A method and system for identifying a runway excursion instability mode of an aircraft

Abstract

The application relates to the technical field of airport operation safety monitoring, in particular to a method and system for judging an aircraft take-off and landing sliding instability mode. The method comprises the following steps: based on airborne side data, runway side data and basic state data in the process of aircraft take-off and landing sliding, the stability margin of each instability mode at the current sampling time is calculated, the instability modes include: shimmy, aquaplaning and braking; based on the stability margin of each instability mode at the current sampling time, the instability competition coefficient of each instability mode at the current sampling time is calculated; based on the instability competition coefficient of each instability mode, the dominant instability mode at the current sampling time is determined, and according to the stability margin of the dominant instability mode at the current sampling time, the instability condition of the aircraft at the current sampling time is determined. The application realizes the comparison of potential instability risks of different instability modes in the same architecture and the same scale, and can improve the judgment accuracy of the dominant instability mode.

Description

Technical Field

[0001] This invention relates to the field of airport operation safety monitoring technology, specifically to a method and system for identifying aircraft takeoff and landing runway instability modes. Background Technology

[0002] Different instability modes exist during aircraft takeoff and landing, typically including yawing instability, hydroplaning instability, and braking instability. In existing technologies, each instability mode employs its own unique analytical method when analyzing the potential risks. These methods have different analytical frameworks, making it impossible to make a unified comparison of the potential instability risks of multiple modes. This can lead to incorrect identification of the dominant mode, causing the initiated stabilization strategy to fail. Furthermore, existing instability analysis methods usually rely on numerical solutions of nonlinear dynamic equations based on operating parameters such as tire pressure and wheel yawing during aircraft takeoff and landing. This process is computationally intensive, lacks real-time performance, and results in a delay between the calculated and actual results. Using these delayed calculations further reduces the accuracy of identifying the dominant instability mode. Summary of the Invention

[0003] To address the technical problem of low accuracy in identifying the dominant instability mode during aircraft takeoff and landing, the present invention aims to provide a method and system for identifying aircraft takeoff and landing runway instability modes. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a method for identifying aircraft takeoff and landing roll instability modes, comprising the following steps: Based on airborne data, runway data, and basic state data during aircraft takeoff and landing, the stability margin of each instability mode at the current sampling time is calculated. The instability modes include: yaw, hydroplaning, and braking. Based on the stability margin of each instability mode at the current sampling time, calculate the instability competition coefficient of each instability mode at the current sampling time; Based on the instability competition coefficients of each instability mode, the dominant instability mode at the current sampling time is determined, and the instability status of the aircraft at the current sampling time is determined according to the stability margin of the dominant instability mode at the current sampling time.

[0004] In conjunction with the first aspect above, in some possible implementations, the airborne side data includes front wheel steering angle data and yaw rate data, and the method for calculating the stability margin of the shimmy mode at the current sampling moment includes: Based on the front wheel angle data within a set time period prior to the current sampling time, determine the maximum and minimum values ​​of the front wheel angle, calculate half of the difference between the two, and obtain the swing angle oscillation amplitude. Based on the yaw rate data within a set time period prior to the current sampling time, determine the maximum and minimum yaw rate values, calculate half of the difference between the two values, and obtain the yaw swing amplitude. Based on the yaw oscillation amplitude and the yaw swing amplitude, the yaw risk ratio is calculated, and a negative correlation mapping is performed on the yaw risk ratio to obtain the stability margin of the yaw mode at the current sampling time.

[0005] In conjunction with the first aspect mentioned above, among some possible implementation methods, the methods for calculating the sway risk ratio include: Calculate the ratio of the oscillation amplitude to the allowable oscillation amplitude, and calculate the ratio of the yaw oscillation amplitude to the allowable oscillation amplitude. Take the larger of these two ratios as the oscillation risk ratio.

[0006] In conjunction with the first aspect mentioned above, among some possible implementations, methods for calculating the stability margin of the waterskiing mode at the current sampling time include: Based on runway-side data, determine the water film thickness at the location of the aircraft wheels at the current sampling time; If the water film thickness is less than or equal to the first water film thickness threshold, then the stability margin of the water-skimming mode at the current sampling time is set to the stability margin of the first water-skimming mode; if the water film thickness is greater than or equal to the second water film thickness threshold, then the stability margin of the water-skimming mode at the current sampling time is set to the stability margin of the second water-skimming mode; if the water film thickness is greater than the first water film thickness threshold and less than the second water film thickness threshold, then the stability margin of the water-skimming mode at the current sampling time is set to the stability margin of the third water-skimming mode. The stability margin of the first water skidding mode is a fixed value. The stability margin of the second water skidding mode is calculated based on airborne data and basic state data. The stability margin of the third water skidding mode is obtained by fusing the stability margins of the first and second water skidding modes.

