Reliability evaluation method of UAV's time in the air based on multi-source uncertainty analysis

Abstract

The present invention provides a method for assessing the reliability of drone hang time based on multi-source uncertainty analysis. The method includes: constructing a drone aerodynamic model, numerically simulating the drone's aerodynamic parameters, obtaining how aerodynamic parameters such as the lift coefficient and drag coefficient vary with flight state, constructing a drone hang time model, deriving the governing equations for hang time, analyzing how hang time varies under parameter uncertainty, environmental uncertainty, and flight state uncertainty, conducting drone hang time simulation tests under multi-source uncertainty, quantifying drone hang time uncertainty and calculating the hang time distribution, constructing a drone hang time reliability model, and evaluating its reliability. Based on the impact of complex environments on drone hang time during flight, the method constructs a hang time reliability model and quantitatively assesses the reliability of drone hang time in actual use. The method is practical, highly accurate, and has strong engineering applicability.

Description

Technical Field

[0001] The present invention belongs to the field of UAV reliability, and in particular relates to a UAV airtime reliability assessment method based on multi-source uncertainty analysis. Background Art

[0002] Unmanned aerial vehicle (UAV) technology, with its advantages of high manufacturability, low cost, and zero risk of casualties during service, has garnered extensive attention and research in many fields. However, the size and weight limitations of UAVs, coupled with a lack of real-time monitoring, increase the potential risks of mission execution. To expand the application scope of UAVs and ensure the successful completion of flight missions, UAV performance is crucial. Flight duration is a key performance factor in UAVs, and ensuring its accuracy is a significant challenge, as flight duration is affected by multiple sources of uncertainty, including geometric characteristics, aerodynamic performance, and the atmospheric environment. Therefore, predicting the aerodynamic performance of UAVs and analyzing the impact of the environment on them are effective methods for assessing the reliability of UAV flight duration, and possess significant engineering value.

[0003] For drones, their aerodynamic performance is closely related to their design and can be simulated using numerical models. Due to their light weight and lower speed than typical aircraft, the atmospheric environment significantly impacts their flight time. Therefore, the primary consideration is the impact of the drone's aerodynamic parameters, flight environment, and flight state on its flight time reliability. In numerical modeling, based on dynamic and kinematic equations, a drone's aerodynamic model describes the relationship between its motion parameters, such as attitude, velocity, and position, and its aerodynamic parameters. Numerical simulation of the aerodynamic model allows the determination of the drone's aerodynamic parameters, which are crucial for flight control and optimization. Another important consideration is the impact of the environment on drone flight performance, primarily the influence of wind dynamics. Especially for small aircraft, wind direction and intensity can have an exponential impact on flight time and energy consumption. In actual flight, environmental disturbances pose a significant threat to flight stability and safety, making it essential to account for these disturbances in simulations.

[0004] Uncertainty is an important aspect of the simulation, modeling, and evaluation of complete aircraft. For UAVs, it is important to quantify and analyze the uncertainty of numerical simulation and modeling results, and to consider the uncertain environment in non-deterministic aerodynamic simulations with known aerodynamic parameters. However, current research usually considers the uncertainty of the aircraft at the design stage, or is based only on the uncertainty of a single parameter. There is still a lack of research on the flight performance of UAVs that considers multi-source uncertainty. UAV applications are often mission- and safety-critical, so the reliability of performing functions within the predetermined mission profile to provide the required services is crucial. On the other hand, current research on UAV reliability focuses on components of UAV systems, UAV swarms, and UAV network communications. Few studies have examined the reliability of individual UAV flight performance, and there is currently a lack of research on the reliability of flight time.

