Test method, apparatus and medium based on grid force under dynamic derivative prediction

Abstract

The application provides a test method, device and medium based on grid force under dynamic derivative prediction. According to the method, the grid force technology is combined with the dynamic derivative prediction technology, the real aerodynamic interference environment of a parent stage vehicle to a substage is simulated in a wind tunnel test, the substage model is driven to perform a controlled attitude angle oscillation motion at a plurality of predetermined grid force station points, and an integrated high-frequency response driving mechanism, real-time sensing feedback and active control system are used to accurately measure and calculate the combined dynamic derivative of the substage at each station point. The application solves the technical problem that the traditional method cannot directly obtain dynamic stability parameters in an interference flow field, realizes efficient integrated acquisition of dynamic derivatives in multiple station points and multiple attitude directions, significantly improves the test efficiency and data accuracy, and provides reliable data support for vehicle separation control law design and dynamic stability analysis.

Description

Technical Field

[0001] This document relates to the field of dynamic derivative prediction technology, and in particular to an experimental method, equipment and medium for dynamic derivative prediction based on grid force measurement. Background Technology

[0002] The dynamic derivative is a key aerodynamic parameter characterizing the dynamic stability of an aircraft and is crucial for the design of its control law. Currently, the mature international method for obtaining the dynamic derivative is forced vibration testing, which can drive an isolated model to undergo controlled oscillations and measure its dynamic response. On the other hand, grid force measurement testing is a standard method for studying aircraft separation problems. Through densely distributed measurements, it is possible to obtain high-precision aerodynamic data of the separated body under different static poses in a disturbed flow field.

[0003] However, existing technologies have systemic limitations. Traditional forced vibration tests cannot be conducted in the real and complex aerodynamic disturbance flow fields generated by the mother-class aircraft. Traditional grid force measurement tests are essentially static measurements and cannot provide the dynamic stability derivatives necessary for the separation body during dynamic oscillations. These two key technologies have long been separated, leading to the limitation of post-hoc comprehensive analysis of only two types of data in engineering. This approach introduces significant data gaps in regions of strong nonlinear disturbances, resulting in uncertainty in predicting the initial trajectory and stability of the separation body, becoming a technical bottleneck restricting the design of highly safe separation schemes.

[0004] Therefore, existing technologies lack an integrated method that can simultaneously simulate real aerodynamic disturbance environments in wind tunnel tests and directly measure the dynamic stability of the separated body. This invention aims to overcome this limitation by providing an innovative testing scheme to achieve efficient and accurate prediction of the dynamic characteristics of the separated body under real disturbance conditions, thus providing more comprehensive data support for the design of aircraft separation control laws. Summary of the Invention

[0005] According to embodiments of the present invention, an experimental method, device, and medium for predicting dynamic derivatives based on grid force measurement are provided, aiming to solve the above-mentioned problems.

[0006] According to an embodiment of the present invention, an experimental method for predicting dynamic derivatives based on grid force measurement is provided, characterized by comprising: S1. Set the initial conditions for the experiment; S2. Based on the initial conditions, start the wind tunnel flow field and position the sub-model to the predetermined grid force measurement station location; S3. At the station point, the sub-level model is driven to perform attitude angle oscillation motion by the driving mechanism of the dynamic derivative motion response; S4. The motion state of the sub-model is monitored in real time by sensors and fed back to the dynamic derivative prediction control system. The dynamic derivative prediction control system performs active compensation control according to the control algorithm so that the actual motion of the sub-model approximates the specified oscillating motion model. S5. Once the motion meets the requirements, the aerodynamic force and torque are measured using the dynamic balance built into the sub-model, and the combined dynamic derivative under the current working condition is calculated. S6. Traverse all predetermined grid force measurement stations to obtain dynamic derivative data for multiple stations.

[0007] According to an embodiment of the present invention, an electronic device is provided, comprising: Processor; and, A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the above-described experimental method for predicting dynamic derivatives based on grid force measurement.

[0008] According to an embodiment of the present invention, a storage medium is provided for storing computer-executable instructions, which, when executed, implement the steps of the above-described experimental method for predicting dynamic derivatives based on grid force measurement.