[0007] In conjunction with the first aspect mentioned above, among some possible implementations, the method for calculating the stability margin of the second hydroplaning mode includes: The airborne data includes the aircraft's tire inflation pressure and ground speed, and the basic status data includes the aircraft's total mass and wheel wear indicators. The arithmetic square root of the tire inflation pressure at the current sampling moment is combined with the hydroplaning critical speed coefficient and the wheel wear condition index to obtain the hydroplaning critical speed. Based on airborne data and basic state data, the absolute value of aerodynamic lift deviation is calculated. The ratio of the ground speed to the critical speed for hydroplaning at the current sampling time is calculated to obtain the hydroplaning risk coefficient. The total weight of the aircraft is calculated based on the total mass of the aircraft. The ratio of the absolute value of the aerodynamic lift deviation to the total weight of the aircraft is calculated to obtain the aerodynamic lift deviation rate. The negative correlation mapping result of the hydroplaning risk coefficient and the negative correlation mapping result of the aerodynamic lift deviation rate are multiplied to obtain the stability margin of the second hydroplaning mode.

[0008] In conjunction with the first aspect above, in some possible implementations, the airborne data also includes the aircraft's pitch angle, vertical acceleration, and the runway's upward support force on the landing gear. Methods for calculating the absolute value of the aerodynamic lift deviation include: Based on the aircraft's pitch angle at the current sampling time, the lift coefficient is calculated. Combined with the aircraft's ground speed at the current sampling time, the estimated aerodynamic lift of the aircraft at the current sampling time is calculated. Based on the total mass of the aircraft, the vertical acceleration at the current sampling time, and the upward support force of the runway on the landing gear, calculate the measured aerodynamic lift of the aircraft at the current sampling time. Calculate the absolute value of the difference between the estimated aerodynamic lift and the measured aerodynamic lift at the current sampling time to obtain the absolute value of the aerodynamic lift deviation.

[0009] In conjunction with the first aspect above, in some possible implementations, the airborne data includes the aircraft's ground speed and main wheel speed, and the method for calculating the stability margin of the braking mode at the current sampling moment includes: Based on the ground speed and main wheel speed at the current sampling time, the slip ratio is calculated; the ratio of the slip ratio to the preset slip ratio threshold is used as the degree of braking instability development, and a negative correlation mapping is performed on the degree of braking instability development to obtain the stability margin of the braking mode.

[0010] In conjunction with the first aspect mentioned above, among some possible implementations, methods for calculating the stability margins of the oscillation mode, the hydroplaning mode, and the braking mode at the current sampling time include: The airborne data, runway data, and basic state data at the current sampling time are input into the trained stability margin calculation model to obtain the stability margins of the shimmy mode, the hydroplaning mode, and the braking mode at the current sampling time.

[0011] In conjunction with the first aspect mentioned above, among some possible implementations, methods for calculating the instability competition coefficients of each instability mode at the current sampling time include: For any instability mode, based on the stability margin of the instability mode at the current sampling time and the stability margin of the instability mode at historical times with a predetermined interval, calculate the rate of decrease of the stability margin of the instability mode at the current sampling time. By fusing the negative correlation mapping results between the rate of decrease of the stability margin of the unstable mode at the current sampling time and the stability margin of the unstable mode at the current sampling time, the instability competition coefficient of each unstable mode at the current sampling time can be obtained.

[0012] Secondly, the present invention also provides an aircraft takeoff and landing runway instability mode discrimination system, including a data acquisition module and an instability mode discrimination module. The data acquisition module is used to collect airborne side data, runway side data and basic state data during the aircraft takeoff and landing process, and send the collected data to the instability mode discrimination module. The instability mode discrimination module is used to determine the instability mode of the aircraft takeoff and landing and determine the instability status of the aircraft based on the received data and using the aircraft takeoff and landing runway instability mode discrimination method of the present invention.

[0013] The present invention has the following beneficial effects: The method and system for identifying aircraft takeoff and landing instability modes of the present invention calculates the stability margin of each instability mode at the current sampling time based on airborne data, runway data, and basic state data during the aircraft takeoff and landing process. Then, based on the stability margin of each instability mode at the current sampling time, it calculates the instability competition coefficient of each instability mode at the current sampling time. By calculating the stability margin, the potential risk level of different instability modes is quantified under the same architecture and the same index, providing a comparative benchmark and numerical basis for determining the dominant instability mode. This enables the comparison of the potential instability risks of different instability modes on the same architecture and scale. Finally, based on the instability competition coefficient of each instability mode, the dominant instability mode at the current sampling time is determined. Based on the stability margin of the dominant instability mode at the current sampling time, the instability status of the aircraft at the current sampling time is determined, thereby improving the accuracy of determining the dominant instability mode. Attached Figure Description

[0014] Figure 1 This is a structural diagram of the aircraft takeoff and landing runway instability mode discrimination system according to an embodiment of the present invention; Figure 2 This is a flowchart of the aircraft takeoff and landing runway instability mode discrimination method according to an embodiment of the present invention. Detailed Implementation

[0015] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0016] The present invention provides an aircraft takeoff and landing runway instability mode discrimination system, the specific structure of which is as follows: Figure 1As shown, the system includes a data acquisition module and an instability mode discrimination module. The data acquisition module collects airborne data, runway-side data, and basic state data during aircraft takeoff and landing, and sends the collected data to the instability mode discrimination module. The instability mode discrimination module determines the instability mode of aircraft takeoff and landing based on the received data, using a method for determining aircraft takeoff and landing instability modes according to this invention.