[0005] In summary, multiple factors introduce uncertainty into the drone's flight time, but few studies have comprehensively considered the impact of these factors and further evaluated the reliability of flight flight time under multi-source uncertainty. Therefore, it is extremely important to combine multi-source uncertainty and reliability assessment to establish a drone flight time reliability assessment method based on multi-source uncertainty analysis to efficiently achieve drone flight time reliability assessment. Summary of the Invention

[0006] In response to the above-mentioned defects in the existing technology, the present invention proposes a method for evaluating the reliability of drone's airborne time based on multi-source uncertainty analysis. First, a drone aerodynamic model is established, and the drone's aerodynamic parameters are numerically simulated to obtain the changes in aerodynamic parameters such as the lift coefficient and the drag coefficient with the flight state. A drone airborne time model is constructed, and the changes in airborne time under parameter uncertainty, environmental uncertainty, and flight state uncertainty are analyzed. The distribution of airborne time is calculated, and a Monte Carlo experiment is performed to simulate the drone's airborne time under multi-source uncertainty. A reliability model for the drone's airborne time is constructed and its reliability is evaluated. The present invention takes into account the impact of the complex environment on the drone's airborne time during flight, constructs a reliability model for the airborne time, and is used to quantitatively evaluate the reliability of the drone's airborne time in actual use. The present invention is practical and has strong engineering applicability.

[0007] Specifically, the present invention provides a method for evaluating the reliability of a UAV's time in the air based on multi-source uncertainty analysis, which comprises the following steps:

[0008] S1. Construct a UAV aerodynamic model and perform numerical simulation of the UAV aerodynamic parameters: Use the UAV aerodynamic model to perform flight simulation of the UAV, obtain the changes of aerodynamic parameters with flight status, and obtain the numerical simulation results of aerodynamic parameters;

[0009] S2. Establish the control equation for the UAV's time in the air. The specific method is as follows: Based on the numerical simulation results of the UAV parameters and aerodynamic parameters, obtain the propeller efficiency η, aircraft mass W, flight airspeed V, and aircraft lift-to-drag ratio K. The control equation for the UAV's time in the air is as follows:

[0010]

[0011] Where K is the lift-to-drag ratio of the aircraft, and the aerodynamic parameters are related to the aircraft mass W and the flight airspeed V. Multi-source uncertainty analysis is performed based on this equation; c f is the engine fuel consumption rate;

[0012] S3. Analyze the changes of the time in the air under the uncertainty of parameters, environment and flight status;

[0013] S4. Conduct a Monte Carlo experiment to simulate the drone's flight time under multi-source uncertainty. Calculate the flight time T for each flight based on the flight time model under multi-source uncertainty and perform statistical analysis:

[0014]

[0015] S5. Construct a reliability model for the drone’s flight time:

[0016] R=Pr(T>T c )(3)

[0017] Among them, R is the reliability model of the UAV’s airborne time, T c The time for staying in the air is the time required to meet the mission flight time, and T is the time for staying in the air for each flight;

[0018] S6. Quantify the uncertainty of the UAV's flight time using the moment integration method and obtain the distribution of the flight time T. Evaluate the reliability of the UAV's flight time based on the UAV's flight time reliability model. This specifically includes the following sub-steps:

[0019] S61. Under the condition that the numerical values ​​of the UAV parameters are determined, obtain the value ranges of the airspeed V and the lift-to-drag ratio K;

[0020] S62. Determine the random variable, which is:

[0021] x=(V,K)=μ x +R T u; (4)

[0022] Where x is the sample value of the random variable, μ x is the average value of the random variable x within the range of values, R is the upper triangular matrix formed by Cholesky decomposition of the variance-covariance matrix of the random variable x, and u is the weighted eigenvalue;

[0023] This step specifically includes the following sub-steps:

[0024] S621. Design a random variable x = (V, K), where x follows a bivariate normal distribution and is converted into two independent Gaussian variables to obtain the mean value μ of the random variable. x , the variance-covariance matrix Σ of the random variables x ;

[0025] S622, for Σ x Perform Cholesky decomposition: R T R=Σ x , where the Cholesky matrix R is:

[0026]

[0027] Among them, r k+1,k+1 is the Cholesky matrix element, k is the order of the random variable Jacobian matrix;

[0028] S623. Assemble the Jacobian matrix J according to the Cholesky matrix R:

[0029]

[0030] in, is the main diagonal element of the Jacobian matrix, is the diagonal element on both sides of the main diagonal of the Jacobian matrix, r jj is the main diagonal element parameter of the Jacobian matrix, j is the subscript number of the main diagonal element of the Jacobian matrix, j∈1,2,…,k;

[0031] S624, solve the Jacobian matrix J eigenvalue ξ=[ξ1,ξ2,…,ξ k ], the corresponding column standard orthogonal eigenvector matrix V is:

