A transonic cascade wind tunnel test system

Abstract

The application belongs to the technical field of wind tunnel test, and particularly relates to a transonic fan blade engine wind tunnel test system. By introducing a high-precision data synchronous recording device, it is ensured that the displacement signals captured by the vibration sensing component and the pressure data obtained by the dynamic pressure acquisition unit can be completely aligned in time. At the same time, the system also integrates an environmental parameter monitor, which can track and compensate the changes of physical quantities such as temperature, humidity and air pressure in real time, thereby significantly improving the accuracy of experimental data. Finally, the addition of an analysis platform and a result visualization interface realizes in-depth analysis of the blade vibration mode, provides intuitive and detailed data support for researchers, and greatly improves the understanding of the dynamic behavior of the blade under transonic conditions.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wind tunnel test, and particularly relates to a transonic fan blade engine wind tunnel test system. BACKGROUND

[0002] In the field of aeronautical engineering, studying the dynamic behavior of blades under transonic conditions is crucial for improving the performance and safety of aircraft. Traditional wind tunnel test systems are mainly used to evaluate the static characteristics of blades in steady airflow, but as the requirements for aircraft performance increase, especially the complexity of supersonic and transonic flight environments, existing test systems have deficiencies in capturing the transient vibration response of blades.

[0003] Currently, most wind tunnel test systems rely on fixedly installed sensors to measure the displacement, pressure and other parameters of blades. These systems usually include basic wind tunnel structures, blade mounting devices and simple data acquisition equipment. However, such systems often lack precise time synchronization recording capabilities, making it difficult to achieve high-precision matching between the sensing signals and pressure data. In addition, the influence of environmental parameters such as temperature, humidity and air pressure on experimental results has not been fully considered, limiting the accuracy and reliability of experimental data.

[0004] In view of the above situation, a significant technical problem is that the existing wind tunnel test system cannot accurately synchronize the recording of the subtle displacement changes on the blade surface and the instantaneous pressure fluctuations caused by airflow, which limits the study of the true vibration pattern of the blade under transonic conditions. SUMMARY

[0005] The purpose of the present application is to provide a transonic fan blade engine wind tunnel test system, which realizes in-depth analysis of the vibration pattern of the blade, provides intuitive and detailed data support for researchers, and greatly improves the understanding of the dynamic behavior of the blade under transonic conditions, to solve the problems raised in the background art.

[0006] To achieve the above object, the application adopts the following technical scheme: A transonic blade machine wind tunnel test system, comprising: a wind tunnel main body, a stable airflow environment is formed in the wind tunnel main body, which is suitable for simulating the aerodynamic characteristics under real flight conditions; a blade mounting structure connected with the wind tunnel main body, used for fixing the blade to be tested and ensuring that it is in a predetermined position in the airflow; a vibration sensing component attached to the blade mounting structure, used to detect the displacement change of the blade surface; a dynamic pressure acquisition unit arranged adjacent to the vibration sensing component, responsible for capturing the instantaneous pressure fluctuation caused by the airflow; a data synchronous recording device connected with the dynamic pressure acquisition unit, realizing the simultaneous recording of the sensing signal and the pressure data; an environmental parameter monitor located inside the wind tunnel main body, tracking the changes of temperature, humidity and air pressure physical quantities; a control panel connected to the environmental parameter monitor, providing an interface for the operator to set and monitor the experimental conditions; an analysis platform receiving information from the data synchronous recording device, processing to reveal the blade vibration mode; and a result visualization interface integrated in the analysis platform, presenting the processed data.

[0007] Preferably, the wind tunnel main body comprises:

[0008] An airflow stabilization zone is located at the entrance of the wind tunnel main body, which adjusts the airflow speed and direction to ensure the uniformity and stability of the airflow before entering the experimental section;

[0009] An adjustable converging-diverging section is connected after the airflow stabilization zone, the internal shape of this section can be adjusted according to the experimental requirements to change the airflow cross-sectional area A, and the force F acting on the blade is calculated according to the formula F=1 / 2*ρ*V^2*A*C_d, wherein ρ represents the air density, V is the airflow speed, and C_d is the drag coefficient;

[0010] A dynamic adjustment wall is arranged after the adjustable converging-diverging section, which can respond to the force F calculated and adjust the wall position in real time to maintain the preset aerodynamic parameters unchanged;

[0011] A flow field feedback loop is established based on the dynamic adjustment wall, which uses sensors to monitor the actual flow field parameters and compares these parameters with the set values, if the deviation exceeds the allowed range, a signal is triggered to the adjustable converging-diverging section for corresponding adjustment.

[0012] Preferably, the blade mounting structure comprises:

[0013] A fixed base connected with the wind tunnel main body provides basic support to ensure that the blade to be tested maintains a fixed position during the entire experiment;

[0014] An adjustable support arm is connected to the fixed base, and has multi-axis rotation capability to allow precise adjustment of the angle θ of the blade. The bending moment M acting on the blade is calculated using the formula M = F * L * sin(θ), where F represents the force applied to the blade, and L is the length of the force arm.

[0015] A displacement adjustment device is integrated in the adjustable support arm to adjust the front-to-back position x of the blade. The pressure difference ΔP caused by the change in position is determined using the formula ΔP = ρ * V^2 * (C_p1 - C_p2) / 2, where ρ is the air density, V is the air flow speed, and C_p1 and C_p2 represent the pressure coefficients at the front and back of the blade, respectively.

