epoxy adhesive were defined, including thermal conductivity, density, specific heat capacity, coefficient of linear expansion, elastic modulus, and Poisson's ratio. Thermal conductivity represents the material's ability to conduct heat; density and specific heat capacity determine the material's heat capacity; the coefficient of linear expansion represents the linear expansion rate of the material per unit degree of temperature change; the elastic modulus represents the material's resistance to deformation; and Poisson's ratio represents the ratio of transverse strain to longitudinal strain.

[0046] The temperature values ​​of each spatial point in the three-dimensional temperature field reconstruction data are assigned to the corresponding mesh element nodes as the initial temperature field. A third type of boundary condition is applied to the nodes on the outer surface of the model. The third type of boundary condition considers both convective heat transfer and thermal radiation. The heat flux density of the convective heat transfer part is calculated based on the convective heat transfer coefficient and the temperature difference between the wall temperature and the ambient temperature. The heat flux density of the radiation absorption part is calculated based on the surface solar radiation absorptivity and the three-dimensional coupled heat flux intensity. The sum of the two heat flux densities constitutes the thermal boundary condition of the outer surface. Natural convection boundary conditions are applied to the nodes on the inner surface of the model. The wall temperature values ​​of the nodes on the inner surface of the model are extracted from the initial temperature field. The difference between the wall temperature value and the ambient temperature inside the test chamber is calculated to obtain the inner surface temperature difference. The absolute value of the inner surface temperature difference is raised to a fractional power to obtain the temperature difference correction factor. The natural convection heat transfer coefficient is obtained by multiplying the preset natural convection basic coefficient with the temperature difference correction factor. A fixed temperature boundary condition equal to the ambient temperature is applied to the bottom surface. The transient heat conduction equation is solved by setting a time step. The temperature evolution process of each time step within the scaled period is calculated iteratively to obtain the node temperature distribution at each time.

[0047] Thermal strain is calculated based on the difference between the nodal temperature distribution and the reference temperature, where the reference temperature is the temperature of the structure in its stress-free state. Thermal strain represents the ratio of the material's expansion or contraction caused by temperature changes to its original length; it is equal to the coefficient of linear expansion multiplied by the difference between the nodal temperature and the reference temperature. The thermal stress tensor is calculated using constitutive relations, which describe the physical relationship between stress and strain. For linear elastic materials, the total strain includes mechanical strain and thermal strain. Mechanical strain is caused by stress, while thermal strain is suppressed under constrained conditions, generating thermal stress. The thermal stress tensor is calculated using the mathematical relationship between the elastic modulus, Poisson's ratio, total strain, and thermal strain. The thermal stress tensor includes normal stress components and shear stress components. The equivalent stress distribution is calculated from the thermal stress tensor. The equivalent stress is converted from the three-dimensional stress state into a single scalar value according to the Mises yield criterion to facilitate the assessment of whether the material yields.

[0048] The square of the size ratio is used as the time correction factor because the heat diffusion time is proportional to the square of the size. The time corresponding to the actual tower at a certain moment in the scaled model is equal to the scaled time multiplied by the time correction factor. This correction maps the rapid thermal response of the scaled model to the slow thermal response of the actual tower. The gap correction factor is calculated based on the ratio of the thermal conductivity of the copper foil heat transfer enhancement sheet to the adhesive layer. The thermal conductivity ratio reflects the difference in heat transfer capacity between the two materials. The thermal conductivity of the copper foil is much higher than that of the epoxy adhesive, resulting in a smaller temperature gradient at the gap in the scaled model compared to the actual tower. The gap correction factor is equal to the ratio of thermal conductivity multiplied by the thickness ratio and then multiplied by the thermal resistance ratio. This correction factor is applied to the temperature gradient at the longitudinal and transverse gap positions. Multiplying the temperature gradient at the gap position by the gap correction factor restores the temperature gradient characteristics of the actual tower. The time coordinates of each node in the equivalent stress distribution are multiplied by the time correction factor, the spatial coordinates are multiplied by the size ratio, and the stress value at the gap position is recalculated based on the corrected temperature gradient. This completes the mapping of the equivalent stress distribution to the coordinate system of the actual tower, obtaining the thermal stress distribution of the actual tower at the real time and spatial scales.