[0032]

[0033] Among them, v kk is the element of the Jacobian matrix characteristic matrix;

[0034] S625. Extract the first column component of the feature vector and calculate the weight value For each pair of eigenvalues ​​and weights (ξ, w), perform Cholesky transform to solve for the random parameter x = (V, K);

[0035] S63, assign different weights to random variables according to the eigenvalues, calculate the sample values ​​x of different aerodynamic parameters, and cross-form k 2 Group is the aerodynamic parameter test group, k is the order of the random variable Jacobian matrix;

[0036] S64, will k 2 The aerodynamic parameter test group is substituted into the hovering time calculation model to calculate the drone's hovering time and evaluate the drone's hovering time reliability based on the drone's hovering time reliability model.

[0037] Preferably, the specific process of establishing the drone aerodynamic model in step S1 is:

[0038] S11. Use Solidworks to build a clean geometry model of the drone.

[0039] S12. Obtain the appropriate aerodynamic parameter values. Use the clean-sheet UAV geometry model in the simulation software to construct a computational fluid dynamics (CFD) numerical model. Perform CFD calculations in Ansys Fluent. Use a pressure-based solver and perform a steady-state solution in single precision. Select air as the fluid material and the SST k-omega turbulence model with a low Reynolds number correction. Set the angle of attack to 0° and the incoming velocity to 20 m / s.

[0040] S13, performing grid division;

[0041] S14. Calculate the aerodynamic behavior of the UAV at different angles of attack based on the numerical model of computational fluid dynamics, obtain the changes in aerodynamic parameters with flight status, and obtain numerical simulation results of aerodynamic parameters.

[0042] Preferably, the specific process of establishing the control equation for the drone's airborne time in step S2 is as follows:

[0043] S21. The UAV is in a straight and level flight state. The relationship between the available power and the required power for level flight is established as follows:

[0044]

[0045] S22. According to the fuel consumption calculation formula of a single-engine piston propeller aircraft, the hourly fuel consumption c f,t Equal to the engine fuel consumption rate c f The product of the engine power P is:

[0046] c f,t =c f P (5);

[0047] S23. Consider the amount of fuel consumed in flight per unit time, dQ f The hourly fuel consumption c f,t *dt, and accordingly, the mass of the drone is reduced by dW, and we get:

[0048]

[0049] S24. Based on the above three steps, the control equation of the UAV's airborne time is obtained, and η and c are taken according to experience. f is a reasonable constant value. The lift-to-drag ratio K is expressed according to the flight states W and V in combination with the aircraft's own aerodynamic characteristics. The aircraft's angle of attack α is an intermediate variable. The calculation formula for the lift-to-drag ratio K is:

[0050] K=K(W,V) (7).

[0051] Preferably, step S3 specifically includes the following sub-steps:

[0052] S31, performing polynomial fitting on the aerodynamic parameters of the UAV and generating residuals in the fitting process, thereby generating parameter uncertainty in the calculation process;

[0053] S32. Analyze the UAV flight environment, perform statistical analysis based on weather station data, and add random fluctuations to simulate environmental uncertainty;

[0054] S33. Numerical simulation of the UAV flight state is performed. Based on the mutual correlation between flight states, the uncertainty of aerodynamic parameters and flight environment will propagate during the calculation process, resulting in flight state uncertainty.

[0055] Preferably, the uncertainty analysis of the UAV in step S3 is quantified using Gaussian distribution, wherein the variance of the Gaussian distribution is quantified by the fitting residual.

[0056] Preferably, the uncertainty analysis of the UAV aerodynamic parameters in step S31 includes angle of attack-lift coefficient polynomial fitting, angle of attack-drag coefficient polynomial fitting, angle of attack-lift-drag ratio polynomial fitting, and statistical calculation of the residuals of each polynomial fitting.

[0057] Preferably, the uncertainty analysis of the UAV flight environment and flight state in step S32 and step S33 is quantified by random simulation of wind speed.

[0058] Preferably, the parameters in the UAV airborne time calculation model in step S4 are quantified according to different UAV models, different environments and different flight missions.

[0059] Preferably, the UAV's airborne time reliability model in step S5 is established using Gaussian distribution.