[0016] An automatic alignment system relies on feedback provided by the displacement adjustment device to automatically correct the attitude of the blade. When a deviation δ is detected, the value of x is adjusted until the deviation is minimized, ensuring that the blade is always located at the predetermined position, according to the formula δ = x_target - x_actual.

[0017] Preferably, the vibration sensing assembly includes:

[0018] A displacement sensor is attached to the blade mounting structure and can capture extremely subtle displacement changes on the surface of the blade. The sensor output signal S is linearly related to the actual displacement d, and the expression is S = k * d, where k represents the sensitivity coefficient of the sensor.

[0019] A signal amplification circuit is connected to the displacement sensor to enhance the original signal strength, ensuring that the subsequent processing unit can effectively receive and analyze the signal. The amplified signal S' follows the formula S' = A1 * S, where A1 represents the amplification factor.

[0020] A filtering processing unit is connected to the signal amplification circuit to filter the amplified signal, remove unnecessary noise interference, and retain useful vibration information. The filtered signal S'' is calculated using the formula S'' = F(S'), where F() represents the filtering function.

[0021] A data sampling module relies on the filtered signal to collect data points at a set time interval Δt, ensuring data continuity and integrity. The sampling process follows the Nyquist criterion, which states that the sampling frequency f_s must be at least twice the highest frequency f_max of the signal, expressed as f_s ≥ 2 * f_max. The data points D_n obtained after sampling are determined by the formula D_n = S''(n * Δt), where n is the sampling sequence number.

[0022] Preferably, the dynamic pressure acquisition unit includes:

[0023] a pressure sensor, arranged adjacent to the vibration sensing component, for capturing instantaneous pressure fluctuations caused by the gas flow in real time, the relationship between the sensor output signal P and the actual pressure p follows the formula P=k_p*p, where k_p represents the conversion coefficient of the pressure sensor;

[0024] a time synchronization interface, connected to the pressure sensor, for ensuring that the time stamp of the pressure data is consistent with the data of the vibration sensing component, the time stamp t is calculated by the formula t=T+Δt_s, where T is the reference time and Δt_s is the time delay specific to the sensor;

[0025] a data preprocessing circuit, arranged immediately after the time synchronization interface, for performing preliminary processing on the raw signal from the pressure sensor, the preprocessed signal P' is calculated by the formula P'=P-P_offset, where P_offset represents the sensor offset;

[0026] a pressure fluctuation analysis module, dependent on the preprocessed pressure signal, for analyzing the instantaneous pressure change characteristics in the gas flow, converting the time-domain signal into a frequency-domain signal, expressed as F(ω)=∫[P'(t)*e^(-jωt)]dt, where ω represents the angular frequency and F(ω) is the pressure fluctuation distribution in the frequency domain.

[0027] Preferably, the data synchronization recording device comprises:

[0028] a synchronization clock generator, connected to the dynamic pressure acquisition unit, for generating a time reference signal T_b to ensure that all sensing signals and pressure data are recorded within the same time frame, the time reference signal follows the formula T_b=f_t*n, where f_t is the fixed frequency and n is the incremental count;

[0029] a multi-channel data buffer, connected to the synchronization clock generator, for receiving data streams from different sensors and temporarily storing them according to the time reference signal T_b, the data D_c of each channel is represented by the formula D_c(t)=S_i(t-Δt_i), where S_i is the raw signal of the i-th sensor and Δt_i is the time difference of the sensor relative to the reference signal;

[0030] a data alignment processor, arranged immediately after the multi-channel data buffer, for adjusting the time stamps of the channel data to completely align the sensing signals and pressure data in time, the processed data D_a is calculated by the formula D_a(t)=D_c(t+Δt_adj), where Δt_adj is the adjusted time offset;

[0031] The high-speed data writing unit records the sensing signal and the pressure data into the non-volatile storage medium simultaneously according to the data after the alignment processing, and the writing speed R_w satisfies the formula R_w≥max(R_s), wherein R_s represents the maximum data generation rate of each sensor.

[0032] Preferably, the environmental parameter monitor comprises:

[0033] The temperature sensing probe is located inside the wind tunnel main body and measures the temperature T in the experimental area in real time, and the output signal of the temperature sensing probe is linearly related to the temperature, and the expression is V_T=α*T+β, wherein α and β are specific constants of the sensor.

[0034] The humidity sensing element is arranged close to the temperature sensing probe and is used for detecting the air humidity H, and the output voltage V_H of the humidity sensing element is calculated by the formula V_H=γ*H+δ, wherein γ and δ are specific conversion coefficients of the humidity sensing element.

[0035] The air pressure sensing unit is integrated near the humidity sensing element and is responsible for capturing the change of the air pressure P, and the output electrical signal V_P of the air pressure sensing unit follows the formula V_P=ε*P+ζ, wherein ε and ζ represent the conversion parameters of the air pressure sensing unit.

[0036] The comprehensive correction module performs comprehensive correction processing according to the data from the temperature sensing probe, the humidity sensing element and the air pressure sensing unit, and the corrected physical quantity X_c is calculated by the formula X_c=X_m+ΔX, wherein X_m represents the original measured value, and ΔX is the correction value calculated according to the change of other physical quantities.

[0037] Preferably, the control panel comprises:

[0038] The experimental condition setting interface is connected to the environmental parameter monitor and provides an intuitive operation interface for an operator to input the target temperature T_t, the humidity H_t and the air pressure P_t, and the set value is expressed by the formula S_i=k_s*X_t, wherein S_i represents the set signal, X_t is the target physical quantity (T_t, H_t, P_t), and k_s is the conversion coefficient.

