KR20220096658A - Method and apparatus for improving the accuracy of 6-dof flight simulation using flight test data - Google Patents

Abstract

The present invention relates to a method for improving accuracy of a 6 degree of freedom flight simulation using flight test data and an apparatus therefor, wherein the method for improving accuracy of a 6 degree of freedom flight simulation comprises: a step of building a flight simulation model; a flight test performing step of performing a longitudinal maneuver flight test in order to build a flight test database with flight test data collected during the flight test; a preliminary model tuning step of adjusting a parameter of a simulation model in order to improve accuracy of the flight simulation model by using the flight test data; an aerodynamic DB correction step of correcting an aerodynamic database; and a flight test verification step of performing a simulation verification by using the flight test data to provide an improved simulation model. Therefore, accuracy of the flight simulation model may be improved in the longitudinal movement of a plane.

Description

Method and apparatus for improving the accuracy of 6-DOF flight simulation using flight test data

The present invention relates to a technique for improving the accuracy of 6-DOF flight simulation using flight test data, and more specifically, to improve the accuracy of 6-DOF simulation of light aircraft by implementing model tuning and aerodynamic database correction using flight test data. It relates to a method and apparatus for improving the accuracy of 6-DOF flight simulation using available flight test data.

Today, flight simulation plays an important role in aerospace engineering and contributes to almost the entire development process, from preliminary design through certification compliance and pilot training.

Also, one of the most promising applications of flight simulation is Virtual Flight Test and Certification (VFTC), which is expected to partially replace real-world flight testing for verification of aircraft flight quality and performance.

Therefore, VFTC can greatly reduce the cost and risk of certification stage through actual flight test. For this reason, the accuracy of flight simulators is becoming increasingly important to ensure that the simulator can reproduce real flight trajectories.

In general, the accuracy of the flight simulation model is affected by the accuracy of several model databases (databases) such as aerodynamics DB, propulsion DB, weight DB, and control system DB. The level of precision and the time required for interpretation are proportional and inversely proportional to the range of interpretation and amount of data. For example, the low-precision empirical method calculates the aerodynamic coefficient much faster and provides a wider range than the analysis method through CFD (Computational Fluid Dynamic) or wind tunnel test (W/T test). It can be dealt with, but due to the limitations of the methodology, the accuracy is lowered. Therefore, in order to improve the accuracy of flight simulation, it is necessary to use data obtained by fusion of various analysis methodologies.

Several procedures and verification methodologies have been proposed based on numerous flight simulation platforms such as JSBSim, Matlab/Simulink or X-Plane simulators. Leong et al. described the evaluation of three different modeling and simulation techniques to support the development of UAVs for use in the study of glacial ice. Here, the response of each dynamic model and flight test data were compared together. Cantarelo et al. presented the verification process of the flight dynamics model for the UAV in the specific case of verifying the simulation part without flight test results in the JSBSim FDM (Flight Dynamics Model) environment.

Some limitations exist in these studies. First of all, aerodynamic analysis tools do not guarantee high precision because they are based on analysis results obtained at low or medium fidelity like empirical formulas or panel methods. In addition, it is necessary to clearly define the evaluation criteria for the purpose of flight simulation verification. Therefore, in order to verify the reliability of the simulation, it is necessary to include a procedure to verify the 6 DOF simulation based on the regulations contained in the certification regulation handbook.

Korean Patent Registration No. 10-1744721 (registered on June 1, 2017)

An embodiment of the present invention is to provide a method and apparatus for improving the accuracy of a 6-DOF flight simulation using flight test data that can improve the accuracy of a light aircraft simulation model by verifying the 6 DOF simulation using actual flight test data .