[0009] This invention combines grid force measurement technology with dynamic derivative prediction technology to achieve rapid and accurate prediction of dynamic stability derivatives for daughter-stage aircraft under aerodynamic disturbances of the parent-stage aircraft. Through the integration of an innovative piezoelectric ceramic drive mechanism, a real-time feedback control system, and an integrated data flow, this invention can efficiently acquire combined dynamic derivatives of pitch, yaw, and roll in a single wind tunnel test, significantly improving testing efficiency and reducing development costs. The obtained data can be directly used for control law design under complex separation conditions, significantly enhancing the separation attitude control capability and safety of the aircraft under unsteady aerodynamic disturbances. This provides a novel technical approach and reliable data support for aircraft dynamic stability research, separation trajectory design, and control strategy verification. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of the experimental method for predicting dynamic derivatives based on grid force measurement according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the dynamic derivative response driving mechanism according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the connection between the sub-level pose driving mechanism and the forced attitude angle oscillation motion mechanism in an embodiment of the present invention; Figure 4 This is a general block diagram of the dynamic derivative prediction control system according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the three-degree-of-freedom angular displacement driving mechanism of the parent model in an embodiment of the present invention; Figure 6 This is a schematic diagram of the safe position before the grid force measurement test according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the initial station location for grid force measurement according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the test station location for predicting the dynamic derivative under grid force measurement according to an embodiment of the present invention. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0013] Method Implementation Examples According to embodiments of the present invention, an experimental method for predicting dynamic derivatives based on grid force measurement is provided. Figure 1 This is a flowchart of an experimental method for predicting dynamic derivatives based on grid force measurement, according to an embodiment of the present invention. Figure 1 As shown, the experimental method for predicting dynamic derivatives based on grid force measurement in this embodiment of the invention specifically includes: S1. Set the initial conditions for the experiment. S1 specifically includes: S101. Determine the scale, feature size, and separation parameters of the parent and child experimental models, including: The scale of the mother-stage model to the full-size mother-stage aircraft, the center of mass position, reference length, and reference area of ​​the full-size mother-stage aircraft; the scale of the daughter-stage model to the full-size daughter-stage aircraft, the center of mass position, reference length, and reference area of ​​the full-size daughter-stage aircraft; Axial separation distance, longitudinal separation distance, and lateral separation distance of the full-size sub-stage aircraft relative to the initial position of the full-size parent aircraft; pitch angle, yaw angle, and roll angle of the full-size sub-stage aircraft relative to the initial attitude angle of the full-size parent aircraft; S102. Determine the experimental model and support form for the prediction of the dynamic derivative, including: The aerodynamic shape of the parent and daughter test models, and the support forms adopted by the daughter and parent models, including: back support, tail support, belly support, etc. S103. Determine the mathematical models for motion prediction based on the dynamic derivatives, including: forced pitch motion model, forced yaw motion model, and forced roll motion model. These models are obtained using the following formulas: Formula 1; Formula 2; Formula 3; In formula 1 Angle of attack that varies over time. Initial angle of attack; Angle of attack amplitude; The pitch oscillation frequency, The vibration time; In formula 2 The sideslip angle varies over time. The initial sideslip angle; The amplitude of the sideslip angle; The sideslip angle oscillation frequency, The vibration time; In formula 3 The roll angle varies over time. This is the initial roll angle; This refers to the roll angle amplitude; The roll angle oscillation frequency, The vibration time.