[0039] The real-time data display screen is integrated with the experimental condition setting interface and synchronously displays the current temperature T_c, the humidity H_c and the air pressure P_c from the environmental parameter monitor, and the data display on the display screen follows the formula D_d=V_X / k_v, wherein V_X is the output voltage of the sensor, and k_v is the display proportion factor.

[0040] An automatic adjustment controller is connected to the real-time data display screen, and calculates an adjustment instruction C_a according to a difference ΔX between a current physical quantity and a target physical quantity, the adjustment instruction being calculated by a formula C_a = K_p * ΔX + K_i * ∫ΔXdt + K_d * d(ΔX) / dt, K_p, K_i and K_d being proportional, integral and differential coefficients, respectively;

[0041] A state feedback loop monitors the adjustment process and feeds back an actual adjustment effect to the control panel according to the instruction from the automatic adjustment controller, a feedback signal F_b being determined by a formula F_b = f(C_a, R_r), R_r representing an adjusted actual response, and f() being a state feedback function.

[0042] Preferably, the analysis platform comprises:

[0043] A data import interface receives information from the data synchronization recording device, and ensures that the vibration signal S_v and the pressure data P_d can be transmitted to the analysis platform, a data transmission rate R_t satisfying a formula R_t = L / T_t, where L is a data length and T_t is a required transmission time;

[0044] A signal preprocessing unit is connected to the data import interface, and performs preliminary processing on the imported data, a preprocessed vibration signal S'_v being calculated by a formula S'_v = f(S_v), f() representing a preprocessing function;

[0045] A vibration characteristic extractor is connected to the signal preprocessing unit, and is configured to extract characteristic parameters, including an amplitude A2, a frequency f and a phase φ, from the preprocessed vibration signal, the characteristic parameters being expressed by A2(f) = |∫[S'_v(t)*e^(-j2πft)]dt| and φ(f) = arg[∫[S'_v(t)*e^(-j2πft)]dt];

[0046] A mode recognition engine is configured to analyze a vibration mode of the blade according to the characteristic parameters provided by the vibration characteristic extractor, the engine constructing a blade vibration mode M according to the characteristic parameters, the formula being M = g(A, f, φ), g() being a mode recognition function.

[0047] Preferably, the result visualization interface comprises:

[0048] A data mapping unit is integrated in the analysis platform, and is configured to convert the processed vibration mode M into a visual graphical element, a mapping process being performed according to a formula G = h(M), where G represents the generated graphical element, and h() is a data-to-graphic conversion function;

[0049] A dynamic chart generator connected to the data mapping unit creates a dynamic chart C from the graph elements G, the chart generation following the formula C = j(G, t), t representing the time variable and j() being the chart construction function.

[0050] An interactive exploration tool, following the dynamic chart generator, allows the user to explore the vibration details within a time period or frequency range by adjusting parameters, the tool operation following the formula E = k(C, P_u), E representing the exploration result, P_u being the parameter set set by the user and k() being the interactive response function.

[0051] A comprehensive report generator, relying on the feedback provided by the interactive exploration tool, summarizes all analysis results and generates a comprehensive report R, the report content being determined by the formula R = l(E, M), l() being the report synthesis function.

[0052] The technical effects and advantages of the present application: The cross-sonic blade machine wind tunnel test system proposed in the present application has the following advantages compared with the prior art:

[0053] The present application introduces a high-precision data synchronization recording device to ensure that the displacement signals captured by the vibration sensing assembly and the pressure data obtained by the dynamic pressure acquisition unit are completely aligned in time. At the same time, the system also integrates an environmental parameter monitor, which can track and compensate for changes in physical quantities such as temperature, humidity and air pressure in real time, thereby significantly improving the accuracy of experimental data. Finally, the addition of the analysis platform and the result visualization interface enables in-depth analysis of the blade vibration mode, providing researchers with intuitive and detailed data support and greatly improving the understanding of the dynamic behavior of the blade under cross-sonic conditions. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The block diagram of the cross-sonic blade machine wind tunnel test system of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0056] The present application provides a method for testing the dynamic behavior of a blade under cross-sonic conditions, comprising the following steps: Figure 1The transonic wind tunnel test system comprises a tunnel main body, a blade mounting structure, a vibration sensing component, a dynamic pressure acquisition unit, a data synchronous recording device, an environmental parameter monitor, a control panel, an analysis platform, and a result visualization interface.

[0057] The wind tunnel main body forms a stable airflow environment inside, suitable for simulating aerodynamic characteristics under real flight conditions. The wind tunnel main body further comprises the following interrelated sub-modules:

[0058] The airflow stabilization zone is located at the entrance of the wind tunnel main body. By adjusting the airflow speed and direction, the uniformity and stability of the airflow before entering the experimental section are ensured.

[0059] The adjustable converging-diverging section is connected after the airflow stabilization zone. The internal shape of this section can be adjusted according to experimental requirements to change the airflow cross-sectional area A. The force F acting on the blade is calculated according to the formula F = 1 / 2 * p * V^2 * A * C_d, where p represents air density, V is airflow speed, and C_d is the drag coefficient.

[0060] The dynamic adjustment wall is set after the adjustable converging-diverging section. It can respond to the force F calculated and adjust the wall position in real time to maintain the preset aerodynamic parameters unchanged.

