Design method of hydrogen energy power logistics unmanned aerial vehicle propeller

Abstract

The invention discloses a design method for a propeller of a hydrogen energy power logistics unmanned aerial vehicle. The design method comprises the steps that overall design parameters of the unmanned aerial vehicle and basic parameters of blades of the propeller are determined; selecting a basic airfoil based on the overall design parameters, and constructing a proxy model for airfoil optimization by taking a target lift coefficient as a design point, keeping the maximum thickness of the airfoil unchanged to a constraint and taking a minimum resistance coefficient as targets; based on the blade design objective function and the design variable, blade appearance design optimization is carried out, and chord length distribution parameters and torsion angle distribution parameters of airfoils of all station positions of the blade are determined according to the minimum energy loss principle; performing three-dimensional modeling on the propeller based on the chord length distribution parameters and the torsion angle distribution parameters of the airfoils of all the station positions of the blades to obtain a propeller three-dimensional model; performing performance calculation on the propeller three-dimensional model to obtain propeller tension and power at different rotating speeds and wind speeds; and comparing the pulling force and power of the propeller at different rotating speeds and wind speeds with design parameters, and determining the initial shape of the propeller.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft design, and in particular to a method for designing a propeller for a hydrogen-powered logistics UAV. Background Art

[0002] The integration of hydrogen power and air logistics, capitalizing on the dual advantages of "green + logistics," will accelerate the formation of new momentum for the green aviation economy and a sustainable growth engine. Air logistics is evolving towards a model characterized by "mainline logistics using large manned transport aircraft, branch distribution using medium- and large-sized fixed-wing drones, and terminal delivery using small rotary-wing drones." Currently, the mainline logistics system for large manned transport aircraft is well established, primarily dominated by passenger-to-cargo conversions developed by Airbus and Boeing. Terminal delivery using small rotary-wing drones has been successfully implemented, featuring a wide variety of models, ease of use, and a low barrier to entry. Large-scale deployment is anticipated once low-altitude airspace is opened. However, the market for medium- and large-sized fixed-wing logistics drones remains relatively undeveloped, with high technical barriers and limited research and development efforts by a small number of aviation organizations.

[0003] As the core component of the UAV power system, the propeller's propulsion performance plays a vital role in the UAV. However, conventional propeller design methods have problems such as long cycle and high cost. Therefore, the patent application document with publication number CN114139279A proposes to first clarify the overall design technical requirements of the propeller; determine the design rated speed and design diameter, airfoil configuration, chord length and torsion distribution; use the strip theory to evaluate the propeller performance; continuously adjust the design parameters until the propeller calculated thrust meets the design required thrust; calculate the energy consumption within the design cruising time and the minimum required battery weight until the design allowable battery weight is met. This can meet the rapid design and precision requirements of the propeller in the overall scheme demonstration stage of the electric propulsion UAV. This scheme only uses the strip theory of a single propeller for theoretical design. During the propeller design process, it does not consider that the propeller needs to take into account both low-speed and high-speed application scenarios, and cannot meet the performance requirements of the propeller at different speeds. Moreover, this scheme optimizes the airfoil and blade shape, and the designed propeller shape may not be the optimal propeller shape; in actual applications, in order to meet the needs of logistics and transportation, the performance design of hydrogen-powered drones usually includes indicators such as high cruising speed and short take-off and landing distance. Therefore, the propeller is required to have high efficiency at high speed, high force efficiency at low speed and be able to adapt to the high speed of the motor. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to quickly design a propeller with low speed, high force efficiency, high speed and high efficiency that meets the design requirements.

[0005] The present invention solves the above technical problems through the following technical means:

[0006] A propeller design method for hydrogen-powered logistics UAV is proposed, including:

[0007] Determine the overall design parameters of the UAV and the basic parameters of the propeller blades;

[0008] Based on the overall design parameters, a basic airfoil is selected to construct a proxy model to automatically optimize the airfoil and obtain airfoil data;

[0009] Based on the blade design objective function and design variables, the blade shape design is optimized using the CST parameterization method, and the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade station are determined according to the principle of minimum energy loss;

[0010] The propeller is three-dimensionally modeled based on the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each position of the blade to obtain a three-dimensional propeller model;

[0011] Calculate the performance of the propeller 3D model to obtain the propeller thrust and power at different rotational speeds and wind speeds;

[0012] Compare the propeller thrust and power at different rotational speeds and wind speeds with the design parameters to determine the preliminary propeller shape.