An embodiment of the present invention combines high fidelity flight test data with data to which a variable fidelity methodology is applied to generate an accurate flight simulation model DB, flight test data that can improve the accuracy of flight simulation To provide a method and apparatus for improving the accuracy of 6-DOF flight simulation using

An embodiment of the present invention provides a method and apparatus for improving the accuracy of 6-DOF flight simulation using flight test data that can evaluate the reliability of the flight simulation model by verifying the reliability of the 6 DOF simulation based on the regulations contained in the certification regulation handbook want to

Among the embodiments, the method for improving the accuracy of 6-DOF flight simulation using flight test data includes building a flight simulation model, performing a longitudinal maneuver flight test to build a flight test database with flight test data collected during the flight test A flight test performing step for, a preliminary model tuning step of adjusting the parameters of the simulation model to improve the accuracy of the flight simulation model using the flight test data, aerodynamic DB correction step of correcting an aerodynamic database, and the flight It includes a flight test validation step that provides an improved simulation model by performing simulation validation using test data.

In the flight test execution step, an actual flight test of a light aircraft is conducted according to flight conditions and control input based on the maneuver description contained in the flight simulator evaluation handbook, and raw data collected during the flight test is used to identify flight maneuvers. It may include data processing.

The raw data may include parameters related to direction, speed, acceleration, control surface displacement, and throttle setting operation variables and flight status.

In the flight test data processing step, after filtering through the moving average filter through the raw data, preprocessing is performed to extract a single maneuver segment for each maneuver, and the extracted single maneuver segments are specified using a signal pattern similarity search technique. It can be classified by type.

The preliminary model tuning step may include a sensitivity analysis step of providing parameter effects by performing sensitivity analysis of flight dynamics in longitudinal motion based on the shape DB, weight DB, and propulsion DB of the aircraft.

The preliminary model tuning step provides a response error by comparing and analyzing the flight response and the simulation response, and based on the sensitivity analysis and flight response analysis, tuning to adjust the sensitive parameters affecting the longitudinal motion in the flight simulation environment. can

The preliminary model tuning step may be defined as a function of Equation 1 below by using the damping rate and natural frequency as longitudinal motion characteristic parameters.

[Equation 1]

는 감쇠율(ζ) 및 고유 진동수(ω_n)에 대한 운동 특성 매개변수,

는 무게중심(CG)의 종방향 위치(X_CG), 공력 중심(AC)의 종방향 위치(X_AG) 및 y축에 대한 관성 모멘트(Iyy)와 같은 민감도 매개변수를 의미한다.From here,

is the kinetic characteristic parameters for damping factor (ζ) and natural frequency (ω _n ),

denotes the sensitivity parameters such as the longitudinal position (X CG ) of the center of gravity ( _CG ), the longitudinal position (X _AG ) of the aerodynamic center (AC), and the moment of inertia about the y-axis (Iyy).

In the preliminary model tuning step, the sensitivity parameter may be tuned by applying Equation 2 below.

[Equation 2]

Here, the superscript “tuned” denotes a tuning parameter, “init” denotes an initial parameter, and “test” and “sim” denote parameters obtained from flight test and simulation data, respectively.

The aerodynamic DB correction step estimates parameters through maximum likelihood (ML) estimation under control input and flight conditions among flight test data, calculates related aerodynamic coefficients from the estimated parameters, and uses a linear correction technique to perform existing aerodynamics. DB can be calibrated.

In the flight test verification step, a 6-DOF simulation is performed based on the regulations contained in the certification regulation handbook, the response error is calculated by comparing the actual flight response and the simulation response, and when the calculated response error meets the tolerance, The reliability of the simulation model can be verified.

Among the embodiments, the device for improving the accuracy of 6-DOF flight simulation using flight test data is through the flight test DB unit in which the flight test data measured during the flight test is recorded, the wind tunnel (W/T) test, CFD analysis, and empirical calculations. In order to preliminarily increase the accuracy of the aircraft DB unit including the configured aerodynamic (aerodynamic) DB, propulsion DB, and weight DB, the model parameters are adjusted by performing sensitivity analysis on the parameters using the flight test data. A preliminary model tuning unit that estimates parameters from the flight test data and combines the estimated parameters and the aerodynamic DB through linear correction to correct the aerodynamic DB by combining the aerodynamic DB correcting unit, and flight response and flight of the simulation model It includes a flight test verification unit that performs simulation verification by comparing the test data with respect to the evaluation criteria.