[0061] The flow field feedback loop is established based on the dynamic adjustment wall. Sensors are used to monitor actual flow field parameters and compare them with set values. If the deviation exceeds the allowed range, a signal is triggered to the adjustable converging-diverging section for corresponding adjustment to ensure stable operation of the entire system.

[0062] In summary, the design of the wind tunnel main body not only enhances the stability of the airflow and the flexibility of the experiment, but also realizes real-time monitoring and automatic adjustment of aerodynamic parameters, ensuring high consistency of experimental conditions. These improvements enable researchers to accurately measure the vibration response of the blade under conditions closer to real flight conditions, providing a solid foundation for optimizing blade design and improving aircraft performance.

[0063] The blade mounting structure is connected to the wind tunnel main body and is used to fix the blade to be tested and ensure that it is in a predetermined position in the airflow. The blade mounting structure further comprises the following interrelated sub-modules:

[0064] The fixed base is closely connected to the wind tunnel main body, providing stable foundation support to ensure that the blade to be tested maintains a fixed position during the entire experiment.

[0065] An adjustable support arm is connected to the fixed base, with multi-axis rotation capability, allowing precise adjustment of the blade angle θ to adapt to different experimental conditions. The bending moment M acting on the blade is calculated by the formula M = F * L * sin(θ), where F represents the force applied to the blade, and L is the length of the force arm.

[0066] A displacement adjustment device is integrated into the adjustable support arm, which can finely adjust the forward and backward position x of the blade to ensure that the relative position of the blade in the airflow meets the experimental settings. The pressure difference ΔP caused by the change in position is determined by the formula ΔP = ρ * V^2 * (C_p1 - C_p2) / 2, where ρ is the air density, V is the airflow speed, and C_p1 and C_p2 represent the pressure coefficients before and after the blade, respectively.

[0067] An automatic alignment system relies on feedback provided by the displacement adjustment device to automatically correct the blade's posture, ensuring that it is in the optimal measurement position at the start of the experiment. When a deviation δ is detected, the system calculates according to the formula δ = x_target - x_actual and adjusts the x value until the deviation is minimized, ensuring that the blade is always located at the predetermined position.

[0068] The blade mounting structure design not only provides stable support and multi-axis precise adjustment capability, but also realizes precise control and automatic correction of the blade position. These improvements work together to enable researchers to accurately measure the blade's vibration response and other dynamic characteristics under complex and variable experimental conditions. Ultimately, this design provides solid technical support for optimizing blade design, improving aircraft performance, and deepening the understanding of blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data.

[0069] By way of example, a vibration sensing component is attached to the blade mounting structure to detect subtle displacement changes on the blade surface; the vibration sensing component further includes the following interrelated sub-modules:

[0070] A displacement sensor is attached to the blade mounting structure and can capture extremely subtle displacement changes on the blade surface. The sensor output signal S is linearly related to the actual displacement d, expressed as S = k * d, where k represents the sensitivity coefficient of the sensor.

[0071] A signal amplification circuit is connected to the displacement sensor to enhance the original signal strength, ensuring that the subsequent processing unit can effectively receive and analyze the signal. The amplified signal S' follows the formula S' = A1 * S, where A1 represents the amplification factor.

[0072] A filtering processing unit is arranged next to the signal amplification circuit to filter the amplified signal, remove unnecessary noise interference, and retain useful vibration information. The filtered signal S" is calculated by the formula S" = F(S'), where F() represents a filtering function designed to retain signal components within a specific frequency range.

[0073] A data sampling module collects data points at a set time interval At based on the filtered signal, ensuring data continuity and integrity. The sampling process follows the Nyquist criterion, which requires that the sampling frequency f_s be at least twice the highest frequency f_max of the signal, expressed as f_s ≥ 2*f_max. The data points D_n obtained after sampling are determined by the formula D_n = S"(n*At), where n is the sampling sequence number.

[0074] The vibration sensing assembly not only captures subtle displacement changes on the blade surface with high sensitivity, but also ensures high quality and reliability of the data through signal amplification, filtering, and precise sampling. These improvements collectively enable researchers to accurately measure the blade's vibration response and other dynamic characteristics under complex experimental conditions. Ultimately, this design provides solid technical support for optimizing blade design, improving aircraft performance, and gaining a deeper understanding of blade behavior under transonic conditions, ensuring highly accurate and reliable experimental data. In addition, high-quality data also lays a good foundation for subsequent analysis and modeling.

[0075] By way of example, a dynamic pressure acquisition unit is arranged adjacent to the vibration sensing assembly and is responsible for capturing transient pressure fluctuations caused by airflow. The dynamic pressure acquisition unit further includes the following interrelated sub-modules:

[0076] A pressure sensor is arranged adjacent to the vibration sensing assembly to capture real-time transient pressure fluctuations caused by airflow. The relationship between the sensor output signal P and the actual pressure p follows the formula P = k_p * p, where k_p represents the conversion coefficient of the pressure sensor.

[0077] A time synchronization interface is connected to the pressure sensor to ensure that the timestamp of the pressure data is consistent with the data of the vibration sensing assembly, facilitating subsequent analysis. The timestamp t is calculated by the formula t = T + At_s, where T is the reference time and At_s is the time delay specific to the sensor.

[0078] A data preprocessing circuit is arranged next to the time synchronization interface to perform preliminary processing on the raw signal from the pressure sensor, such as offset correction and linearization adjustment. The preprocessed signal P' is calculated by the formula P' = P - P_offset, where P_offset represents the sensor offset.

