Wind tunnel grid structure deformation prediction and compensation scheme determination method

Abstract

The invention provides a wind tunnel grid structure deformation prediction and compensation scheme determination method, and belongs to the technical field of wind tunnels. Multi-physics field parameters are collected, a real-time data matrix is established, and a high-precision deformation prediction equation set including a temperature field influence equation and a stress field calculation equation is constructed based on a multi-physics field coupling theory; structural response is accurately determined through fluid-solid-heat coupling calculation, the deviation value of predicted deformation and actually-measured deformation is calculated, prediction model parameters are adaptively corrected according to the deviation range, and a deformation compensation control equation set containing a displacement compensation equation and a stress compensation equation is established. Accurate structural deformation control is realized by adjusting support prestress, temperature control parameters or airflow parameters, a closed-loop control system for cyclic monitoring and self-adaptive compensation is formed, and the technical problem that the compensation effect is poor due to the fact that deformation prediction precision of a wind tunnel grid structure is insufficient under the multi-physics coupling effect is solved.

Description

Technical Field

[0001] This invention belongs to the field of wind tunnel technology, and more specifically, relates to a method for predicting and determining the compensation scheme for deformation of wind tunnel grid structures. Background Technology

[0002] Wind tunnel space frame structures are critical experimental facilities in aerospace, automotive, and other industries. Their structural deformation control directly impacts the accuracy of experimental data and the safety of the equipment. Traditional wind tunnel space frame deformation monitoring primarily employs conventional strain gauges and displacement meters for single-point monitoring, combined with simplified finite element analysis for deformation prediction. Compensation measures are then determined based on human experience. This method provides basic deformation prediction and control under static loads or single physical fields. However, existing technologies have significant limitations in handling the multi-physics coupling effects of temperature, stress, and flow fields. Traditional prediction models often rely on single-physics assumptions or simple superposition principles, neglecting the nonlinear interactions between different physical fields. This leads to severely insufficient prediction accuracy. When there is a large deviation between the predicted and actual deformation, compensation strategies based on erroneous predictions will inevitably fail to achieve the desired results. During current wind tunnel operation, complex coupling effects are generated by the simultaneous action of multiple factors such as temperature gradient changes, airflow load fluctuations, and structural thermal expansion and contraction. Existing deformation prediction methods cannot accurately capture this multi-field coupling characteristic. Compensation operations are often based on inaccurate prediction data, leading to errors in compensation calculation. Even after compensation, structural deformation cannot be controlled within the allowable range, and there are even problems such as overcompensation or incorrect compensation direction. Summary of the Invention

[0003] In view of this, the present invention provides a method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure, which can solve the technical problem in the prior art where the deformation prediction accuracy of the wind tunnel space frame structure under the coupling effect of multiple physical fields is insufficient, resulting in poor compensation effect.

[0004] This invention is implemented as follows: A method for predicting and determining the compensation scheme for wind tunnel space frame structure deformation is provided. Low-temperature sensitive fiber optic strain sensors, shape sensors, and temperature sensors are deployed at key nodes of the wind tunnel space frame structure to collect multi-physics parameters during wind tunnel operation, including temperature gradient, stress-strain, structural displacement, wind speed, airflow density, and pressure distribution, establishing a real-time data matrix containing each monitoring point. Based on multi-physics coupling theory, a set of wind tunnel space frame deformation prediction equations is established, and the structural response is determined through fluid-structure-thermal coupling calculations. The deviation between the predicted deformation and the measured deformation is calculated. Monitoring continues when the deviation is within a set range; when the deviation exceeds the set range, compensation adjustment is initiated and the monitoring frequency is increased. A compensation strategy is determined based on the deformation deviation range. For small deviations, linear interpolation is used to correct the prediction model parameters; for large deviations, a nonlinear iterative algorithm is used to recalculate the multi-physics coupling coefficient. A set of deformation compensation control equations is established, and the compensation amount is solved using the Newton-Raphson iterative method. Compensation operations are performed by manually adjusting the support prestress, correcting temperature control parameters, or changing airflow parameters to achieve structural deformation control.

[0005] Specifically, the low-temperature sensitive fiber optic strain sensor is mounted on the surface of the steel structure member by surface bonding. The sensor's temperature sensitivity is controlled within 1 με / ℃. Wavelength division multiplexing technology is used to achieve multi-point series monitoring. The sensor is connected to the data acquisition system through optical fiber to obtain stress and strain data.

[0006] Specifically, the shape sensor is composed of a PVC sleeve with an outer diameter of 70mm and an inner thin-walled steel pipe. A set of triangular array fiber optic strain sensors is arranged every 200mm along the axial direction on the outer surface of the thin-walled steel pipe. The length is adjusted by a sliding clamp. The shape sensor transmits the structural displacement data to the data processing system through optical fiber transmission.

[0007] Specifically, the temperature sensor is directly fixed to the space frame member using a fastener. The temperature sensor and the member are kept in close contact to ensure accurate temperature transmission. The temperature sensor transmits the temperature gradient data to the monitoring system via a signal line.

[0008] Specifically, the key nodes are located in the wind tunnel space frame structure where the maximum stress or deformation occurs, including the maximum bending moment point at the mid-span of the space frame, the support connection, the nodes around the airflow channel with drastic temperature changes, and the structural transition nodes.

[0009] Specifically, the multi-physics parameter acquisition steps involve setting the sampling frequency to 100Hz, and when the deviation exceeds 15mm, initiating secondary compensation adjustment and increasing the monitoring frequency to 200Hz.

[0010] Specifically, the multiphysics coupling theory is based on a step-by-step iterative algorithm to perform coupled calculations of the flow field, solid field, and temperature field. Sub-loop iterations ensure that the convergence accuracy of each physical field calculation reaches 10⁻⁶. -6 Magnitude.

[0011] Specifically, the wind tunnel space frame deformation prediction equation set includes a temperature field influence equation and a stress field calculation equation. The temperature field influence equation takes temperature gradient, thermal expansion coefficient, elastic modulus and material density as inputs, and the stress field calculation equation takes load coefficient, section moment of inertia, stress concentration factor and yield strength as inputs.

[0012] Specifically, the step of judging the deviation value is to continue monitoring when the deviation value is in the range of 0 to 5 mm, to start a compensation adjustment when the deviation value is in the range of 5 to 15 mm, and to start a second compensation adjustment when the deviation value exceeds 15 mm.

[0013] Specifically, the steps for determining the compensation strategy involve using a linear interpolation method to correct the prediction model parameters for the first compensation adjustment, and using a nonlinear iterative algorithm to recalculate the multiphysics coupling coefficient for the second compensation adjustment, while simultaneously adjusting the temperature compensation coefficient and the load correction factor.

[0014] Specifically, the deformation compensation control equation set includes displacement compensation equation and stress compensation equation. The displacement compensation equation is input with the current displacement, target displacement, structural stiffness and damping coefficient, and the stress compensation equation is input with the measured stress value, allowable stress value, safety factor and material fatigue limit.

[0015] Specifically, the Newton-Raphson iterative method is a nonlinear equation solving algorithm that obtains the iterative increment by calculating the product of the inverse of the Jacobian matrix and the residual vector.