The disclosed technology may have the following effects. However, this does not mean that a specific embodiment should include all of the following effects or only the following effects, so the scope of the disclosed technology should not be understood as being limited thereby.

The method and apparatus for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment of the present invention increase the precision (Fidelity) of the simulation model by performing preliminary model tuning and aerodynamic DB correction using actual flight test data. can improve the accuracy of the flight simulation model for light aircraft by validating the 6 DOF simulation.

A method and apparatus for improving the accuracy of six degrees of freedom flight simulation using flight test data according to an embodiment of the present invention is accurate flight simulation by combining high fidelity flight test data with data to which a variable fidelity methodology is applied. By creating a model DB, the accuracy of flight simulation can be improved.

The method and apparatus for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment of the present invention can evaluate the reliability of the flight simulation model by verifying the reliability of the 6-DOF simulation based on the regulations contained in the certification regulation handbook. .

1 is a view showing an apparatus for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment of the present invention.
2 is a flowchart for explaining a method for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment of the present invention.
FIG. 3 is a view for explaining a detailed process of the method for improving the accuracy of the 6-DOF flight simulation shown in FIG. 2 .
FIG. 4 is a view for explaining the flight test data processing process of the flight test execution stage in FIG. 2 .
5 is a diagram illustrating a simulation verification result according to an embodiment.

Since the description of the present invention is merely an embodiment for structural or functional description, the scope of the present invention should not be construed as being limited by the embodiment described in the text. That is, since the embodiment may have various changes and may have various forms, it should be understood that the scope of the present invention includes equivalents capable of realizing the technical idea. In addition, since the object or effect presented in the present invention does not mean that a specific embodiment should include all of them or only such effects, it should not be understood that the scope of the present invention is limited thereby.

On the other hand, the meaning of the terms described in the present application should be understood as follows.

Terms such as “first” and “second” are for distinguishing one component from another, and the scope of rights should not be limited by these terms. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

The singular expression is to be understood as including the plural expression unless the context clearly dictates otherwise, and terms such as "comprises" or "have" refer to the embodied feature, number, step, action, component, part or these It is intended to indicate that a combination exists, and it should be understood that it does not preclude the possibility of the existence or addition of one or more other features or numbers, steps, operations, components, parts, or combinations thereof.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs, unless otherwise defined. Terms defined in general used in the dictionary should be interpreted as having the meaning consistent with the context of the related art, and cannot be interpreted as having an ideal or excessively formal meaning unless explicitly defined in the present application.

The present invention relates to a simulation model improvement and verification procedure, and the 6-DOF simulation accuracy improvement procedure may consist of three steps: preliminary model tuning, aerodynamic DB correction, and flight test verification.

1 is a view showing an apparatus for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment of the present invention.

Referring to FIG. 1 , a 6-DOF flight simulation accuracy improvement device using flight test data (hereinafter referred to as a '6-DOF flight simulation accuracy improvement device') 100 is a flight test DB unit 110 and an aircraft DB unit. 120 , a preliminary model tuning unit 130 , an aerodynamic DB correction unit 140 , a flight test verification unit 150 and a control unit 160 .

The flight test DB unit 110 includes flight maneuvers processed from raw data measured during the flight test using an onboard acquisition system. In one embodiment, the flight test DB unit 110 may include control inputs, flight conditions, and flight responses in at least longitudinal behavior.

The aircraft DB unit 120 is a multi-module database constructed using several analysis methods such as wind tunnel (W/T) testing, CFD analysis, and empirical calculations. In an embodiment, the aircraft DB unit 120 may include at least a shape DB, an aerodynamic DB, a propulsion DB, and a mass and balance DB.

The preliminary model tuning unit 130 may adjust model parameters using flight test data to preliminarily increase the accuracy of the simulation model. In one embodiment, the preliminary model tuning unit 130 may be adjusted according to the influence of the relevant parameters of the simulation model on the longitudinal motion. Here, the preliminary model tuning unit 130 adjusts the parameters by analyzing the sensitivity to the parameters.

Sensitivity analysis is commonly used to determine the impact of model parameters for validation and reference purposes. In the context of validation, sensitivity analysis can be defined as a systematic investigation of the simulation response to the input of a model.