[0013] Furthermore, the overall design parameters include the UAV cruising altitude, cruising speed, cruising thrust, cruising efficiency and size constraints; the basic blade parameters include the number of blades, propeller diameter and propeller speed.

[0014] Furthermore, the calculation formula for the propeller diameter is:

[0015]

[0016] Where C is the speed of sound at the flight altitude, n is the propeller speed, M tip is the Mach number of the blade tip resultant velocity, M uav is the flight Mach number of the UAV.

[0017] Furthermore, the base airfoil is selected based on the overall design parameters, and a proxy model is constructed with the target lift coefficient as the design point, the maximum thickness of the airfoil remaining unchanged as the constraint, and the minimum drag coefficient as the goal, so as to automatically optimize the airfoil to obtain airfoil data, including:

[0018] The basic airfoil is selected based on the overall design parameters to construct the surrogate model. The parameterized expression of the surrogate model is:

[0019] y'=y+Δy

[0020]

[0021] Where y' is the new airfoil, y is the coordinate value of the discrete point of the initial airfoil, Δy is the perturbation amount; C(x) is the class function used to control the overall shape of the curve; S(x) is the class function used to describe the local shape change of the curve; x is the chord coordinate; y T Indicates the leading edge thickness; A i is the coefficient of the shape function, used to adjust the local shape; ΔA i represents the change in the shape function coefficient; N1 is the shape parameter used to control the leading edge; N2 is the shape parameter used to control the trailing edge; N represents the order or number of terms;

[0022] The proxy model is optimized using the following optimization function:

[0023] Cov[Z(x (i) ), Z(x (j) )]=σ 2 R[r(x (i) , x (j) )]

[0024] Where, Cov[Z(x (i) ), Z(x (j) )] is the covariance of the sample points; R is the correlation matrix; r is the correlation function, θ k is an unknown correlation parameter, p is used to adjust the smoothness of the correlation function r, n is the number of design variables; x (i) , x (j) is the sample point, σ 2 is the variance.

[0025] Furthermore, the blade shape design optimization is performed based on the blade design objective function and design variables and the CST parameterization method, and the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each position of the blade are determined according to the minimum energy loss principle, including:

[0026] Based on the fitting formula of the blade geometric distribution function, the blade design variables are determined:

[0027] C RR =C(x)·S c (x)+x·C R (1)+(1-x)·C R (0)

[0028] β R R=C(x)·S β (x)+x·β R (1)+(1-x)·β R (0)

[0029] Where x = r / R is the chord coordinate, R is the correlation matrix, and r is the correlation function; CRR is the relative chord length relative to the blade radius, β R R is the torsion angle, C(x) is the class function, x is the chord coordinate, S c (x) and S β (x) is the corresponding type function, C R (1) represents the blade end chord length, C R (0) is the chord length of the blade starting end (0.2R of the blade), β R (1) represents the blade tip torsion angle, β R (0) represents the torsion angle at the starting end of the blade (0.2R of the blade);

[0030] The chord length and twist angle of the airfoil at each station are used as design variables. The optimal solution model is built based on the EI method. With the minimum energy as the goal, the chord length and twist angle distribution parameters of the airfoil at each station of the blade are optimized and determined. The optimal solution model is as follows:

[0031]

[0032] Where, f min is the minimum value of the objective function of all sample points, I(x) is the improvement, is the predicted value of the Kriging model at point x, is any variable, s is the root mean square error RMSE of the Kriging model prediction, Φ and represent the cumulative distribution function and probability density function of the standard normal distribution, respectively.

[0033] Furthermore, the three-dimensional modeling of the propeller is performed based on the chord length distribution parameters and the torsion angle distribution parameters of the airfoil at each position of the blade to obtain the three-dimensional propeller model, including:

[0034] Based on the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade position, the three-dimensional surface of the blade, the three-dimensional surface of the hub, and the transition surface between the blade and the hub are constructed using CATIA.

[0035] A three-dimensional propeller model is constructed based on the three-dimensional surface of the blade, the three-dimensional surface of the installation hub, and the transition surface between the blade and the hub.