[0014] S104. Determine the driving mechanism for realizing the dynamic derivative motion response of the motion, and its connection form with the support rod, balance, and sub-stage model, and determine the natural frequency of the connection structure therein; The specific driving mechanism for dynamic derivative motion response (the driving mechanism for forced attitude angle oscillation motion) consists of a preload bolt, a contact spherical surface, a piezoelectric ceramic actuator, a mounting base, an actuator mounting slot, a cover plate, and a support rod, such as... Figure 2This is a schematic diagram of the dynamic derivative response drive mechanism according to an embodiment of the present invention. The piezoelectric ceramic actuator has a stacked structure and can only withstand axial loads; larger shear forces would damage the original structure of the piezoelectric ceramic. To prevent damage from large shear forces during installation and output, a contact spherical surface is designed at the top of the ceramic, which is then bonded to the top of the piezoelectric ceramic with epoxy resin. Axial pre-tightening bolts are used for pre-tightening, with the bolts acting directly on the spherical surface, ensuring that the ceramic is not affected by shear forces during use. To prevent the complex surface shape of the ceramic mounting section from causing chaotic flow around the drive mechanism, an arc-shaped cover plate is added around the four ceramics to maintain the overall aerodynamic shape of the drive mechanism consistent with the outer circle of the support rod.

[0015] The drive mechanism employs multiple sets of piezoelectric ceramic actuators arranged symmetrically in the circumference. By coordinating the extension and retraction of each set of actuators, a small-amplitude angular displacement around a specific axis can be synthesized at the end of the drive strut, thereby achieving forced oscillations of pitch, yaw, or roll of the sub-stage model. The preload mechanism, consisting of the contact spherical surface at the actuator tip and the preload bolt, is designed to keep the actuator under constant pressure, preventing tensile instability under alternating loads and effectively transmitting thrust, while also eliminating motion nonlinearity caused by installation gaps. The arc-shaped cover plate maintains the aerodynamic smoothness of the drive section's shape, reducing interference with the main flow of the wind tunnel.

[0016] Figure 3 This is a schematic diagram of the connection between the sub-level pose driving mechanism and the forced attitude angle oscillation motion mechanism according to an embodiment of the present invention. Figure 3 It can be seen that the tail fairing, the dynamic derivative motion response drive mechanism (the drive mechanism for forced attitude angle oscillation motion), the motor, the Z-shaped support rod, the built-in balance, and the model are connected in series from front to back, and then connected to the parallel six-degree-of-freedom mechanism, thereby efficiently realizing the functions of six-degree-of-freedom displacement and attitude motion, forced attitude angle oscillation motion, and dynamic force measurement of the sub-level model.

[0017] This serially integrated connection decouples and integrates two major functions: large-range pose adjustment (achieved by a six-DOF parallel mechanism) and high-precision, high-dynamic attitude micro-amplitude forced oscillation (achieved by a dynamic derivative response drive mechanism). The parallel six-DOF mechanism is responsible for quickly and accurately positioning the sub-model to the designated space station position and static attitude angle according to the grid force measurement plan. Once positioning is complete, the piezoelectric ceramic-driven dynamic derivative response mechanism begins to work, superimposing a high-frequency, micro-amplitude harmonic angular motion on the static reference provided by the parallel mechanism without interfering with the overall static pose of the model. The built-in dynamic balance is located between the model and the drive mechanism, directly measuring the unsteady aerodynamic forces and torques experienced by the model during forced oscillation.

[0018] S105. Determine the signal generator and power amplifier models, the hysteresis force of the piezoelectric ceramic actuator, the structural dimensions of the piezoelectric ceramic actuator, and the excitation power of the exciter; Conventional displacement and velocity sensors cannot perform real-time measurement and feedback of model vibration under experimental conditions. Therefore, small, fast-response accelerometers are attached inside the model as vibration measurement sensors. The dynamic derivative prediction control system hardware consists of a high-performance computer, a power amplifier, a vibrator, and accelerometers. Its working principle is as follows: when the sub-model moves to the grid force measurement station, the signal generator provides an excitation signal, which is transmitted to the vibrator through the power amplifier. The vibrator is excited according to the preset angular displacement oscillation pattern. At this time, the accelerometers inside the sub-model measure and feedback the dynamic angular displacement signal in real time, and transmit it to the high-performance computer. The high-performance computer compares various control algorithms and provides the optimal control strategy. This strategy is converted into discrete electrical signals and transmitted to the amplifier to drive the piezoelectric ceramic to actuate, ultimately compensating for the angular displacement oscillation motion of the sub-model, balance, and support rod to achieve the specified angular displacement oscillation motion pattern. Figure 4 This is a general block diagram of the dynamic derivative prediction control system according to an embodiment of the present invention.

