KR102484249B1 - 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 and apparatus for improving the accuracy of 6 degree of freedom flight simulation using flight test data. A flight test performance step for building a flight test database with collected flight test data, a preliminary model tuning step for adjusting the parameters of the simulation model to improve the accuracy of the flight simulation model using the flight test data, and aerodynamics An aerodynamic DB correction step of correcting the database, and a flight test verification step of providing an improved simulation model by performing simulation verification using the flight test data. Accordingly, it is possible to improve the accuracy of the flight simulation model in the longitudinal motion of the aircraft.

Description

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

The present invention relates to a technology for improving the accuracy of 6 DOF flight simulation using flight test data, and more particularly, 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 degree of freedom flight simulation using flight test data that can be used.

Today, flight simulation plays an important role in aerospace engineering, contributing to nearly the entire development process, from preliminary design to 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 tests for verifying aircraft flight quality and performance.

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

In general, the accuracy of a flight simulation model is affected by the accuracy of several model databases such as aerodynamics DB, propulsion DB, weight DB, and control system DB. The level of precision and time required for analysis are proportional and inversely proportional to the scope of analysis and the amount of data. For example, the method using the low-precision empirical formula calculates the aerodynamic coefficient much faster and covers 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 limited. Therefore, in order to improve the accuracy of flight simulation, it is necessary to use data obtained from various analysis methodologies through data fusion.

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

There are several limitations in these studies. First of all, since most aerodynamic analysis tools are based on analysis results obtained in low fidelity or medium fidelity, such as empirical formulas or panel methods, they do not guarantee high precision. Also, 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 for verifying the 6 DOF simulation based on the regulations listed in the certification regulation handbook.

Korean Registered Patent 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 a 6 DOF simulation using actual flight test data. .

An embodiment of the present invention is flight test data that can improve the accuracy of flight simulation by generating an accurate flight simulation model DB by combining high fidelity flight test data with variable fidelity methodology applied data. It is intended to provide a method and apparatus for improving the accuracy of flight simulation with 6 degrees of freedom 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 a flight simulation model by verifying the reliability of a 6 DOF simulation based on the regulations listed in the certification regulation handbook. want to do

Among the embodiments, a method for improving the accuracy of flight simulation using flight test data includes building a flight simulation model, performing a longitudinal maneuver flight test, and constructing a flight test database with flight test data collected during the flight test. A flight test performed for the flight test, 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, an aerodynamic DB correction step of correcting the aerodynamics database, and the flight It includes a flight test verification step of providing an improved simulation model by performing simulation verification using test data.

The flight test execution step is to conduct an actual flight test of a lightweight aircraft according to flight conditions and control inputs based on the maneuver descriptions contained in the flight simulator evaluation handbook, and to use raw data collected during the flight test to identify flight maneuvers. Data processing may be included.

The raw data may include direction, speed, acceleration, displacement of the control surface, operation variables of throttle settings, and parameters related to flight conditions.

In the flight test data processing step, preprocessing is performed to extract a single maneuver segment for each maneuver after filtering through a moving average filter through the raw data, 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 performing a sensitivity analysis of flight dynamics in the longitudinal motion based on the shape DB, weight DB, and propulsion DB of the aircraft to provide parameter effects.

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

The preliminary model tuning step may be defined as a function of Equation 1 below using a damping rate and a 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 motion characteristic parameter for the damping rate (ζ) and natural frequency (ω _n ),

Means sensitivity parameters such as the longitudinal position of the center of gravity (CG) (X _CG ), the longitudinal position of the center of aerodynamics (AC) (X _AG ), 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 tuning parameters, “init” denotes initial parameters, 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 in control inputs and flight conditions among flight test data, calculates related aerodynamic coefficients from the estimated parameters, and uses the existing aerodynamics through a linear correction technique. DB correction can be performed.