[0060] Preferably, in step S12, the calculation is performed in Ansys Fluent, a pressure-based solver is selected, a steady-state solution is performed under single precision, air is selected as the fluid material, the SST k-omega model is selected as the turbulence model, and a low Reynolds number correction is included.

[0061] Compared with the prior art, the beneficial technical effects of the present invention are:

[0062] (1) The reliability assessment method for drone's airborne time based on multi-source uncertainty analysis proposed in this invention obtains the drone's flight state parameters as calculation variables for airborne time according to the numerical simulation results of drone parameters and aerodynamic parameters, and establishes the control equation for drone's airborne time. The parameter uncertainty is calculated based on the polynomial relationship between the aerodynamic parameters lift coefficient, drag coefficient, lift-to-drag ratio and angle of attack; statistical analysis is performed based on meteorological station data, and random fluctuations are added on this basis to simulate the uncertainty of the drone's flight environment; the drone's flight state is numerically simulated to complete the uncertainty propagation calculation of aerodynamic parameters and flight environment, generating the uncertainty of the flight state. This method analyzes and quantifies the multi-source uncertainty of airborne time from three aspects, and constructs a drone uncertainty analysis framework that can analyze and calculate the multi-source uncertainty problem during flight.

[0063] (2) The present invention proposes a method for evaluating the reliability of drone flight time based on multi-source uncertainty analysis. Based on a Monte Carlo experiment simulating the flight time of a drone under multi-source uncertainty, the flight time is calculated according to the flight time model under multi-source uncertainty, and statistical analysis is performed. A reliability model for the drone flight time is constructed, and its reliability is evaluated based on the distribution and probability of the flight time. This method can be used to calculate the reliability of drone flight time under multi-source uncertainty conditions with high accuracy, providing a relatively simple solution to practical engineering problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0065] Figure 1 This is a flow chart of the reliability assessment method of UAV's airborne time based on multi-source uncertainty analysis of the present invention;

[0066] Figure 2 This is a diagram of a complete model of a UAV in a clean configuration according to an embodiment of the present invention;

[0067] Figure 3 is a schematic diagram of a computational fluid dynamics model in an embodiment of the present invention;

[0068] Figure 4 Schematic diagram of wind speed variation in a wind field with uncertainty in an embodiment of the present invention;

[0069] Figure 5 This is a graph showing the relationship between flight airspeed, wind speed, and drone weight in an embodiment of the present invention;

[0070] Figure 6 is a schematic diagram of the probability density distribution of the drone's airborne time in an embodiment of the present invention;

[0071] Figure 7 2 is a schematic diagram of the calculation results of the reliability of the UAV's airborne time in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0073] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0074] This paper proposes a method for evaluating the reliability of UAV's airborne time based on multi-source uncertainty analysis. Figure 1 As shown, the method includes the following steps:

[0075] S1. Construct a UAV aerodynamic model and perform numerical simulation of the UAV aerodynamic parameters: Perform flight simulation of the UAV to obtain the changes of aerodynamic parameters such as lift coefficient and drag coefficient with flight state. Step S1 specifically includes the following sub-steps:

[0076] S11. Use Solidworks to build a clean configuration UAV geometric structure model. Figure 2 1 is a model diagram of the UAV 100 in a clean configuration according to an embodiment of the present invention.

[0077] S12. Obtain the corresponding aerodynamic parameter values ​​and construct a computational fluid dynamics numerical model in ANSYS Fluent using the clean-profile UAV geometry. Specifically, in step S12, the calculation is performed in ANSYS Fluent using a pressure-based solver and a steady-state solution in single precision. Air is selected as the fluid material, the SST k-omega turbulence model is selected with a low Reynolds number correction, and the angle of attack is set to 0° with an incoming flow velocity of 20 m / s. Figure 3 This is a computational fluid dynamics model in an embodiment of the present invention.

[0078] S13. Ensure the accuracy and efficiency of CFD analysis through appropriate grid division, so as to better predict the fluid flow behavior. The grid independence of the model is studied. When the number of grids is 6×10 6 , the optimal result can be obtained.

[0079] S14. Calculate the aerodynamic behavior of the UAV at different angles of attack based on the numerical model.