[0049] In one specific embodiment, the three-dimensional temperature field reconstruction data is used as the volume load input, and convective heat transfer boundary conditions and radiation absorption boundary conditions are applied to the outer surface to obtain the nodal temperature distribution at each time step, including: The temperature values ​​of each spatial point in the three-dimensional temperature field reconstruction data are assigned to the corresponding grid cell nodes as the initial temperature field. Apply a third type of boundary condition to the nodes on the outer surface of the model, and input the convective heat transfer coefficient, wall temperature, ambient temperature, and surface solar radiation absorptivity along with the three-dimensional coupled heat flux intensity into the outer surface boundary condition; Apply natural convection boundary conditions to the nodes on the inner surface of the model, and apply a fixed temperature boundary condition equal to the ambient temperature to the bottom surface. The transient heat conduction is solved by setting a time step, and the temperature evolution process within the scaled period is calculated iteratively to obtain the nodal temperature distribution at each time point.

[0050] Specifically, the temperature values ​​of each spatial point in the three-dimensional temperature field reconstruction data are assigned to the corresponding mesh element nodes as the initial temperature field. The assignment process is achieved through spatial position matching. For each spatial point in the three-dimensional temperature field reconstruction data, the nearest element node is found in the finite element mesh model according to its three-dimensional coordinates, and the temperature value of the spatial point is assigned to the corresponding node. The initial temperature field assignment of the entire mesh model is completed by traversing all spatial points. The initial temperature field provides the starting state for the transient heat conduction solution.

[0051] A third type of boundary condition is applied to the nodes on the outer surface of the model. The third type of boundary condition considers both convective heat transfer and thermal radiation heat exchange. For the convective heat transfer part, the convective heat transfer coefficient, wall temperature and ambient temperature are input into the boundary condition formula. The convective heat flux density is equal to the convective heat transfer coefficient multiplied by the temperature difference between the wall temperature and the ambient temperature. For the radiation absorption part, the surface solar radiation absorptivity and the three-dimensional coupled heat flux intensity are input into the boundary condition formula. The radiation absorption heat flux density is equal to the surface solar radiation absorptivity multiplied by the three-dimensional coupled heat flux intensity. The total heat flux density of the outer surface nodes is equal to the convective heat flux density plus the radiation absorption heat flux density. Natural convection boundary conditions are applied to the nodes on the inner surface of the model. The wall temperature values ​​of the nodes on the inner surface of the model are extracted from the initial temperature field. The difference between the wall temperature value and the ambient temperature in the test chamber is calculated to obtain the inner surface temperature difference. The absolute value of the inner surface temperature difference is raised to a fractional power to obtain the temperature difference correction factor. The preset natural convection basic coefficient is multiplied by the temperature difference correction factor to obtain the natural convection heat transfer coefficient. The natural convection heat flux density is equal to the natural convection heat transfer coefficient multiplied by the temperature difference between the inner surface wall temperature and the ambient temperature. A fixed temperature boundary condition equal to the ambient temperature is applied to the bottom surface. The fixed temperature boundary condition forces all nodes on the bottom surface to maintain a constant temperature.

[0052] The transient heat conduction problem is solved by setting a time step, which is the time interval between two adjacent time points in the transient calculation. The selection of the time step must balance computational accuracy and efficiency; an excessively large time step will distort the calculation results, while a too-small time step will increase the computation time. The transient heat conduction equation describes the temperature variation with time and space. The right side of the equation includes a heat conduction term representing the heat diffusion caused by the temperature gradient, while the left side includes a heat capacity term representing the heat required for the material's temperature change. The temperature evolution process within the scaled period is calculated iteratively, starting from the initial moment and progressing step by step according to the time step. At each time step, the temperature distribution for the next moment is solved based on the current temperature field and boundary conditions, using the temperature distribution of the previous moment as the initial condition for the next moment. This process is repeated until the entire scaled period is covered, obtaining the nodal temperature distribution at each moment. The nodal temperature distribution records the temperature value of each node in the mesh model at each moment, forming time series data.