[0016] The coefficient of thermal expansion mentioned above is specifically an inherent physical property of steel, with a value of 12 × 10⁻⁶. -6 / ℃, obtained by consulting the material standard handbook, is used to calculate the thermal deformation of the structure using the temperature field influence equation.

[0017] The elastic modulus, specifically, is the ratio of stress to strain in steel within its elastic range, with a typical value of 206 GPa. It is obtained through material mechanical property testing and is used to calculate structural stiffness in the temperature field influence equation and stress field calculation equation.

[0018] The yield strength, specifically, is the stress value at which the material begins to undergo plastic deformation. For Q345 steel, the typical value is 345 MPa, obtained through tensile testing, and used in stress field calculation equations to determine whether the material has entered a plastic state.

[0019] Specifically, the compensation operation involves returning to the multiphysics parameter acquisition step after the compensation operation is completed, and then performing cyclic monitoring until the deformation deviation value stabilizes within the allowable range.

[0020] This invention addresses the problem of insufficient deformation prediction accuracy leading to poor compensation in existing technologies by establishing an accurate deformation prediction model based on multiphysics coupling theory, correcting prediction parameters using real-time monitoring data, and formulating effective compensation strategies based on accurate prediction results. The invention employs a fluid-structure-thermal coupling calculation method, accurately describing the interaction mechanism between various physical fields through the coupled solution of temperature field influence equations and stress field calculation equations. The deviation between predicted and measured deformation is controlled within a small range, providing a reliable data foundation for formulating compensation strategies. The deformation compensation control equations established in this invention calculate the compensation amount based on accurate prediction results, and determine specific compensation operation parameters through displacement compensation equations and stress compensation equations. This achieves a complete control process from accurate prediction to effective compensation, ensuring that the compensation operation can control structural deformation within the target range, significantly improving the reliability and accuracy of the compensation effect. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a stress ratio cloud diagram for the space frame construction stage in Example 2.

[0023] Figure 3 This is a stress ratio cloud diagram of the wind tunnel operation stage in Example 2.

[0024] Figure 4 This is a diagram of the fiber optic shape sensor in Example 2. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0026] like Figure 1 The diagram shown is a flowchart of a method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure provided by the present invention. This method includes the following steps:

[0027] S01. Low-temperature sensitive fiber optic strain sensors, shape sensors and temperature sensors are arranged at key nodes of the wind tunnel grid structure. The low-temperature sensitive fiber optic strain sensors are arranged in the middle of the members with stress concentration coefficient greater than 2.5, the shape sensors are installed at the mid-span of the span exceeding 20m, and the temperature sensors are fixed at the support connection points that are significantly affected by heat.

[0028] S02. Collect multi-physics parameters during wind tunnel operation, including temperature gradient, stress and strain, structural displacement, wind speed, airflow density and pressure distribution, and establish a real-time data matrix containing each monitoring point. The sampling frequency is set to 100Hz.

[0029] S03. Based on the multiphysics coupling theory, establish a set of wind tunnel space frame deformation prediction equations. Determine the structural response through fluid-structure-thermal coupling calculations. The temperature field influence equations input temperature gradient, thermal expansion coefficient, elastic modulus and material density. The stress field calculation equations input load coefficient, section moment of inertia, stress concentration factor and yield strength.

[0030] S04. Calculate the deviation between the predicted deformation and the measured deformation. When the deviation is within the range of 0 to 5 mm, continue monitoring. When the deviation is within the range of 5 to 15 mm, initiate a compensation adjustment. When the deviation exceeds 15 mm, initiate a second compensation adjustment and increase the monitoring frequency to 200 Hz.

[0031] S05. Determine the compensation strategy based on the deformation deviation range. For the first compensation adjustment, use the linear interpolation method to correct the prediction model parameters. For the second compensation adjustment, use the nonlinear iterative algorithm to recalculate the multiphysics coupling coefficient, and adjust the temperature compensation coefficient and load correction factor at the same time.

[0032] S06. Establish a set of deformation compensation control equations and solve for the compensation amount using the Newton-Raphson iteration method. The displacement compensation equation is input with the current displacement, target displacement, structural stiffness and damping coefficient, and the stress compensation equation is input with the measured stress value, allowable stress value, safety factor and material fatigue limit.

[0033] S07. Perform compensation operation by manually adjusting the prestress of the support, correcting the temperature control parameters, or changing the airflow parameters to control structural deformation. After the compensation operation is completed, return to S02 for cyclic monitoring until the deformation deviation value stabilizes within the allowable range.

[0034] The low-temperature sensitive fiber optic strain sensor is mounted on the surface of the steel structure member by surface bonding. The temperature sensitivity of the sensor is controlled within 1με / ℃. Wavelength division multiplexing technology is used to realize multi-point series monitoring. The sensor is connected to the data acquisition system through optical fiber to obtain stress and strain data.

[0035] The shape sensor consists of a PVC sleeve with an outer diameter of 70 mm and an inner thin-walled steel tube. A set of triangular array fiber optic strain sensors is arranged every 200 mm along the axial direction on the outer surface of the thin-walled steel tube. The length is adjusted by a sliding clamp. The shape sensor transmits the structural displacement data to the data processing system through optical fiber transmission.

[0036] The temperature sensor is directly fixed to the space frame member by a fastener, and the temperature sensor and the member are kept in close contact to ensure accurate temperature transmission. The temperature sensor transmits the temperature gradient data to the monitoring system through a signal line.

[0037] The key nodes are located in the wind tunnel space frame structure at the locations that bear the greatest stress or deformation, including the maximum bending moment point at the mid-span of the space frame, the support connection, the nodes around the airflow channel with drastic temperature changes, and the structural transition nodes.

[0038] The key monitoring locations are determined based on the finite element analysis results and are located in areas with a stress concentration factor greater than 2.5, including the connection nodes between the main truss and the secondary truss, the boundary constraint points of the space frame, the middle of large-span members, and the connection points of support members that are significantly affected by temperature.

[0039] The locations of each monitoring point include the monitoring point at the mid-span of the east main truss, the monitoring point at the mid-span of the west main truss, the monitoring point at the south end support connection, the monitoring point at the north end support connection, the monitoring point at the central intersection node, the monitoring point of the member at the airflow inlet, the monitoring point of the member at the airflow outlet, the monitoring point at the boundary of the temperature change area, the monitoring point in the high stress area of ​​the structure, and the monitoring point in the vibration sensitive area.

[0040] The multiphysics coupling theory is based on a step-by-step iterative algorithm to perform coupled calculations of the flow field, solid field, and temperature field. Sub-loop iteration ensures that the convergence accuracy of each physical field calculation reaches 10⁻⁶. -6 The magnitude and coupling theory are realized by establishing a finite element model using ANSYS software.

[0041] The temperature gradient is obtained by measuring temperature sensors arranged at different heights and positions on the grid structure, reflecting the temperature distribution changes of the structure during wind tunnel operation. The temperature gradient value is used to calculate the temperature field influence equation.

[0042] The stress concentration factor is calculated through finite element analysis and represents the ratio of local stress to average stress. The stress concentration factor value is used to determine key monitoring locations and input to the stress field calculation equation.

[0043] The coefficient of thermal expansion is an inherent physical property of steel, with a value of 12 × 10⁻⁶. -6 / ℃, the coefficient of thermal expansion value is obtained by consulting the material standard handbook, and is used to calculate the thermal deformation of the structure by the temperature field influence equation.