[0017] Airborne data includes: real-time ground speed, spatial coordinate position, pitch angle, vertical acceleration, nose wheel steering angle, yaw rate, runway upward support force on the landing gear, main wheel speed, and tire inflation pressure. In this embodiment, real-time ground speed and spatial coordinate position are acquired using GNSS (Global Navigation Satellite System) or IRS (Inertial Reference System), with a sampling rate of 50Hz; pitch angle is acquired by an IRU (Inertial Reference Unit) attitude sensor, with a sampling frequency of 50Hz; vertical acceleration is acquired using an airborne IMU (Inertial Measurement Unit), with a sampling rate of 200Hz; and the runway upward support force on the landing gear can be measured by a load sensor installed on the main landing gear shock absorber strut, with a sampling frequency set to 200Hz.

[0018] The main wheel speed is obtained by an onboard wheel speed sensor with a sampling frequency of 200Hz; the tire inflation pressure is obtained by a hydraulic line pressure sensor with a sampling frequency of 100Hz.

[0019] Runway-side data includes real-time water film thickness distribution. In this embodiment, the airport runway is divided into multiple preset units, and water film monitoring sensors are pre-installed on the ground of each preset unit. The installed sensors need to ensure that they are consistent with the original pavement condition and do not affect aircraft take-off and landing operations; or a "airport infrared array + laser telemetry" technical solution with multiple preset units can be used to obtain the real-time water film thickness on the runway surface, with a sampling frequency of 1Hz.

[0020] In this embodiment, the basic condition data includes: the total mass of the aircraft and the wheel wear condition indicators before takeoff and landing.

[0021] In this embodiment, the method for determining the total mass of the aircraft before takeoff and landing includes: summing the aircraft's own mass, passenger mass, and cargo mass to obtain the zero-fuel mass; and summing the zero-fuel mass and the remaining fuel mass before takeoff and landing to obtain the total mass *m* of the aircraft before takeoff and landing. The zero-fuel mass is calculated by the load balancer based on passengers, baggage, and cargo, and is usually provided via the manifest before takeoff; the fuel mass is calculated based on the fuel density and the remaining fuel volume (remaining fuel volume), and the density is a fixed value; the remaining fuel volume is measured in real time by the aircraft's fuel level indicator system. In this embodiment, it is approximately assumed that the fuel mass remains constant during takeoff and landing.

[0022] In this embodiment, the method for determining the wear condition index of the wheel includes: Before takeoff, the remaining tread depth of each tire is measured and divided by the initial tread depth to obtain the wear condition index of each tire before takeoff. The minimum value among the wear condition indices is taken as the tire wear condition index. The method for measuring the remaining tread depth includes: selecting multiple equally spaced points along the circumference of each main drainage groove of the tire and measuring the average tread depth at different points for each groove; then, arithmetically averaging the average values ​​of all main drainage grooves to obtain the remaining tread depth. Measurement methods can include manual contact measurement or automated non-contact measurement, such as three-dimensional structured light scanning.

[0023] In this embodiment, for aircraft takeoff roll, the wheel wear condition index determined before takeoff can be used directly. For aircraft landing roll, the wheel wear condition index at the time of landing roll is predicted based on the wheel wear condition index determined before takeoff, under the most unfavorable assumptions. For example, the wheel wear condition index at the time of landing roll is predicted by subtracting the maximum difference between the wheel wear condition indices before two consecutive flights from the wheel wear condition index determined before takeoff.

[0024] In another alternative implementation, since the aircraft wheels do not wear during flight, the wheel wear condition index during the landing roll is also the same as the wheel wear condition index determined before takeoff.

[0025] The specific process of the method for determining the instability mode of aircraft takeoff and landing roll according to the present invention is as follows: Figure 2 As shown, it includes the following steps: Step 1: Preprocess the airborne and runway data during aircraft takeoff and landing to obtain preprocessed airborne and runway data.

[0026] In this embodiment, the method for preprocessing airborne and runway-side data during aircraft takeoff and landing includes time alignment. The time alignment method includes: Using the high-precision time synchronization of airborne GNSS as the absolute physical time reference, the highest sampling rate (such as 200Hz of IMU) is selected as the discrete network with a unified time axis. During the calibration phase, the hardware and transmission fixed delay of each sensor pipeline are measured and used as compensation values ​​to directly correct the initial timestamp of the corresponding data. The compensated data are then interpolated and resampled according to the unified time axis to remove the influence of the sampling resolution difference of different data and obtain the time-aligned data.