[0079] The pressure fluctuation analysis module relies on the pre-processed pressure signal to analyze the instantaneous pressure change characteristics in the airflow. Based on the Fourier transform principle, this module converts the time domain signal into a frequency domain signal, with the expression F(ω)=∫[P'(t)*e^(-jωt)]dt, where ω represents the angular frequency and F(ω) is the pressure fluctuation distribution in the frequency domain.

[0080] This dynamic pressure acquisition unit design not only achieves high-precision real-time capture of instantaneous pressure fluctuations caused by airflow, but also ensures high-quality and reliable data through time synchronization, data preprocessing, and frequency domain analysis. These improvements work together to enable researchers to accurately measure and analyze the impact of airflow on blades under complex and variable experimental conditions. Ultimately, this design provides solid technical support for studying blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data. Furthermore, the high-quality pressure data lays a good foundation for subsequent vibration mode analysis and aircraft performance optimization.

[0081] For example, the data synchronization recording device is connected to the dynamic pressure acquisition unit to simultaneously record the sensed signal and pressure data; the data synchronization recording device further includes the following interrelated sub-modules:

[0082] The synchronous clock generator is connected to the dynamic pressure acquisition unit to generate a precise time base signal T_b, ensuring that all sensing signals and pressure data are recorded within the same time frame. The time base signal follows the formula T_b=f_t*n, where f_t is a fixed frequency and n is an incrementing count.

[0083] A multi-channel data buffer is connected to the synchronous clock generator to receive data streams from different sensors and temporarily store them according to the time reference signal T_b. The data D_c of each channel is represented by the formula D_c(t)=S_i(t-Δt_i), where S_i is the original signal of the i-th sensor and Δt_i is the time difference of the sensor relative to the reference signal.

[0084] The data alignment processor, which follows the multi-channel data buffer, is responsible for adjusting the timestamps of each channel's data to ensure that the sensing signal and pressure data are fully aligned in time. The processed data D_a is calculated using the formula D_a(t)=D_c(t+Δt_adj), where Δt_adj is the adjusted time offset.

[0085] The high-speed data writing unit relies on the aligned data to simultaneously record the sensing signal and pressure data into the non-volatile storage medium. The writing rate R_w satisfies the formula R_w≥max(R_s), where R_s represents the maximum data generation rate of each sensor.

[0086] This data synchronization recording device not only achieves precise time-synchronized recording of inductive signals and pressure data, but also ensures high data consistency and integrity through efficient temporary storage, precise time alignment, and high-speed stable writing. These improvements work together to enable researchers to accurately capture and preserve the vibration response and other dynamic characteristics of blades under complex and variable experimental conditions at transonic speeds. Ultimately, this design provides solid technical support for optimizing blade design, improving aircraft performance, and gaining a deeper understanding of blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data.

[0087] For example, the environmental parameter monitoring instrument is located inside the wind tunnel body and tracks changes in physical quantities such as temperature, humidity, and air pressure; the environmental parameter monitoring instrument further includes the following interrelated sub-modules:

[0088] The temperature sensing probe is located inside the wind tunnel and measures the temperature T in the experimental area in real time. The output signal of the sensing probe is linearly related to the temperature, and the expression is V_T=α*T+β, where α and β are specific constants of the sensor.

[0089] A humidity sensing element is disposed adjacent to the temperature sensing probe and is used to detect air humidity H. The output voltage V_H of the humidity sensing element is calculated by the formula V_H=γ*H+δ, where γ and δ are conversion coefficients unique to the humidity sensing element.

[0090] The barometric pressure sensing unit, integrated near the humidity sensing element, is responsible for capturing changes in barometric pressure P. The electrical signal V_P output by the barometric pressure sensing unit follows the formula V_P=ε*P+ζ, where ε and ζ represent the conversion parameters of the barometric pressure sensing unit.

[0091] The integrated calibration module relies on data from the temperature sensing probe, humidity sensing element, and air pressure sensing unit to perform integrated calibration processing to ensure the accuracy of the measured values ​​of each physical quantity. The calibrated physical quantity X_c is calculated using the formula X_c=X_m+ΔX, where X_m represents the original measured value and ΔX is the calibration value calculated based on the changes in other physical quantities.

[0092] This environmental parameter monitoring instrument not only achieves high-precision real-time tracking of changes in physical quantities such as temperature, humidity, and air pressure, but also ensures the accuracy of each physical quantity measurement through comprehensive correction processing. These improvements work together to enable researchers to accurately grasp the changes in environmental parameters under complex and variable experimental conditions, thus allowing for a more comprehensive study of the dynamic behavior of blades under transonic conditions. Ultimately, this design provides solid technical support for optimizing blade design, improving aircraft performance, and gaining a deeper understanding of blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data.

[0093] For example, the control panel is connected to the environmental parameter monitor, providing an interface for operators to set and monitor experimental conditions; the control panel further includes the following interrelated sub-modules:

[0094] The experimental condition setting interface connects to the environmental parameter monitoring instrument and provides an intuitive operation interface for operators to input target temperature T_t, humidity H_t, and air pressure P_t. The set value is expressed by the formula S_i=k_s*X_t, where S_i represents the setting signal, X_t is the target physical quantity (T_t,H_t,P_t), and k_s is the conversion coefficient.

[0095] The real-time data display screen is integrated with the experimental condition setting interface and synchronously displays the current temperature T_c, humidity H_c and air pressure P_c from the environmental parameter monitor. The data displayed on the screen follows the formula D_d=V_X / k_v, where V_X is the sensor output voltage and k_v is the display scaling factor.