[0019] This system constructs a closed-loop active control circuit. An accelerometer, acting as a feedback sensor, measures the real-time angular acceleration signal of the model; after integration, angular velocity and angular displacement are obtained. A high-performance computer compares the measured actual motion state with a preset mathematical model (Equations 1-3) and generates control commands to drive the piezoelectric ceramic actuator. The key challenge lies in the fact that aerodynamic loads are strong disturbances acting on the model, and piezoelectric ceramics exhibit nonlinear characteristics such as hysteresis and creep. Therefore, the control algorithm not only needs to respond quickly to track commands but also must possess anti-interference capabilities and the ability to compensate for actuator nonlinearities. Multiple control algorithms (PID, quadratic optimal, fuzzy control) are selected and switched or fused in real time to adapt to control requirements under different flow conditions (such as different aerodynamic damping characteristics at subsonic and transonic speeds) and different oscillation modes (pitch, yaw, roll).

[0020] S106. Determine the active vibration control algorithm in the dynamic derivative predictive control system; In this embodiment of the invention, the control program of the dynamic derivative prediction control system includes: digital-to-analog / analog-to-digital conversion at the input / output terminals, low-pass filtering, control law, and limiting circuitry (simulation operating voltage exceeds the safe operating voltage of the piezoelectric ceramic). The control algorithms include: PID control algorithm, quadratic linear optimal control algorithm, and fuzzy control algorithm. The low-pass filter is mainly used to filter out high-frequency noise introduced by high-frequency vibration of the model structure and turbulence in the accelerometer signal, and extract the effective signal related to the forced oscillation frequency. The limiting circuit is used to protect the piezoelectric ceramic actuator and prevent its driving voltage from exceeding the safe operating range due to excessive control commands, thus avoiding damage. The various control algorithms each have their own focus: the PID control algorithm has a simple structure and is easy to implement, providing stable control under conditions where the dynamic characteristics of the model do not change much; the quadratic linear optimal control is based on the state-space model design and can systematically balance control accuracy and energy consumption, making it suitable for applications with higher performance requirements; fuzzy control is good at handling system nonlinearity and uncertainty, and exhibits stronger robustness when aerodynamic disturbances are complex or model parameters drift.

[0021] S107. Calibrate the positioning accuracy and precision of the pitch angle, yaw angle, and roll angle of the motion drive mechanism of the parent model; Figure 5 This is a schematic diagram of the three-degree-of-freedom angular displacement driving mechanism of the parent model in an embodiment of the present invention. The inverse kinematic model of the three-degree-of-freedom angular displacement driving mechanism of the first-level parent model is as follows:

[0022]

[0023] Formula 4;

[0024]

[0025] In formula 4, The preset angle between the main spindle and the tail shaft The angle of attack of the device. The angle of rotation of the main shaft around its own axis. The angle of rotation of the tail shaft around its own axis. For the tail shaft angle of attack, For tail shaft sideslip angle, This is the tail shaft roll angle.

[0026] S108, the parent model's three-degree-of-freedom angular displacement drive mechanism performs position inverse kinematics based on the inverse kinematics model described in Formula 5, issuing motor drive commands to complete the three-degree-of-freedom attitude angular motion. Subsequently, using a spatial coordinate system established by the absolute measurement wall, the actual attitude angular motion of the parent model is measured, ultimately obtaining 100 sets of actual attitude angles. The actual measured attitude angles are then compared with those obtained from the inverse kinematics model. , , As input, the error parameters of the three-degree-of-freedom angular displacement drive mechanism of the parent model are obtained using the nonlinear least squares method. These error parameters are then substituted into Equation 5, and a new set of command attitude angles is given to drive the first-stage motion mechanism. After the attitude angle motion is completed, the attitude angles are measured again using an absolute measurement wall. If the difference between the actual attitude angle and the command attitude angle meets the positioning accuracy requirements, the calibration ends. If not, another set of command attitude angles needs to be measured until the accuracy requirements are met, thus completing the calibration. The essence of this calibration process is to accurately obtain the error mapping relationship between the theoretical kinematic model (Equation 4) of the drive mechanism and the actual physical system through system identification methods. A high-precision absolute measurement wall (such as a laser tracker) is used as an external measurement benchmark to obtain the true values ​​of the model's attitude angles. By comparing the command values, the theoretical model output values, and the measured true values, the geometric and kinematic errors of the system can be identified in reverse. The identified error parameters are compensated into the control model to form a corrected inverse kinematic model (Formula 5). Thus, in subsequent experiments, even if there are manufacturing and assembly errors in the mechanism, high-precision attitude positioning can be achieved through software compensation.