[0027] Step 2: Based on the preprocessed airborne data, runway data, and basic state data, calculate the stability margin of each instability mode at the current sampling time.

[0028] In this embodiment, the instability modes include: oscillation, hydroplaning, and braking. Therefore, the stability margins for each instability mode include: the stability margin for the oscillation mode, the stability margin for the hydroplaning mode, and the stability margin for the braking mode.

[0029] Methods for calculating the stability margin of oscillation modes include: Shimming instability is a type of torsional forced vibration (self-excited vibration) that occurs in the front landing gear during taxiing. Its destructive force is manifested in both the "deformation position (angle)" and the "deformation rate (angular velocity)". The front wheel steering mechanism has a physical limit on the maximum shimming angle. If the shimming amplitude exceeds the maximum allowable shimming angle of the structure, it will cause the relevant struts and connecting rods to break mechanically. Even if the shimming amplitude does not exceed the maximum allowable shimming angle of the structure, if the shimming angular velocity is extremely high, it will generate a huge alternating inertial torque (angular velocity is related to angular acceleration) in a very short time. This will cause strong oscillations leading to tire detachment or severe uneven wear, which is also a type of instability failure. Therefore, in this embodiment, the method for calculating the stability margin of the shimming mode at the current sampling time includes: First, based on the current sampling time The previously set time period [ , Determine the maximum value of the front wheel steering angle data within the range []. and minimum value Calculate half of the difference between the two to obtain the amplitude of the pendulum oscillation. The unit is radians. In this embodiment, the set time period is 0.2 seconds, and therefore the number of sampling moments included within the set time period is determined by this setting. The relative swing angle is calculated from the absolute rotation angle using the half-difference envelope method.

[0030] Then, based on the current sampling time The previously set time period [ , Determine the maximum yaw rate from the yaw rate data within the range. and minimum value Calculate half of the difference between the two to obtain the yaw swing amplitude. The unit is radians per second.

[0031] Finally, calculate the ratio of the swing angle oscillation amplitude to the allowable swing angle amplitude. Calculate the ratio of yaw sway amplitude to allowable sway amplitude. Compare these two ratios and take the larger one as the oscillation risk ratio. By performing a negative correlation mapping on the oscillation risk ratio, the stability margin of the oscillation mode can be obtained. In this embodiment, the negative correlation mapping adopts a 1-x type negative correlation mapping, therefore It should be noted that if If it is less than 0, then The value is set to 0.

[0032] Methods for calculating the stability margin of a water skidding mode include: Hydroplaning instability refers to the loss of effective contact between the tires and the runway surface due to the water film on the runway during takeoff and landing. The critical condition is that the aircraft ground speed reaches the hydroplaning critical speed corresponding to the current tire pressure. Therefore, the stability margin of the hydroplaning mode characterizes the redundancy between the current ground speed and the hydroplaning critical speed. In this embodiment, the method for calculating the stability margin of the hydroplaning mode includes: Determine the current sampling time based on runway-side data. Water film thickness at the location of the aircraft landing gear The unit is millimeters, which is compared with a preset first water film thickness threshold. and the second water film thickness threshold A comparison is made. In one embodiment, the first water film thickness threshold is... The setting is based on the empirical lower limit of when the tire tread begins to lose its effective drainage capacity; at this thickness, the water film has not yet fully supported the tire. Second water film thickness threshold. The thickness is set based on the lower limit at which typical dynamic water pressure causes the tire to just begin to fully float and lift; this thickness is the critical point for water accumulation danger. The area formed by these two factors constitutes a smooth transition zone. In this embodiment, the first water film thickness threshold is... Using an empirical value of 0.4 mm, the second water film thickness threshold is determined. Take the empirical value of 0.6 mm.

[0033] like This indicates that the water accumulation conditions for water skidding are not met, and the stability margin of the water skidding mode should be set. .in, This represents the stability margin for the first water skidding mode. If... Then set the stability margin for the water skidding mode. For the stability margin of the second water skidding mode .like Then set the stability margin for the water skidding mode. For the stability margin of the third water skidding mode .