[0096] An automatic adjustment controller is connected to the real-time data display screen. It calculates the adjustment command C_a based on the difference ΔX between the current physical quantity and the target physical quantity. The adjustment command is calculated by the formula C_a=K_p*ΔX+K_i*∫ΔXdt+K_d*d(ΔX) / dt, where K_p, Ki and K_d are the proportional, integral and differential coefficients, respectively.

[0097] The state feedback loop, relying on the instructions issued by the automatic adjustment controller, monitors the adjustment process and feeds back the actual adjustment effect to the control panel. The feedback signal F_b is determined by the formula F_b=f(C_a,R_r), where R_r represents the actual response after adjustment and f() is the state feedback function.

[0098] This control panel design not only enables intuitive and convenient setting of experimental conditions and real-time data visualization, but also ensures a high degree of consistency and stability of experimental conditions through precise automatic adjustment and dynamic status feedback. These improvements work together to allow researchers to precisely set and monitor the experimental environment under complex and variable conditions, thereby enabling more accurate research on the dynamic behavior of blades under transonic conditions.

[0099] For example, the analysis platform receives information from the data synchronization and recording device, processes it to reveal blade vibration modes; the analysis platform further includes the following interrelated sub-modules:

[0100] The data import interface receives information from the data synchronization recording device to ensure that the vibration signal S_v and pressure data P_d can be accurately transmitted to the analysis platform. The data transmission rate R_t satisfies the formula R_t=L / T_t, where L is the data length and T_t is the transmission time.

[0101] The signal preprocessing unit is connected to the data import interface and performs preliminary processing on the imported data, including noise reduction and smoothing. The preprocessed vibration signal S'_v is calculated by the formula S'_v=f(S_v), where f() represents the preprocessing function, which aims to reduce noise interference.

[0102] The vibration characteristic extractor, immediately following the signal preprocessing unit, is used to extract characteristic parameters from the preprocessed vibration signal, such as amplitude A2, frequency f, and phase φ. These parameters are obtained by Fourier transform, and the expressions are A2(f)=|∫[S'_v(t)*e^(-j2πft)]dt|, φ(f)=arg[∫[S'_v(t)*e^(-j2πft)]dt].

[0103] The pattern recognition engine, relying on the feature parameters provided by the vibration characteristic extractor, analyzes the vibration pattern of the blade. The engine constructs the blade vibration pattern M based on the feature parameters, with the formula M = g(A, f, φ), where g() is the pattern recognition function, which aims to reveal the vibration behavior under different conditions.

[0104] This analysis platform not only achieves efficient data transmission and high-quality signal preprocessing for blade vibration modes, but also ensures a comprehensive understanding and accurate revelation of blade vibration behavior through precise feature parameter extraction and in-depth pattern recognition. These improvements work together to enable researchers to accurately capture and analyze the dynamic response of blades under complex and varied experimental conditions, thereby allowing for a deeper study of their behavior under transonic conditions.

[0105] For example, the results visualization interface is integrated into the analysis platform to present the processed data, facilitating an intuitive understanding of blade behavior. The results visualization interface further includes the following interrelated sub-modules:

[0106] The data mapping unit, integrated into the analysis platform, is responsible for converting the processed vibration mode M into visualized graphical elements. The mapping process follows the formula G = h(M), where G represents the generated graphical element and h() is the data-to-graphic conversion function.

[0107] The dynamic chart generator, connected to the data mapping unit, creates a dynamic chart C based on the graphic element G to show the vibration behavior of the blade under different conditions. The chart is generated according to the formula C = j(G,t), where t represents the time variable and j() is the chart construction function, ensuring that the vibration characteristics that change over time are presented intuitively.

[0108] The interactive exploration tool, which follows the dynamic chart generator, allows users to explore vibration details in a specific time period or frequency range by adjusting parameters. The tool operation follows the formula E = k(C, P_u), where E represents the exploration result, P_u is the parameter set set by the user, and k() is the interactive response function.

[0109] The comprehensive report generator, relying on feedback provided by the interactive exploration tool, summarizes all analysis results and generates a comprehensive report R. The report content is determined by the formula R = l(E,M), where l() is the report synthesis function, which aims to integrate vibration patterns and user exploration results to provide a comprehensive data interpretation.

[0110] The visualization interface not only enables efficient mapping and dynamic chart display of processed data, but also provides user-friendly data exploration and comprehensive results summaries through interactive exploration tools and a comprehensive report generator. These improvements work together to allow researchers to intuitively and interactively understand the behavior of blades under complex and varied experimental conditions, thereby enabling a deeper study of their dynamic response under transonic conditions.