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

Among the embodiments, the 6-degree-of-freedom flight simulation accuracy improvement device using flight test data is a flight test DB unit in which the flight test data measured during the flight test is recorded, through a wind tunnel (W / T) test, CFD analysis and empirical calculation. In order to preliminarily increase the accuracy of the aircraft DB including the configured aerodynamics (aerodynamics) DB, propulsion DB, and weight DB, and the simulation model, the model parameters are adjusted by analyzing the sensitivity of 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 with the aerodynamic DB through linear correction to correct the aerodynamic DB; and the flight response and flight of the simulation model. It includes a flight test verification unit that performs simulation verification by comparing test data with respect to evaluation criteria.

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

According to an embodiment of the present invention, the method and apparatus for improving the accuracy of 6 degree of freedom flight simulation using flight test data can improve the fidelity of the simulation model by performing preliminary model tuning and aerodynamic DB correction using actual flight test data. The accuracy of flight simulation models for light aircraft can be improved by verifying 6 DOF simulations.

According to an embodiment of the present invention, a method and apparatus for improving the accuracy of flight simulation with 6 degrees of freedom using flight test data is an accurate flight simulation by combining high fidelity flight test data with variable fidelity methodology applied data. The accuracy of flight simulation can be improved by creating a model DB.

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

Since the description of the present invention is only an embodiment for structural or functional description, the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, since the embodiment can be changed in various ways and can 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, the scope of the present invention should not be construed as being limited thereto.

Meanwhile, the meaning of terms described in this application should be understood as follows.

Terms such as "first" and "second" are used to distinguish one component from another, and the scope of rights should not be limited by these terms. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

Expressions in the singular number should be understood to include plural expressions unless the context clearly dictates otherwise, and terms such as “comprise” or “having” refer to an embodied feature, number, step, operation, component, part, or these. It should be understood that it is intended to indicate that a combination exists, and does not preclude the possibility of the presence or addition of one or more other features, 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 defined otherwise. Terms defined in commonly used dictionaries should be interpreted as consistent with meanings in the context of the related art, and cannot be interpreted as having ideal or excessively formal meanings 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 can be composed of three steps: preliminary model tuning, aerodynamic DB correction, and flight test verification.

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

Referring to FIG. 1, the 6 degree of freedom flight simulation accuracy improvement device using flight test data (hereinafter referred to as '6 degree of freedom flight simulation accuracy improvement device') 100 includes 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 may be included.

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

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

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

Sensitivity analysis is commonly used to determine the influence of model parameters referring to validation and validation purposes. In the context of validation, sensitivity analysis can be defined as a systematic examination of the response of a simulation to the inputs of a model.

In the flight simulation environment, the behavior of the flight response depends on the location of the root 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 as Equation 1 below as a state equation and a characteristic equation for short-period approximation.

[Equation 1]

Here, α is the angle of attack, q is the pitch speed, u ₀ is the forward speed, Z _α is the longitudinal derivative due to the angle of attack, M _α is the pitching moment in units of the angle of attack, and M is the pitching moment in units of the rate of change of the angle of attack , M _q is the pitching moment in pitch velocity units, and λ means the characteristic root of the characteristic equation.

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

[Equation 2]

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

) 및 감쇠율(

)은 하기 수학식 3으로 계산되어질 수 있다.frequency (

) and decay rate (

) can be calculated by Equation 3 below.

[Equation 3]

는 받음각 단위 피칭 모멘트를 의미한다. From here,

denotes the pitching moment per angle of attack.