[0027]

[0028]

[0029] Formula 5;

[0030]

[0031] S109. Calibrate the positioning accuracy and precision of the six-degree-of-freedom parallel mechanism drive sub-stage model for axial displacement, lateral displacement, longitudinal displacement, pitch angle, yaw angle, and roll angle motion.

[0032] The calibration principle of the six-DOF parallel mechanism in this embodiment of the invention includes: First, given a theoretical pose value, the motor encoder value is obtained by inverse solving according to the six-DOF parallel mechanism kinematic mathematical model (see Formula 6). The rotation of the motor encoder drives the static ball joints of the six links of the parallel mechanism to perform linear motion, and finally, the actual pose motion is completed by the end-stage secondary aircraft model of the parallel mechanism. Simultaneously, 100 sets of pose commands are measured using an absolute measurement wall, and the unknown parameters in the kinematic mathematical model are obtained using the nonlinear least squares method. Then, 20 sets of command attitude angles are re-given. If the pose command and the actual measured pose error do not meet the accuracy requirements, the 20 sets of command attitude angles need to be re-measured until the accuracy requirements are met.

[0033] Formula 6; In formula 6, The static coordinate transformation of the moving ball hinge point of the i-th branch of a six-degree-of-freedom parallel mechanism in the ground coordinate system coordinate; Let x be the x-coordinate of the moving ball hinge point of the i-th branch of the six-degree-of-freedom parallel mechanism in the ground coordinate system after static coordinate transformation; Let y be the y-coordinate of the moving ball hinge point of the i-th branch of the six-degree-of-freedom parallel mechanism in the ground coordinate system after static coordinate transformation; Let z be the z-coordinate of the moving ball hinge point of the i-th branch of the six-degree-of-freedom parallel mechanism in the ground coordinate system after the static coordinate transformation. , Let x be the slope and intercept of the x-coordinate of the stationary ball hinge point of the i-th branch of a six-degree-of-freedom parallel mechanism with respect to the value of the motor encoder. , Let y-coordinate of the i-th branch static ball hinge point of a six-DOF parallel mechanism be the slope and intercept of the motor encoder value with respect to the value of the motor encoder. , Let z be the slope and intercept of the z-coordinate of the stationary ball hinge point of the i-th branch of a six-degree-of-freedom parallel mechanism with respect to the value of the motor encoder. The encoder value of the i-th branch motor; Let be the length of the i-th branch; Formula 7; In formula 7, Let x be the x-coordinate of the moving ball hinge point of the i-th branch of the six-degree-of-freedom parallel mechanism in the model coordinate system; Let y be the y-coordinate of the moving ball hinge point of the i-th branch of the six-degree-of-freedom parallel mechanism in the model coordinate system; Let z be the z-coordinate of the moving ball hinge point of the i-th branch of the six-degree-of-freedom parallel mechanism in the model coordinate system; This is the transformation matrix from the coordinate system of the secondary aircraft model to the ground coordinate system.

[0034] S110. Before the test, the child model is moved to a safe position below the parent model using a six-degree-of-freedom parallel mechanism: that is, the six-degree-of-freedom parallel mechanism moves the child model 150mm downwards from the bracket to avoid collision between the parallel six-degree-of-freedom mechanism and the parent model and its drive mechanism during the initial start-up after the wind blows. Figure 6 The diagram shows the safe position before the grid force measurement test according to an embodiment of the present invention.