[0053] In one specific embodiment, Apply natural convection boundary conditions to the surface nodes within the model, and apply fixed temperature boundary conditions equal to the ambient temperature to the bottom surface, including: The natural convection heat transfer coefficient is calculated based on the temperature difference between the wall temperature of the model's inner surface and the ambient temperature inside the test chamber. The natural convection heat transfer coefficient, the inner surface wall temperature, and the ambient temperature inside the test chamber are input into the boundary conditions of the inner surface nodes of the model. The ambient temperature inside the test chamber is assigned as a fixed temperature value to the boundary conditions of all nodes on the bottom surface of the model. The boundary conditions of the inner surface nodes and the boundary conditions of the bottom surface nodes of the model are combined with the boundary conditions of the outer surface to form a thermal boundary condition system.

[0054] Specifically, the natural convection heat transfer coefficient is calculated based on the temperature difference between the wall temperature of the inner surface of the model and the ambient temperature inside the test chamber. The wall temperature values ​​of the nodes on the inner surface of the model are extracted from the initial temperature field. The difference between the wall temperature values ​​and the ambient temperature inside the test chamber is calculated to obtain the inner surface temperature difference. The inner surface temperature difference reflects the temperature gradient between the inner surface and the surrounding air. The absolute value of the inner surface temperature difference is raised to a fractional power to obtain the temperature difference correction factor. The fractional power operation reflects the nonlinear relationship between the intensity of natural convection heat transfer and the temperature difference. The larger the temperature difference, the more intense the natural convection, but the rate of increase of the heat transfer coefficient gradually decreases. The natural convection heat transfer coefficient is obtained by multiplying the preset natural convection baseline coefficient by the temperature difference correction factor. The natural convection baseline coefficient is an empirical value that reflects the heat transfer benchmark level under a specific geometry and fluid properties. The temperature difference correction factor corrects the baseline coefficient based on the actual temperature difference.

[0055] The natural convection heat transfer coefficient, the inner surface wall temperature, and the ambient temperature inside the test chamber are input into the boundary conditions of the inner surface nodes of the model. In the boundary condition calculation formula, the natural convection heat flux density is equal to the natural convection heat transfer coefficient multiplied by the temperature difference between the inner surface wall temperature and the ambient temperature inside the test chamber. When the inner surface wall temperature is higher than the ambient temperature, the heat flux density is positive, indicating that heat is transferred from the wall to the air; when the inner surface wall temperature is lower than the ambient temperature, the heat flux density is negative, indicating that heat is transferred from the air to the wall. The calculated natural convection heat flux density is applied to each node on the inner surface of the model as the thermal boundary condition for that node. The ambient temperature inside the test chamber is assigned as a fixed temperature value to the boundary conditions of all nodes on the bottom surface of the model. The fixed temperature boundary condition forces the temperature of all nodes on the bottom surface to remain equal to the ambient temperature throughout the solution process. This boundary condition assumes that the bottom of the model is in full contact with the ground of the test chamber and that the ground temperature is constant and equal to the ambient temperature.

[0056] The boundary conditions of the inner surface nodes and the bottom surface nodes of the model are combined with the outer surface boundary conditions to form a thermal boundary condition system. The outer surface boundary conditions include both convective heat transfer and radiative absorption, the inner surface boundary conditions are natural convective heat transfer, and the bottom surface boundary conditions are fixed temperature. These three types of boundary conditions are applied to different surface nodes of the mesh model. The thermal boundary condition system describes all heat exchange methods between the model and the surrounding environment, providing the necessary boundary constraints for solving the transient heat conduction equation. The finite element solver iteratively calculates the node temperature distribution at each time step based on the initial temperature field and the thermal boundary condition system.

[0057] In one specific embodiment, the natural convection heat transfer coefficient is calculated based on the temperature difference between the wall temperature of the model's inner surface and the ambient temperature inside the test chamber, including: Extract the wall temperature values ​​of the inner surface nodes of the model from the initial temperature field; The difference between the wall temperature value and the ambient temperature inside the test chamber is calculated to obtain the inner surface temperature difference. The temperature difference correction factor is obtained by raising the absolute value of the temperature difference on the inner surface to a fractional power. The natural convection heat transfer coefficient is obtained by multiplying the preset natural convection basic coefficient by the temperature difference correction factor.