[0034] Stability margin of the second water skidding mode The calculation methods include: The square root of the tire inflation pressure at the current sampling time. With the critical speed coefficient of hydroplaning and wheel wear condition indicators By combining the results, the critical velocity for hydroplaning can be obtained. The absolute value of the aircraft's aerodynamic lift deviation is calculated, and the stability margin of the skidding mode is calculated based on the critical speed for water skidding, the total mass of the aircraft, and the absolute value of the aerodynamic lift deviation. Specific methods include: calculating the ground speed of the aircraft at the current sampling time. Critical speed for hydroplaning The ratio is used to obtain the hydroplaning risk coefficient. The total mass of the aircraft, m, is multiplied by the acceleration due to gravity to obtain the total weight of the aircraft, G. The absolute value of the aerodynamic lift deviation is then calculated. And the total weight of the aircraft The ratio of the two values ​​is used to obtain the aerodynamic lift deviation rate. Multiplying the negative correlation mapping result of the hydroplaning risk coefficient and the negative correlation mapping result of the aerodynamic lift deviation rate yields the stability margin of the second hydroplaning mode. This allows us to obtain the stability margin of the water skiing mode at this point. In this embodiment, the negative correlation mapping adopts a 1-x type negative correlation mapping. Therefore, the values ​​of the hydroplaning risk coefficient and the aerodynamic lift deviation rate are both in the range of [0,1]. When the value of is greater than 1, the water skiing risk coefficient is set to 1. When the value of is greater than 1, the aerodynamic lift deviation rate is set to 1.

[0035] When an aircraft taxis at high speed over water, the hydrodynamic pressure generated by the water wedge in front of the tires is proportional to the square of the velocity. The average pressure at the tire's contact patch is approximately equal to the tire's internal inflation pressure. Meanwhile, as tires wear down and tread depth decreases, wheel wear indicators... As tire wear decreases, the tire's water drainage capacity also decreases. Retained water is more likely to form a water wedge at the tire's leading edge, accumulating dynamic pressure. This physical change lowers the critical speed required for the water film to support the tire, thus necessitating the use of tire wear indicators. By attenuating the velocity, the critical speed for hydroplaning is derived. .

[0036] In this embodiment, the critical velocity coefficient of hydroplaning NASA's empirical formula (section) = (Pounds per square inch) Unit conversion: 1 unit equals 0.5144 meters per second, 1 pound per square inch equals 6.8948 kilopascals. Substituting, we get... The unit is meters per second. .

[0037] like To improve the stability margin of the water skiing mode The value is in and A smooth transition occurs between these modes, at which point the stability margin of the waterskiing mode is [missing information]. The value is obtained by weighted fusion of the first two values. Specifically, ,in, For the stability margin of the third hydroplaning mode, As a transition weight, in this embodiment, .

[0038] In this embodiment, the method for calculating the absolute value of the aerodynamic lift deviation includes: Based on the aircraft's pitch angle at the current sampling time The lift coefficient was calculated. Combined with the aircraft's ground speed at the current sampling time Calculate the estimated aerodynamic lift of the aircraft at the current sampling time. ,in, air density, This is the reference area of ​​the wing. In this embodiment, the lift coefficient... pitch angle The function is obtained through wind tunnel testing or flight testing.

[0039] Based on the aircraft's total gravity G, total mass m, and vertical acceleration at the current sampling moment. (With vertical upward as the positive direction) and the upward supporting force of the runway on the landing gear Calculate the measured aerodynamic lift of the aircraft at the current sampling moment. The specific method includes: combining the principle of force balance, to calculate the measured aerodynamic lift of the aircraft at the current sampling moment. .

[0040] Calculate the estimated aerodynamic lift at the current sampling time. and measured aerodynamic lift The absolute value of the difference is used to obtain the absolute value of the aerodynamic lift deviation. .

[0041] Methods for calculating the stability margin of braking modes include: Braking instability refers to a situation where the braking torque of an aircraft exceeds the maximum available adhesion between the tires and the runway during takeoff and landing, resulting in tire lock-up. The most direct and observable measurement during tire lock-up is the tire slip ratio, which is the degree of deviation between the main wheel speed and the ground speed.

[0042] In this embodiment, the method for calculating the stability margin of the braking mode includes: Ground velocity based on the current sampling time and main wheel speed Calculate the slip ratio ; the slip ratio With the preset slip ratio threshold The ratio of these values ​​is used as the degree of braking instability development. A negative correlation mapping is performed on the degree of braking instability development to obtain the stability margin of the braking mode. Preset slip ratio threshold The value range is 0.2 to 0.5. In this embodiment, the preset slip ratio threshold is... .

[0043] One method for calculating slip ratio is to calculate the speed of the main wheel. Ground velocity at the current sampling time ratio And by performing a negative correlation mapping on this ratio, the slip ratio is obtained. It should be noted that when the aircraft's ground speed... When the ground speed is less than the preset braking ground speed threshold, it indicates that the aircraft has completed braking. At this point, the stability margin of the braking mode is set. Therefore, calculations using the above formula are unnecessary. In this embodiment, the preset braking ground speed threshold is 10 m / s. At this point, the aircraft has exited the high-risk area of ​​high-speed taxiing and entered the final stage of low-speed taxiing. The aerodynamic lift of the aircraft is almost completely unloaded (most of the gravity is on the wheels), and the braking energy is significantly reduced. It is no longer necessary to rely on the anti-skid braking system for high-frequency anti-lock intervention. The physical risk of serious braking instability (such as tail-skid due to full wheel lock-up) has essentially disappeared.