[0035] S2. Based on the initial conditions, start the wind tunnel flow field and position the sub-model to the predetermined grid force measurement station location; Start the exciter, accelerometer, and power amplifier; start the wind tunnel. After the wind tunnel flow field stabilizes, keep the attitude angle of the parent model constant and drive the child model to the initial grid force measurement station point: The position is obtained by accurately measuring the first pneumatic data using a ranging fixture, such as... Figure 7The diagram shown is a schematic diagram of the initial station location for grid force measurement according to an embodiment of the present invention.

[0036] S3. At the station point, the sub-level model is driven to perform attitude angle oscillation motion by the driving mechanism of the dynamic derivative motion response; The driving mechanism that initiates the dynamic derivative motion response of the sub-level model is the driving mechanism that forces the attitude angle motion, driving the sub-level model to perform fixed-angle amplitude motion at the grid force measurement station point; S4. The motion state of the sub-model is monitored in real time by sensors and fed back to the dynamic derivative prediction control system. The dynamic derivative prediction control system performs active compensation control according to the control algorithm so that the actual motion of the sub-model approximates the specified oscillating motion model. When forced attitude angle motion is performed at each grid force measurement station, the accelerometer measures the attitude angle motion form and frequency in real time and feeds it back to the dynamic derivative prediction control system. The dynamic derivative prediction control system provides the excitation frequency and amplification power in real time according to the control algorithm to realize the specified angular displacement motion form, frequency and amplitude of the sub-level model. Using a high-performance computer, we can observe whether the control system it carries can achieve the specified angular displacement motion form, frequency and amplitude of the sub-level model within 3 seconds. If this can be achieved, the wind will continue to blow for 2 seconds, and the combined dynamic derivative under the current attitude angle condition will be calculated by using the built-in balance of the sub-model based on the established dynamic derivative prediction calculation method. If this is not possible, the sub-model is driven to move to the next grid force measurement station through a six-degree-of-freedom parallel mechanism to continue forced attitude angle motion; S5. Once the motion meets the requirements, the aerodynamic force and torque are measured using the dynamic balance built into the sub-model, and the combined dynamic derivative under the current working condition is calculated. S6. Traverse all predetermined grid force measurement stations to obtain dynamic derivative data for multiple stations.

[0037] Once the dynamic derivative prediction for the last grid force measurement station is completed, dynamic derivative data for all grid force measurement stations is generated. This involves performing a specified attitude angle motion at each grid force measurement station and acquiring the dynamic derivatives at each station. These dynamic derivatives include: pitch combined dynamic derivative, yaw combined dynamic derivative, and roll combined dynamic derivative. A single airflow operation can establish a database of dynamic derivatives for the sub-stage model under aerodynamic disturbances from the parent model, significantly improving experimental efficiency and reducing computational costs for both simulation and experimentation. Figure 8 This is a schematic diagram of the test station location for predicting the dynamic derivative under grid force measurement according to an embodiment of the present invention.

[0038] Finally, the experiment ended, and the mechanism for forced attitude angle motion was stopped. The sub-model was driven back to a safe position through a parallel six-degree-of-freedom mechanism, and the parallel six-degree-of-freedom mechanism of the sub-model and the attitude angle driving mechanism of the parent model were stopped. The wind tunnel was shut down. Combining the aerodynamic data under the same working conditions and at the same station point under the grid force measurement test, including aerodynamic force coefficients and aerodynamic torque coefficients, a short-period motion model of the sub-model under a certain attitude angle state was generated. In the grid force measurement test, both the sub-model and the parent model need to obtain aerodynamic force and aerodynamic torque coefficients through a built-in balance. The built-in balance is directly connected to the balance measurement signal acquisition device to form a closed loop. When the aerodynamic force and aerodynamic torque act on the model, the built-in balance deforms accordingly, and the resistance value changes, generating electrical signal fluctuations. Based on short-cycle motion modes and PID parameter tuning methods, a separation control law was constructed and embedded into the trajectory capture test system to achieve separation attitude control of the sub-level model under aerodynamic disturbances of the parent level. Separation trajectory capture tests with attitude control were carried out on the sub-level and parent level to verify the control capability and effect of the sub-level model under dangerous separation conditions.