[0080] S2. Construct a model for the drone's time in the air and derive the control equation for the time in the air. The specific method is as follows: Based on the numerical simulation results of the drone's parameters and aerodynamic parameters, obtain the engine power P, propeller efficiency η, aircraft mass W, flight airspeed V, and aircraft lift-to-drag ratio K. Since the drone's flight state is a fixed straight and level flight condition, the control equation for the drone's time in the air can be established:

[0081]

[0082] In the formula, the aerodynamic parameter K is related to the UAV flight state W and V, and the flight state is affected by environmental uncertainty. Based on this equation, subsequent multi-source uncertainty analysis can be performed.

[0083] S21. Since the UAV is in a straight and level flight state, the relationship between available power and required power for level flight can be established as follows:

[0084]

[0085] S22. According to the fuel consumption calculation formula of a single-engine piston propeller aircraft, the hourly fuel consumption c f,t Equal to the engine fuel consumption rate c f The product of the engine power P is:

[0086] c f,t =c f P (3).

[0087] S23. Consider the amount of fuel consumed in flight per unit time, dQ f The hourly fuel consumption c f,t *dt, accordingly, the mass of the drone is reduced by dW, so we can get:

[0088]

[0089] S24. Based on the above three steps, the control equation of the UAV's airborne time can be obtained. According to experience, η and c are taken as f For a reasonable constant, the lift-to-drag ratio K can be expressed based on the aircraft's aerodynamic characteristics and flight conditions W and V. The aircraft's angle of attack α is an intermediate variable in this calculation.

[0090] K=x(W,V,α) (5).

[0091] S3. Analyze the changes in the time spent in the air under parameter uncertainty, environmental uncertainty, and flight state uncertainty. The uncertainty analysis of the UAV is quantified using a Gaussian distribution, where the variance of the Gaussian distribution is quantified by the fitting residual. This includes the following sub-steps:

[0092] S31. Uncertainty analysis of UAV aerodynamic parameters including angle of attack α-lift coefficient C L 、Angle of attack α-Drag coefficient C D , polynomial fitting of angle of attack α-lift-to-drag ratio K, and statistical calculation of polynomial fitting residuals. Polynomial fitting of UAV aerodynamic parameters will generate parameter uncertainty during the calculation process due to residuals generated in the fitting process.

[0093] A cubic polynomial was fitted based on the aerodynamic parameters obtained from the UAV flight simulation in S1. The specific results are shown below:

[0094] C L =0.000124α 3 -0.00248α 2 +0.0870α+0.617 (6)

[0095] C D =0.0000112α 3 +0.00100α 2 +0.00310α+0.0367 (7)

[0096] K=0.00394α 3 -0.172α 2 +0.845α+14.4 (8).

[0097] Residual analysis was performed based on the fitting results. The fitting residuals of angle of attack-lift coefficient, angle of attack-drag coefficient, and angle of attack-lift-to-drag ratio were 0.0501, 0.00554, and 2.52, respectively. The fitting residuals were used as the variance of the Gaussian distribution to quantify the uncertainties of the lift coefficient, drag coefficient, and lift-to-drag ratio.

[0098] S32. Uncertainty analysis of the drone's flight environment and flight status is quantified through random wind speed simulation. Specifically, this is done by analyzing the drone's flight environment, performing statistical analysis based on weather station data, and adding random fluctuations to simulate environmental uncertainty.

[0099] Based on the wind speed data obtained from the meteorological station, its wind speed characteristics can be statistically analyzed, and on this basis, a wind field sequence containing turbulence can be established. Taking into account the strong changes in the wind field during actual flight, in the simulation, a time interval of 1 minute is used to generate 50% light turbulence, 30% moderate turbulence, and 20% heavy turbulence to simulate the fluctuations of actual wind speed.

[0100] S33. Perform numerical simulation on the UAV flight state. Since the flight states are interrelated, the uncertainty of aerodynamic parameters and flight environment will propagate during the calculation process, resulting in uncertainty in the flight state.