The behavior of the flight response in the flight simulation environment depends on the root position of the characteristic equation, which is converted into values of characteristic parameters such as frequency and damping ratio.

The preliminary model tuning unit 130 may provide sensitivity analysis of flight dynamics in longitudinal motion, which may be defined by Equation 1 below as a state equation and characteristic equation for short-period approximation.

[Equation 1]

where α is the angle of attack, q is the pitch speed, u ₀ is the forward speed, Z _α is the longitudinal differential coefficient by the angle of attack, M _α is the pitching moment per unit of angle of attack, and M is the pitching moment per unit of rate of change of the angle of attack. , M _q is the pitching moment in units of pitch speed, and λ is the characteristic root of the characteristic equation.

The short-period root may be obtained from the above characteristic equation, which may be defined by Equation 2 below.

[Equation 2]

Here, the subscript “sp” stands for SPPO (Short Period Pitching Oscillation).

) 및 감쇠율(

)은 하기 수학식 3으로 계산되어질 수 있다.frequency (

) and the damping factor (

) can be calculated by Equation 3 below.

[Equation 3]

는 받음각 단위 피칭 모멘트를 의미한다. From here,

is the pitching moment in units of the angle of attack.

In previous modeling of longitudinal motion, the characteristic parameters for short-period motion are functions of a very large parameter set. Some of these parameters are considered to be fixed quantities in the initial design phase, such as aircraft geometric dimensions, while some others are known to change nominally with the flight range of the aircraft, such as center of gravity (CG) position. Therefore, the sensitivity analysis for short-period motion focuses on the effects associated with fluctuations in the parameter list in Table 1 below.

[Table 1]

(받음각 단위 피칭 모멘트)에 직접적인 의미를 갖는 받음각으로 인한 피칭 모멘트 계수의 변화

에 포함된 1차 공기 역학적 계수를 나타낸다. 무게중심(CG) 및 공력 중심(AC)의 종방향 위치는 종방향 동적 안정성에 큰 영향을 미친다. 유사한 방식으로, 이 분석은 영향을 받는 모든 매개변수에 적용될 수 있다. For example, Equation 3 above

Change in pitching moment coefficient due to angle of attack with direct meaning to (pitching moment in units of angle of attack)

It represents the first aerodynamic coefficient included in . The longitudinal positions of the center of gravity (CG) and the center of aerodynamics (AC) have a great influence on the longitudinal dynamic stability. In a similar way, this analysis can be applied to all affected parameters.

,

)에서 영향을 받은 매개변수

의 영향을 운동 특성 방정식에 기반하여 구분할 수 있다. 가장 민감한 매개변수는 특정 동작에 영향을 미치는 매개변수의 효과를 기반으로 선택될 수 있다. 운동 특성이 하기 수학식 4와 같이, 영향을 받는 매개변수의 함수로 설명될 수 있다고 가정하면, Preliminary model tuning unit 130 is a characteristic parameter (

,

) affected parameters

can be distinguished based on the motion characteristic equation. The most sensitive parameter can be selected based on the parameter's effect on a particular behavior. Assuming that the motion characteristics can be described as a function of the affected parameter as in Equation 4 below,

[Equation 4]

는 감쇠율(ζ) 및 고유 진동수(ω_n)와 같은 운동 특성 매개변수이고,

는 무게중심(CG)의 종방향 위치(X_CG), 공력 중심(AC)의 종방향 위치(X_AG) 및 y축에 대한 관성 모멘트(Iyy)와 같은 튜닝 매개변수이다. 민감도 매개변수는 하기 수학식 5를 적용하여 튜닝될 수 있다.From here,

is the kinetic characteristic parameters such as damping factor (ζ) and natural frequency (ω _n ),

are the tuning parameters such as the longitudinal position (X CG ) of the center of gravity ( _CG ), the longitudinal position (X _AG ) of the aerodynamic center (AC), and the moment of inertia about the y-axis (Iyy). The sensitivity parameter can be tuned by applying Equation 5 below.