[0044] The elastic modulus is the ratio of stress to strain in steel within its elastic range, with a typical value of 206 GPa. The elastic modulus is obtained through material mechanical property testing and is used to calculate structural stiffness in the temperature field influence equation and stress field calculation equation.

[0045] The density of the material is the mass of steel per unit volume, with a typical value of 7850 kg / m³. 3 The material density was obtained by consulting the material's technical parameters and was used to calculate the influence of inertial forces in the temperature field influence equation.

[0046] The load factor is determined through wind tunnel operation analysis and includes the comprehensive influence coefficient of static load, dynamic load and temperature load. The load factor is obtained through statistical analysis of measured data and is used to calculate the structural response under load in the stress field calculation equation.

[0047] The moment of inertia of the cross section is calculated based on the geometric dimensions of the cross section of the space frame member, reflecting the bending stiffness characteristics of the cross section. The value of the moment of inertia of the cross section is used in the stress field calculation equation to calculate the bending deformation of the member.

[0048] The yield strength is the stress value at which the material begins to undergo plastic deformation. The typical value for Q345 steel is 345 MPa. The yield strength is obtained through a tensile test and is used in the stress field calculation equation to determine whether the material has entered the plastic state.

[0049] The current displacement is obtained in real time through shape sensor monitoring, reflecting the actual deformation state of the structure under the current working conditions. The current displacement value is used to calculate the compensation control quantity in the displacement compensation equation.

[0050] The target displacement is determined according to wind tunnel design requirements and structural safety standards, representing the ideal deformation state of the structure under normal working conditions. The target displacement value is used to calculate the displacement deviation in the displacement compensation equation.

[0051] The structural stiffness is obtained through structural mechanics calculations or finite element analysis, reflecting the structure's ability to resist deformation. The structural stiffness value is used to calculate the structural response using displacement compensation equations and stress compensation equations.

[0052] The damping coefficient is determined by structural dynamic characteristic tests and reflects the energy dissipation characteristics of structural vibration. The damping coefficient value is used to calculate the dynamic response using the displacement compensation equation.

[0053] The measured stress value is obtained by monitoring a low-temperature sensitive fiber optic strain sensor, reflecting the actual stress state of the member under the current load. The measured stress value is used to calculate the stress safety margin using the stress compensation equation.

[0054] The allowable stress value is calculated based on the material strength and safety factor, and represents the maximum stress that the material is allowed to withstand within a safe range. The allowable stress value is used in the stress compensation equation to determine the stress safety of the structure.

[0055] The safety factor is determined based on the importance of the structure and the load characteristics, with a typical value of 1.5 to 2.0. The safety factor is used to calculate the allowable stress value and stress safety margin in the stress compensation equation.

[0056] The material fatigue limit is the maximum stress value at which the material does not fail under an infinite number of cyclic loads. The material fatigue limit is obtained through fatigue testing and is used to evaluate the fatigue safety of the structure using the stress compensation equation.

[0057] The fluid-structure-thermal coupling calculation is implemented using ANSYS finite element software. By establishing the coupling interface between the fluid domain and the solid domain, a step-by-step iterative solution method is used to obtain the response of the structure under the action of multiple physics fields. The calculation results are used to predict the deformation calculation.

[0058] The Newton-Raphson iterative method is a nonlinear equation solving algorithm. It obtains the iteration increment by calculating the product of the inverse of the Jacobian matrix and the residual vector. The iterative method is used to solve the compensation amount of the deformation compensation control equation system.

[0059] The predicted deformation is calculated using a set of multiphysics field coupled deformation prediction equations, representing the theoretical deformation value of the structure under the current working conditions. The predicted deformation is used to compare with the measured deformation to calculate the deviation value.

[0060] The measured deformation is obtained in real time through shape sensor monitoring and represents the actual deformation value of the structure under the current working condition. The measured deformation is used to compare with the predicted deformation to calculate the deviation value.

[0061] The deviation value is the absolute value of the difference between the predicted deformation and the measured deformation. The deviation value is used to determine the accuracy of the prediction model and to determine the type of compensation strategy.

[0062] The temperature compensation coefficient is used to correct the influence of temperature changes on structural deformation. The temperature compensation coefficient is obtained through regression analysis of historical monitoring data and is used to correct the parameters of the temperature field influence equation in the compensation strategy.

[0063] The load correction factor is used to correct the difference between the actual load and the design load. The load correction factor is calculated by the ratio of the measured load to the design load and is used to correct the parameters of the stress field calculation equation in the compensation strategy.

[0064] The multiphysics coupled deformation prediction equation set includes a temperature field influence equation and a stress field calculation equation. The temperature field influence equation is used to calculate the effect of temperature change on structural deformation. The inputs include temperature gradient, coefficient of thermal expansion, elastic modulus and material density, and the output is the deformation caused by temperature. The stress field calculation equation is used to calculate the structural deformation response under load. The inputs include load coefficient, moment of inertia of section, stress concentration factor and yield strength, and the output is the deformation caused by load.

[0065] The deformation compensation control equation set includes displacement compensation equations and stress compensation equations. The displacement compensation equation is used to calculate the compensation amount required to achieve the target displacement. The inputs include the current displacement, the target displacement, the structural stiffness, and the damping coefficient. The output is the displacement compensation control amount. The stress compensation equation is used to calculate the compensation measures to maintain the stress within the safe range. The inputs include the measured stress value, the allowable stress value, the safety factor, and the material fatigue limit. The output is the stress compensation control amount.

[0066] The specific implementation methods of the above steps are described in detail below.

[0067] The specific implementation of step S01 involves deploying a layered monitoring system with multiple types of sensors at key nodes of the wind tunnel space frame structure. First, the locations of key nodes are identified based on finite element analysis results. Stress concentration factors greater than 2.5 are calculated to determine the mid-span locations of members. Low-temperature sensitive fiber optic strain sensors are then installed at these locations using surface bonding, ensuring the sensor temperature sensitivity is controlled within 1 με / ℃. Next, shape sensors are installed at the mid-span locations of large-span members exceeding 20m. These sensors consist of a 70mm outer diameter PVC sleeve and an inner thin-walled steel tube. A set of triangular array fiber optic strain sensors is arranged axially every 200mm on the outer surface of the thin-walled steel tube. Then, temperature sensors are fixed at support connections where heat significantly affects performance, ensuring a tight fit between the sensor and the member to guarantee accurate temperature transmission. Finally, wavelength division multiplexing (WDM) technology is used to achieve multi-point series monitoring, and sensor data is transmitted to the data acquisition system via fiber optic connections. The purpose of this step is to establish a comprehensive monitoring network covering key structural locations, providing accurate foundational data for subsequent deformation prediction and compensation control.

