An optimization design method for the propeller blades of a multi-rotor bomb-dropping fire-fighting UAV

Abstract

The present invention provides an optimized design method for the blades of a multi-rotor bomb-dropping fire-fighting UAV, which relates to the technical field of UAVs. The present invention obtains the geometric design data of the fuselage and the blade design data, constructs a fire-fighting UAV model using CFD software, performs aerodynamic analysis using this model to obtain flight force data; generates a blade flight evaluation coefficient based on the blade material property data and the flight force data; obtains the blade flight evaluation coefficients at each rotational speed gear and analyzes them to obtain a flight quality evaluation coefficient; constructs and trains a flight quality evaluation model with the historical blade design data as the input and the corresponding historical flight quality evaluation coefficient as the label; uses the blade design data as the initial solution and maximizes the blade flight evaluation coefficient as the optimization objective, and uses the simulated annealing algorithm to optimize the blade design data to obtain the optimal blade design data.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and particularly to an optimized design method for the blades of a multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle. Background Art

[0002] Traditional fire-fighting methods mainly rely on fire trucks and manual fire-fighting, and usually have problems such as long response time, inconvenient operation, and complex working environment. This makes traditional fire-fighting means appear inadequate when dealing with sudden fires. Especially when facing fires in high-rise buildings, narrow alleys, and complex terrains, the operation difficulty for firefighters is greater, and the rescue efficiency is also greatly reduced. Therefore, it is particularly important to develop new fire-fighting equipment and improve fire-fighting efficiency.

[0003] As an emerging high-tech fire-fighting auxiliary tool, unmanned aerial vehicles have gradually attracted attention due to their flexibility and rapid deployment capabilities. The application of multi-rotor unmanned aerial vehicles at the fire scene can effectively improve fire-fighting efficiency by means of aerial fire extinguishing agent delivery, fire situation reconnaissance, etc. However, existing fire-fighting unmanned aerial vehicles still have deficiencies in flight performance, payload capacity, and operation stability, especially the lack of optimization in blade design, resulting in their inability to exert the best performance in actual operations. Traditional blade designs often ignore the interaction between material properties and aerodynamic properties, causing problems such as low flight efficiency and insufficient aviation power.

[0004] In addition, for the simulation and optimization of unmanned aerial vehicle blade design, traditional methods mostly rely on experience and simple calculations, lacking scientific systematic analysis. This makes the designed blades difficult to meet the requirements of efficient fire-fighting in actual applications, especially when facing different types of fires, the performance of the blades may be affected. Therefore, comprehensive optimization in multiple dimensions such as the aerodynamic characteristics, material properties, and flight evaluation of the blades has become the key to improving the performance of fire-fighting unmanned aerial vehicles. In this context, developing an integrated and systematic blade optimization design method can not only improve the working efficiency of multi-rotor bomb-dropping fire-fighting unmanned aerial vehicles, but also provide new ideas and directions for the further development of unmanned aerial vehicles.

[0005] In the prior art, the published number CN113408044A discloses a method, system, device and storage medium for optimizing the design of the propeller blades of a multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle, which includes: determining the working condition parameter and selecting a reference airfoil; parameterizing the reference airfoil to obtain a basic airfoil; optimizing the basic airfoil to obtain an optimal airfoil; and verifying the three-dimensional simulation of the optimal airfoil. The method for optimizing the design of the propeller blades of the multi-rotor unmanned aerial vehicle in the prior art has many advantages such as wide applicability, high optimization dimension, high optimization efficiency and good optimization effect. However, there are still defects in the prior art. When optimizing the propeller blade parameters, the prior art only considers the influence of the geometric characteristics of the propeller blades on the flight effect during flight, and ignores the important influence of the material of the propeller blades themselves on the flight situation and the important influence of the pressure borne by the propeller blades on the flight effect and the service life of the propeller blades.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for optimizing the design of the propeller blades of a multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle to solve the problems raised in the above background art.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] A method for optimizing the design of the propeller blades of a multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle, the specific steps include:

[0010] Step 1: Obtain the maximum rotation speed of the propeller blades of the multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle, divide the multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle into N rotation speed gears through the maximum rotation speed of the propeller blades of the multi-rotor bomb-dropping fire-fighting unmanned aerial vehicle, and obtain the rotation speed intermediate value of each rotation speed gear;

[0011] Step 2: Obtain the fuselage geometric design data and the propeller blade design data, use CFD software to construct a fire-fighting unmanned aerial vehicle model, use the fire-fighting unmanned aerial vehicle model to conduct aerodynamic analysis, and obtain the flight force data under different rotation speed intermediate values; obtain the propeller blade material property data, and generate corresponding propeller blade flight evaluation coefficients according to the propeller blade material property data and the flight force data;