[Equation 5]

Here, the superscript “tuned” indicates the tuned parameter, “init” indicates the initial parameter, and “test” and “sim” are parameters obtained from flight test and simulation data, respectively.

Aerodynamic DB correction unit 140 receives the flight test data stored in the flight test DB unit 110 to estimate the parameters, and based on the estimated parameters and the aircraft DB unit 120, linear correction for the creation of the aerodynamic DB carry out In one embodiment, the aerodynamic DB corrector 140 may perform parameter estimation in flight test data using a Maximum Likelihood (ML) method.

The ML method is a method to obtain the parameters of a random variable based on the values sampled from that random variable. It is a method of selecting a parameter that maximizes the likelihood that desired values will be obtained when a certain parameter is given.

The aerodynamic DB corrector 140 is basically considered to be modeled as a series of dynamic equations including unknown parameters for the system under investigation. To determine the value of an unknown parameter, the system is excited by an appropriate input and then the actual system response is measured. The difference between the measured response and the expected response (hereafter, response error) is in the ML cost function module. By applying a Gaussian-Newton (GN) calculation algorithm, the cost function, which is a difference function between the measured time history and the calculated time history, is minimized. The probability that the aircraft response time history will reach a value near the observed value is defined for each estimate of the unknown parameter. The ML estimate is defined as the estimate that maximizes this probability. The values of unknown parameters are inferred based on the requirement that the mathematical model respond to given conditions. The input matches the actual flight test response. Since the aircraft is a continuous-time dynamic system, when measurements are made at discrete time intervals, the system equation can be defined as Equation 6 below.

[Equation 6]

의 값을 추정한다. 잘 알려지지 않은 매개변수

의 ML 추정은 하기 수학식 7의 비용함수 최소 값이다. where x(t) is the state, u(t) is the input, and z(t) is the response of the dynamic system. Assuming that the measurement noise (η _i ) is a sequence of independent Gaussian random vectors with zero mean and covariance (GG*), the forms of the f and g functions are well known. The parameter estimation task is a parameter that is not well known.

estimate the value of unknown parameters

Estimation of ML of is the minimum value of the cost function of Equation 7 below.

[Equation 7]

는 알려지지 않은 매개변수 벡터

의 주어진 값에 대해 t_i에서 z의 예측된 응답 추정이다. From here,

is the unknown parameter vector

Estimation of the predicted response of z at t _i for a given value of .

Aerodynamic DB correction unit 140 combines the existing aerodynamic DB with parameters estimated from flight test data. Initially, the data formats of the aerodynamic DBs obtained from different analysis sources are not similar. For example, reference aerodynamic coefficients are mostly in 3D and 2D tabular format, while flight test estimate coefficients are all in linear format. Therefore, the aerodynamic DB correction unit 140 applies a correction technique to correct the linear region of the data in order to update the existing aerodynamic DB. The aerodynamic DB corrector 140 is recalculated by two linear coefficients in the 2D form coefficient through Equation 8 below.

[Equation 8]

Here, the superscript “mod” indicates the changed coefficient, “init” indicates the initial coefficient of the aircraft model database, and “est” indicates the value estimated from the flight test data.

The flight test verification unit 150 may perform simulation verification using flight test data. In one embodiment, the flight test verification unit 150 compares the flight response of the simulation model and the flight test data with respect to the evaluation criteria. According to the flight quality section of the evaluation handbook, the validation procedure is applied to the short-term dynamics of longitudinal motion. In the flight test verification unit 150, the modified simulation model is performed in JSBSim FDM using the adjusted parameters of the first stage and the aerodynamic DB correction of the second stage.

The controller 160 generally controls the operation of the 6-DOF flight simulation accuracy improvement device 100 using flight test data, and the flight test DB unit 110 , the aircraft DB unit 120 , and the preliminary model tuning unit 130 . ), it is possible to control and manage the flow of data between the aerodynamic DB correction unit 140 and the flight test verification unit 150 .

2 is a flowchart for explaining a method for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment of the present invention, and FIG. 3 is a detailed process of the 6-DOF flight simulation accuracy improvement method in FIG. It is a drawing for explanation.