[0039] More specifically, one concrete implementation of this application is as follows: An experiment was conducted to predict the dynamic derivative based on grid-based force measurement, obtaining the dynamic derivative under different attitude angle conditions at each grid-based force measurement station. The specific process is as follows: Figure 1 As shown.

[0040] Based on the requirements of the aerodynamic test missions of the daughter and mother stages, the separation state of the daughter stage under strong aerodynamic interference from the mother stage was designed, and grid force measurement test and dynamic derivative prediction test under grid force measurement were carried out for each separation condition.

[0041] The experiment began by moving the sub-model to a safe position using a parallel six-degree-of-freedom mechanism, based on the separation condition at the strong aerodynamic interference point. The exciter, accelerometer, and power amplifier were then activated, and the wind tunnel was started. After the wind tunnel's own flow field stabilized, the forced attitude angle motion drive mechanism of the sub-model was activated, driving the sub-model to perform fixed-angle amplitude motion at the grid force measurement station. The high-performance computer monitored the process. The attitude angle motion pattern and frequency were observed using the accelerometer. If the sub-model could achieve the specified angular displacement motion pattern, frequency, and amplitude within 3 seconds, airflow was continued for another 2 seconds. The unsteady aerodynamic forces were acquired in real-time using the sub-model's built-in dynamic balance, and the combined dynamic derivative under this condition was calculated. If the sub-model could not achieve the specified angular displacement motion pattern, frequency, and amplitude within 3 seconds, the six-degree-of-freedom parallel mechanism was used to drive the sub-model to the next grid force measurement station to continue acquiring the dynamic derivative under this condition. After the dynamic derivative prediction at the last station point is completed, the six-degree-of-freedom parallel mechanism drives the sub-stage model back to a safe position, and the parent-stage three-degree-of-freedom attitude angle drive mechanism returns to the initial attitude angle. The wind tunnel is then shut down.

[0042] After obtaining the predicted dynamic derivative data of all grid force measurement stations, and combining it with the aerodynamic data obtained from the completed grid force measurement test under the same working conditions, the short-cycle motion mode of the sub-level model was analyzed and the PID control law was debugged. Finally, the designed separation control law was embedded into the trajectory capture test system to realize the attitude control of the sub-level model when the sub-level and parent-level are separated. At the same time, the control capability and effect of the sub-level model under dangerous working conditions were verified.

[0043] By employing the embodiments of the present invention, the following beneficial effects are achieved: By combining grid force measurement technology with dynamic derivative prediction technology, this invention enables rapid and accurate prediction of dynamic stability derivatives for daughter-stage aircraft at multiple stations and in multiple attitude angles under aerodynamic disturbances of the parent-stage aircraft. Through the integration of an innovative piezoelectric ceramic drive mechanism, a real-time feedback control system, and an integrated data flow, this embodiment of the invention can efficiently acquire combined dynamic derivatives of pitch, yaw, and roll in a single wind tunnel test, significantly improving test efficiency and reducing R&D costs. The obtained data can be directly used for control law design under complex separation conditions, significantly enhancing the separation attitude control capability and safety of the aircraft under unsteady aerodynamic disturbances. This provides a new technical approach and reliable data support for aircraft dynamic stability research, separation trajectory design, and control strategy verification.

[0044] Device Example 1 According to an embodiment of the present invention, an electronic device is provided, comprising: Processor; and, A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the above-described experimental method for predicting dynamic derivatives based on grid force measurement.