[0044] The larger the stability margin of the braking mode, the greater the probability that the current braking state is within the stable region; conversely, the smaller the stability margin of the braking mode, the greater the probability that braking instability has occurred (tires lock up).

[0045] The above-described methods for calculating the stability margins of the oscillation mode, the hydroplaning mode, and the braking mode are only optimal implementations. In another implementation, to improve computational speed and enhance the real-time performance of the algorithm, the methods for calculating the stability margins of the oscillation mode, the hydroplaning mode, and the braking mode may further include: Based on historical airborne, runway-side, and baseline state data from aircraft takeoff and landing, training samples are constructed. The stability margins for the yaw mode, hydroplaning mode, and braking mode of each sample are calculated using the aforementioned method and used as labels for each sample, thus obtaining the training dataset. The stability margin calculation model is then trained using this training dataset, resulting in the trained stability margin calculation model. The airborne, runway-side, and baseline state data at the current sampling time are input into the trained stability margin calculation model to obtain the stability margins for the yaw mode, hydroplaning mode, and braking mode at the current sampling time.

[0046] The stability margin calculation model includes an input layer, three fully connected hidden layers, and an output layer.

[0047] The input layer receives the state vector during the aircraft's taxiing process. The state vector includes an airborne data vector consisting of real-time ground speed, spatial coordinates, pitch angle, vertical acceleration, nose wheel angle, yaw rate, main wheel speed, and tire inflation pressure; a runway-side data vector consisting of water film thickness; and a basic state data vector consisting of the aircraft's total mass and wheel wear indicators. Each of the three fully connected hidden layers contains 64 neurons, and the activation function is the Sigmoid linear unit function (SiLU). The output layer outputs three-dimensional stability margins: stability margins for yaw mode, hydroplaning mode, and braking mode.

[0048] The stability margin calculation model is trained using a dual-constraint loss function. This function constrains both the fitting residual between the network output and the measured stability margin, and the consistency between the network output and the numerical integration results of aerodynamic forces. The specific form of the loss function is as follows: in, This represents the training loss function of the stability margin calculation model; This represents the mean square error loss of the measured parameter residuals; This represents the mean square error loss in the aerodynamic fitting. The weighting coefficient represents the loss of the mean square error in aerodynamic fitting. It is dynamically adjusted according to the fitting effect during the training process, and its value ranges from 0.3 to 0.7, specifically 0.5.

[0049] The calculation formula is: in, Indicates the number of training samples; Indicates the first The measured stability margin vector of each sample is expressed in the form of a three-dimensional vector and is calculated by the corresponding formula above. The stability margin calculation model represents the first... The predicted output vector for each sample. for and The square of the L2 norm.

[0050] The calculation formula is: in, Indicates the first The stability margin data of each sample after aerodynamic numerical integration was obtained by offline numerical integration of the aircraft takeoff and landing dynamics equations.

[0051] The constant 2 in the denominators of the two formulas above is used to cancel out the constant 2 obtained by differentiating the squared terms during backpropagation of the neural network to calculate the gradient.

[0052] Step 3: Based on the stability margin of each unstable mode at the current sampling time, calculate the instability competition coefficient of each unstable mode at the current sampling time.

[0053] In this embodiment, the calculation method for the instability competition coefficient is the same for the three instability modes: oscillation, hydroplaning, and braking. The specific calculation method is as follows: For any instability mode, the stability margin of that instability mode is based on the current sampling time. Stability margin of the instability mode at historical time intervals. Calculate the rate of decrease in the stability margin of the instability mode at the current sampling time. Then, the instability competition coefficient of the instability mode at the current sampling time is calculated. .in, This is an unstable mode. , Indicates oscillation, Indicates water skiing, Indicates braking. To set the interval, in this embodiment, the set interval is 20 to 50 sampling times, corresponding to a time of 0.1 to 0.25 seconds.

[0054] Calculate the rate of decrease in the stability margin of this instability mode at the current sampling time. The methods include: The difference between the stability margin of the instability mode at the previous set interval and the stability margin of the instability mode at the current sampling time is divided by the time difference between the two sampling times. ( In this embodiment, the sampling interval is... (seconds) to obtain the rate of decrease in stability margin of the unstable mode at the current sampling time. The rate of decrease in stability margin is taken as positive and the rate of increase is taken as negative.