[0101] The case calculation is performed based on the drone parameters obtained from the airborne model in S2. The specific parameters are as follows: take-off weight 60kg, empty weight 26kg, wing area 3m 2 , average fuel consumption rate is 0.0006kg / w*h, engine-propeller efficiency is 60%. According to steps S31 and S32, aerodynamic parameters and wind field are calculated to obtain the real-time correspondence between flight airspeed, wind speed and UAV weight, and finally quantify the uncertainty of flight state K, W, V. Figure 4 Graph showing wind speed variations in a wind field containing uncertainty in an embodiment of the present invention. Figure 5 This is a diagram showing the corresponding relationship between flight airspeed, wind speed, and drone weight in an embodiment of the present invention.

[0102] S4. Conduct Monte Carlo experiments to simulate the drone's flight time under multi-source uncertainty. The parameters can be quantified based on different drone models, different environments, and different flight missions. Calculate the flight time of each flight based on the flight time model under multi-source uncertainty. Figure 6 This is a schematic diagram of the probability density distribution of the drone's time in the air in an embodiment of the present invention. Statistical analysis is then performed:

[0103]

[0104] S5. Construct a reliability model for the drone's time in the air, where the reliability model for the drone's time in the air is established using normal distribution. Figure 7 This is a schematic diagram of the calculation results of the reliability of the drone's airborne time in an embodiment of the present invention. The reliability model of the drone's airborne time is as follows:

[0105] R=Pr(T>T c ) (10).

[0106] S6. Quantify the uncertainty of the UAV's flight time using the moment integration method and obtain the distribution of the flight time T. Evaluate the reliability of the UAV's flight time based on the UAV's flight time reliability model. This specifically includes the following sub-steps:

[0107] S61. Under the condition that the numerical values ​​of the UAV parameters are determined, obtain the value ranges of the airspeed V and the lift-to-drag ratio K.

[0108] S62: Identify uncertainty parameters, the random parameter vector is:

[0109] x=(V,K)=μ x +R T u; (11)

[0110] Where x is the sample value of the uncertainty parameter; μ xis the average value of the random variable x within the range of values; R is the upper triangular matrix formed by Cholesky decomposition of the variance-covariance matrix of the random variable x; u is the weighted eigenvalue. The specific calculation process includes the following sub-steps:

[0111] S621. Design a random variable x = (V, K), where x follows a bivariate normal distribution and can be converted into two independent Gaussian variables. The mean value μ of the random variable is obtained. x , the variance-covariance matrix Σ of the random variables x .

[0112] S622: For Σ x Perform Cholesky decomposition: R T R=Σ x , where the Cholesky matrix R is:

[0113]

[0114] Among them, r k+1,k+1 is the Cholesky matrix element, and k is the order of the Jacobian matrix of the random variable.

[0115] S623: According to the Cholesky matrix R, the Jacobian matrix J is assembled as follows:

[0116]

[0117] in, is the main diagonal element of the Jacobian matrix, is the diagonal element on both sides of the main diagonal of the Jacobian matrix, r jj is the parameter of the main diagonal element of the Jacobian matrix, j is the subscript number of the main diagonal element of the Jacobian matrix, j∈1,2,…,k.

[0118] S624: Solve the Jacobian matrix J eigenvalue ξ=[ξ1,ξ2,…,ξ k ], the corresponding column standard orthogonal eigenvector matrix V is:

[0119]

[0120] Among them, v kk are the elements of the Jacobian matrix characteristic matrix.

[0121] S625: Extract the first column component of the eigenvector and calculate the weight value For each pair of eigenvalues ​​and weights (ξ, w), a Cholesky transform is performed to solve for the uncertainty parameter x = (V, K).

[0122] S63: Assign different weights based on the eigenvalues ​​and calculate different aerodynamic parameter sample values ​​x. The uncertainty distribution of V is N(0,1), and the uncertainty distribution of K is N(0,2.52). Different airspeeds V and lift-to-drag ratios K are selected and cross-formed into 9 test groups. The final fitting yields a probability distribution of the drone's time in the air as N(84.52,0.94).

[0123] S64: Substitute the aerodynamic parameter sample values ​​into the time-in-the-air calculation model to calculate the drone's time in the air and evaluate the reliability of the drone's time in the air based on the reliability model. Based on the performance parameter requirements of the case drone, its mission flight time is 77.8 hours, and the calculated reliability is 0.99.