[0012] Step 3: Analyze the propeller blade flight evaluation coefficients under each rotation speed intermediate value to obtain the flight quality evaluation coefficients;

[0013] Step 4: Construct a flight quality evaluation model, obtain the historical propeller blade design data and the historical flight quality evaluation coefficients, and use the historical propeller blade design data as the input and the corresponding historical flight quality evaluation coefficients as the labels to train the flight quality evaluation model;

[0014] Step 5: Use the blade design data obtained in Step 2 as the initial solution, and with the maximization of the blade flight evaluation coefficient as the optimization goal, use the simulated annealing algorithm to optimize the blade design data to obtain the optimal blade design data.

[0015] Further, the specific logic for dividing the rotational speed gears is as follows: Obtain the maximum rotational speed of the multi-rotor bomb-dropping fire-fighting drone blades, and divide the multi-rotor bomb-dropping fire-fighting drone into N rotational speed gears based on the maximum rotational speed of the multi-rotor bomb-dropping fire-fighting drone blades. The specific formula for dividing the rotational speed gears is:

[0016]

[0017] where, SP i is the rotational speed of the i-th rotational speed gear, SP 0 is the maximum rotational speed gear, N is the number of rotational speed gears, and i is the rotational speed gear index.

[0018] Further, the blade design data includes the total length, total width, maximum thickness, minimum thickness, surface area, and maximum bending angle of the multi-rotor bomb-dropping fire-fighting drone blades; the flight force data includes the drone lift, drone drag, blade root pressure, and blade tip pressure.

[0019] Further, the specific logic for generating the blade flight evaluation coefficient is as follows: Obtain the blade material property data, generate the material performance coefficient based on the blade material property data, and generate the blade flight evaluation coefficient based on the material performance coefficient and the flight force data; the material property data includes the hardness, fatigue strength, compressive strength, tensile strength, and Poisson's ratio of the blade material.

[0020] The specific formula for generating the material performance coefficient is:

[0021]

[0022] where, CL is the material performance coefficient, Ha is the hardness of the blade material, R is the fatigue strength of the blade material, F is the tensile strength of the blade material, B is the compressive strength of the blade material, and SB is the Poisson's ratio of the blade material.

[0023] The specific formula for generating the blade flight evaluation coefficient is:

[0024]

[0025] where, Y is the blade flight evaluation coefficient, F u is the drone lift, F do is the drone drag, P g is the blade root pressure, P j is the blade tip pressure.

[0026] Further, the specific logic for generating the flight quality evaluation coefficient is as follows: calculate the blade flight evaluation coefficient corresponding to the intermediate rotation speed value; analyze the blade flight evaluation coefficients at each intermediate rotation speed value to obtain the flight quality evaluation coefficient; the specific formula for generating the flight quality evaluation coefficient is:

[0027]

[0028] where FY is the flight quality evaluation coefficient, and Y i is the blade flight evaluation coefficient corresponding to the intermediate rotation speed value of the rotation speed of the i-th gear, and SP i is the intermediate rotation speed value of the i-th rotation speed gear, N is the number of rotation speed gears, and i is the rotation speed gear index.

[0029] Further, the optimization goal for determining the blade design data is to maximize the flight quality evaluation coefficient. The blade design data obtained in step 2 is used as the initial solution of the blade design data in the simulated annealing algorithm;

[0030] Search for the optimal blade design data through the simulated annealing algorithm. Set the initial annealing temperature and the cooling coefficient. At the initial annealing temperature, perform a neighborhood search on the initial solution, randomly select a neighboring solution within the parameter range, use the flight quality evaluation model to predict the flight quality evaluation coefficient of the neighboring solution, and determine the probability of accepting the new solution according to the Metropolis criterion.

[0031] Further, the formula for determining the probability of accepting the new solution according to the Metropolis criterion is:

[0032]

[0033] where P is the probability that the new flight quality evaluation coefficient is accepted, and FY k+1 is the flight quality evaluation coefficient generated at the (k + 1)-th time, and FY k is the flight quality evaluation coefficient generated at the k-th time, k is the generation order index of the flight quality evaluation coefficient, T is the annealing temperature, and the annealing temperature is continuously adjusted through the cooling coefficient until the termination condition is reached;

[0034] The set termination condition of the simulated annealing algorithm is:

[0035] The total number of solutions for generating the flight quality evaluation coefficient is X. For a currently found optimal flight quality evaluation coefficient FY L , if in the subsequent X - L iterations, the flight quality evaluation coefficients are all FY L , then the iteration ends and outputs the blade design data corresponding to the flight quality evaluation coefficients all being FY L .