[0036] Furthermore, the performance calculation of the propeller three-dimensional model is performed to obtain the propeller thrust and power at different rotational speeds and wind speeds, including:

[0037] The propeller 3D model is meshed and CFD numerical simulation is performed based on the mesh space to obtain the propeller thrust and power at different rotational speeds and wind speeds.

[0038] The grid space of the propeller three-dimensional model is divided into a stationary domain and a rotating domain. The rotating domain contains the propeller grid, which is used to simulate the rotating flow in the area near the propeller; the stationary domain contains the far-field boundary grid, which is used to simulate the computational space flow away from the propeller.

[0039] Furthermore, the propeller thrust and power at different rotational speeds and wind speeds are compared with design parameters to determine the preliminary shape of the propeller, including:

[0040] Determine whether the calculated propeller thrust is greater than the target thrust. If so, calculate the required battery weight. If not, change the propeller-related parameters and redesign the initial propeller shape.

[0041] Determine whether the required battery weight is less than the allocated battery weight;

[0042] If so, the initial shape of the propeller is obtained. If not, the propeller-related parameters are changed and the initial shape of the propeller is redesigned.

[0043] Furthermore, the required battery weight is calculated based on the power and flight time requirements of the UAV.

[0044] In addition, the present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the propeller design method for the hydrogen-powered logistics drone as described above is implemented.

[0045] The advantages of the present invention are:

[0046] In order to avoid the reduction of aircraft load factor due to complex structure, the present invention proposes a propeller design method for hydrogen-powered logistics UAV. According to the principle of minimum energy loss, the chord length and torsion angle at different propeller positions are determined to take into account the balanced fixed-pitch propeller design of high speed and high efficiency / low speed and high force efficiency. The propeller shape that meets the propulsion performance requirements of hydrogen-powered logistics UAV can be quickly determined, which greatly shortens the propeller design time, solves the problems of long design cycle and high cost of existing propellers, and can meet the performance requirements of propellers at different speeds.

[0047] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for designing a propeller for a hydrogen-powered logistics drone, according to one embodiment of the present invention;

[0049] Figure 2 This is a complete flow chart of a method for designing a propeller for a hydrogen-powered logistics drone, according to one embodiment of the present invention;

[0050] Figure 3 is a schematic diagram of an optimized blade airfoil in one embodiment of the present invention;

[0051] Figure 4 is a schematic diagram of a distribution function in one embodiment of the present invention;

[0052] Figure 5 Schematic diagram of the optimal solution found under the design power constraint conditions in one embodiment of the present invention;

[0053] Figure 6 1 is a schematic diagram of the chord length and torsion angle distribution under the optimal solution in one embodiment of the present invention;

[0054] Figure 7 3D digital model diagram of a propeller formed by optimized design in one embodiment of the present invention;

[0055] Figure 8 Schematic diagram of mesh division for propeller CFD calculation in one embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] like Figures 1 to 2 As shown, an embodiment of the present invention proposes a method for designing a propeller for a hydrogen-powered logistics drone, the method comprising the following steps:

[0058] S10. Determine the overall design parameters of the UAV and the basic parameters of the propeller blades;

[0059] S20, selecting a basic airfoil based on the overall design parameters, and constructing a proxy model with the target lift coefficient as the design point, the maximum thickness of the airfoil remaining unchanged as the constraint, and the minimum drag coefficient as the goal, to automatically optimize the airfoil and obtain airfoil data;

[0060] S30. Based on the blade design objective function and design variables, optimize the blade shape design using the CST parameterization method, and determine the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade station according to the principle of minimum energy loss;

[0061] S40, performing three-dimensional modeling on the propeller based on the chord length distribution parameters and the torsion angle distribution parameters of the airfoil at each position of the blade to obtain a three-dimensional propeller model;

[0062] S50, performing performance calculation on the three-dimensional propeller model to obtain propeller thrust and power at different rotational speeds and wind speeds;

[0063] S60. Compare the propeller thrust and power at different rotational speeds and wind speeds with the design parameters to determine the preliminary propeller shape.

[0064] Conventional propeller designs, if not optimized, require a redesign, verification of compliance, redesign, parameter adjustment, and re-verification. This repetitive process is time-consuming and labor-intensive. Unlike conventional propeller designs, the propeller designed in this embodiment accommodates both low-speed and high-speed applications, achieving optimal propeller efficiency in both scenarios. Furthermore, since it is based on an optimization model, the optimal solution can be quickly designed without repeated iterations, resulting in a short design cycle and low costs.