[0111] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wind tunnel testing system for a transonic bladed turbine, characterized in that, include: The main body of the wind tunnel creates a stable airflow environment, which is suitable for simulating the aerodynamic characteristics under real flight conditions. The blade mounting structure is connected to the wind tunnel body and is used to fix the blade to be tested and ensure that it is in a predetermined position in the airflow; A vibration sensing component is attached to the blade mounting structure to detect displacement changes on the blade surface. A dynamic pressure acquisition unit is located adjacent to the vibration sensing component and is responsible for capturing instantaneous pressure fluctuations caused by airflow. A data synchronization recording device is connected to the dynamic pressure acquisition unit to simultaneously record the sensing signal and pressure data; The data synchronization and recording device includes: The synchronous clock generator is connected to the dynamic pressure acquisition unit to generate a time reference signal T_b, ensuring that all sensing signals and pressure data are recorded within the same time frame. The time reference signal follows the formula T_b=f_t*n, where f_t is a fixed frequency and n is an incrementing count. A multi-channel data buffer is connected to the synchronous clock generator to receive data streams from different sensors and temporarily store them according to the time reference signal T_b. The data D_c of each channel is represented by the formula D_c(t)=S_i(t-Δt_i), where S_i is the original signal of the i-th sensor and Δt_i is the time difference of the sensor relative to the reference signal. The data alignment processor, which follows the multi-channel data buffer, is responsible for adjusting the timestamps of each channel's data to ensure that the sensing signal and pressure data are fully aligned in time. The processed data D_a is calculated using the formula D_a(t)=D_c(t+Δt_adj), where Δt_adj is the adjusted time offset. The high-speed data writing unit relies on the aligned data to simultaneously record the sensing signal and pressure data into the non-volatile storage medium. The writing rate R_w satisfies the formula R_w≥max(R_s), where R_s represents the maximum data generation rate of each sensor. An environmental parameter monitoring instrument, located inside the wind tunnel, tracks changes in physical quantities such as temperature, humidity, and air pressure. The control panel is connected to the environmental parameter monitor and provides an interface for operators to set and monitor experimental conditions. The analysis platform receives information from the data synchronization and recording device and processes it to reveal the blade vibration mode. The results visualization interface is integrated into the analysis platform and presents the processed data.

2. The transonic bladed wind tunnel testing system according to claim 1, characterized in that: The wind tunnel body includes: The airflow stabilization zone is located at the entrance of the wind tunnel main body. The uniformity and stability of the airflow before entering the experimental section are ensured by adjusting the airflow speed and direction. The adjustable convergent expansion section is connected after the airflow stabilization zone. The internal shape of this section can be adjusted according to experimental requirements to change the airflow cross-sectional area A. The force F acting on the blade is calculated according to the formula F=1 / 2*ρ*V^2*A*C_d, where ρ represents the air density, V is the airflow velocity, and C_d is the drag coefficient. The dynamically adjustable wall surface, positioned after the adjustable convergent expansion section, can respond to the calculated force F and adjust the wall surface position in real time to maintain the preset aerodynamic parameters unchanged. The flow field feedback loop is built on the basis of the dynamically adjustable wall. It uses sensors to monitor the actual flow field parameters and compares these parameters with the set values. If the deviation exceeds the allowable range, a signal is triggered to the adjustable convergence and expansion section for corresponding adjustment.

3. The transonic bladed wind tunnel testing system according to claim 2, characterized in that: The blade mounting structure includes: The fixed base is connected to the main body of the wind tunnel, providing basic support and ensuring that the blade under test remains in a fixed position throughout the experiment; An adjustable support arm is connected to the fixed base. The support arm has multi-axis rotation capability, allowing precise adjustment of the blade angle θ. The bending moment M acting on the blade is calculated by the formula M=F*L*sin(θ), where F represents the force applied to the blade and L is the lever arm length. The displacement adjustment device is integrated in the adjustable support arm to adjust the front and rear position x of the blade. The pressure difference ΔP caused by the position change is determined by the formula ΔP=ρ*V^2*(C_p1-C_p2) / 2, where ρ is the air density, V is the airflow velocity, and C_p1 and C_p2 represent the pressure coefficients in front of and behind the blade, respectively. The automatic alignment system relies on the feedback provided by the displacement adjustment device to automatically correct the blade attitude. When a deviation δ is detected, it calculates according to the formula δ=x_target-x_actual and adjusts the x value until the deviation is minimized, ensuring that the blade is always in the predetermined position.

4. The transonic bladed wind tunnel testing system according to claim 3, characterized in that: The vibration sensing component includes: The displacement sensor, attached to the blade mounting structure, can capture extremely minute displacement changes on the blade surface. The sensor output signal S is linearly related to the actual displacement d, expressed as S=k*d, where k represents the sensor's sensitivity coefficient. A signal amplification circuit, connected to the displacement sensor, is used to enhance the original signal strength and ensure that the subsequent processing unit can effectively receive and analyze the signal. The amplified signal S' follows the formula S'=A1*S, where A1 represents the amplification factor. The filtering unit, which is located immediately after the signal amplification circuit, filters the amplified signal to remove unnecessary noise interference and retain useful vibration information. The filtered signal S'' is calculated by the formula S''=F(S'), where F() represents the filtering function. The data sampling module relies on the filtered signal and collects data points at set time intervals Δt to ensure data continuity and integrity. The sampling process follows the Nyquist criterion, that is, the sampling frequency f_s is at least twice the highest frequency of the signal f_max, which can be expressed by the formula f_s≥2*f_max. The data points D_n obtained after sampling are determined by the formula D_n=S''(n*Δt), where n is the sampling sequence number.