[0058] Specifically, the wall temperature values ​​of the inner surface nodes of the model are extracted from the initial temperature field. The initial temperature field is the temperature distribution formed by assigning the three-dimensional temperature field reconstruction data to each node of the mesh model. The inner surface nodes of the model refer to all nodes on the inner wall of the mesh model. The extraction process identifies the nodes located on the inner surface by traversing the node list of the mesh model and reading their temperature values, thereby obtaining a set of wall temperature values ​​of each node on the inner surface. This set reflects the temperature differences at different locations on the inner surface.

[0059] The inner surface temperature difference is obtained by calculating the difference between the wall temperature value and the ambient temperature inside the test chamber. The ambient temperature inside the test chamber is obtained by real-time monitoring by a simple weather station, representing the temperature of the air inside the test chamber. The inner surface temperature difference of each node is obtained by subtracting the ambient temperature from the wall temperature value of the test chamber. A positive inner surface temperature difference indicates that the wall temperature is higher than the ambient temperature, and a negative inner surface temperature difference indicates that the wall temperature is lower than the ambient temperature. The magnitude of the inner surface temperature difference determines the driving force of natural convection heat transfer.

[0060] The temperature difference correction factor is obtained by performing a fractional power operation on the absolute value of the internal surface temperature difference. Taking the absolute value of the internal surface temperature difference eliminates the influence of the positive and negative signs and only retains the magnitude of the temperature difference. Performing a fractional power operation on the absolute value means using the absolute value as the base and performing a power operation with an exponent less than one. The fractional power operation reflects the nonlinear relationship between the natural convection heat transfer coefficient and the temperature difference. According to fluid mechanics theory, the natural convection heat transfer coefficient increases with the increase of the temperature difference, but the rate of increase decreases. The fractional power exponent is usually taken as 0.25 to reflect this nonlinear characteristic. The temperature difference correction factor is the result of the fractional power calculation of the absolute value of the temperature difference, which represents the correction factor of the temperature difference on the natural convection heat transfer coefficient.

[0061] The natural convection heat transfer coefficient is obtained by multiplying the preset natural convection baseline coefficient by the temperature difference correction factor. The natural convection baseline coefficient is an empirical coefficient preset based on the geometry, air properties and flow state inside the test chamber. This coefficient represents the benchmark value of natural convection heat transfer intensity under unit temperature difference conditions. Multiplying the baseline coefficient by the temperature difference correction factor achieves adaptation to the actual temperature difference conditions. The obtained natural convection heat transfer coefficient reflects the actual convection heat transfer capacity between the inner surface of the model and the air inside the test chamber under the current temperature difference. This coefficient is used to calculate the natural convection heat flux density boundary conditions of the inner surface nodes.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation, characterized in that, The method includes: Step S1: Determine the size ratio and thermal diffusivity correction value of the scaled model based on the parameters of the physical tower, pre-embed copper foil heat transfer enhancement sheets at the gaps of the ring sheet, and calculate the solar trajectory parameters of the target geographical location. Step S2: Compare the LED spectrum with the standard solar spectrum to calculate the spectral mismatch factor. When the spectral mismatch factor exceeds the threshold, a filter is superimposed for shaping. Drive the LED module according to the solar trajectory parameters and synchronously control the infrared radiation plate to form a three-dimensional coupled heat flow. Step S3: Calculate and correct the wind speed value according to the different height layers of the scaled model to drive the fan group and superimpose turbulent disturbance. Calculate the convective heat transfer coefficient according to the surface heat flux density and wall temperature under the three-dimensional coupled heat flow. When the deviation of the convective heat transfer coefficient exceeds the range, output the speed correction amount. Step S4: Arrange nested temperature measurement nodes in layers along the scaled model and densify them at the location of the copper foil heat transfer enhancement sheet. After collecting temperature signals, use a spatial interpolation algorithm to generate three-dimensional temperature field reconstruction data. Step S5: Use the reconstructed three-dimensional temperature field data as a load to solve the nodal temperature distribution and calculate the thermal stress distribution in the finite element model. Map the thermal stress distribution to the solid tower using the scaling correction coefficient corresponding to the size ratio and the gap correction coefficient corresponding to the copper foil heat transfer enhancement sheet.

2. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 1, characterized in that, Step S1 includes: Based on the height, outer diameter, and wall thickness of the solid tower, the height, outer diameter, and wall thickness of the scaled-down model are calculated according to a geometric scaling ratio of 1:15 to obtain the aforementioned size ratio; The thermal diffusivity of the solid tower is calculated based on the thermal conductivity, density, and specific heat capacity of the concrete. The thermal diffusivity of the solid tower is then compared with the square of the size ratio to obtain the target thermal diffusivity of the scaled-down model. By adjusting the material ratio of the scaled-down model, the actual thermal diffusivity of the scaled-down model is made to reach the target thermal diffusivity, thus obtaining the thermal diffusivity correction value. The gap thermal resistance of the solid tower is calculated based on the thickness of the adhesive layer of the solid tower and the thermal conductivity of the epoxy adhesive. The thickness of the copper foil heat transfer enhancement sheet is determined by finite element simulation inversion based on the size ratio and the gap thermal resistance, so that the ratio of the equivalent contact thermal resistance of the copper foil heat transfer enhancement sheet to the gap thermal resistance is equal to the size ratio. The solar declination angle is calculated based on the latitude of the target geographical location and the Julian day of the simulated date. The solar altitude angle and solar azimuth angle at different times are calculated based on the solar declination angle and the latitude to obtain the solar trajectory parameters.

3. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 1, characterized in that, Step S2 includes: The spectral irradiance of a single LED bead at 200 wavelength sampling points is measured using a spectroradiometer. The difference between the spectral irradiance and the standard solar spectral irradiance is summed to obtain the spectral mismatch factor. When the spectral mismatch factor is greater than 0.15, a combination of narrow-band filters of different thicknesses is superimposed in front of the LED beads. The spectral irradiance is multiplied by the pre-calibrated filter transmittance function and then summed with the difference of the standard solar spectral irradiance to obtain the corrected spectral mismatch factor. Based on the solar azimuth and solar altitude angles in the solar trajectory parameters, the meridional and pitch angles of 14 LED modules are calculated using an inverse kinematics algorithm, and the servo motors are driven to move the LED modules to the target position. The ground reflected radiation intensity is calculated based on the ground albedo and the solar altitude angle. The surface temperature of the bottom infrared radiation plate is adjusted by the PID temperature control module to satisfy the Stefan-Boltzmann law. At the same time, the output power of the side wall auxiliary infrared plate is set according to the sky scattered radiation intensity to obtain the three-dimensional coupled heat flow.

4. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 1, characterized in that, Step S3 includes: Based on the ground roughness length of the target simulation site and the reference wind speed at a height of 10m, combined with the ground clearance of different height layers of the scaled model, the wind speed profile value corresponding to each height layer is calculated. The wind speed profile value is then multiplied by the size ratio to the power of 0.5 to obtain the corrected wind speed value. The speed of the three-layer centrifugal fan group is adjusted by the frequency converter according to the corrected wind speed value. The turbulence intensity is multiplied by the Gaussian white noise signal to obtain the turbulence disturbance component. The turbulence disturbance component is superimposed on the corrected wind speed value to drive the fan group to output pulsating wind speed. Heat flux meters are arranged circumferentially on the surface of the scaled model to measure the surface heat flux density under the action of the three-dimensional coupled heat flux. Combined with the wall temperature measured by the surface temperature sensor and the ambient temperature in the test chamber, the surface heat flux density is calculated as a ratio of the temperature difference between the wall temperature and the ambient temperature to obtain the actual convective heat transfer coefficient. When the ratio of the deviation between the actual convective heat transfer coefficient and the theoretical convective heat transfer coefficient is greater than 0.1, the difference between the actual convective heat transfer coefficient and the theoretical convective heat transfer coefficient is input into the PID controller for proportional-integral-derivative calculation to obtain the speed correction amount.

5. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 1, characterized in that, Step S4 includes: A ring-shaped measuring point layer is set every eighth of the model height along the height direction of the scaled model. Each layer has nested temperature measuring nodes containing K-type thermocouples, infrared temperature measuring modules and fiber optic grating sensors arranged every 45 degrees along the circumference. Thermocouple triple groups are densely arranged at the longitudinal and transverse seams of the annular sheet at the location of the copper foil heat transfer enhancement sheet, forming a fine measurement layout for the temperature field in the gap area. Temperature signals from the nested temperature measurement nodes and the thermocouple triplet are synchronously acquired at a sampling frequency of 10Hz using a hybrid architecture of RS485 bus and Ethernet to obtain 192 channels of temperature data. For any point inside the tower, the weighting coefficients are obtained by solving the Kriging equations using a semi-variogram model. The weighting coefficients are then weighted and summed with the known temperature at the measuring point to obtain the reconstructed three-dimensional temperature field data.

6. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 1, characterized in that, Step S5 includes: The three-dimensional temperature field reconstruction data is imported into the finite element platform to establish a mesh model including the ring body, the longitudinal seam copper foil region and the transverse seam adhesive layer, and the thermal parameters of concrete, copper foil and epoxy adhesive are defined. The three-dimensional temperature field reconstruction data is used as the volume load input. Convective heat transfer boundary conditions and radiation absorption boundary conditions are applied to the outer surface, and the nodal temperature distribution at each time moment is obtained by solving. The thermal strain is calculated based on the difference between the nodal temperature distribution and the reference temperature, and the thermal stress tensor is calculated through constitutive relations to obtain the equivalent stress distribution. The square of the size ratio is used as the time correction factor. The gap correction factor is calculated based on the ratio of the thermal conductivity of the copper foil heat transfer enhancement sheet to the adhesive layer. The equivalent stress distribution is then mapped to the solid tower coordinate system.

7. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 6, characterized in that, The process of using the reconstructed three-dimensional temperature field data as a volume load input, applying convective heat transfer boundary conditions and radiative absorption boundary conditions to the outer surface, and solving for the nodal temperature distribution at each time step includes: The temperature values ​​of each spatial point in the reconstructed three-dimensional temperature field data are assigned to the corresponding grid cell nodes as the initial temperature field. A third type of boundary condition is applied to the nodes on the outer surface of the model, and the convective heat transfer coefficient, wall temperature, ambient temperature, and surface solar radiation absorptivity, along with the three-dimensional coupled heat flux intensity, are input into the outer surface boundary condition. Apply natural convection boundary conditions to the nodes on the inner surface of the model, and apply a fixed temperature boundary condition equal to the ambient temperature to the bottom surface. The transient heat conduction is solved by setting a time step, and the temperature evolution process within the scaled period is calculated iteratively to obtain the nodal temperature distribution at each time point.

8. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 7, characterized in that, The application of natural convection boundary conditions to the surface nodes within the model and the application of fixed temperature boundary conditions equal to the ambient temperature to the bottom surface include: The natural convection heat transfer coefficient is calculated based on the temperature difference between the wall temperature of the model's inner surface and the ambient temperature inside the test chamber. The natural convection heat transfer coefficient, the inner surface wall temperature, and the ambient temperature inside the test chamber are input into the boundary conditions of the inner surface nodes of the model. The ambient temperature inside the test chamber is assigned as a fixed temperature value to the boundary conditions of all nodes on the bottom surface of the model. The boundary conditions of the inner surface nodes and the boundary conditions of the bottom surface nodes of the model are combined with the boundary conditions of the outer surface to form a thermal boundary condition system.

9. The method for simulating the temperature field of prefabricated concrete towers based on multidimensional environmental simulation according to claim 8, characterized in that, The calculation of the natural convection heat transfer coefficient based on the temperature difference between the wall temperature of the model's inner surface and the ambient temperature inside the test chamber includes: Extract the wall temperature values ​​of the inner surface nodes of the model from the initial temperature field; The difference between the wall temperature value and the ambient temperature inside the test chamber is calculated to obtain the inner surface temperature difference. The absolute value of the temperature difference on the inner surface is raised to a fractional power to obtain the temperature difference correction factor; The natural convection heat transfer coefficient is obtained by multiplying the preset natural convection baseline coefficient by the temperature difference correction factor.

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