[0068] The specific implementation of step S02 involves collecting multiphysics parameters during wind tunnel operation and establishing a real-time data matrix. First, the sampling frequency is set to 100Hz. Temperature gradient data is synchronously collected using sensors positioned at different heights and locations on the grid structure to reflect the temperature distribution changes during wind tunnel operation. Next, low-temperature sensitive fiber optic strain sensors monitor stress-strain data in real time to obtain the actual stress state of the members under the current load. Then, shape sensors monitor structural displacement, and sliding clamps are used to adjust the length to accommodate different monitoring ranges. Simultaneously, flow field parameters such as wind speed, airflow density, and pressure distribution are collected, and airflow status data are obtained using flow meters and pressure sensors. Finally, a real-time data matrix is ​​established by arranging the data from all monitoring points in a time series, with each matrix element containing the physical parameter values ​​of the corresponding monitoring point at a specific time. The purpose of this step is to obtain the complete dataset required for multiphysics coupling analysis, providing data support for establishing an accurate deformation prediction model.

[0069] The specific implementation of step S03 is to establish a set of wind tunnel grid deformation prediction equations based on multiphysics coupling theory. First, a step-by-step iterative algorithm is used to perform coupled calculations of the flow field, solid field, and temperature field. By establishing the coupling interface between the fluid domain and the solid domain, the interaction between the various physical fields is ensured to be accurately simulated. Next, the temperature field influence equation is established, with the temperature gradient and thermal expansion coefficient (12 × 10⁻⁶) as input. -6 Temperature: ℃, Elastic modulus: 206 GPa, and Material density: 7850 kg / m³ 3 The effect of temperature changes on structural deformation was calculated. Then, a stress field calculation equation was established, and the load factor, section moment of inertia, stress concentration factor, and yield strength of 345 MPa were input to calculate the structural deformation response under load. Finally, a sub-loop iteration method was used to ensure that the convergence accuracy of each physical field calculation reached 10. -6 The magnitude is determined by establishing a computational model using ANSYS finite element software to achieve coupled solution. The purpose of this step is to establish a theoretical prediction model that can comprehensively consider multiple physical factors, providing a mathematical basis for accurately predicting structural deformation.

[0070] The specific implementation of step S04 involves calculating the deviation between the predicted deformation and the measured deformation and determining the monitoring strategy. First, the predicted deformation is calculated using a multiphysics-coupled deformation prediction equation set, representing the theoretical deformation value of the structure under the current working conditions. Next, the measured deformation is obtained in real-time through shape sensors, representing the actual deformation value of the structure under the current working conditions. Then, the absolute value of the difference between the predicted and measured deformations is calculated to obtain the deviation value, which is used to determine the accuracy of the prediction model. Finally, a monitoring and compensation strategy is determined based on the magnitude of the deviation. When the deviation is within the range of 0 to 5 mm, normal monitoring continues; when the deviation is within the range of 5 to 15 mm, a first compensation adjustment is initiated; and when the deviation exceeds 15 mm, a second compensation adjustment is initiated, and the monitoring frequency is increased to 200 Hz. The purpose of this step is to evaluate the accuracy of the prediction model by comparing the theoretical prediction value with the actual measurement value and to formulate corresponding control strategies based on the degree of deviation.

[0071] The specific implementation of step S05 involves determining the corresponding compensation strategy and parameter correction method based on the deformation deviation range. First, the range of the deviation value is determined. For primary compensation adjustments within the range of 5 to 15 mm, a linear interpolation method is used to correct the prediction model parameters. Key coefficients in the model are adjusted through linear relationships to reduce prediction errors. Next, for secondary compensation adjustments exceeding 15 mm, a nonlinear iterative algorithm is used to recalculate the multiphysics coupling coefficient. Nonlinear solution methods such as Newton's iteration method are employed to obtain more accurate coupling parameters. Then, the temperature compensation coefficient is adjusted. This coefficient is obtained through regression analysis of historical monitoring data and is used to correct the impact of temperature changes on structural deformation. Finally, the load correction factor is corrected. This factor is calculated by the ratio of the measured load to the design load and is used to correct the difference between the actual load and the design load. The purpose of this step is to employ parameter correction methods of varying complexity based on the prediction accuracy, ensuring that the prediction model can adapt to changes in actual working conditions.

[0072] The specific implementation of step S06 involves establishing a set of deformation compensation control equations and solving for the compensation amount. First, a displacement compensation equation is established, taking the current displacement, target displacement, structural stiffness, and damping coefficient as input, and outputting the displacement compensation control amount. This equation is used to calculate the compensation amount required to achieve the target displacement. Next, a stress compensation equation is established, taking the measured stress value, allowable stress value, safety factor of 1.5 to 2.0, and material fatigue limit as input, and outputting the stress compensation control amount. This equation is used to calculate compensation measures to maintain stress within a safe range. Then, the Newton-Raphson iterative method is used to solve for the compensation amount. This method is a nonlinear equation solving algorithm, obtaining the iteration increment by calculating the product of the inverse of the Jacobian matrix and the residual vector. Finally, the optimal compensation control amount is obtained through iterative calculation, ensuring that structural deformation can be controlled within the design allowable range. The purpose of this step is to establish a mathematical optimization model to calculate specific compensation operation amounts, providing quantitative guidance for actual structural adjustments.

[0073] The specific implementation of step S07 involves performing a compensation operation and establishing a cyclic monitoring and control system. First, the prestress of the supports is manually adjusted based on the calculated compensation amount. Adjusting the preload at the support connections changes the constraint state of the structure, thus affecting overall deformation. Next, the temperature control parameters are corrected. Adjusting the temperature distribution system inside the wind tunnel alters the thermal environment of the structure, thereby controlling thermal deformation. Then, airflow parameters are changed. Adjusting parameters such as wind speed and airflow density alters the aerodynamic load acting on the structure, thereby controlling load deformation. Finally, a cyclic monitoring mechanism is established. After the compensation operation is completed, the process returns to step S02 for cyclic monitoring, continuously collecting multiphysics parameters and recalculating deviation values ​​until the deformation deviation value stabilizes within the allowable range. The purpose of this step is to transform the theoretically calculated compensation amount into actual engineering operations and ensure the continuous effectiveness of the compensation effect through closed-loop control.

[0074] It should be noted that the first key technical idea of ​​this invention is the application of multiphysics coupling deformation prediction theory. Traditional methods typically employ single-physics field analysis, neglecting the interactions between the temperature field, stress field, and flow field, resulting in insufficient prediction accuracy. This invention achieves fluid-structure-thermal three-field coupling calculation through a step-by-step iterative algorithm, accurately simulating the complex physical phenomena and interactions during wind tunnel operation, significantly improving the accuracy of deformation prediction. Compared to traditional linear superposition methods, multiphysics coupling theory can capture nonlinear interaction effects, making the prediction results closer to actual working conditions.

[0075] The second key technical approach is a hierarchical compensation strategy based on deviation values. Traditional compensation methods typically employ fixed compensation algorithms, failing to adjust the complexity of the compensation strategy according to the actual degree of deviation, leading to low compensation efficiency or overcompensation. This invention uses compensation strategies with varying complexity based on the deviation range: linear interpolation correction for small deviations and nonlinear iterative algorithms for large deviations, achieving adaptive adjustment of the compensation strategy. This hierarchical strategy ensures both rapid response for small deviations and accurate compensation for large deviations, improving the overall compensation effect.