In previous modeling of longitudinal motion, the characteristic parameters for short-period motion are functions of very large parameter sets. It is known that some of these parameters are considered fixed quantities in the initial design phase, such as the dimensions of the aircraft shape, while some others change nominally over the flight range of the aircraft, such as the center of gravity (CG) position. Therefore, the sensitivity analysis for short-period motion focuses on effects related to variations in the list of parameters 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 the pitching moment coefficient due to the angle of attack, which has a direct meaning in (pitching moment per angle of attack)

represents the primary aerodynamic coefficient included in The longitudinal position of the center of gravity (CG) and center of aerodynamics (AC) has a great influence on 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, The preliminary model tuning unit 130 has a characteristic parameter (

,

) parameters affected by

The effect of 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 shown 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 a motion characteristic parameter such as damping rate (ζ) and natural frequency (ω _n ),

is a tuning parameter such as the longitudinal position of the center of gravity (CG) (X _CG ), the longitudinal position of the aerodynamic center (AC) (X _AG ), 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 tuned parameters, “init” indicates initial parameters, and “test” and “sim” are parameters obtained from flight test and simulation data, respectively.

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

The ML method is a method of obtaining parameters of a random variable based on values sampled from a certain random variable. It is a method of selecting a parameter that maximizes the likelihood of a desired value given a given parameter.

The aerodynamic DB compensator 140 basically assumes that the system under investigation is modeled as a series of dynamic equations including unknown parameters. To determine the value of an unknown parameter, the system is excited by the appropriate input and then the actual system response is measured. The difference between the measured response and the expected response (hereafter referred to as response error) is in the ML cost function module. A Gaussian-Newton (GN) calculation algorithm is applied to minimize the cost function, which is a function of the difference between the measured time history and the calculated time history. 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 the unknown parameters are inferred based on the requirement that the mathematical model respond to a given condition. Inputs correspond to actual flight test responses. Since an aircraft is a continuous-time dynamic system, if 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 shapes of the f and g functions are well known. The parameter estimation task is for unknown parameters.

estimate the value of unknown parameters

The ML estimate 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

is the predicted response estimate of z at t _i for a given value of

The aerodynamic DB compensator 140 combines the existing aerodynamic DB with parameters estimated from the flight test data. Initially, the data format of the aerodynamics DB obtained from different analytical sources is not similar. For example, reference aerodynamic coefficients are mostly in 3D and 2D tabular form, whereas flight test estimate coefficients are all in linear form. Accordingly, the aerodynamic DB corrector 140 applies a correction technique for correcting the linear region of data in order to update the existing aerodynamic DB. In the aerodynamic DB corrector 140, the 2D form coefficient is recalculated by two linear coefficients through Equation 8 below.

[Equation 8]

Here, the superscript “mod” represents the modified coefficient, “init” represents the initial coefficient of the aircraft model database, and “est” represents 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 evaluation criteria. According to the Flight Quality section of the Assessment Handbook, validation procedures are applied to the short-term dynamics of longitudinal motion. In the flight test verification unit 150, a modified simulation model is performed in JSBSim FDM using the adjusted parameters in step 1 and aerodynamic DB correction in step 2.

The control unit 160 controls the overall operation of the six degree of freedom flight simulation accuracy improving device 100 using flight test data, and controls the flight test DB unit 110, the aircraft DB unit 120, and the preliminary model tuning unit 130. ), the flow of data between the aerodynamic DB correction unit 140 and the flight test verification unit 150 may be controlled and managed.

2 is a flowchart illustrating 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 shows a detailed process of the method for improving the accuracy of the 6-DOF flight simulation shown in FIG. It is a drawing for explanation.

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

Then, a longitudinal maneuvering 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 using actual flight test data, preliminary model tuning for adjusting parameters of the simulation model is performed (step S230).