[0065] As a further preferred technical solution, the overall design parameters include the UAV cruising altitude, cruising speed, cruising thrust, cruising efficiency and size constraints; the basic parameters of the blades include the number of blades, propeller diameter and propeller speed.

[0066] Specifically, this embodiment, based on the overall design requirements for hydrogen-powered logistics drones, specifies the drone's cruising altitude, cruising speed, cruising force, cruising efficiency, and size constraints as overall design parameters. For example, a certain hydrogen-powered logistics drone has a cruising altitude of H = 1 km, a cruising speed of V = 250-350 km / h, a cruising force T ≥ 164 N, and a cruising efficiency η ≥ 83%. At a low speed of 162 km / h, the force efficiency reaches above 2 kg / kw. Due to the ground-scrubbing angle constraint, the blade diameter is less than 1 meter.

[0067] Furthermore, this embodiment uses empirical engineering formulas and reference values ​​to quickly estimate the number of propeller blades and propeller diameter, and determines the propeller speed based on motor characteristics. Increasing the number of blades improves the theoretical design efficiency of the propeller system, but this increases frictional resistance, which does not significantly improve propeller efficiency. At the same time, as the number of blades increases, the total weight of the propeller increases significantly, and the structure becomes relatively complex. To ensure propeller efficiency, this embodiment uses the principle that fewer blades increase efficiency and references the number of blades of similar types to determine the number of blades to be two.

[0068] Specifically, the calculation formula for the propeller diameter is:

[0069]

[0070] Where C is the speed of sound at the flight altitude, n is the propeller speed, M tipis the Mach number of the blade tip resultant velocity, M uav is the flight Mach number of the UAV; the low-speed propeller M of the relatively thick airfoil of about 10% is tip ≤0.8, for high-speed propellers with thinner airfoils of about 6% of the relative thickness, M uav ≤(0.85~0.9).

[0071] It should be noted that, according to the motor speed characteristics, the propeller speed n=4050 rpm, so the propeller diameter D is determined to be 0.9 m.

[0072] As a further preferred technical solution, step S20: selecting a basic airfoil based on the overall design parameters to construct a proxy model to automatically optimize the airfoil to obtain airfoil data, specifically includes the following steps:

[0073] S21. Based on the overall design parameters, a basic airfoil is selected to construct a proxy model. The airfoil is parametrically modeled using a difference-based class function / shape function method. The parameterized expression of the proxy model is:

[0074]

[0075] Where y' is the new airfoil, y is the coordinate value of the discrete point of the initial airfoil, Δy is the perturbation amount; C(x) is the category function used to control the overall shape of the curve; S(x) represents the shape function used to describe the local shape change of the curve; x is the chord coordinate; y T Indicates the leading edge thickness; A i is the coefficient of the shape function, used to adjust the local shape; ΔA i represents the change in the shape function coefficient; N1 and N2 are the exponents of the shape function; N represents the order or number of terms;

[0076] It should be noted that, compared with the prior art, the airfoil in this embodiment has more shape function weights and higher fitting accuracy. In addition, a class function is introduced to describe the airfoil more accurately.

[0077] Specifically, this embodiment uses the Clark-Y airfoil as the base airfoil, based on the cruise design requirements of a flight altitude of 1 km above sea level, a flight speed of 250-350 km / h, and a motor speed of 4500-5500 rpm. A Kriging proxy model is constructed to automatically optimize the airfoil, with a lift coefficient CL = 0.7 as the design point, a constant maximum airfoil thickness as the constraint, and a minimum drag coefficient Cd as the goal. Furthermore, during parametric modeling of the airfoil, the new airfoil is represented by the coordinate values ​​of the discrete points of the initial airfoil plus a perturbation value, accurately describing the airfoil in the design space.

[0078] S22. Optimize the proxy model and use the covariance related to the sample points as the optimization function:

[0079] Cov[Z(x (i) ), Z(x (j) )]=σ 2 R[r(x (i) , x (j) )]

[0080] Where, Cov[Z(x (i) ), Z(x (j) )] is the covariance of the sample points; R is the correlation matrix and is symmetric and positive; r is the correlation function, θ k is the correlation parameter, p is the smoothness used to adjust the correlation function r, and n is the number of design variables; x (i) , x (j) is the sample point, i=1,…,ns,j=1,…,ns,ns is the number of sample points, and σ is the variance.