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] On the basis of considering the influence of the blade geometric design data on the flight effect of the drone, the present invention further comprehensively considers the influence of the blade material characteristics and the pressure borne by the blade during flight on the flight performance. This multi-dimensional analysis can more accurately evaluate the overall flight effect of the drone, and also fully considers the service life of the blade. By integrating all relevant factors, we generate a flight quality evaluation coefficient reflecting the comprehensive performance of the blade as the main direction of optimization. The optimized blade design data will exhibit more excellent performance, providing strong support for the application of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0040] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0041] Embodiment:

[0042] Please refer to Figure 1 , the present invention provides a technical solution:

[0043] A method for optimizing the design of the blades of a multi-rotor bomb-dropping fire-fighting drone, the specific steps including:

[0044] Step 1: Obtain the maximum rotational speed of the blades of the multi-rotor bomb-dropping fire-fighting drone, divide the multi-rotor bomb-dropping fire-fighting drone into N rotational speed gears according to the maximum rotational speed of the blades of the multi-rotor bomb-dropping fire-fighting drone, and obtain the rotational speed intermediate value of each rotational speed gear;

[0045] The specific logic for dividing the rotational speed gears is as follows: Obtain the maximum rotational speed of the propeller blades of the multi-rotor bomb-dropping fire-fighting drone. Divide the multi-rotor bomb-dropping fire-fighting drone into N rotational speed gears based on the maximum rotational speed of the propeller blades of the multi-rotor bomb-dropping fire-fighting drone. The specific formula for obtaining the rotational speed intermediate value is:

[0046]

[0047] where SP i is the rotational speed intermediate value of the i-th rotational speed gear, SP 0 is the maximum rotational speed gear, N is the number of rotational speed gears, and i is the rotational speed gear index.

[0048] Step 2: Obtain the fuselage geometric design data and propeller blade design data. Use CFD software to construct a fire-fighting drone model. Use the fire-fighting drone model to conduct aerodynamic analysis to obtain flight force data at different rotational speed intermediate values; Obtain the propeller blade material property data. Generate corresponding propeller blade flight evaluation coefficients based on the propeller blade material property data and the flight force data;

[0049] The propeller blade design data includes the total length, total width, maximum thickness, minimum thickness, surface area, and maximum bending angle of the propeller blades of the multi-rotor bomb-dropping fire-fighting drone; The flight force data includes the drone lift, drone drag, propeller blade root pressure, and propeller blade tip pressure;

[0050] The specific logic for generating the propeller blade flight evaluation coefficient is as follows: Obtain the propeller blade material property data. Generate a material performance coefficient based on the propeller blade material property data. Generate a propeller blade flight evaluation coefficient based on the material performance coefficient and the flight force data; The material property data includes the hardness, fatigue strength, compressive strength, tensile strength, and Poisson's ratio of the propeller blade material;

[0051] The specific formula for generating the material performance coefficient is:

[0052]

[0053] where CL is the material performance coefficient, Ha is the hardness of the propeller blade material, R is the fatigue strength of the propeller blade material, F is the tensile strength of the propeller blade material, B is the compressive strength of the propeller blade material, and SB is the Poisson's ratio of the propeller blade material; The material performance coefficient CL reflects the performance of the propeller blade material. The larger its value, the better the performance of the propeller blade material. The generation of this coefficient can provide an important basis for the evaluation of flight quality.

[0054] The specific formula for generating the propeller blade flight evaluation coefficient is:

[0055]

[0056] where Y is the propeller blade flight evaluation coefficient, Fu is the lift of the drone, F do is the drag of the drone, P g is the pressure at the root of the blade, P j is the pressure at the tip of the blade. The blade flight evaluation coefficient Y reflects the comprehensive performance of the blade during flight. The comprehensive performance includes the flight effect of the drone, the stability of the blade, and the blade life. The larger its value, the better the comprehensive performance of the blade during flight. is the lift-to-drag ratio, which reflects the ability of the multi-rotor bomb-dropping fire-fighting drone to adjust its speed and attitude during flight. The larger its value, the stronger the ability of the multi-rotor bomb-dropping fire-fighting drone to adjust its speed and attitude. is the main pressure borne by the blade, which can reflect the pressure borne by the blade during flight. The larger its value, the greater the pressure borne by the blade during flight, which is less conducive to the normal flight of the aircraft and the lower the blade life.

