Testing system for transonic-speed grid wind tunnel test

Abstract

The invention belongs to the technical field of wind tunnel tests, and particularly relates to a transonic wind tunnel test system for a grid fan, which ensures that displacement signals captured by a vibration sensing assembly can be completely aligned with pressure data acquired by a dynamic pressure acquisition unit in time by introducing a high-precision data synchronous recording device. Meanwhile, the system is also integrated with an environmental parameter monitor which can track and compensate the change of physical quantities such as temperature, humidity and air pressure in real time, so that the accuracy of experimental data is remarkably improved. Finally, by adding an analysis platform and a result visualization interface, deep analysis of the blade vibration mode is realized, visual and detailed data support is provided for researchers, and understanding of dynamic behaviors of the blade under the transonic speed condition is greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of wind tunnel tests, and in particular relates to a wind tunnel test system for a transonic Y-270 aircraft. Background Art

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

[0003] At present, most wind tunnel test systems rely on fixed sensors to measure parameters such as blade displacement and pressure. These systems usually include a basic wind tunnel structure, a blade mounting device, and a simple data acquisition device. However, such systems often lack accurate time synchronization recording capabilities, making it difficult to achieve high-precision matching between the sensing signal and the pressure data. In addition, the impact of changes in environmental parameters (such as temperature, humidity, and air pressure) on the experimental results has not been fully considered, limiting the accuracy and reliability of the experimental data.

[0004] In response to the above situation, a significant technical problem is that the existing wind tunnel test system is unable to accurately and synchronously record the subtle displacement changes on the blade surface and the instantaneous pressure fluctuations caused by the airflow, which limits the study of the true vibration mode of the blade under transonic conditions. Summary of the invention

[0005] The purpose of the present invention is to provide a transonic blade wind tunnel test system, which realizes the in-depth analysis of the blade vibration mode, provides researchers with intuitive and detailed data support, greatly improves the understanding of the dynamic behavior of blades under transonic conditions, and solves the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a transonic blade-type aircraft wind tunnel test test system, comprising: a wind tunnel main body, in which a stable airflow environment is formed, suitable for simulating aerodynamic characteristics under real flight conditions; a blade mounting structure, connected to the wind tunnel main body, used to fix the blade to be tested and ensure 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 synchronization recording device, connected to the dynamic pressure acquisition unit, to achieve simultaneous recording of the sensing signal and the pressure data; an environmental parameter monitor, located inside the wind tunnel main body, tracking the changes in the physical quantities of temperature, humidity and air pressure; a control panel, connected to the environmental parameter monitor, providing an interface for operators to set and monitor experimental conditions; an analysis platform, receiving information from the data synchronization recording device, processing it to reveal the blade vibration mode; a result visualization interface, integrated into the analysis platform, presenting the processed data.

[0007] Preferably, the wind tunnel body comprises:

[0008] The airflow stabilization zone is located at the entrance of the wind tunnel body. It adjusts the airflow speed and direction to ensure the uniformity and stability of the airflow before entering the experimental section.

[0009] The adjustable convergent expansion section is connected to 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. 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;

[0010] A dynamically adjustable wall surface is arranged after the adjustable convergent-expandable section, and can respond to the calculated force F, adjust the wall surface position in real time, and maintain the preset aerodynamic parameters unchanged;

[0011] The flow field feedback loop is established on the basis of the dynamic adjustment wall surface, 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 trigger signal is sent to the adjustable convergence and expansion section for corresponding adjustment.

[0012] Preferably, the blade mounting structure comprises:

[0013] The fixed base is connected to the wind tunnel body and provides basic support to ensure that the blade to be tested remains in a fixed position during the entire experiment;

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

[0015] A displacement adjustment device, integrated in the adjustable support arm, adjusts the front and rear position x of the blade, and determines the pressure difference ΔP caused by the position change using the formula ΔP=ρ*V^2*(C_p1-C_p2) / 2, where ρ is the air density, V is the air flow velocity, and C_p1 and C_p2 represent the pressure coefficients before and after the blade, respectively;

[0016] The automatic alignment system automatically corrects the blade posture by relying on the feedback provided by the displacement adjustment device. When a deviation δ is detected, it is calculated according to the formula δ=x_target-x_actua l and the x value is adjusted until the deviation is minimized to ensure that the blade is always in the predetermined position.