[0055] If the rate of decrease in the stability margin of this instability mode at the current sampling time If the value is less than or equal to 0, it indicates that the stability margin of the instability mode is gradually increasing or remaining stable, and there is no risk of instability. Therefore, the instability competition coefficient of this instability mode is set to 0. If the rate of decrease of the stability margin of this instability mode at the current sampling time is... A value greater than 0 indicates a risk of instability. In this case, the rate of decrease in the stability margin of the instability mode at the current sampling time is considered. Perform normalization processing and obtain the normalized result. Stability margin of the unstable mode at the current sampling time The negative correlation mapping results are fused to obtain the oscillation instability competition coefficient of this instability mode at the current sampling time. The formula is expressed as follows: In the formula, and These are the preset first and second weighting coefficients, respectively. In this embodiment, , The values ​​of both can be adjusted based on actual conditions. In this embodiment, the normalization process uses maximum-minimum value normalization. During the processing, the minimum value is set to 0, and the maximum value is the maximum value of the stability margin decrease rate of all instability modes in the historical data of aircraft instability. If the value exceeds this maximum value, then set .

[0056] Step 4: Based on the competition coefficients for yaw instability, hydroplaning instability, and braking instability, determine the dominant instability mode. Based on the stability margin of the dominant instability mode, determine the aircraft instability situation.

[0057] In this embodiment, the method for determining the dominant instability mode includes: taking the instability mode corresponding to the maximum value among the oscillation instability competition coefficient, hydroplaning instability competition coefficient and braking instability competition coefficient at the current sampling time as the dominant instability mode.

[0058] In this embodiment, the method for determining aircraft instability includes: If the stability margin of the dominant instability mode is greater than the preset first stability threshold, it is determined that the stability margin is sufficient, the operating state is safe, and there is no risk of instability. The current control strategy is maintained. If the stability margin of the dominant instability mode is greater than the preset second stability threshold but less than or equal to the preset first stability threshold, it is determined that the margin is decreasing and there is a tendency for instability. An early warning is initiated, and continuous monitoring is carried out. If the stability margin of the dominant instability mode is greater than the preset third stability threshold but less than or equal to the preset second stability threshold, it is determined that the margin is extremely low and close to the instability boundary. The aircraft is about to experience instability. Preparatory compensation is triggered, and the crew is notified to prepare for aircraft instability. If the stability margin of the dominant instability mode is less than or equal to the preset third stability threshold, it is determined that aircraft instability has already occurred, and the stability compensation control strategy for the dominant instability mode is immediately executed.

[0059] The first stability threshold is greater than the second stability threshold, which in turn is greater than the third stability threshold. In this embodiment, the first stability threshold is 0.5, the second stability threshold is 0.2, and the third stability threshold is 0.

[0060] This concludes the invention. The aircraft takeoff and landing runway instability mode discrimination method and system of this invention calculates the stability margin of different instability modes based on airborne data, runway data, and basic state data. Combining the stability margin descent rate, it calculates the instability competition coefficient for each instability mode, enabling comparison of the three instability modes within the same order of magnitude and system. Furthermore, it determines the dominant instability state based on the instability competition coefficient and, based on the stability margin of the dominant instability state, determines the aircraft instability condition. This eliminates the need to evaluate the three instability modes separately using three different dynamic systems, offering advantages such as computational simplicity and real-time performance.

Claims

1. A method for identifying aircraft takeoff and landing runway instability modes, characterized in that, Includes the following steps: Based on airborne data, runway data, and basic state data during aircraft takeoff and landing, the stability margin of each instability mode at the current sampling time is calculated. The instability modes include: yaw, hydroplaning, and braking. Based on the stability margin of each instability mode at the current sampling time, calculate the instability competition coefficient of each instability mode at the current sampling time; Based on the instability competition coefficients of each instability mode, the dominant instability mode at the current sampling time is determined, and the instability status of the aircraft at the current sampling time is determined according to the stability margin of the dominant instability mode at the current sampling time.

2. The method for determining aircraft takeoff and landing runway instability modes according to claim 1, characterized in that, The airborne data includes front wheel steering angle data and yaw rate data. The method for calculating the stability margin of the shimmy mode at the current sampling moment includes: Based on the front wheel angle data within a set time period prior to the current sampling time, determine the maximum and minimum values ​​of the front wheel angle, calculate half of the difference between the two, and obtain the swing angle oscillation amplitude. Based on the yaw rate data within a set time period prior to the current sampling time, determine the maximum and minimum yaw rate values, calculate half of the difference between the two values, and obtain the yaw swing amplitude. Based on the yaw oscillation amplitude and the yaw swing amplitude, the yaw risk ratio is calculated, and a negative correlation mapping is performed on the yaw risk ratio to obtain the stability margin of the yaw mode at the current sampling time.

3. The method for determining aircraft takeoff and landing runway instability modes according to claim 2, characterized in that, Methods for calculating the sway risk ratio include: Calculate the ratio of the oscillation amplitude to the allowable oscillation amplitude, and calculate the ratio of the yaw oscillation amplitude to the allowable oscillation amplitude. Take the larger of these two ratios as the oscillation risk ratio.