[0076] The third key technological approach is a closed-loop control system for real-time monitoring and dynamic compensation. Traditional methods typically employ offline analysis and static compensation, which cannot cope with the dynamic changes during wind tunnel operation, and the compensation effect easily decays over time. This invention establishes a complete closed-loop system from sensor monitoring, deviation calculation, strategy selection to compensation operation, capable of tracking structural state changes in real time and dynamically adjusting compensation measures. Through 100Hz high-frequency sampling and real-time data processing, the system can quickly respond to structural deformation changes, ensuring the continuity and stability of the compensation effect.

[0077] The synergistic effect of these key technological approaches offers significant advantages over existing technologies. Multiphysics coupling prediction provides an accurate theoretical basis for the hierarchical compensation strategy, which in turn provides an efficient execution scheme for the closed-loop control system. The closed-loop control system, in turn, provides continuous data feedback and model optimization for multiphysics coupling prediction. These three elements form a mutually reinforcing technological system, achieving optimization throughout the entire process from theoretical prediction to actual control. Compared to traditional empirical compensation methods, this synergistic technological system enables more precise deformation control, faster dynamic response, and more stable long-term effects, providing reliable technical assurance for the safe and stable operation of wind tunnel space frame structures.

[0078] It should be noted that this invention also solves the following technical problems: Existing technologies suffer from a lack of scientific basis and quantitative control in compensation operations. Traditional wind tunnel space frame structure deformation compensation mainly relies on the operator's experience and judgment, determining compensation measures by observing structural deformation phenomena and equipment operating conditions. This qualitative compensation method lacks scientific theoretical basis and precise numerical calculation support, and the determination of the compensation amount is often highly subjective and arbitrary, making it difficult to guarantee the consistency and repeatability of the compensation effect. This invention, by establishing a set of deformation compensation control equations, transforms the compensation problem into a mathematical optimization problem. The displacement compensation equation calculates the displacement compensation control amount based on objective parameters such as the current displacement, target displacement, structural stiffness, and damping coefficient. The stress compensation equation calculates the stress compensation control amount based on engineering parameters such as measured stress value, allowable stress value, safety factor, and material fatigue limit. The Newton-Raphson iterative method is used to solve the nonlinear equation set to obtain the optimal compensation scheme. This quantitative compensation method based on mathematical models not only provides a scientific theoretical basis but also enables precise calculation of the compensation amount according to specific structural parameters and operating conditions, achieving a technological leap from experience-based compensation to scientific compensation, significantly improving the accuracy and effectiveness of compensation operations.

[0079] Specifically, the principle of this invention is as follows: The fundamental reason why the technical solution of this invention can solve the problem of insufficient deformation prediction accuracy leading to poor compensation effect in the prior art lies in its construction of an accurate multi-physics coupling prediction model and an adaptive compensation mechanism based on accurate prediction. First, the multi-physics coupling theory established by this invention based on a step-by-step iterative algorithm can accurately describe the nonlinear interaction between the temperature field, stress field, and flow field. The temperature field influence equation calculates thermally induced deformation by inputting parameters such as temperature gradient, thermal expansion coefficient, elastic modulus, and material density. The stress field calculation equation calculates load-induced deformation by inputting parameters such as load coefficient, cross-sectional moment of inertia, stress concentration factor, and yield strength. The two equations achieve data exchange and mutual influence through a coupling interface, ensuring that the convergence accuracy of each physical field calculation reaches the order of 10 to the power of -6, thereby obtaining high-precision deformation prediction results. Secondly, this invention acquires accurate boundary conditions and load parameters through a real-time monitoring system, compares and analyzes the measured data with the prediction model, and automatically initiates a model parameter correction program when the deviation exceeds a set threshold. For small deviations, a linear interpolation method is used to correct the prediction model parameters; for large deviations, a nonlinear iterative algorithm is used to recalculate the multiphysics coupling coefficient. This adaptive adjustment mechanism ensures that the prediction model always maintains high accuracy. Finally, based on the accurate prediction results, this invention establishes a set of deformation compensation control equations, solves for the optimal compensation amount using the Newton-Raphson iterative method, and transforms the difference between the current deformation state and the target deformation state into specific compensation operation parameters. By adjusting the support prestress, temperature control parameters, or airflow parameters, precise deformation control is achieved, forming a closed-loop control system from accurate prediction to effective compensation.

[0080] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0081] In this embodiment, the specific implementation of step S01 is the same as described above, and will not be repeated in detail here.

[0082] The specific implementation of step S02 involves collecting multiphysics parameters during wind tunnel operation and establishing a real-time data matrix. The real-time data matrix is ​​specifically represented as follows:

[0083]

[0084] In the formula, D is the real-time data matrix; T ij S represents the temperature gradient at the i-th monitoring point at time j, in °C / m. ij U represents the stress and strain at the i-th monitoring point at time j, in MPa. ij V represents the structural displacement at the i-th monitoring point at time j, in meters. ij Let ρ be the wind speed at the i-th monitoring point at time j, in m / s; gijLet be the airflow density at the i-th monitoring point at time j, in kg / m³. 3 ;P ij Let represent the pressure distribution at the i-th monitoring point at time j, in Pa; n is the total number of monitoring points, dimensionless.

[0085] The specific implementation of step S03 is based on establishing a set of wind tunnel grid deformation prediction equations according to multiphysics coupling theory. The temperature field influence equation is specifically expressed as follows:

[0086]

[0087] In the formula, Δ T The value is the deformation caused by temperature, in meters (m); α is the coefficient of thermal expansion, with a value of 12 × 10⁻⁶. -6 / ℃; Temperature gradient, unit: °C / m; L, component length, unit: m; E, elastic modulus, value: 206 GPa; ρ mat The material density is 7850 kg / m³. 3 g is the acceleration due to gravity, with a value of 9.8 m / s². 2 ;ε T This represents the error term in the temperature field calculation, expressed in meters (m). The specific equation for the stress field calculation is as follows:

[0088]

[0089] In the formula, Δ S K represents the deformation caused by the load, in meters (m). L 1 is the load factor, dimensionless; M is the bending moment, in N·m; I is the moment of inertia of the section, in m. 4 ;K c σ is the stress concentration factor, dimensionless; β is the correction factor, dimensionless, ranging from 0.8 to 1.2; y ε is the yield strength, with a value of 345 MPa; h is the section height, in meters (m); ω is the angular frequency, in rad / s; t is the time, in seconds (s); ε S This represents the error term in the stress field calculation, expressed in meters (m).

[0090] The specific implementation of step S04 involves calculating the deviation between the predicted deformation and the measured deformation. The total deformation prediction equation is specifically expressed as follows:

[0091] Δ pred =Δ T +Δ S +λ·Δ T ·Δ S +ε coup ;

[0092] In the formula, Δpred The deformation is predicted in meters (m); λ is the coupling coefficient, also in meters (m). -1 ;ε coup This represents the error term in the coupling calculation, expressed in meters (m). The equation for calculating the deviation value is as follows:

[0093] δ=||Δ pred -Δ meas ||;

[0094] In the formula, δ is the deviation value, with units of m; Δ meas The measured deformation is expressed in meters (m).