[0045] Device Example 2 According to an embodiment of the present invention, a storage medium is provided for storing computer-executable instructions, which, when executed, implement the steps of the above-described experimental method for predicting dynamic derivatives based on grid force measurement.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An experimental method for predicting dynamic derivatives based on grid force measurement, characterized in that... include: S1. Set the initial conditions for the experiment; S2. Based on the initial conditions, start the wind tunnel flow field and position the sub-model to the predetermined grid force measurement station location; S3. At the station point, the sub-level model is driven to perform attitude angle oscillation motion by the driving mechanism of the dynamic derivative motion response; S4. The motion state of the sub-model is monitored in real time by sensors and fed back to the dynamic derivative prediction control system. The dynamic derivative prediction control system performs active compensation control according to the control algorithm so that the actual motion of the sub-model approximates the specified oscillating motion model. S5. Once the motion meets the requirements, the aerodynamic force and torque are measured using the dynamic balance built into the sub-model, and the combined dynamic derivative under the current working condition is calculated. S6. Traverse all predetermined grid force measurement stations to obtain dynamic derivative data for multiple stations.

2. The method according to claim 1, characterized in that, The setting of initial test conditions includes: Determine the scale, feature size, and separation parameters of the parent and child experimental models; Determine the experimental model and support form for predicting the dynamic derivative; Determine the mathematical model of motion for predicting dynamic derivatives; Determine the driving mechanism for achieving the dynamic derivative motion response of the motion, its connection form with the support rod, balance, and sub-stage model, and determine the natural frequency of the connection structure therein; Determine the signal generator and power amplifier models, the hysteresis force of the piezoelectric ceramic actuator, the structural dimensions of the piezoelectric ceramic actuator, and the excitation power of the exciter; Determine the active vibration control algorithm in the dynamic derivative predictive control system; The positioning accuracy and precision of the pitch, yaw, and roll angles of the motion drive mechanism of the parent model are calibrated. The positioning accuracy and precision of the six-degree-of-freedom parallel mechanism drive sub-level model for axial displacement, lateral displacement, longitudinal displacement, pitch angle, yaw angle, and roll angle motion are calibrated.

3. The method according to claim 1, characterized in that, Before activating the wind tunnel flow field, the method uses a six-degree-of-freedom parallel mechanism to drive the child-level model to a safe position below the parent-level model.

4. The method according to claim 2, characterized in that, The driving mechanism for the dynamic derivative motion response includes: a preload bolt, a contact spherical surface, a piezoelectric ceramic actuator, a mounting base, and a cover plate. The piezoelectric ceramic actuator has a stacked structure, with the top end bonded to the contact spherical surface by epoxy resin. The preload bolt acts axially on the contact spherical surface to achieve preload. The cover plate is arc-shaped and covers the actuator to maintain its aerodynamic shape.

5. The method according to claim 2, characterized in that, The sub-model is connected in series to form a series body through the following components: tail fairing, drive mechanism for the dynamic derivative motion response, motor, Z-shaped support rod, built-in dynamic balance, and sub-model body. The series body is connected to a six-degree-of-freedom parallel mechanism.

6. The method according to claim 1, characterized in that, The sensor is an accelerometer installed inside the sub-level model. The dynamic derivative prediction control system includes a signal generator, a power amplifier, an exciter, an accelerometer, and a high-performance computer. The control algorithm includes at least one of PID control algorithm, quadratic linear optimal control algorithm, and fuzzy control algorithm.

7. The method according to claim 1, characterized in that, S5 specifically includes: determining whether the sub-model can achieve the specified angular displacement motion form, frequency and amplitude within 3 seconds; if it can, then continuously blow air for 2 seconds to collect data and calculate the combined dynamic derivative; if it cannot, then drive the sub-model to the next grid force measurement station to continue the test.

8. The method according to claim 1, characterized in that, The method further includes: After acquiring the positional derivative data of all stations, a short-period motion model of the sub-model is generated by combining the grid force measurement aerodynamic data under the same working conditions. Based on the aforementioned short-cycle motion model and PID parameter tuning method, a separate control law is constructed. The separation control law was embedded into the trajectory capture test system, and sub-level and parent-level separation trajectory capture tests were carried out to verify the separation attitude control effect.

9. An electronic device, comprising: processor; as well as, A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the experimental method for predicting dynamic derivatives based on grid force measurement as described in any one of claims 1-8.

10. A storage medium for storing computer-executable instructions, which, when executed, perform the steps of the experimental method for predicting dynamic derivatives based on grid force measurement as described in any one of claims 1-8.

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