[0017] Preferably, the vibration sensing component comprises:

[0018] The 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;

[0019] A signal amplification circuit, connected to the displacement sensor, is used to enhance the original signal strength to 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;

[0020] A filtering processing unit, which is immediately after the signal amplification circuit, performs filtering processing on 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 a filtering function;

[0021] The data sampling module relies on the filtered signal to collect data points at the set time interval 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 f_max of the signal, which is expressed as f_s≥2*f_max. The data point D_n obtained after sampling is determined by the formula D_n=S”(n*Δt), where n is the sampling number.

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

[0023] The pressure sensor is arranged adjacent to the vibration sensing component and is used to capture the instantaneous pressure fluctuation caused by the 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;

[0024] A time synchronization interface, 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, T is the reference time, Δt_s is the sensor-specific time delay;

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

[0026] The pressure fluctuation analysis module relies on the preprocessed pressure signal to analyze the instantaneous pressure change characteristics in the airflow and convert the time domain signal into a frequency domain signal. The expression is 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] The synchronous clock generator is connected to the dynamic pressure acquisition unit to generate a time reference signal T_b to ensure that all sensing signals and pressure data are recorded in 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 increasing count;

[0029] A multi-channel data buffer is connected to the synchronous clock generator, receives data streams from different sensors, and temporarily stores the data 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;

[0030] A data alignment processor, which is located immediately after the multi-channel data buffer, is responsible for adjusting the timestamp of each channel data so that the sensing signal and the pressure data are completely aligned 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, relying on the aligned data, records the sensing signal and the pressure data into the non-volatile storage medium at the same time. 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.

[0032] Preferably, the environmental parameter monitor comprises:

[0033] The temperature sensing probe is located inside the wind tunnel body 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, expressed as V_T = α*T + β, where α and β are sensor-specific constants;

[0034] A humidity sensing element is arranged adjacent to the temperature sensing probe and is used to detect the 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 specific to 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. The electrical signal V_P output by the air pressure sensing unit follows the formula V_P=ε*P+ζ, where ε and ζ represent conversion parameters of the air pressure sensing unit.

[0036] The comprehensive correction module relies on the data from the temperature sensing probe, humidity sensing element and air pressure sensing unit to perform comprehensive correction processing. The corrected physical quantity X_c is calculated by the formula X_c=X_m+ΔX, where X_m represents the original measurement value and ΔX is the correction value calculated based on the changes in 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 operators to input the 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;

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

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

[0041] The state feedback loop depends 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), R_r represents the actual response after adjustment, and f() is the state feedback function.

[0042] Preferably, the analysis platform comprises:

[0043] Data import interface, receiving information from the data synchronization recording device, ensuring that the vibration signal S_v and pressure data P_d can be transmitted to the analysis platform, and 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 time required for transmission;

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

[0045] A vibration characteristic extractor, immediately after 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];

[0046] The pattern recognition engine relies on the characteristic parameters provided by the vibration characteristic extractor to analyze the vibration mode of the blade. The engine constructs the blade vibration mode M according to the characteristic parameters. The formula is M=g(A, f, φ), where g() is the pattern recognition function.

[0047] Preferably, the result visualization interface includes:

[0048] The data mapping unit, integrated into the analysis platform, is responsible for converting the processed vibration mode M into a visual graphic element. The mapping process follows the formula G = h(M), where G represents the generated graphic element and h() is the data-to-graphic conversion function;

[0049] A dynamic chart generator, connected to the data mapping unit, creates a dynamic chart C according to the graphic element G, and the chart is generated according to the formula C=j(G,t), t represents the time variable, and j() is the chart construction function;

[0050] An interactive exploration tool, which follows 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 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;

[0051] 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 report content is determined by the formula R=l(E,M), l() is the report synthesis function.

[0052] Technical effects and advantages of the present invention: Compared with the prior art, the transonic Ye Shan aircraft wind tunnel test system proposed by the present invention has the following advantages:

[0053] The present invention introduces a high-precision data synchronization recording device to ensure that the displacement signal captured by the vibration sensing component and the pressure data obtained by the dynamic pressure acquisition unit can be fully aligned in time. At the same time, the system also integrates an environmental parameter monitor that 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 an analysis platform and a result visualization interface enables an in-depth analysis of the blade vibration mode, providing researchers with intuitive and detailed data support, greatly enhancing the understanding of the dynamic behavior of blades under transonic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The present invention is a block diagram of a transonic Ye Shan machine wind tunnel test system. DETAILED DESCRIPTION

[0055] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] The present invention provides Figure 1A transonic blade engine wind tunnel test system is shown, comprising: a tunnel body, a blade mounting structure, a vibration sensing component, a dynamic pressure acquisition unit, a data synchronization recording device, an environmental parameter monitor, a control panel, an analysis platform and a result visualization interface.