[0095] The specific implementation of step S05 involves determining a compensation strategy based on the deformation deviation range. The linear interpolation correction equation is specifically expressed as follows:

[0096]

[0097] In the formula, θ new The corrected model parameters have dimensions that depend on the specific parameter type; θ old The original model parameters, with dimensions equal to θ. new Same; k1 is the proportionality coefficient, in units of s. -1 The range is 0.1 to 0.3; k2 is the differential coefficient, dimensionless, ranging from 0.05 to 0.15. The nonlinear iterative algorithm uses Newton's iteration method, specifically expressed as follows:

[0098] θ k+1 =θ k -J -1 (θ k )·F(θ k );

[0099] In the formula, θ k+1 θ represents the parameter value for the (k+1)th iteration, with dimensions depending on the specific parameter type. k Let be the parameter value for the k-th iteration, with dimensions equal to θ. k+1 Same; J(θ) k F(θ) is a Jacobian matrix whose dimensions depend on the specific types of the function and its parameters; k ) is the residual function, and its dimensions depend on the specific function type.

[0100] The specific implementation of step S06 is to establish a set of deformation compensation control equations. The displacement compensation equations are specifically expressed as follows:

[0101]

[0102] In the formula, F comp The compensating force is expressed in N; K s This is the structural stiffness matrix, in N / m; utarget The target displacement is expressed in meters (m); u current The current displacement is expressed in meters (m); C is the damping matrix, in N·s / m; u comp K represents the displacement compensation control quantity, in meters (m). adj To adjust stiffness, the unit is N / m. The stress compensation equation is expressed as follows:

[0103]

[0104] In the formula, σ comp This is the stress compensation control quantity, in MPa; σ allow σ represents the allowable stress value, in MPa. meas The measured stress value is expressed in MPa; n s The safety factor is dimensionless and ranges from 1.5 to 2.0; σ fatigue The fatigue limit of the material is expressed in MPa; N represents the number of cycles, which is dimensionless.

[0105] The specific implementation method of step S07 is the same as described above, and will not be repeated in detail here.

[0106] The parameter acquisition method is as follows: The temperature (M) is obtained experimentally, including step 1: deploying an array of temperature sensors at different heights on the space frame; and step 2: calculating the ratio of the temperature difference to the distance between adjacent measuring points using a numerical difference method. The bending moment (I) is obtained computationally, including step 1: calculating the force on each member based on the load distribution; and step 2: calculating the bending moment distribution using material mechanics theory. The moment of inertia (K) is obtained geometrically, including step 1: measuring the cross-sectional dimensions of the members; and step 2: calculating the moment of inertia using formulas based on the geometric properties of the cross-section. L The following parameters were obtained: λ was obtained using statistical analysis, including step 1: collecting wind tunnel operating condition data; and step 2: determining the relationship between load coefficients and operating condition parameters through regression analysis. λ was obtained using parameter identification, including step 1: establishing a coupling effect test; and step 2: fitting the coupling coefficient using the least squares method. β was obtained using calibration testing, including step 1: conducting deformation tests under standard loads; and step 2: determining correction coefficients by comparing theoretical and measured values. h was obtained using direct measurement, including step 1: measuring the section height of the member using vernier calipers; and step 2: taking the average of multiple measurements as the section height. ω was obtained using vibration testing, including step 1: conducting modal analysis tests on the structure; and step 2: determining the main vibration frequencies through spectral analysis. K adj The stiffness is obtained through a stiffness test, including step 1: applying a standard force to the compensation device; and step 2: measuring the corresponding displacement and calculating the adjustment stiffness. ρ matThe density is obtained using a material standard lookup method, including step 1: consulting the material technical specifications to determine the standard density value; and step 2: verifying the material density through laboratory density testing. ρ gij The data is obtained using fluid measurement methods, including step 1: measuring the airflow density at different locations within the wind tunnel using a gas densitometer; and step 2: establishing a model relating airflow density to temperature and pressure. allow The material safety design approach is used to obtain the required strength, including step 1: determining the baseline strength by consulting material strength standards; and step 2: calculating the allowable stress value based on the safety factor. σ fatigue The fatigue life test method was used, including step 1: conducting material fatigue life tests; and step 2: determining the fatigue limit value through the SN curve. The load history (N) was obtained through load history statistics, including step 1: recording the number of load cycles borne by the structure; and step 2: calculating the equivalent number of cycles using the rainflow counting method. T The range is 0.02~0.05mm, ε S The range is 0.01~0.03mm, ε coup The range is 0.03 to 0.08 mm.

[0107] It should be explained that the temperature field influence equation is based on thermoelastic theory, and the first term of the equation is a linear thermal deformation term:

[0108]

[0109] The second term in the equation is the second-order effect term of the temperature gradient:

[0110]

[0111] Compared with the traditional linear thermal deformation formula, this equation adds higher-order terms and material nonlinear effects, which can more accurately predict the thermal deformation of structures under large temperature difference conditions, improve the accuracy of temperature deformation prediction, and is particularly suitable for complex working conditions with uneven temperature distribution inside wind tunnels.

[0112] The stress field calculation equation combines elasticity theory and dynamic effects. The first term of the equation is the static bending deformation term:

[0113]

[0114] The second term of the equation is the dynamic load effect term:

[0115]

[0116] Compared with the traditional static analysis method, this equation introduces stress concentration factor and dynamic effect, which can accurately reflect the influence of the dynamic characteristics of aerodynamic load on structural deformation during wind tunnel operation, and significantly improve the prediction accuracy of load deformation.

[0117] The total deformation prediction equation adopts multiphysics coupling theory, and the coupling terms are expressed as:

[0118] λ·Δ T ·Δ S ;

[0119] Compared with the traditional independent field analysis method, this equation takes into account the inter-field coupling effect, avoids the prediction error caused by simple superposition, and realizes accurate prediction of structural deformation under complex multiphysics field environment.

[0120] The linear interpolation correction equation is based on the proportional-derivative control concept in control theory, and the proportional control term is:

[0121] k1·δ;

[0122] The differential control term is:

[0123]

[0124] Compared to fixed-parameter methods, this equation has the ability to dynamically adjust, automatically correcting model parameters based on prediction errors, thus improving the adaptability and robustness of the prediction model.

[0125] Newton's iterative algorithm uses the inverse of the Jacobian matrix to solve the nonlinear equation system. The iterative formula is as follows:

[0126] θ k+1 =θ k -J -1 (θ k )·F(θ k );

[0127] Compared with the first-order algorithm, this algorithm has a faster convergence speed and higher solution accuracy. It is particularly suitable for handling complex nonlinear compensation problems with large deviations, ensuring the fast response and accurate calculation of the compensation strategy.

[0128] The displacement compensation equation is based on structural dynamics theory, and the static stiffness term is:

[0129] K s ·(u target -u current );

[0130] The dynamic damping term is:

[0131]

[0132] Compared with the traditional static compensation method, this equation adds a damping term, which can suppress oscillations during the compensation process, achieve smooth displacement control, and improve the stability and accuracy of the compensation operation.

[0133] The stress compensation equation takes into account the material safety factor and fatigue characteristics, and the safety margin term is:

[0134]

[0135] The fatigue life item is:

[0136]

[0137] Compared with traditional stress control methods, this equation introduces a fatigue life influencing factor, which can comprehensively consider instantaneous safety and long-term fatigue performance, ensuring the long-term safe operation of the structure under dynamic loads.