Referring to FIG. 2, first, a flight simulation model is built (step S210).

Then, a longitudinal maneuver flight test is performed to build a flight test DB unit with flight test data collected during the flight test (step S220).

In order to improve the accuracy of the flight simulation model by using the actual flight test data, preliminary model tuning is performed by adjusting the parameters of the simulation model (step S230).

Referring to FIG. 3 , the preliminary model tuning step S230 may include a sensitivity analysis step, a flight response analysis step, and a parameter tuning step. The sensitivity analysis step analyzes the influence of parameters on flight behavior. In one embodiment, the sensitivity analysis step provides parameter influences by performing sensitivity analysis of flight dynamics in longitudinal motion based on data related to aircraft shape, weight, propulsion, etc. recorded in the aircraft DB unit 120 . In the flight response analysis step, a response error is provided by comparing and analyzing the flight response recorded in the flight test DB unit 110 and the simulation response. Here, the simulation response may be obtained in the flight test verification step. The parameter tuning step adjusts the sensitive parameters based on the sensitivity analysis and flight response analysis. In one embodiment, the parameter tuning step is performed by tuning sensitive parameters affecting longitudinal motion with motion characteristic parameters such as damping rates or natural frequencies for initial parameters and parameters obtained from flight test and simulation data. can provide

Again, returning to Figure 2, performing aerodynamic DB correction (step S240).

Referring to FIG. 3 , the aerodynamic DB correction step S240 may include an ML estimation step, an aerodynamic coefficient calculation step, and an aerodynamic DB correction step. In the ML estimation step, parameters can be estimated from flight test data through Maximum Likelihood estimation. In one embodiment, the ML estimation step may receive a control input and flight conditions recorded in the flight test DB unit 110 to estimate parameters. The aerodynamic coefficient calculation step may calculate a related aerodynamic coefficient from the estimated parameter. The calculated aerodynamic coefficients are shown in Table 2 below.

[Table 2]

The aerodynamic DB correction step performs aerodynamic DB correction using a linear correction technique. In one embodiment, the aerodynamic DB correction step may be performed by combining the existing aerodynamic DB with parameters estimated from flight test data to perform the correction. Here, the aerodynamic DB calibration step may update the aerodynamic coefficients using a linear calibration technique.

Again, returning to FIG. 2, simulation verification using flight test data is performed to provide an improved simulation model (step S250).

Referring to FIG. 3 , the flight test verification step S250 may include a six-degree-of-freedom simulation step, a response error calculation step, and a reliability verification step. The 6 degree of freedom simulation step can perform a 6 degree of freedom simulation based on the rules contained in the certification rule handbook. Six degrees of freedom (DOF) refers to the six directions of motion of an aircraft. The aircraft moves about three axes around the center of gravity. The change in the attitude of the aircraft during flight is that it is rotating around three axes. The three axes intersect each other at right angles to the other axes at the central position. Therefore, when the horizontal axis and vertical axis, respectively, the rightward and upward movements are called positive movement, and the opposite is called negative movement, There are six directions of motion. In the 6 degree of freedom simulation step, flight simulation is performed based on the regulations contained in the certification regulations handbook. Here, the flight simulation model is performed in JSBSim FDM using the same control inputs and flight conditions as the actual flight test using the adjusted parameters and the corrected aerodynamic DB.

, 피치 자세에 대한 허용 오차는

이다.The response error calculation step calculates a response error by comparing the simulated response with the actual flight response recorded in the flight test DB unit 110 . In the reliability verification step, if the calculated response error meets the tolerance, it is determined that the simulation model is reliable, and if it does not meet the tolerance, the simulation response is fed back to the preliminary model tuning step of step S230, from step S230 to be performed repeatedly. According to the regulations contained in the Certification Regulations Handbook, the tolerance for pitch speed when applied to short-period motion is

, the tolerance for pitch attitude is

to be.

FIG. 4 is a view for explaining the flight test data processing process of the flight test execution stage in FIG. 2 .