Referring to FIG. 3 , 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 effects 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 can be obtained in the flight test verification step. The parameter tuning step adjusts sensitive parameters based on sensitivity analysis and flight response analysis. In one embodiment, the parameter tuning step is to tune sensitive parameters that affect 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 FIG. 2, aerodynamic DB correction is performed (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 estimate parameters by receiving control inputs and flight conditions recorded in the flight test DB unit 110 . The step of calculating aerodynamic coefficients may calculate related aerodynamic coefficients from the estimated parameters. 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 perform correction by combining the existing aerodynamic DB with parameters estimated from flight test data. Here, the aerodynamic DB correction step may update the aerodynamic coefficient using a linear correction 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 6 degree of freedom simulation step, a response error calculation step, and a reliability verification step. In the 6 degree-of-freedom simulation step, 6-degree-of-freedom simulation can be performed based on the regulations listed in the certification regulation handbook. The six degrees of freedom (DOF) are the six directions of motion of an aircraft. The aircraft moves around three axes centered on the center of gravity. The change in attitude of an aircraft in flight is that it rotates around three axes. The three axes intersect each other at right angles to each other at the center position. Therefore, when the aircraft moves left and right and up and down around the three axes, the rightward and upward motions of the horizontal and vertical axes, respectively, are called positive direction motions, and the opposite is called negative direction motions. There are six directions of motion. In the 6 degree of freedom simulation step, flight simulation is performed based on the regulations listed in the certification regulation handbook. Here, the flight simulation model is performed in JSBSim FDM using the same control input and flight conditions as the actual flight test with the modified model using the tuned parameters and the calibrated aerodynamic DB.

, 피치 자세에 대한 허용 오차는

이다.In the response error calculation step, a response error is calculated by comparing an actual flight response recorded in the flight test DB unit 110 with a simulated response. In the reliability verification step, if the calculated response error satisfies the tolerance, it is determined that the corresponding 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, and steps from step S230 to be repeated. According to the regulations contained in the Certification Regulations Handbook, when applied to short cycle motion, the tolerance for pitch speed is

, the tolerance for the pitch attitude is

to be.

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

Referring to FIG. 4, a flight test is performed (step S410). In one embodiment, the flight test (Flight Testing) used a full-size prototype manufactured for the purpose of performance verification and certification compliance of the light 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 maneuvering description in 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 operational variables and flight conditions, such as direction, speed, acceleration, control surface displacement, and throttle setting.

Data pre-processing is performed to identify the flight motion (step S440). In one embodiment, segments including noise filtering and flight test maneuvers may be extracted through data preprocessing. Here, the data pre-processing 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. The entire data is then extracted into multiple single maneuver segments using extraction rules specified for each maneuver, such as load factor or control input, and the extracted single maneuver segments are recorded.

The extracted single maneuver segments are classified according to specific types (step S450). In one embodiment, the extracted maneuver may be automatically classified as a specific type of maneuver using a signal pattern similarity search technique. Signal pattern similarity search techniques use distance matrices to recognize time-varying segments that closely match sample signals. The idea behind 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 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 created with respect to a general characteristic description. For example, short-time motion is a fast-responding longitudinal mode that is initiated in level level flight by applying an elevator or other longitudinal control input, while a fugoid (long-period) mode can be excited through elevator pulses or thrust fluctuations. In this case, an elevator single doublet was applied for short-period longitudinal motion.

The 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 into 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 longitudinal motion and lateral motion, respectively.

5 is a diagram showing simulation verification results according to an embodiment.

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

Comparison of simulation results according to simulation models 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 first-stage model tuned model, and the second-stage model after correcting the aerodynamic DB, 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 six degree of freedom flight simulation accuracy improvement method using flight test data according to an embodiment satisfies the specified accuracy requirements in longitudinal motion.

A method and apparatus for improving the accuracy of a 6 degree of freedom flight simulation using flight test data according to an embodiment can efficiently reduce the inconsistency by modifying sensitive parameters related to related motion, and apply the data obtained from the flight test. Through this, the accuracy of the simulation model can be improved.