[0138] To better understand and implement this invention, a specific application scenario is provided below as Example 2: A wind tunnel employs a double-layer space frame structure, primarily bearing airflow loads, temperature loads, and its own weight loads. According to design requirements, the maximum allowable deformation of the space frame structure under operating conditions is 20 mm, and the stress safety factor must not be less than 1.8. The technical team adopted the wind tunnel space frame structure deformation prediction and compensation scheme of this invention to establish a complete monitoring and control system.

[0139] During the sensor deployment phase, the technical team first conducted finite element analysis, identifying 36 critical locations with stress concentration factors greater than 2.5. Seventy-two low-temperature sensitive fiber Bragg grating strain sensors were installed at locations such as the middle of the main truss, secondary truss connection nodes, and support connections, with sensor temperature sensitivity controlled at 0.8 με / ℃. Shape sensors were installed at the mid-span of eight large-span members exceeding 20m. Each sensor consisted of a 70mm outer diameter PVC sleeve and a 60mm inner diameter thin-walled steel pipe, with triangular array fiber Bragg grating strain sensors arranged every 200mm axially. Temperature sensors were installed at 12 support connections significantly affected by heat, ensuring tight contact between the sensors and the members. Wavelength division multiplexing (WDM) technology was used to achieve multi-point series monitoring, with all sensors connected to the data acquisition system via fiber optic cables, establishing a comprehensive monitoring network covering key structural locations.

[0140] The data acquisition system is set to a sampling frequency of 100Hz, simultaneously acquiring multi-physics parameters. The temperature monitoring range is from ambient temperature (20℃) to the maximum operating temperature (65℃), with a maximum temperature gradient of 45℃ / m. The stress-strain monitoring range is 0 to 350MPa, corresponding to the yield strength of Q345 steel (345MPa). The structural displacement monitoring accuracy reaches 0.1mm, with a monitoring range of ±50mm. Simultaneously, wind speed is collected within the range of 80–120m / s, and airflow density is collected within the range of 1.1–1.3kg / m³. 3Flow field parameters such as pressure distribution variation range ±500Pa were established. A real-time data matrix containing 92 monitoring points was constructed. Each matrix element contains six physical parameters: temperature, strain, displacement, wind speed, density, and pressure, forming a 92×6 data matrix structure.

[0141] Deformation prediction equations were established based on multiphysics coupling theory. A finite element model was built using ANSYS software, with 156,000 mesh elements and 68,000 nodes. The input parameters for the temperature field influence equation included: a temperature gradient of 45℃ / m and a thermal expansion coefficient of 12×10⁻⁶. -6 / ℃, elastic modulus 206GPa, material density 7850kg / m³ 3 The input parameters for the stress field calculation equations include: load factor 1.2, and section moment of inertia 2.8 × 10⁻⁶. -4 m 4 The stress concentration factor is 3.2, and the yield strength is 345 MPa. A step-by-step iterative algorithm is used to perform fluid-structure-thermal coupling calculations, achieving a sub-loop iteration convergence accuracy of 1×10⁻⁶. -6 The scale is such that the time for a single calculation is controlled within 15 minutes.

[0142] During actual operation, the system continuously monitors the deformation deviation. In the initial operation phase, the predicted deformation was 12.5 mm, the measured deformation was 11.8 mm, and the deviation was 0.7 mm, within the normal range of 0–5 mm. The system continued normal monitoring. As operating conditions changed, when the airflow temperature rose to 58℃, the predicted deformation reached 18.2 mm, the measured deformation was 25.6 mm, and the deviation was 7.4 mm, within the range of 5–15 mm. The system then initiated a compensation adjustment. A linear interpolation method was used to correct the prediction model parameters. The temperature compensation coefficient was adjusted from 1.05 to 1.12, and the load correction factor was adjusted from 0.98 to 1.03. After the correction, the prediction accuracy improved to a deviation of 3.2 mm.

[0143] When the wind tunnel entered its extreme operating condition, the airflow temperature reached 65℃, the wind speed increased to 115m / s, the predicted deformation was 22.8mm, and the measured deformation was 39.5mm, with a deviation of 16.7mm, exceeding the 15mm threshold. The system immediately initiated secondary compensation adjustment, increasing the monitoring frequency to 200Hz. A nonlinear iterative algorithm was used to recalculate the multiphysics coupling coefficient, and the compensation control quantity was solved using the Newton-Raphson iterative method. The displacement compensation equation was input with the current displacement of 39.5mm, the target displacement of 18mm, and a structural stiffness of 8.5×10⁻⁶. 8With a damping coefficient of 0.02 and a measured stress value of 298 MPa, an allowable stress value of 192 MPa, a safety factor of 1.8, and a material fatigue limit of 180 MPa, the calculated stress compensation control value is -106 MPa.

[0144] The compensation operation employed a multi-pronged approach. First, the prestress of the eight main supports was adjusted by using hydraulic jacks to increase the preload from 850kN to 1200kN, altering the structural constraint state. Second, temperature control parameters were corrected, and the auxiliary cooling system was activated to reduce the temperature in critical areas from 65℃ to 52℃, controlling the impact of thermal deformation. Finally, airflow parameters were adjusted by regulating the fan speed, reducing the wind speed from 115m / s to 95m / s, thus minimizing aerodynamic loads. After the compensation operation, the system returned to cyclic monitoring mode, continuously monitoring changes in various parameters.

[0145] The data for verifying the compensation effect are shown in Table 1:

[0146] Table 1 Comparison of key parameters before and after compensation

[0147] Monitoring parameters Values ​​before compensation Compensated values Improvement range Maximum displacement 39.5mm 17.2mm 56.5％ Maximum stress value 298MPa 185MPa 37.9％ Temperature gradient 45℃ / m 28℃ / m 37.8％ Prediction deviation value 16.7mm 2.8mm 83.2％

[0148] After 48 hours of continuous operation and monitoring, the system performance stability data are shown in Table 3:

[0149] Table 3. Results of Long-Term Stability Monitoring of the System

[0150] Time period Average deviation Maximum deviation value Number of compensations Monitoring reliability 0～12h 2.1mm 4.3mm 0 99.8％ 12～24h 3.6mm 8.2mm 2 99.6％ 24～36h 2.8mm 6.1mm 1 99.7％ 36～48h 3.2mm 7.4mm 2 99.5％

[0151] The sensor performance verification data are shown in Table 4:

[0152] Table 4 Main Sensor Performance Indicators

[0153] Sensor type Installation quantity Measurement accuracy Response time operational reliability strain sensor 72 ±2με 0.1s 98.6％ Shape sensor 8 ±0.1mm 0.5s 97.8％ Temperature sensor 12 ±0.2℃ 1.0s 99.2％

[0154] The entire monitoring and compensation system achieved the expected control effect under complex working conditions. The prediction model established through multiphysics coupling theory has high accuracy, with prediction deviation controlled within 3mm under normal working conditions. The hierarchical compensation strategy can adaptively adjust the control intensity according to the degree of deviation, ensuring that structural deformation is always controlled within a safe range. The low-temperature sensitive fiber optic strain sensor performs stably in high-temperature environments, with the temperature influence coefficient controlled within the design specifications. The shape sensor, arranged in a triangular array, achieves high-precision deformation measurement, meeting the monitoring needs of large-span structures. The temperature sensor arrangement is reasonable and can accurately reflect the temperature field distribution characteristics of the structure.