5. The transonic bladed wind tunnel testing system according to claim 4, characterized in that: The dynamic pressure acquisition unit includes: The pressure sensor, located near the vibration sensing component, is used to capture instantaneous pressure fluctuations caused by airflow in real time. The relationship between the sensor output signal P and the actual pressure p follows the formula P=k_p*p, where k_p represents the conversion coefficient of the pressure sensor. A time synchronization interface is connected to the pressure sensor to ensure that the timestamp of the pressure data is consistent with the data of the vibration sensing component. The timestamp t is calculated by the formula t=T+Δt_s, where T is the reference time and Δt_s is the sensor-specific time delay. The data preprocessing circuit, which is immediately following the time synchronization interface, performs preliminary processing on the raw signal from the pressure sensor. The preprocessed signal P' is calculated using the formula P'=P-P_offset, where P_offset represents the sensor offset. The pressure fluctuation analysis module relies on the pre-processed pressure signal to analyze the instantaneous pressure change characteristics in the airflow, converting the time domain signal into a frequency domain signal, expressed as F(ω)=∫[P'(t)*e^(-jωt)]dt, where ω represents the angular frequency and F(ω) is the pressure fluctuation distribution in the frequency domain.

6. The transonic bladed wind tunnel testing system according to claim 5, characterized in that: The environmental parameter monitoring instrument includes: The temperature sensing probe is located inside the wind tunnel and measures the temperature T in the experimental area in real time. The output signal of the sensing probe is linearly related to the temperature, and the expression is V_T=α*T+β, where α and β are specific constants of the sensor. A humidity sensing element is disposed adjacent to the temperature sensing probe and is used to detect air humidity H. The output voltage V_H of the humidity sensing element is calculated by the formula V_H=γ*H+δ, where γ and δ are conversion coefficients unique to the humidity sensing element. The barometric pressure sensing unit, integrated near the humidity sensing element, is responsible for capturing changes in barometric pressure P. The electrical signal V_P output by the barometric pressure sensing unit follows the formula V_P=ε*P+ζ, where ε and ζ represent the conversion parameters of the barometric pressure sensing unit. The integrated calibration module relies on data from the temperature sensing probe, humidity sensing element, and air pressure sensing unit to perform integrated calibration processing. The calibrated physical quantity X_c is calculated using the formula X_c=X_m+ΔX, where X_m represents the original measured value and ΔX is the calibration value calculated based on changes in other physical quantities.

7. The transonic bladed wind tunnel testing system according to claim 6, characterized in that: The control panel includes: The experimental condition setting interface connects to the environmental parameter monitoring instrument and provides an intuitive operation interface for operators to input target temperature T_t, humidity H_t, and air pressure P_t. The set value is expressed by the formula S_i=k_s*X_t, where S_i represents the set signal, X_t is the target physical quantity (T_t,H_t,P_t), and k_s is the conversion coefficient. The real-time data display screen is integrated with the experimental condition setting interface and synchronously displays the current temperature T_c, humidity H_c and air pressure P_c from the environmental parameter monitor. The data displayed on the screen follows the formula D_d=V_X / k_v, where V_X is the sensor output voltage and k_v is the display scaling factor. An automatic adjustment controller is connected to the real-time data display screen. It calculates the adjustment command C_a based on the difference ΔX between the current physical quantity and the target physical quantity. The adjustment command is calculated using the formula C_a=K_p*ΔX+K_i*∫ΔXdt+K_d*d(ΔX) / dt, where K_p, Ki, and K_d are the proportional, integral, and derivative coefficients, respectively. The state feedback loop, relying on the instructions issued by the automatic adjustment controller, monitors the adjustment process and feeds back the actual adjustment effect to the control panel. The feedback signal F_b is determined by the formula F_b=f(C_a,R_r), where R_r represents the actual response after adjustment and f() is the state feedback function.

8. The transonic bladed wind tunnel testing system according to claim 7, characterized in that: The analysis platform includes: The data import interface receives information from the data synchronization and recording device, ensuring that the vibration signal S_v and pressure data P_d can be transmitted to the analysis platform. The data transmission rate R_t satisfies the formula R_t=L / T_t, where L is the data length and T_t is the transmission time. The signal preprocessing unit is connected to the data import interface and performs preliminary processing on the imported data. The preprocessed vibration signal S'_v is calculated by the formula S'_v=f(S_v), where f() represents the preprocessing function. The vibration characteristic extractor, immediately following the signal preprocessing unit, is used to extract characteristic parameters from the preprocessed vibration signal, including amplitude A2, frequency f, and phase φ, expressed as A2(f)=|∫[S'_v(t)*e^(-j2πft)]dt|, φ(f)=arg[∫[S'_v(t)*e^(-j2πft)]dt]. The pattern recognition engine relies on the feature parameters provided by the vibration characteristic extractor to analyze the vibration mode of the blade. The engine constructs the blade vibration mode M based on the feature parameters, with the formula M=g(A,f,φ), where g() is the pattern recognition function.

9. The transonic bladed wind tunnel testing system according to claim 8, characterized in that: The results visualization interface includes: The data mapping unit, integrated into the analysis platform, is responsible for converting the processed vibration mode M into visualized graphical elements. The mapping process follows the formula G=h(M), where G represents the generated graphical element and h() is the data-to-graphic conversion function. A dynamic chart generator is connected to the data mapping unit and creates a dynamic chart C based on the graphic element G. The chart is generated according to the formula C=j(G,t), where t represents the time variable and j() is the chart construction function. The interactive exploration tool, which follows the dynamic chart generator, allows users to explore vibration details within a time period or frequency range by adjusting parameters. The tool operation follows the formula E=k(C,P_u), where E represents the exploration result, P_u is the parameter set set by the user, and k() is the interactive response function. The comprehensive report generator, relying on the feedback provided by the interactive exploration tool, summarizes all analysis results and generates a comprehensive report R. The content of the report is determined by the formula R=l(E,M), where l() is the report synthesis function.

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