[0081] It should be noted that any θ k The value of can generate an interpolation model, and the optimal solution is obtained by solving the unconstrained nonlinear optimal problem of the above formula. Based on the above method, three sets of airfoil data of the propeller root, middle and tip of the propeller suitable for hydrogen energy logistics UAV are optimized (see Figure 3 ), the overall drag reduction effect reaches 10.6%.

[0082] As a further preferred technical solution, step S30: based on the blade design objective function and design variables, optimizing the blade shape design based on the CST parameterization method, and determining the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each position of the blade according to the minimum energy loss principle, specifically includes the following steps:

[0083] S31. Determine the blade design variables based on the fitting formula of the blade geometric distribution function:

[0084] C RR =C(x)·S c (x)+x·C R (1)+(1-x)·C R (0)

[0085] β R R=C(x)·S β (x)+x·β R (1)+(1-x)·β R (0)

[0086] Where x = r / R is the chord coordinate, R is the correlation matrix, and r is the correlation function; C RRis the relative chord length relative to the blade radius, β R is the torsion angle, C(x) is the class function, S c (x) and S β (x) is the corresponding type function, C R (1) represents the blade end chord length, C R (0) is the chord length of the blade starting end (0.2R of the blade), β R (1) represents the blade tip twist angle, β R (0) represents the torsion angle at the starting end of the blade (0.2R of the blade);

[0087] The class function C(x) is defined as follows:

[0088] C(x)=x N1 (1-x) N2

[0089] The type function S is defined as follows:

[0090]

[0091] Among them, N1 and N2 are the exponents of the class function. ci and A βi is the coefficient to be determined. i (x) is the Bernstein polynomial, and N is the polynomial order.

[0092] Assume that the order of the chord length distribution is N c , the order of the torsion angle distribution is N β , taking the chord length and twist angle at the blade root and blade tip as independent variables. Since each distribution curve has two independent function-like exponents, the total number of design variables for blade parameterization is:

[0093] N v =(N v +1+2+2)+(N β +1+2+2)

[0094] In blade design, N c and N β All are set to 2, so there are 14 design variables in total.

[0095] It should be noted that the parameterization of the blade in this embodiment is not a direct parameterization of the geometric coordinates, but rather the cross-sectional airfoil of the blade is kept unchanged, and the chord length distribution and torsion angle distribution of the blade are fitted by the CST method. The chord length distribution function is regarded as the upper surface of the airfoil, and the torsion angle distribution function is regarded as the lower surface of the airfoil. In the blade design, the effective part starts from the relative radius r / R=0.20, so the horizontal coordinate of the distribution function needs to be normalized to (0~1) first, such as Figure 4 shown.

[0096] S32. The chord length and twist angle of the airfoil at each position are used as design variables, and the optimal solution model is built based on the EI method. With the minimum energy as the goal, the chord length and twist angle distribution parameters of the airfoil at each position of the blade are optimized and determined.

[0097] Assuming that any variable (represents the uncertainty of the true response value corresponding to the independent variable x), and the mean is The probability density of a normal distribution with variance s(x) is:

[0098]

[0099] For the energy minimization problem, the improvement I(x) is set to:

[0100]

[0101] The EI (Expected Improvement) value can be modeled as the optimal solution using the following formula:

[0102]

[0103] Where, f min is the minimum value of the objective function of all sample points, I(x) is the improvement, is the predicted value at point x, is any variable, s is the root mean square error RMSE of the Kriging model prediction, Φ and represent the cumulative distribution function and probability density function of the standard normal distribution, respectively.

[0104] It should be noted that this embodiment uses the CST parameterization method and particle swarm optimization algorithm to optimize the blade shape design based on the blade design objective function, variables and other parameters. The parameterization of the blade is not a direct geometric parameterization, but the blade cross-section airfoil is kept unchanged, and the chord length distribution and torsion angle distribution of the blade are fitted by the CST method to achieve rapid parameterized design and evaluation. Based on this, this embodiment is based on the aircraft design point (power constraint point, see Figure 5 ), based on the above optimization model, with the minimum energy as the goal, the chord length and torsion angle distribution parameters of the airfoil at each position of the blade are optimized (see Figure 6 ).