[0155] Appendix Figure 2The stress ratio contour map shown during the construction phase of the space frame clearly identifies stress concentration areas, providing a scientific basis for sensor placement. (Attached) Figure 3 The stress ratio contour plots during wind tunnel operation are presented, reflecting the stress distribution characteristics under actual working conditions and verifying the accuracy of the theoretical analysis. (Attached) Figure 4 The structural design of the fiber optic shape sensor is presented, and its working principle and installation method are explained in detail. The shape sensor consists of a PVC sleeve, thin-walled steel sliding clamps, and fiber optic grating strain sensors. The PVC sleeve has an outer diameter of 70mm and an inner diameter of 60mm, with four grooves, each 1.5mm deep, perpendicular to each other in its cross-section. The thin-walled steel tube has an outer diameter of 27mm and a thickness of 1.5mm. Sliding clamps are arranged at regular intervals along its axial direction. These clamps are installed on the outer surface of the thin-walled steel tube by tightening the top screws. Four ball-head plungers are installed at the ends of the clamps, their positions matching the grooves in the PVC sleeve, ensuring free sliding of the thin-walled steel tube within the sleeve. Two strain measurement points are set between two adjacent sliding clamps. Each measurement point uses a triangular array strain sensor layout, with a clamp-on FBG strain sensor arranged every 120mm circumferentially. The FBG sensor has fixed supports welded to the outer surface of a thin-walled steel tube at both ends, with a mechanical connection between the sensor and the fixed supports. Utilizing wavelength division multiplexing (WDM) technology, the demodulator can independently identify fiber gratings with different center wavelengths connected in series on the same optical fiber, reducing the number of demodulator channels required for multi-sensor signal acquisition and facilitating internal wiring within the shape sensor. Furthermore, fiber optic transmission has strong anti-interference capabilities, enabling long-distance transmission of optical signals, which is beneficial for remote monitoring. The thin-walled steel tube with FBG strain sensors distributed on its surface serves as the sensing element. Different measurement requirements can be met by adjusting the position and number of sliding clamps. The external PVC sleeve not only protects the internal strain sensor but also allows the internal steel tube to be pulled out using the sliding clamps in case of sensor damage, facilitating strain sensor replacement.

[0156] This invention represents a significant technological advancement over traditional deformation monitoring and control methods. Traditional methods rely primarily on periodic manual inspections and experience-based judgment, resulting in low monitoring accuracy, delayed response, and an inability to achieve real-time control. The intelligent monitoring system established in this invention enables all-weather automatic monitoring. Traditional compensation adjustments depend mainly on engineering experience, making it difficult to quantify and evaluate the adjustment effects. This invention, through the establishment of mathematical models and optimization algorithms, achieves precise calculation and automatic adjustment of compensation amounts. Traditional monitoring methods cannot consider the coupling effects of multiple physics fields, resulting in limited prediction accuracy. This invention comprehensively considers the interactions of temperature, stress, and flow fields, establishing a more accurate prediction model and providing strong protection for the safe operation of large wind tunnel space frame structures.

[0157] It should be noted that the variables involved in this invention are explained in detail in Table 5.

[0158] Table 5. Variable Explanation Table

[0159]

[0160]

[0161] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting and determining the compensation scheme for deformation of a wind tunnel space frame structure, characterized in that, Low-temperature sensitive fiber optic strain sensors, shape sensors, and temperature sensors are deployed at key nodes of the wind tunnel space frame structure to collect multiphysics parameters during wind tunnel operation, including temperature gradient, stress-strain, structural displacement, wind speed, airflow density, and pressure distribution, establishing a real-time data matrix containing each monitoring point. Based on multiphysics coupling theory, a set of deformation prediction equations for the wind tunnel space frame is established, and the structural response is determined through fluid-structure-thermal coupling calculations. The deviation between the predicted and measured deformation is calculated; monitoring continues when the deviation is within a set range, and compensation adjustment and increased monitoring frequency are initiated when the deviation exceeds the set range. A compensation strategy is determined based on the deformation deviation range: linear interpolation is used to correct the prediction model parameters for small deviations, and nonlinear iterative algorithms are used to recalculate the multiphysics coupling coefficients for large deviations. A set of deformation compensation control equations is established, and the compensation amount is solved using the Newton-Raphson iterative method. Compensation operations are performed, and structural deformation control is achieved by manually adjusting the support prestress, correcting temperature control parameters, or changing airflow parameters.

2. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 1, characterized in that, The low-temperature sensitive fiber optic strain sensor is specifically mounted on the surface of a steel structure member by surface bonding. The sensor's temperature sensitivity is controlled within 1 με / ℃. Wavelength division multiplexing technology is used to achieve multi-point series monitoring. The sensor is connected to a data acquisition system via optical fiber to acquire stress and strain data.

3. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 2, characterized in that, The shape sensor is specifically composed of a PVC sleeve with an outer diameter of 70mm and an inner thin-walled steel tube. A set of triangular array fiber optic strain sensors is arranged every 200mm along the axial direction on the outer surface of the thin-walled steel tube. The length is adjusted by a sliding clamp. The shape sensor transmits the structural displacement data to the data processing system through optical fiber transmission.

4. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 3, characterized in that, Specifically, the temperature sensor is directly fixed to the space frame member using a fastener. The temperature sensor and the member are kept in close contact to ensure accurate temperature transmission. The temperature sensor transmits the temperature gradient data to the monitoring system via a signal line.

5. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 4, characterized in that, The key nodes are specifically located in the wind tunnel space frame structure where the maximum stress or deformation occurs, including the maximum bending moment point at the mid-span of the space frame, support connections, nodes around airflow channels with drastic temperature changes, and structural transition nodes.

6. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 5, characterized in that, The acquisition steps for the multiphysics parameters are as follows: the sampling frequency is set to 100Hz, and when the deviation exceeds 15mm, a secondary compensation adjustment is initiated and the monitoring frequency is increased to 200Hz.

7. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 6, characterized in that, The aforementioned multiphysics coupling theory specifically utilizes a step-by-step iterative algorithm to perform coupled calculations of the flow field, solid field, and temperature field. Sub-loop iterations ensure that the convergence accuracy of each physical field calculation reaches 10⁻⁶. -6 Magnitude.

8. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 7, characterized in that, The wind tunnel space frame deformation prediction equation set specifically includes a temperature field influence equation and a stress field calculation equation. The temperature field influence equation takes temperature gradient, thermal expansion coefficient, elastic modulus and material density as inputs, while the stress field calculation equation takes load coefficient, section moment of inertia, stress concentration factor and yield strength as inputs.

9. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 8, characterized in that, The steps for judging the deviation value are as follows: when the deviation value is in the range of 0 to 5 mm, continue monitoring; when the deviation value is in the range of 5 to 15 mm, initiate a compensation adjustment; and when the deviation value exceeds 15 mm, initiate a second compensation adjustment.

10. The method for predicting and determining the deformation compensation scheme of a wind tunnel space frame structure according to claim 9, characterized in that, The steps for determining the compensation strategy are as follows: for the first compensation adjustment, a linear interpolation method is used to correct the prediction model parameters; for the second compensation adjustment, a nonlinear iterative algorithm is used to recalculate the multiphysics coupling coefficient, while simultaneously adjusting the temperature compensation coefficient and the load correction factor.

Images