[0057] Exemplarily, a wind tunnel body forms a stable airflow environment therein, which is suitable for simulating aerodynamic characteristics under real flight conditions; the wind tunnel body further includes the following interrelated submodules:

[0058] The airflow stabilization zone is located at the entrance of the wind tunnel body. It adjusts the airflow speed and direction to ensure the uniformity and stability of the airflow before entering the experimental section.

[0059] The adjustable convergent expansion section is connected to 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. 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;

[0060] A dynamically adjustable wall surface is arranged after the adjustable convergent-expandable section, and can respond to the calculated force F, adjust the wall surface position in real time, and maintain the preset aerodynamic parameters unchanged;

[0061] The flow field feedback loop is established on the basis of the dynamic adjustment wall surface, 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 trigger signal is sent to the adjustable convergence and expansion section for corresponding adjustment to ensure the stable operation of the entire system.

[0062] In summary, the main design of the wind tunnel 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 a high degree of consistency in experimental conditions. These improvements enable researchers to accurately measure the vibration response of the blades under conditions closer to real flight conditions, thus providing a solid foundation for optimizing blade design and improving aircraft performance.

[0063] Exemplarily, 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; the blade mounting structure further includes the following interrelated submodules:

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

[0065] An adjustable support arm is connected to the fixed base. The support arm has multi-axis rotation capability, allowing the angle θ of the blade to be accurately adjusted 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 arm;

[0066] A displacement adjustment device, integrated in the adjustable support arm, can finely adjust the front and rear position x of the blade to ensure that the relative position of the blade in the airflow meets the experimental setting, and the pressure difference ΔP caused by the position change is determined using 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 before and after the blade, respectively;

[0067] The automatic alignment system relies on the feedback provided by the displacement adjustment device to automatically correct the blade posture to ensure that it is in the optimal measurement position at the beginning of the experiment. When a deviation δ is detected, the system will calculate according to the formula δ=x_target-x_actual and adjust the x value until the deviation is minimized to ensure that the blade is always in the predetermined position.

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

[0069] Exemplarily, the vibration sensing component is attached to the blade mounting structure to detect slight displacement changes on the blade surface; the vibration sensing component further includes the following interrelated submodules:

[0070] The 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, connected to the displacement sensor, is used to enhance the original signal strength to 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;

[0072] A filtering processing unit, which is immediately after the signal amplification circuit, performs filtering processing on 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 a filtering function, which is intended to retain signal components within a specific frequency range;

[0073] The data sampling module relies on the filtered signal to collect data points at the set time interval 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 f_max of the signal, which is expressed as f_s≥2*f_max. The data point D_n obtained after sampling is determined by the formula D_n=S”(n*Δt), where n is the sampling number.

[0074] The design of the vibration sensing component not only achieves high-sensitivity capture of subtle displacement changes on the blade surface, but also ensures high data quality and reliability through steps such as signal amplification, filtering and precise sampling. These improvements work together to enable researchers to accurately measure the vibration response and other dynamic characteristics of the blades under complex and changing experimental conditions. Ultimately, the design provides solid technical support for optimizing blade design, improving aircraft performance, and in-depth understanding of blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data. In addition, high-quality data also lays a good foundation for subsequent analysis and modeling.

[0075] Exemplarily, the dynamic pressure acquisition unit is disposed adjacent to the vibration sensing component and is responsible for capturing instantaneous pressure fluctuations caused by airflow; the dynamic pressure acquisition unit further includes the following interrelated submodules:

[0076] The pressure sensor is arranged adjacent to the vibration sensing component and is used to capture the instantaneous pressure fluctuation caused by the 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;

[0077] A time synchronization interface, connected to the pressure sensor, ensures that the timestamp of the pressure data is consistent with the data of the vibration sensing component for subsequent analysis. 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;

[0078] A data preprocessing circuit, immediately after the time synchronization interface, performs preliminary processing on the raw signal from the pressure sensor, such as offset correction and linearization adjustment, and 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 preprocessed pressure signal to analyze the instantaneous pressure change characteristics in the airflow. This module converts the time domain signal into the frequency domain signal according to the Fourier transform principle. The expression is 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] The design of the dynamic pressure acquisition unit not only achieves high-precision real-time capture of instantaneous pressure fluctuations caused by airflow, but also ensures the high quality and reliability of the data through steps such as 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 changing experimental conditions. Ultimately, the design provides solid technical support for studying blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data. In addition, high-quality pressure data also lays a good foundation for subsequent vibration mode analysis and aircraft performance optimization.