[0105] As a further preferred technical solution, step S40: performing three-dimensional modeling on the propeller based on the chord length distribution parameters and the torsion angle distribution parameters of the airfoil at each position of the blade to obtain a three-dimensional propeller model specifically includes the following steps:

[0106] S41. Based on the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade station, construct the blade 3D surface, the hub 3D surface, and the transition surface between the blade and hub using CATIA;

[0107] S42. Construct a three-dimensional propeller model based on the three-dimensional surface of the blade, the three-dimensional surface of the installation hub, and the transition surface between the blade and the hub.

[0108] Specifically, this embodiment uses CATIA to perform three-dimensional modeling of the propeller based on the above-mentioned propeller geometric parameters (airfoil position, chord length, torsion angle, etc.), as shown in FIG. Figure 7 .

[0109] According to the chord length and torsion angle distribution parameters of the airfoil at each station, three positions, namely the blade root, the maximum chord length and the blade tip, are selected. The airfoil data are used to construct three-dimensional airfoils 1, 2 and 3 respectively. Based on the principle of coplanarity, spline curves are used to connect the three leading edge points of the airfoils to construct leading edge curve 4. According to the torsion angles of the three airfoils, spline curves are used to connect the three trailing edge points of the airfoils to construct trailing edge curve 8. With curves 1, 2 and 3 as cross-sectional curves and 4 and 8 as guide lines, the three-dimensional surface of the blade is constructed by sweeping.

[0110] According to the blade installation size requirements, circle 6 is constructed and stretched in the vertical direction to construct the three-dimensional surface for installing the hub; spline curves are used to construct the tangent line 5 between curves 4 and 6, as well as the tangent line 7 between curves 8 and 6; curves 5, 6, and 7 are selected, and surface filling is used to construct the transition surface between the blade and the hub through tangent constraints with the three-dimensional surfaces of the blade and the hub.

[0111] Finally, a three-dimensional propeller model can be constructed based on the above-mentioned blade three-dimensional surface, hub three-dimensional surface and transition surface.

[0112] As a further preferred technical solution, step S50: performing performance calculation on the three-dimensional propeller model to obtain propeller thrust and power at different rotational speeds and wind speeds, specifically includes:

[0113] The propeller 3D model is meshed and CFD numerical simulation is performed based on the mesh space to obtain the propeller thrust and power at different rotational speeds and wind speeds.

[0114] The grid space of the propeller three-dimensional model is divided into a stationary domain and a rotating domain. The rotating domain contains the propeller grid, which is used to simulate the rotating flow in the area near the propeller; the stationary domain contains the far-field boundary grid, which is used to simulate the computational space flow away from the propeller.

[0115] Specifically, if Figure 8As shown, in terms of mesh division, the working state of the propeller in this embodiment is an unsteady flow state of rotational / static interference. The CFD simulation must consider the rotational motion of the propeller, as well as the mutual interference between the propeller and the free flow. To this end, it is proposed to divide the computational grid space of the propeller into two regions, one is the stationary domain, and the other is the rotating domain. The rotating domain contains the propeller grid, which simulates the rotational flow in the area near the propeller; the stationary domain contains the far-field boundary grid, which simulates the computational space flow away from the propeller. The flow field information is transmitted between the stationary domain and the rotating domain through the rotational / static slip surface interpolation, which effectively considers both the rotational flow of the propeller and the mutual interference between the propeller and the free flow, thereby effectively simulating the complex interference flow and overall performance of the propeller under different wind speed and rotation speed conditions. See the computational grid diagram of the project. Figure 7 .

[0116] Furthermore, in terms of numerical simulation calculation settings, this embodiment is a propeller viscous flow numerical simulation program based on the NS equations and nested grid technology, which introduces the SA equation and the SST k-ω two-equation turbulence model combined with the full-speed domain spatial discrete AUSM+up format, and adopts a rotationally symmetric boundary to ensure the calculation accuracy while greatly improving the calculation efficiency.

[0117] In the rotating non-inertial coordinate system fixed to the propeller, the integral form of the Reynolds-averaged NS equation can be written as follows:

[0118]

[0119] Where w is the conserved flow variable, H and H v are the inviscid and viscous fluxes, respectively, and G is the Coriolis force source term introduced by the rotational motion. The above equations are solved using the finite volume method, and the time marching adopts the modified LU-SGS implicit scheme.