[0057] Step 3: Analyze the blade flight evaluation coefficients at each intermediate speed value to obtain the flight quality evaluation coefficient;

[0058] The specific logic for generating the flight quality evaluation coefficient is as follows: Calculate the blade flight evaluation coefficient corresponding to the intermediate speed value; Analyze the blade flight evaluation coefficients at each intermediate speed value to obtain the flight quality evaluation coefficient; The specific formula for generating the flight quality evaluation coefficient is:

[0059]

[0060] where FY is the flight quality evaluation coefficient, Y i is the blade flight evaluation coefficient corresponding to the intermediate speed value of the i-th gear speed, SP i is the intermediate speed value of the i-th speed gear, N is the number of speed gears, and i is the speed gear index.

[0061] The flight quality evaluation coefficient FY reflects the comprehensive flight ability of the drone at each speed. The larger its value, the stronger the comprehensive flight ability of the drone at each speed, which can provide an important direction for optimizing the parameters of the drone blade.

[0062] Step 4: Build a flight quality evaluation model, obtain historical blade design data and historical flight quality evaluation coefficients, and train the flight quality evaluation model with the historical blade design data as the input and the corresponding historical flight quality evaluation coefficients as the labels;

[0063] A feedforward neural network is adopted, and the flight quality evaluation model is trained and optimized using historical blade design data with the flight quality evaluation coefficient label. It should be noted that training and optimizing the flight quality evaluation model using historical blade design data with the flight quality evaluation coefficient label can adopt existing technologies, specifically including: an input layer, a hidden layer, an output layer, and an activation function, and the root mean square error loss function is adopted. The input data is calculated through the network once to obtain the output result. The loss function is calculated based on the predicted value and the true value, and the gradient of the loss function with respect to each weight and bias is calculated through the chain rule. The gradient descent algorithm is used to update the weights and biases of the network to minimize the loss function.

[0064] Step 5: Using the blade design data obtained in Step 2 as the initial solution and maximizing the blade flight evaluation coefficient as the optimization goal, the simulated annealing algorithm is used to optimize the blade design data to obtain the optimal blade design data.

[0065] It is determined that the optimization goal of the blade design data is to maximize the flight quality evaluation coefficient, and the blade design data obtained in Step 2 is used as the initial solution of the blade design data in the simulated annealing algorithm.

[0066] The optimal blade design data is searched for through the simulated annealing algorithm. The initial annealing temperature and the cooling coefficient are set. At the initial annealing temperature, a neighborhood search is performed on the initial solution. A neighboring solution is randomly selected within the parameter range, and the flight quality evaluation coefficient of the neighboring solution is predicted using the flight quality evaluation model. The probability of accepting the new solution is determined according to the Metropolis criterion.

[0067] The formula for determining the probability of accepting the new solution according to the Metropolis criterion is:

[0068]

[0069] where P is the probability that the new flight quality evaluation coefficient is accepted, FY k+1 is the flight quality evaluation coefficient generated at the (k + 1)-th time, FY k is the flight quality evaluation coefficient generated at the k-th time, k is the index of the order of generating the flight quality evaluation coefficient, T is the annealing temperature, and the annealing temperature is continuously adjusted through the cooling coefficient until the termination condition is reached.

[0070] The set termination condition of the simulated annealing algorithm is:

[0071] The total number of solutions generating the flight quality evaluation coefficient is X. For a currently found optimal flight quality evaluation coefficient FY L , if in the subsequent X - L iterations, the flight quality evaluation coefficients are all FY L , then the iteration ends and the output flight quality evaluation coefficients are all FY LThe corresponding blade design data.

[0072] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0074] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] As described above, only the specific implementation manners of this application are provided, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application.