[0081] Exemplarily, the data synchronization recording device is connected to the dynamic pressure acquisition unit to realize simultaneous recording of the sensing signal and the pressure data; the data synchronization recording device further includes the following interrelated submodules:

[0082] The synchronous clock generator is connected to the dynamic pressure acquisition unit to generate an accurate time reference signal T_b to ensure that all sensing signals and pressure data are recorded in 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 increasing count;

[0083] A multi-channel data buffer is connected to the synchronous clock generator, receives data streams from different sensors, and temporarily stores the data 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] A data alignment processor, which is located immediately after the multi-channel data buffer, is responsible for adjusting the timestamp of each channel data so that the sensing signal and the pressure data are completely aligned 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;

[0085] The high-speed data writing unit, relying on the aligned data, records the sensing signal and the pressure data into the non-volatile storage medium at the same time, and 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] The design of the data synchronization recording device not only realizes the precise time synchronization recording of the sensing signal and pressure data, but also ensures the high consistency and integrity of the data through steps such as efficient temporary storage, precise time alignment, and high-speed stable writing. These improvements work together to enable researchers to accurately capture and save the vibration response and other dynamic characteristics of the blades under transonic conditions under complex and changing experimental conditions. Ultimately, the design provides solid technical support for optimizing blade design, improving aircraft performance, and in-depth understanding of blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data.

[0087] Exemplarily, the environmental parameter monitor is located inside the wind tunnel body to track changes in physical quantities such as temperature, humidity and air pressure; the environmental parameter monitor further includes the following interrelated submodules:

[0088] The temperature sensing probe is located inside the wind tunnel body 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, expressed as V_T = α*T + β, where α and β are sensor-specific constants;

[0089] A humidity sensing element is provided adjacent to the temperature sensing probe and is used to detect the 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 specific to the humidity sensing element;

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

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

[0092] The design of the environmental parameter monitor 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 the measured values ​​of each physical quantity through comprehensive correction processing. These improvements work together to enable researchers to accurately grasp the changes in environmental parameters under complex and changing experimental conditions, thereby more comprehensively studying the dynamic behavior of blades under transonic conditions. Ultimately, the design provides solid technical support for optimizing blade design, improving aircraft performance, and in-depth understanding of blade behavior under transonic conditions, ensuring the high accuracy and reliability of experimental data.

[0093] Exemplarily, 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 submodules:

[0094] The experimental condition setting interface is connected to the environmental parameter monitor and provides an intuitive operation interface for operators to input the 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] A real-time data display screen is integrated with the experimental condition setting interface to synchronously display the current temperature T_c, humidity H_c and air pressure P_c from the environmental parameter monitor. The data displayed on the display screen follows the formula D_d=V_X / k_v, where V_X is the sensor output voltage and k_v is the display scale factor;

[0096] An automatic adjustment controller, connected to the real-time data display screen, calculates an adjustment instruction C_a according to the difference ΔX between the current and target physical quantities, and the adjustment instruction is calculated by the formula C_a=K_p*ΔX+K_i*∫ΔXdt+K_d*d(ΔX) / dt, where K_p, K_i and K_d are proportional, integral and differential coefficients respectively;

[0097] The state feedback loop depends 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] The control panel design not only enables intuitive and convenient setting of experimental conditions and real-time data visualization, but also ensures high consistency and stability of experimental conditions through precise automatic adjustment and dynamic state feedback. These improvements work together to enable researchers to accurately set and monitor the experimental environment under complex and changing experimental conditions, thereby more accurately studying the dynamic behavior of blades under transonic conditions.

[0099] Exemplarily, the analysis platform receives information from the data synchronization recording device and processes it to reveal the blade vibration mode; the analysis platform further includes the following interrelated submodules:

[0100] Data import interface, receiving information from the data synchronization recording device, ensuring that the vibration signal S_v and pressure data P_d can be accurately transmitted to the analysis platform, and 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 time required for transmission;

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

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

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

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

[0105] Exemplarily, the result visualization interface is integrated into the analysis platform to present the processed data for intuitive understanding of the blade behavior. The result visualization interface further includes the following interrelated submodules:

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

[0107] A 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, and the chart is generated according to the formula C=j(G,t), where t represents the time variable and j() is a chart construction function to ensure that the vibration characteristics changing with time can be intuitively presented;

[0108] An interactive exploration tool, which follows the dynamic chart generator, allows the user to deeply explore the vibration details within 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 the feedback provided by the interactive exploration tool, summarizes all analysis results and generates a comprehensive report R, the content of which is determined by the formula R=l(E,M), where l() is a report synthesis function, which aims to integrate the vibration mode and the results of user exploration to provide a comprehensive data interpretation.