[0120] As a further preferred technical solution, step S60: comparing the propeller thrust and power at different rotational speeds and wind speeds with the design parameters to determine the preliminary shape of the propeller, specifically includes:

[0121] Determine whether the calculated propeller thrust is greater than the target thrust. If so, calculate the required battery weight. If not, change the propeller-related parameters and redesign the initial propeller shape.

[0122] Determine whether the required battery weight is less than the allocated battery weight;

[0123] If so, the initial shape of the propeller is obtained. If not, the propeller-related parameters are changed and the initial shape of the propeller is redesigned.

[0124] It should be noted that this embodiment has been designed and optimized through an optimization algorithm. By changing the shape of the airfoil, as well as the chord length and twist angle of the airfoil at different positions, the optimal propeller shape that meets the indicators is obtained. After CFD verification, the design is completed when the thrust, power, and force efficiency meet the design requirements.

[0125] It should be noted that if the calculated thrust is greater than the target thrust, the requirements are initially met. Otherwise, parameters such as the airfoil, chord length, and torsion angle are changed, and the subsequent steps of determining the basic propeller blade parameters are continued until the propeller thrust requirements, efficiency, and force efficiency requirements are met. Because in the design process, after the basic blade parameters are determined, the propeller that meets the requirements is designed by optimizing the airfoil and changing the local chord length and torsion angle of the blade. Therefore, the step of determining the basic blade parameters is not included in the loop.

[0126] Specifically, at flight speeds of 250 km / h and 350 km / h, the thrust reached 166.8 N and 281.4 N, respectively, with propeller efficiency exceeding 0.83. Furthermore, at low speeds of 162 km / h, the force efficiency exceeded 2 kg / kW.

[0127] Furthermore, assuming propeller thrust meets requirements, the propeller cruise power is converted from propeller torque and speed. Combined with the flight time requirements of the hydrogen-powered logistics drone, the minimum required hydrogen source and battery weight is calculated. If this weight does not exceed the required index, the designed propeller meets the requirements. Otherwise, further parameters such as the airfoil, chord length, and twist angle are modified to determine the basic propeller blade parameters and subsequent steps until the overall design requirements are met.

[0128] Specifically, under flight conditions of 250 km / h-350 km / h, the power is 13.6 kW and 32.4 kW respectively. According to the cruise time requirement of 6 hours / 2 hours, the battery capacity is 64.8-81.6 kWh. Based on the maximum power consumption of 81.6 kWh, the weight of the hydrogen source is 1.5 kg and the battery is 15.8 kg, which meets the design requirements.

[0129] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0130] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0133] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for designing propellers for hydrogen-powered logistics drones, characterized in that: include: Determine the overall design parameters of the UAV and the basic parameters of the propeller blades; Based on the overall design parameters, a basic airfoil is selected to construct a proxy model to automatically optimize the airfoil and obtain airfoil data; Based on the blade design objective function and design variables, the blade shape design is optimized using the CST parameterization method, and the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade station are determined according to the principle of minimum energy loss; The propeller is three-dimensionally modeled based on the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each position of the blade to obtain a three-dimensional propeller model; Calculate the performance of the propeller 3D model to obtain the propeller thrust and power at different rotational speeds and wind speeds; Compare the propeller thrust and power at different rotational speeds and wind speeds with the design parameters to determine the preliminary propeller shape.

2. The hydrogen-powered logistics drone propeller design method according to claim 1, characterized in that: The overall design parameters include the UAV's cruising altitude, cruising speed, cruising thrust, cruising efficiency and size constraints; the basic blade parameters include the number of blades, propeller diameter and propeller speed.

3. The hydrogen-powered logistics drone propeller design method according to claim 2, characterized in that: The calculation formula of the propeller diameter is: Where C is the speed of sound at the flight altitude, n is the propeller speed, M tip is the Mach number of the blade tip resultant velocity, M uav is the flight Mach number of the UAV.