[0124] The present invention proposes a method for evaluating the reliability of drone airtime based on multi-source uncertainty analysis. This method constructs a drone aerodynamic model, numerically simulates the drone's aerodynamic parameters, obtains how aerodynamic parameters such as the lift coefficient and drag coefficient change with flight state, constructs a drone airtime model, derives the governing equations for airtime, analyzes how airtime changes under parameter uncertainty, environmental uncertainty, and flight state uncertainty, calculates the distribution of airtime, conducts Monte Carlo experiments to simulate drone airtime under multi-source uncertainty, constructs a reliability model for drone airtime, and evaluates its reliability. The present invention considers the impact of complex environments on drone airtime during flight and constructs a reliability model for airtime, which is used to quantitatively evaluate the reliability of drone airtime in actual use. The present invention is practical, highly accurate, and has strong engineering applicability.

[0125] Finally, it should be noted that the above-described embodiments are only intended to illustrate and not limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the reliability of UAV's airborne time based on multi-source uncertainty analysis, characterized by: It includes the following steps: S1. Construct a UAV aerodynamic model and perform numerical simulation of the UAV aerodynamic parameters: Use the UAV aerodynamic model to perform flight simulation of the UAV, obtain the changes of aerodynamic parameters with flight status, and obtain the numerical simulation results of aerodynamic parameters; S2. Establish the control equation of the UAV's airborne time. The specific method is: According to the numerical simulation results of the UAV parameters and aerodynamic parameters, obtain the propeller efficiency , aircraft quality , flight speed and the aircraft's lift-to-drag ratio , the control equation of the UAV’s airborne time is established as follows: （ ）； in, is the lift-to-drag ratio of the aircraft, is the aircraft mass, is the flight airspeed, is the engine fuel consumption rate, g is the acceleration due to gravity; S3. Analyze the changes of the time in the air under the uncertainty of parameters, environment and flight status; S4. Conduct Monte Carlo experiments to simulate the drone's flight time under multi-source uncertainty. Calculate the flight time for each flight based on the flight time model under multi-source uncertainty. , and statistical analysis is performed. The calculation method for each flight's airtime is as follows: （ ）； S5. Construct a reliability model for the drone’s flight time: （ ）； in, A reliability model for the drone's airtime, To ensure that the air time meets the mission flight time, The time left for each flight, represents the conditions that a reliable model satisfies; S6. Use moment integration method to quantify the uncertainty of the UAV's time in the air and obtain the time in the air The reliability model of the drone's time in the air is used to evaluate the reliability of the drone's time in the air. This includes the following sub-steps: S61. Under the condition that the numerical values ​​of the UAV parameters are determined, obtain the value ranges of the airspeed V and the lift-to-drag ratio K; S62. Determine the random variable as: （ ）； in, is the sample value of the random variable, is a random variable The average value within the range of values, For random variables The upper triangular matrix formed by Cholesky decomposition of the variance-covariance matrix of is the weighted eigenvalue; This step specifically includes the following sub-steps: S621、Design random variables , Follows a bivariate normal distribution and is transformed into two independent Gaussian variables to obtain the average value of the random variable , the variance-covariance matrix of the random variable ; S622, yes Perform Cholesky decomposition: , where the Cholesky matrix for: ； in, is the Cholesky matrix element, is the order of the random variable Jacobian matrix; S623, according to the Cholesky matrix Assemble the Jacobian matrix for: ； in, is the main diagonal element of the Jacobian matrix, is the Jacobian matrix with the diagonal elements on both sides of the main diagonal, is the main diagonal element parameter of the Jacobian matrix, is the subscript number of the main diagonal elements of the Jacobian matrix, ; S624, solve the Jacobian matrix Eigenvalue , the corresponding column standard orthogonal eigenvectors form a matrix for: ； in, is the element of the Jacobian matrix characteristic matrix; S625. Extract the first column component of the feature vector and calculate the weight value ; For each pair of eigenvalues ​​and weights ( ), perform Cholesky transform to solve for random parameters ; S63. Assign different weights to random variables based on eigenvalues ​​and calculate sample values ​​of different aerodynamic parameters. , and cross to form Pneumatic parameter test group, is the order of the random variable Jacobian matrix; S64, will The aerodynamic parameter test group is substituted into the airborne time calculation model in step S4 to calculate the UAV airborne time and evaluate the UAV airborne time reliability according to the UAV airborne time reliability model in step S5.