[0110] The result visualization interface design not only achieves efficient mapping and dynamic chart display of processed data, but also provides user-friendly data exploration and comprehensive result summary through interactive exploration tools and comprehensive report generators. These improvements work together to enable researchers to intuitively and interactively understand the behavior characteristics of blades under complex and changing experimental conditions, thereby further studying their dynamic response under transonic conditions.

[0111] Finally, it should be noted that the above 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 aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A transonic propeller wind tunnel test system, characterized in that: include: The wind tunnel body, which forms a stable airflow environment suitable for simulating aerodynamic characteristics under real flight conditions; A blade mounting structure, connected to the wind tunnel body, used to fix the blade to be tested and ensure that it is in a predetermined position in the airflow; A vibration sensing component, attached to the blade mounting structure, for detecting displacement changes on the blade surface; A dynamic pressure acquisition unit is disposed adjacent to the vibration sensing component and is responsible for capturing instantaneous pressure fluctuations caused by airflow; A data synchronization recording device connected to the dynamic pressure acquisition unit to achieve simultaneous recording of the sensing signal and the pressure data; An environmental parameter monitor, located inside the wind tunnel body, tracks changes in temperature, humidity and air pressure; a control panel, connected to the environmental parameter monitor, providing an interface for an operator to set and monitor experimental conditions; an analysis platform, receiving information from the data synchronization recording device and processing it to reveal the blade vibration mode; The result visualization interface is integrated into the analysis platform to present the processed data.

2. A transonic propeller wind tunnel test system according to claim 1, characterized in that: The wind tunnel body comprises: The airflow stabilization zone is located at the entrance of the wind tunnel body. It adjusts the airflow speed and direction to ensure the uniformity and stability of the airflow before entering the experimental section. The adjustable convergent expansion section is connected to 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. 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; A dynamically adjustable wall surface is arranged after the adjustable convergent-expandable section, and can respond to the calculated force F, adjust the wall surface position in real time, and maintain the preset aerodynamic parameters unchanged; The flow field feedback loop is established on the basis of the dynamic adjustment wall surface, 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 trigger signal is sent to the adjustable convergence and expansion section for corresponding adjustment.

3. A transonic jet engine wind tunnel test system according to claim 2, characterized in that: The blade mounting structure comprises: The fixed base is connected to the wind tunnel body and provides basic support to ensure that the blade to be tested remains in a fixed position during the entire experiment; An adjustable support arm is connected to the fixed base. The support arm has multi-axis rotation capability, allowing the angle θ of the blade to be accurately adjusted. The formula M=F*L*sin(θ) is used to calculate the bending moment M acting on the blade, where F represents the force applied to the blade and L is the length of the arm. A displacement adjustment device, integrated in the adjustable support arm, adjusts the front and rear position x of the blade, and determines the pressure difference ΔP caused by the position change using the formula ΔP=ρ*V^2*(C_p1-C_p2) / 2, where ρ is the air density, V is the air flow velocity, and C_p1 and C_p2 represent the pressure coefficients before and after the blade, respectively; The automatic alignment system automatically corrects the blade posture based on the feedback provided by the displacement adjustment device. When a deviation δ is detected, it is calculated according to the formula δ=x_target-x_actual and the x value is adjusted until the deviation is minimized to ensure that the blade is always in the predetermined position.

4. A transonic jet engine wind tunnel test system according to claim 3, characterized in that: The vibration sensing component comprises: The 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; A signal amplification circuit, connected to the displacement sensor, is used to enhance the original signal strength to 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; A filtering processing unit, which is immediately after the signal amplification circuit, performs filtering processing on 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 a filtering function; The data sampling module relies on the filtered signal to collect data points at the set time interval 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 f_max of the signal, which is expressed as f_s≥2*f_max. The data point D_n obtained after sampling is determined by the formula D_n=S”(n*Δt), where n is the sampling number.