Claims

1. A method for optimizing the blade design of a multi-rotor bomb-dropping fire-fighting drone, characterized in that: The specific steps include: Step 1: Obtain the maximum speed of the blades of the multi-rotor bomb-throwing fire-fighting drone, divide the multi-rotor bomb-throwing fire-fighting drone into N speed gears according to the maximum speed of the blades of the multi-rotor bomb-throwing fire-fighting drone, and obtain the intermediate speed value of each speed gear; Step 2: Obtain the fuselage geometry design data and blade design data, use CFD software to build a fire-fighting UAV model, use the fire-fighting UAV model to perform aerodynamic analysis, and obtain flight power data at different intermediate speeds; obtain blade material property data, and generate corresponding blade flight evaluation coefficients based on the blade material property data and flight power data; Step 3: Analyze the blade flight evaluation coefficient at each intermediate speed value to obtain the flight quality evaluation coefficient; Step 4: Build a flight quality assessment model, obtain historical blade design data and historical flight quality assessment coefficients, and use the historical blade design data as input and the corresponding historical flight quality assessment coefficients as labels to train the flight quality assessment model; Step 5: Taking the blade design data obtained in step 2 as the initial solution and maximizing the blade flight evaluation coefficient as the optimization goal, the blade design data is optimized using the simulated annealing algorithm to obtain the optimal blade design data; The blade design data includes the total length, total width, maximum thickness, minimum thickness, surface area and maximum bending angle of the blades of the multi-rotor bomb-dropping fire-fighting UAV; the flight power data includes the lift of the UAV, the drag of the UAV, the pressure at the root of the blade and the pressure at the tip of the blade; The specific logic for generating the blade flight evaluation coefficient is as follows: obtaining blade material property data, generating a material performance coefficient according to the blade material property data, and generating a blade flight evaluation coefficient according to the material performance coefficient and flight power data; the material property data includes hardness, fatigue strength, compressive strength, tensile strength and Poisson's ratio of the blade material; The specific formula used to generate the material performance coefficient is: Wherein, CL is the material performance coefficient, Ha is the hardness of the blade material, R is the fatigue strength of the blade material, F is the tensile strength of the blade material, B is the compressive strength of the blade material, and SB is the Poisson's ratio of the blade material; The specific formula used to generate the blade flight evaluation coefficient is: Where Y is the blade flight evaluation coefficient, F u is the lift of the drone, F do is the drag of the drone, P g is the blade root pressure, P j is the blade tip pressure.

2. The method for optimizing blade design of a multi-rotor bomb-dropping fire-fighting drone according to claim 1 is characterized in that: The specific logic for dividing the speed gears is: obtain the maximum speed of the blades of the multi-rotor bomb-throwing fire-fighting drone, divide the multi-rotor bomb-throwing fire-fighting drone into N speed gears according to the maximum speed of the blades of the multi-rotor bomb-throwing fire-fighting drone, and obtain the specific formula for the intermediate value of the speed as follows: Among them, SP i is the speed intermediate value of the i-th speed gear, SP0 is the maximum speed gear, N is the speed gear number, and i is the speed gear index.

3. The method for optimizing blade design of a multi-rotor bomb-dropping fire-fighting drone according to claim 1 is characterized in that: The specific logic for generating the flight quality assessment coefficient is as follows: calculate the blade flight assessment coefficient corresponding to the intermediate speed value; analyze the blade flight assessment coefficient at each intermediate speed value to obtain the flight quality assessment coefficient; the specific formula for generating the flight quality assessment coefficient is as follows: Among them, FY is the flight quality assessment coefficient, Y i is the blade flight evaluation coefficient corresponding to the intermediate value of the speed of the i-th gear, SP i is the middle speed value of the i-th speed gear, N is the number of speed gears, and i is the speed gear index.

4. The method for optimizing blade design of a multi-rotor bomb-dropping fire-fighting drone according to claim 1 is characterized in that: Determine that the optimization target of the blade design data is to maximize the flight quality assessment coefficient, and use the blade design data obtained in step 2 as the initial solution of the blade design data in the simulated annealing algorithm; The optimal blade design data is found through simulated annealing algorithm, the initial annealing temperature and the cooling coefficient are set, and a neighborhood search is performed on the initial solution at the initial annealing temperature. A neighboring solution is randomly selected within the parameter range, and the flight quality assessment coefficient of the neighboring solution is predicted using the flight quality assessment model. The probability of accepting the new solution is determined according to the Metropolis criterion.

5. The method for optimizing blade design of a multi-rotor bomb-dropping fire-fighting drone according to claim 4 is characterized in that: The formula for determining the probability of accepting a new solution according to the Metropolis criterion is: Where P is the probability of the new flight quality assessment coefficient being accepted, FY k+1 is the k+1th generated flight quality assessment coefficient, FY k is the kth generated flight quality assessment coefficient, k is the generation order index of the flight quality assessment coefficient, T is the annealing temperature, and the annealing temperature is continuously adjusted by the cooling coefficient until the termination condition is reached; The termination condition of the simulated annealing algorithm is set as: The total number of solutions for generating the flight quality assessment coefficient is X. For a currently found optimal flight quality assessment coefficient FY L , if the flight quality assessment coefficient is FY in the subsequent XL iterations L , then the output flight quality assessment coefficients at the end of the iteration are all FY L The corresponding blade design data.

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