2. The method for evaluating the reliability of UAV's airborne time based on multi-source uncertainty analysis according to claim 1 is characterized in that: The specific process of establishing the UAV aerodynamic model in step S1 is as follows: S11. Establish a clean configuration UAV geometric structure model; S12, obtaining corresponding aerodynamic parameter values, and constructing a numerical model of computational fluid dynamics using the geometric structure model of the UAV in a clean configuration; S13, performing grid division; S14. Calculate the aerodynamic behavior of the UAV at different angles of attack based on the numerical model of computational fluid dynamics, obtain the changes in aerodynamic parameters with flight status, and obtain numerical simulation results of aerodynamic parameters.

3. The method for evaluating the reliability of UAV's airborne time based on multi-source uncertainty analysis according to claim 1 is characterized in that: The specific process of establishing the control equation for the drone's airborne time in step S2 is as follows: S21. The UAV is in a straight and level flight state. The relationship between the available power and the required power for level flight is established as follows: （ ）； S22. According to the fuel consumption calculation formula of single-engine piston propeller aircraft, hourly fuel consumption Equal to engine fuel consumption rate With engine power The product of: （ ）； S23. Consider the amount of fuel consumed in flight per unit time. Hourly fuel consumption , accordingly, the drone's mass is reduced ,get: （ ）； S24, based on the three steps S21, S22, and S23, the control equation of the UAV's air time is obtained, and the control equation of the UAV's air time is obtained according to experience. and is a reasonable constant value, the lift-to-drag ratio The calculation formula is: （ ）。 4. The method for evaluating the reliability of UAV's time in the air based on multi-source uncertainty analysis according to claim 1 is characterized in that: Step S3 specifically includes the following sub-steps: S31, performing polynomial fitting on the aerodynamic parameters of the UAV and generating residuals in the fitting process, thereby generating parameter uncertainty in the calculation process; S32. Analyze the UAV flight environment, perform statistical analysis based on weather station data, and add random fluctuations to simulate environmental uncertainty; S33. Numerical simulation of the UAV flight state is performed. Based on the mutual correlation between flight states, the uncertainty of aerodynamic parameters and flight environment will propagate during the calculation process, resulting in flight state uncertainty.

5. The method for evaluating the reliability of UAV's airborne time based on multi-source uncertainty analysis according to claim 4 is characterized in that: In step S3, the uncertainty analysis of the UAV is quantified using Gaussian distribution, where the variance of the Gaussian distribution is quantified by the fitting residual.

6. The method for evaluating the reliability of UAV's time in the air based on multi-source uncertainty analysis according to claim 4 is characterized in that: The uncertainty analysis of the UAV aerodynamic parameters in step S31 includes polynomial fitting of angle of attack-lift coefficient, polynomial fitting of angle of attack-drag coefficient, polynomial fitting of angle of attack-lift-drag ratio, and statistical calculation of the residuals of each polynomial fitting.

7. The method for evaluating the reliability of UAV's time in the air based on multi-source uncertainty analysis according to claim 4 is characterized in that: The uncertainty analysis of the UAV flight environment and flight status in step S32 and step S33 is quantified through random wind speed simulation.

8. The method for evaluating the reliability of UAV's time in the air based on multi-source uncertainty analysis according to claim 1 is characterized in that: The parameters in the drone's airborne time calculation model in step S4 are quantified according to different drone models, different environments, and different flight missions.

9. The method for evaluating the reliability of UAV's time in the air based on multi-source uncertainty analysis according to claim 1 is characterized in that: In step S5, the reliability model of the drone's airborne time is established using Gaussian distribution.

10. The method for evaluating the reliability of UAV's time in the air based on multi-source uncertainty analysis according to claim 2 is characterized in that: In step S12, calculation is performed in Ansys Fluent, using a pressure-based solver and performing a steady-state solution in single precision. Air is selected as the fluid material, and the SST k-omega model is selected as the turbulence model, including a low Reynolds number correction.

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