Although the above has been described with reference to the preferred embodiments of the present application, those skilled in the art will variously modify the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. And it will be understood that it can 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 performance 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 accuracy of the flight simulation model using the flight test data;
an aerodynamic DB correction step of correcting the aerodynamic database; and
Including a flight test verification step of providing an improved simulation model by performing simulation verification using the flight test data,
The preliminary model tuning step is
A sensitivity analysis step of performing a sensitivity analysis of flight dynamics in longitudinal motion based on the shape DB, weight DB, and propulsion DB of the aircraft to provide parameter effects;
A flight response analysis step of providing a response error by comparing and analyzing the flight response and the simulated response; and
Based on sensitivity analysis and flight response analysis, six degrees of freedom flight simulation accuracy using flight test data, characterized in that it consists of a parameter tuning step that performs tuning to adjust sensitive parameters that affect longitudinal motion in the flight simulation environment. How to improve.
The method of claim 1, wherein the flight test is performed
Based on the maneuver descriptions in the flight simulator evaluation handbook, actual flight tests of lightweight aircraft are conducted according to flight conditions and control inputs, and raw data collected during the flight tests are processed to identify flight maneuvers. A method for improving the accuracy of 6 degree of freedom flight simulation using flight test data, characterized in that.
The method of claim 2, wherein the raw data (Raw Data)
A method for improving the accuracy of six degrees of freedom flight simulation using flight test data, characterized in that it includes parameters related to direction, speed, acceleration, control surface displacement, and motion variables of throttle setting and flight conditions.
The method of claim 2, wherein the flight test is performed
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 classified into specific types using a signal pattern similarity search technique. A method for improving the accuracy of 6 degree of freedom flight simulation using flight test data.
delete delete The method of claim 1, wherein the preliminary model tuning step
A method for improving the accuracy of 6-degree-of-freedom flight simulation using flight test data, characterized in that the damping rate and natural frequency are defined as a function of Equation 1 below using longitudinal motion characteristic parameters.
[Equation 1]

From here,

is the motion characteristic parameter for the damping rate (ζ) and natural frequency (ω _n ),

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

Here, the superscript “tuned” denotes tuning parameters, “init” denotes initial parameters, and “test” and “sim” denote parameters obtained from flight test and simulation data, respectively.
The method of claim 1, wherein the aerodynamic DB correction step
Among the flight test data, parameters are estimated through maximum likelihood (ML) estimation in control inputs and flight conditions, and related aerodynamic coefficients are calculated from the estimated parameters, and Equation 3 below is applied through a linear correction technique to determine the existing A method for improving the accuracy of 6 degree of freedom flight simulation using flight test data, characterized in that the correction of the aerodynamic DB is performed.
[Equation 3]

Here, the superscript “mod” represents the modified coefficient, “init” represents the initial coefficient of the aircraft model database, and “est” represents the value estimated from the flight test data.
The method of claim 1, wherein the flight test verification step
6 degree of freedom simulation is performed based on the regulations listed in the certification regulation handbook, the response error is calculated by comparing the actual flight response with the simulated response, and the reliability of the simulation model is verified when the calculated response error meets the tolerance. A method for improving the accuracy of 6 degree of freedom flight simulation using flight test data, characterized in that for authentication.
a flight test DB unit in which flight test data measured during the flight test are recorded;
An aircraft DB including an aerodynamic DB, a propulsion DB, and a weight DB configured through wind tunnel (W/T) tests, CFD analysis, and empirical calculations;
a preliminary model tuning unit adjusting model parameters by performing sensitivity analysis on parameters using the flight test data in order to preliminary increase the accuracy of the simulation model;
an aerodynamic DB corrector for correcting the aerodynamic DB by estimating parameters from the flight test data and combining the estimated parameters with the aerodynamic DB through linear correction; and
Including a flight test verification unit that performs simulation verification by comparing the flight response and flight test data of the simulation model in relation to the evaluation criteria,
The preliminary model tuning unit
Flight test, characterized by providing a response error by comparing and analyzing flight response and simulation response, and performing tuning to adjust sensitive parameters affecting longitudinal motion in a flight simulation environment based on sensitivity analysis and flight response analysis 6 degree of freedom flight simulation accuracy improvement device using data.

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