5. A transonic jet engine wind tunnel test system according to claim 4, characterized in that: The dynamic pressure acquisition unit comprises: The pressure sensor is arranged adjacent to the vibration sensing component and is used to capture the instantaneous pressure fluctuation caused by the 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, 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, T is the reference time, Δt_s is the sensor-specific time delay; A data preprocessing circuit, immediately after the time synchronization interface, performs preliminary processing on the raw signal from the pressure sensor, and the preprocessed signal P' is calculated by the formula P'=P-P_offset, where P_offset represents the sensor offset; The pressure fluctuation analysis module relies on the preprocessed pressure signal to analyze the instantaneous pressure change characteristics in the airflow and convert the time domain signal into a frequency domain signal. The expression is 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. A transonic jet engine wind tunnel test system according to claim 5, characterized in that: The data synchronization recording device comprises: The synchronous clock generator is connected to the dynamic pressure acquisition unit to generate a time reference signal T_b to ensure that all sensing signals and pressure data are recorded in 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 increasing count; A multi-channel data buffer is connected to the synchronous clock generator, receives data streams from different sensors, and temporarily stores the data 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; A data alignment processor, which is located immediately after the multi-channel data buffer, is responsible for adjusting the timestamp of each channel data so that the sensing signal and the pressure data are completely aligned 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; The high-speed data writing unit, relying on the aligned data, records the sensing signal and the pressure data into the non-volatile storage medium at the same time. 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.

7. A transonic jet engine wind tunnel test system according to claim 6, characterized in that: The environmental parameter monitor comprises: The temperature sensing probe is located inside the wind tunnel body 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, expressed as V_T = α*T + β, where α and β are sensor-specific constants; A humidity sensing element is arranged adjacent to the temperature sensing probe and is used to detect the 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 specific to the humidity sensing element; The air pressure sensing unit is integrated near the humidity sensing element and is responsible for capturing the change of the air pressure P. The electrical signal V_P output by the air pressure sensing unit follows the formula V_P=ε*P+ζ, where ε and ζ represent conversion parameters of the air pressure sensing unit. The comprehensive correction module relies on the data from the temperature sensing probe, humidity sensing element and air pressure sensing unit to perform comprehensive correction processing. The corrected physical quantity X_c is calculated by the formula X_c=X_m+ΔX, where X_m represents the original measurement value and ΔX is the correction value calculated based on the changes in other physical quantities.

8. A transonic jet engine wind tunnel test system according to claim 7, characterized in that: The control panel comprises: The experimental condition setting interface is connected to the environmental parameter monitor and provides an intuitive operation interface for operators to input the 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; A real-time data display screen is integrated with the experimental condition setting interface to synchronously display the current temperature T_c, humidity H_c and air pressure P_c from the environmental parameter monitor. The data displayed on the display screen follows the formula D_d=V_X / k_v, where V_X is the sensor output voltage and k_v is the display scale factor; An automatic adjustment controller, connected to the real-time data display screen, calculates an adjustment instruction C_a according to the difference ΔX between the current and target physical quantities, and the adjustment instruction is calculated by the formula C_a=K_p*ΔX+K_i*∫ΔXdt+K_d*d(ΔX) / dt, where K_p, K_i and K_d are proportional, integral and differential coefficients respectively; The state feedback loop depends 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), R_r represents the actual response after adjustment, and f() is the state feedback function.

9. A transonic jet engine wind tunnel test system according to claim 8, characterized in that: The analysis platform includes: Data import interface, receiving information from the data synchronization recording device, ensuring that the vibration signal S_v and pressure data P_d can be transmitted to the analysis platform, and 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 time required for transmission; A signal preprocessing unit, connected to the data import interface, performs preliminary processing on the imported data, and the preprocessed vibration signal S'_v is calculated by the formula S'_v=f(S_v), where f() represents a preprocessing function; A vibration characteristic extractor, immediately after 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 characteristic parameters provided by the vibration characteristic extractor to analyze the vibration mode of the blade. The engine constructs the blade vibration mode M according to the characteristic parameters. The formula is M=g(A, f, φ), where g() is the pattern recognition function.

10. A transonic jet engine wind tunnel test system according to claim 9, characterized in that: The result visualization interface includes: The data mapping unit, integrated into the analysis platform, is responsible for converting the processed vibration mode M into a visual graphic element. The mapping process follows the formula G = h(M), where G represents the generated graphic element and h() is the data-to-graphic conversion function; A dynamic chart generator, connected to the data mapping unit, creates a dynamic chart C according to the graphic element G, and the chart is generated according to the formula C=j(G,t), t represents the time variable, and j() is the chart construction function; An interactive exploration tool, which follows 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 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 report content is determined by the formula R=l(E,M), l() is the report synthesis function.

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