4. The method for determining aircraft takeoff and landing runway instability modes according to claim 1, characterized in that, Methods for calculating the stability margin of the water skidding mode at the current sampling time include: Based on runway-side data, determine the water film thickness at the location of the aircraft wheels at the current sampling time; If the water film thickness is less than or equal to the first water film thickness threshold, then the stability margin of the water-skimming mode at the current sampling time is set to the stability margin of the first water-skimming mode; if the water film thickness is greater than or equal to the second water film thickness threshold, then the stability margin of the water-skimming mode at the current sampling time is set to the stability margin of the second water-skimming mode; if the water film thickness is greater than the first water film thickness threshold and less than the second water film thickness threshold, then the stability margin of the water-skimming mode at the current sampling time is set to the stability margin of the third water-skimming mode. The stability margin of the first water skidding mode is a fixed value. The stability margin of the second water skidding mode is calculated based on airborne data and basic state data. The stability margin of the third water skidding mode is obtained by fusing the stability margins of the first and second water skidding modes.

5. The method for determining aircraft takeoff and landing runway instability modes according to claim 4, characterized in that, The method for calculating the stability margin of the second hydroplaning mode includes: The airborne data includes the aircraft's tire inflation pressure and ground speed, and the basic status data includes the aircraft's total mass and wheel wear indicators. The arithmetic square root of the tire inflation pressure at the current sampling moment is combined with the hydroplaning critical speed coefficient and the wheel wear condition index to obtain the hydroplaning critical speed. Based on airborne data and basic state data, the absolute value of aerodynamic lift deviation is calculated. The ratio of the ground speed to the critical speed for hydroplaning at the current sampling time is calculated to obtain the hydroplaning risk coefficient. The total weight of the aircraft is calculated based on the total mass of the aircraft. The ratio of the absolute value of the aerodynamic lift deviation to the total weight of the aircraft is calculated to obtain the aerodynamic lift deviation rate. The negative correlation mapping result of the hydroplaning risk coefficient and the negative correlation mapping result of the aerodynamic lift deviation rate are multiplied to obtain the stability margin of the second hydroplaning mode.

6. The method for determining aircraft takeoff and landing runway instability modes according to claim 5, characterized in that, The airborne data also includes the aircraft's pitch angle, vertical acceleration, and the runway's upward support force on the landing gear. Methods for calculating the absolute value of the aerodynamic lift deviation include: Based on the aircraft's pitch angle at the current sampling time, the lift coefficient is calculated. Combined with the aircraft's ground speed at the current sampling time, the estimated aerodynamic lift of the aircraft at the current sampling time is calculated. Based on the total mass of the aircraft, the vertical acceleration at the current sampling time, and the upward support force of the runway on the landing gear, calculate the measured aerodynamic lift of the aircraft at the current sampling time. Calculate the absolute value of the difference between the estimated aerodynamic lift and the measured aerodynamic lift at the current sampling time to obtain the absolute value of the aerodynamic lift deviation.

7. The method for determining aircraft takeoff and landing instability modes according to claim 1, characterized in that, The airborne data includes the aircraft's ground speed and main wheel speed. The method for calculating the stability margin of the braking mode at the current sampling moment includes: Based on the ground speed and main wheel speed at the current sampling time, the slip ratio is calculated; the ratio of the slip ratio to the preset slip ratio threshold is used as the degree of braking instability development, and a negative correlation mapping is performed on the degree of braking instability development to obtain the stability margin of the braking mode.

8. The method for determining aircraft takeoff and landing runway instability modes according to claim 1, characterized in that, Methods for calculating the stability margins of the oscillation mode, hydroplaning mode, and braking mode at the current sampling time include: The airborne data, runway data, and basic state data at the current sampling time are input into the trained stability margin calculation model to obtain the stability margins of the shimmy mode, the hydroplaning mode, and the braking mode at the current sampling time.

9. The method for determining aircraft takeoff and landing runway instability modes according to any one of claims 1-8, characterized in that, Methods for calculating the instability competition coefficients of each instability mode at the current sampling time include: For any instability mode, based on the stability margin of the instability mode at the current sampling time and the stability margin of the instability mode at historical times with a predetermined interval, calculate the rate of decrease of the stability margin of the instability mode at the current sampling time. By fusing the negative correlation mapping results between the rate of decrease of the stability margin of the unstable mode at the current sampling time and the stability margin of the unstable mode at the current sampling time, the instability competition coefficient of each unstable mode at the current sampling time can be obtained.

10. A system for identifying aircraft takeoff and landing runway instability modes, characterized in that, It includes a data acquisition module and an instability mode discrimination module. The data acquisition module is used to collect airborne side data, runway side data and basic state data during the aircraft takeoff and landing process, and send the collected data to the instability mode discrimination module. The instability mode discrimination module is used to determine the instability mode of the aircraft takeoff and landing and the instability status of the aircraft based on the received data, using the aircraft takeoff and landing runway instability mode discrimination method as described in any one of claims 1-9.

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