4, a flight test is performed (step S410). In one embodiment, flight testing (Flight Testing) used a life-size prototype manufactured for the purpose of performance verification and certification compliance of the lightweight aircraft KLA-100. Here, the KLA-100 is a two-seater light aircraft for aviation leisure and pilot training.

The flight test operation team determines flight conditions and control inputs based on the maneuver description of the flight simulator evaluation handbook (step S420).

During the flight test, raw data collected through the on-board data collection system is recorded (step S430). Here, the raw data may include all parameters related to flight status and operating variables such as direction, speed, acceleration, control surface displacement and throttle setting.

Data pre-processing is performed to identify flight motions (step S440). In one embodiment, data preprocessing may be used to extract segments containing noise filtering and flight test maneuvers. Here, data preprocessing uses a moving average filter to smooth the raw data by replacing each data point with the average of adjacent data points defined within the range. Then, using the specified extraction rules for each maneuver, such as load factor or control input, the entire data is extracted into multiple single start segments and the extracted single start segments are recorded.

The extracted single maneuver segments are classified by specific types (step S450). In one embodiment, the extracted maneuvers may be automatically classified as a particular type of maneuver using signal pattern similarity search techniques. The signal pattern similarity search technique uses a distance matrix to recognize time-varying segments that closely match the sample signal. The basic idea of this method is to sweep the sample signal through the data and then find the lowest difference between the sample signal and the data locally at each location. Euclidean distance was used to measure the difference, and also the signal sequences must be distorted in a non-linear way to match each other. A warping algorithm was used to find the optimal alignment between two given time-dependent sequences under certain constraints. A sample signal is generated with respect to a general characteristic description. For example, short-time motion is a fast-responding longitudinal mode initiated in horizontal-level flight by applying an elevator or other longitudinal control input, while fugoid mode can be excited via elevator pulses or thrust fluctuations. In this case, an elevator single doublet is applied for short-period longitudinal motion.

Flight test DB unit 110 may be constructed by recording flight test data in a form that can be applied for validation and identification purposes by filtering raw data and classifying it as a single maneuver. In one embodiment, the flight test DB unit 110 may record flight test data including control inputs, flight conditions and flight responses for each of the longitudinal and lateral motions.

5 is a diagram illustrating a simulation verification result according to an embodiment.

In Fig. 5, the shaded area shows the tolerance band obtained in the flight measurement. Referring to FIG. 5 , it can be seen that the response of the improved model is within an acceptable range. That is, it can be seen that the improved simulation model is close to the flight response of the actual flight test.

A comparison of simulation results according to the simulation model is shown in Table 3 below.

[Table 3]

As shown in Table 3 above, as a result of comparing the simulation verification of each of the initial model, the model in the first stage of tuning the model, and the model in the second stage in which the aerodynamic DB was corrected, the actual flight test and the improved simulation in the first and second stages It can be seen that the error between the models met the regulatory requirements. In conclusion, the simulation model improved through the 6-DOF flight simulation accuracy improvement method using flight test data according to an embodiment satisfies the specified accuracy requirements in the longitudinal motion.

The method and apparatus for improving the accuracy of 6-DOF flight simulation using flight test data according to an embodiment can effectively reduce the discrepancy by modifying the sensitive related parameters for the related motion, and improve the application of the data obtained from the flight test. This can improve the accuracy of the simulation model.

Although the above has been described with reference to the preferred embodiment of the present application, those skilled in the art can variously modify the present invention within the scope without departing from the spirit and scope of the present invention described in the claims below and may be changed.

100: 6 degree of freedom flight simulation accuracy improvement device using flight test data
110: flight test DB unit 120: aircraft DB unit
130: preliminary model tuning unit 140: aerodynamic DB correction unit
150: flight test verification unit 160: control unit