4. The hydrogen-powered logistics drone propeller design method according to claim 1, characterized in that: The process of selecting a basic airfoil based on overall design parameters to construct a proxy model for automatically optimizing the airfoil to obtain airfoil data includes: The basic airfoil is selected based on the overall design parameters to construct the surrogate model. The parameterized expression of the surrogate model is: y′=y+Δy Where y′ is the new airfoil, y is the coordinate value of the discrete point of the initial airfoil, Δy is the perturbation amount; C(x) is the class function used to control the overall shape of the curve; S(x) is the class function used to describe the local shape change of the curve; x is the chord coordinate; y T Indicates the leading edge thickness; A i is the coefficient of the shape function, used to adjust the local shape; ΔA i represents the change in the shape function coefficient; N1 is the shape parameter used to control the leading edge; N2 is the shape parameter used to control the trailing edge; N represents the order or number of terms; The proxy model is optimized using the following optimization function: Cov[Z(x (i) ), Z(x (j) )]=σ 2 R[r(x (i) ,x (j) )] Where, Cov[Z(x (i) ), Z(x (j) )] is the covariance of the sample points; R is the correlation matrix; r is the correlation function, θ k is the correlation parameter, p is the smoothness used to adjust the correlation function r, and n is the number of design variables; x (i) , x (j) is the sample point, and σ is the variance.

5. The hydrogen-powered logistics drone propeller design method according to claim 1, characterized in that: The blade shape design optimization is performed based on the blade design objective function and design variables using the CST parameterization method, and the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade station are determined according to the minimum energy loss principle, including: Based on the fitting formula of the blade geometric distribution function, the blade design variables are determined: C R =C(x)·S c (x)+x·C R (1)+(1-x)·C R (0) b R =C(x)·S β (x)+x·β R (1)+(1-x)·b R (0) Where x = r / R is the chord coordinate, R is the correlation matrix, and r is the correlation function; C R is the relative chord length relative to the blade radius, β R is the torsion angle, C(x) is the class function, S c (x) and S β (x) is the corresponding type function, C R (1) represents the blade end chord length, C R (0) is the chord length of the blade starting end, β R (1) represents the blade tip twist angle, β R (0) represents the torsion angle at the starting end of the blade; The chord length and twist angle of the airfoil at each station are used as design variables. The optimal solution model is built based on the EI method. With the minimum energy as the goal, the chord length and twist angle distribution parameters of the airfoil at each station of the blade are optimized and determined. The optimal solution model is as follows: Where, f min is the minimum value of the objective function of all sample points, I(x) is the improvement, is the predicted value of the Kriging model at point x, is any variable, s is the root mean square error RMSE of the Kriging model prediction, Φ and are the cumulative distribution function and probability density function of the standard normal distribution, respectively.

6. The hydrogen-powered logistics drone propeller design method according to claim 1, characterized in that: The three-dimensional modeling of the propeller is performed based on the chord length distribution parameters and the torsion angle distribution parameters of the airfoil at each position of the blade to obtain the three-dimensional model of the propeller, including: Based on the chord length distribution parameters and torsion angle distribution parameters of the airfoil at each blade position, the three-dimensional surface of the blade, the three-dimensional surface of the hub, and the transition surface between the blade and the hub are constructed using CATIA. A three-dimensional propeller model is constructed based on the three-dimensional surface of the blade, the three-dimensional surface of the installation hub, and the transition surface between the blade and the hub.

7. The hydrogen-powered logistics drone propeller design method according to claim 1, characterized in that: The performance calculation of the propeller three-dimensional model is performed to obtain the propeller thrust and power at different rotation speeds and wind speeds, including: The propeller 3D model is meshed and CFD numerical simulation is performed based on the mesh space to obtain the propeller thrust and power at different rotational speeds and wind speeds. The grid space of the propeller three-dimensional model is divided into a stationary domain and a rotating domain. The rotating domain contains the propeller grid, which is used to simulate the rotating flow in the area near the propeller; the stationary domain contains the far-field boundary grid, which is used to simulate the computational space flow away from the propeller.

8. The hydrogen-powered logistics drone propeller design method according to claim 1, characterized in that: The propeller thrust and power at different rotational speeds and wind speeds are compared with the design parameters to determine the preliminary shape of the propeller, including: Determine whether the calculated propeller thrust is greater than the target thrust. If so, calculate the required battery weight. If not, change the propeller-related parameters and redesign the initial propeller shape. Determine whether the required battery weight is less than the allocated battery weight; If so, the initial shape of the propeller is obtained. If not, the propeller-related parameters are changed and the initial shape of the propeller is redesigned.

9. The hydrogen-powered logistics drone propeller design method according to claim 8, characterized in that: The required battery weight is calculated based on the power and flight time requirements of the drone.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the propeller design method for a hydrogen-powered logistics drone as described in any one of claims 1 to 9 is implemented.

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