Claims (11)

building a flight simulation model;
A flight test execution step for establishing a flight test database with flight test data collected during the flight test by performing a longitudinal maneuver flight test;
a preliminary model tuning step of adjusting parameters of the simulation model to improve the accuracy of the flight simulation model by using the flight test data;
Aerodynamic DB correction step to correct the aerodynamic database; and
6 degrees of freedom flight simulation accuracy improvement method using flight test data comprising a flight test verification step of providing an improved simulation model by performing simulation verification using the flight test data.
According to claim 1, wherein the flight test performing step
Based on the maneuver description contained in the Flight Simulator Evaluation Handbook, it includes a step of conducting an actual flight test of a light aircraft according to flight conditions and control inputs, and processing raw data collected during the flight test to identify flight maneuvers. 6 degrees of freedom flight simulation accuracy improvement method using flight test data, characterized in that.
According to claim 2, wherein the raw data (Raw Data) is
6-DOF flight simulation accuracy improvement method using flight test data, characterized in that it includes parameters related to flight status and operating parameters of direction, speed, acceleration, control surface displacement and throttle setting.
According to claim 2, wherein the flight test data processing step
After filtering through the moving average filter through the raw data, preprocessing is performed for each maneuver to be extracted as a single maneuver segment, and the extracted single maneuver segments are classified by specific type using a signal pattern similarity search technique. A method for improving the accuracy of 6-DOF flight simulation using flight test data.
According to claim 1, wherein the preliminary model tuning step
6 degree of freedom flight simulation using flight test data, characterized in that it includes a sensitivity analysis step of providing parameter effects by performing sensitivity analysis of flight dynamics in longitudinal motion based on the shape DB, weight DB, and propulsion DB of the aircraft How to improve accuracy.
The method of claim 5, wherein the preliminary model tuning step
A flight test characterized in that it provides a response error by comparing and analyzing the flight response and the simulation response, and performing tuning to adjust the sensitive parameters affecting the longitudinal motion in the flight simulation environment based on the sensitivity analysis and flight response analysis A method for improving the accuracy of 6-DOF flight simulation using data.
The method of claim 6, wherein the preliminary model tuning step
A method of improving the accuracy of 6-DOF flight simulation using flight test data, characterized in that the damping rate and natural frequency are defined as a function of Equation 1 below by using the longitudinal motion characteristic parameters.
[Equation 1]

From here,

is the kinetic characteristic parameters for damping factor (ζ) and natural frequency (ω _n ),

denotes the sensitivity parameters such as the longitudinal position (X CG ) of the center of gravity ( _CG ), the longitudinal position (X _AG ) of the aerodynamic center (AC), and the moment of inertia about the y-axis (Iyy).
The method of claim 7, wherein the preliminary model tuning step
6-DOF flight simulation accuracy improvement method using flight test data, characterized in that the sensitivity parameter is tuned by applying Equation 2 below.
[Equation 2]

Here, the superscript “tuned” denotes a tuning parameter, “init” denotes an initial parameter, and “test” and “sim” denote parameters obtained from flight test and simulation data, respectively.
According to claim 1, wherein the aerodynamic DB correction step
In flight test data, parameters are estimated through maximum likelihood (ML) estimation under control input and flight conditions, and related aerodynamic coefficients are calculated from the estimated parameters to correct the existing aerodynamic DB through a linear correction technique. 6 degrees of freedom flight simulation accuracy improvement method using flight test data, characterized in that.
According to claim 1, wherein the flight test verification step
Based on the regulations contained in the certification regulation handbook, a 6-DOF simulation is performed, the response error is calculated by comparing the actual flight response and the simulation response, and the reliability of the simulation model is checked when the calculated response error meets the tolerance. 6-DOF flight simulation accuracy improvement method using flight test data, characterized in that authentication.
Flight test DB section in which flight test data measured during the flight test are recorded;
Aircraft DB unit including aerodynamic (aerodynamic) DB, propulsion DB, and weight DB constructed through wind tunnel (W/T) test, CFD analysis and empirical calculation;
a preliminary model tuning unit for adjusting the model parameters by performing sensitivity analysis on the parameters using the flight test data to preliminarily increase the accuracy of the simulation model;
An aerodynamic DB correction unit for estimating a parameter from the flight test data and correcting the aerodynamic DB by combining the estimated parameter and the aerodynamic DB through linear correction; and
6-DOF flight simulation accuracy improvement device using flight test data including a flight test verification unit that performs simulation verification by comparing the flight response of the simulation model and flight test data with respect to the evaluation criteria.

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