Vertical Takeoff and Landing Aircraft Ducted Fan Design Optimization Method and System

Abstract

The present invention relates to a method and system for optimizing the design of a ducted fan for a vertical take-off and landing (VTOL) aircraft, and in particular, to the application of a Bayesian optimization algorithm based on a variable acquisition function. This method aims to improve the overall efficiency of a VTOL aircraft by optimizing the design of the ducted fan to minimize fuel consumption for a given flight mission profile. By introducing a Bayesian optimization algorithm, the present invention efficiently handles the mixed variable optimization problem in ducted fan design and utilizes a variable acquisition function to dynamically adjust the optimization strategy, achieving comprehensive performance optimization under different mission profiles. This significantly reduces simulation computational costs and improves optimization efficiency and result quality.

Description

Technical Field

[0001] The present invention belongs to the field of aircraft design, and specifically relates to a design optimization method and system for a ducted fan of a vertical take-off and landing aircraft, in particular an optimization method based on a Bayesian optimization algorithm with a variable acquisition function. Background Art

[0002] Vertical take-off and landing (VTOL) vehicles (VTOLs) have great potential for both military and civilian applications due to their vertical take-off and landing capabilities and high-speed cruise flight efficiency. Improving the overall efficiency of VTOL vehicles is crucial, as they face greater challenges in terms of power and energy requirements. One promising approach is the use of ducted fans, which offer enhanced aerodynamic performance and noise shielding. Numerous studies have shown that duct and fan parameters significantly influence the aerodynamic performance of ducted fans and are key factors affecting the overall efficiency of ducted fan VTOL vehicles. However, optimizing the duct and fan parameters for the entire VTOL flight mission is a complex, computationally intensive optimization problem with both continuous and discrete design variables.

[0003] Complex interactions between discrete and continuous variables: Ducted fan optimization involves both continuous design variables (such as blade geometry, tip clearance, duct inlet radius, length, thickness, and outlet expansion angle) and discrete design variables (such as number of blades and fan airfoil selection). The interaction between these variables complicates the optimization problem and prevents access to gradient information during model training. Existing optimization methods are prone to local optimal solutions and suffer from low computational efficiency, especially when dealing with large-scale, nonlinear, and complex systems. Optimization results are often constrained by computational resources and time constraints.

[0004] Ducted fan optimization involves extensive computation: In aircraft design, the aerodynamic performance of the fan and duct is a dynamic and complex process, and ducted fan design must simultaneously consider multiple operating conditions. Solving this type of optimization problem involving multiple design variables requires extensive simulation calculations. This is especially true when considering the aerodynamic performance of an aircraft during multiple flight phases (such as takeoff, hovering, and cruising), which can be computationally intensive and time-consuming. Traditional optimization methods are often inefficient and difficult to obtain in a short period of time. Therefore, existing optimization methods typically optimize under a single operating condition, such as optimizing flight performance only for a specific phase (for example, optimizing only for hovering or cruising).

[0005] The conflict between aerodynamic performance and multi-mission performance: Ducted fan design must balance aerodynamic performance requirements across different flight phases. High thrust is required during takeoff and hovering, while low energy consumption and high aerodynamic efficiency are required during cruise. Existing optimization methods often focus on optimizing under a single operating condition, making it difficult to address the comprehensive aerodynamic performance requirements across multiple mission phases.

[0006] Therefore, the present invention provides a method for designing and optimizing the ducted fan of a vertical take-off and landing aircraft, which can comprehensively optimize the aerodynamic performance in multiple mission stages such as take-off, hovering, acceleration, and cruising, meet the complex requirements of multiple missions and multiple working conditions, and support the fusion optimization of continuous and discrete design variables, and adapt to the interactive relationship between complex variables. Summary of the Invention

[0007] In response to the key technical issues existing in the design of existing ducted fans for vertical take-off and landing aircraft, the present invention proposes a design method based on a Bayesian optimization algorithm with a variable acquisition function. This method aims to address the problems of complex interactions between discrete and continuous design variables, large computational complexity and low optimization efficiency, inconsistencies in comprehensive optimization performance in multi-task stages, black-box objective function optimization, and insufficient adaptability to dynamic and complex environments. By introducing the Bayesian optimization algorithm, the mixed optimization problem in ducted fan design is efficiently handled, and the optimization strategy is dynamically adjusted using the Gaussian process model and a variable acquisition function to achieve comprehensive performance optimization under multiple objectives and multi-task stages, significantly reducing simulation computing costs, improving optimization efficiency and result quality, and thus providing a better design solution for the application of vertical take-off and landing aircraft.

[0008] This paper uses a Bayesian optimization algorithm to efficiently solve the hybrid optimization problem in ducted fan design. Based on the Gaussian process model, the Bayesian optimization algorithm models the objective function using existing sample data and calculates the posterior probability distribution of the existing data points, thereby providing predictions for the mean and variance of the objective function in unsampled regions.

[0009] The technical solutions of the present invention are as follows:

[0010] A design optimization method for a ducted fan of a vertical take-off and landing aircraft is proposed. The method uses a Bayesian optimization algorithm based on a variable acquisition function to solve the mixed design variables in the ducted fan design. Specifically, the method includes:

[0011] Set the hybrid design variables and their value ranges for the ducted fan of a vertical take-off and landing aircraft;

[0012] Construct an objective function based on the mixed design variables, optimization objectives, and constraints;

[0013] Obtain initial data points through simulation calculation and initialize the sampling data set;

[0014] Build an analytical model based on the sampled data set to fit the objective function;

[0015] Iteratively optimize the analytical model using variable acquisition functions to obtain the optimal values of hybrid design variables;

[0016] Wherein, the hybrid design variables include fan design variables, duct design variables and fan-duct interaction design variables;

[0017] The sampling data set is composed of data points; the data points include a set of mixed design variable values and their corresponding objective function values;

[0018] The variable acquisition function includes a parameter exploration weight κ; the exploration weight κ linearly decays as the number of iterations increases.

[0019] This method dynamically adjusts the optimization strategy using a Gaussian process model and a variable acquisition function. The core of the variable acquisition function lies in dynamically adjusting the exploration and exploitation weights during the optimization process to achieve a comprehensive search of the design variable space and prioritized exploitation. In the early stages of optimization, a larger exploration weight is preferred to fully explore the design space. In the later stages of optimization, the exploration weight is gradually reduced, focusing more on exploitation and exploring high-potential solutions near known points, thereby rapidly converging to the global optimal solution.

[0020] Furthermore, in the design optimization method of the present invention, the iterative optimization of the analysis model using the variable acquisition function to obtain the optimal value of the hybrid design variable specifically includes:

[0021] Construct variable acquisition functions based on the analytical model;

[0022] A set of mixed design variable evaluation values are obtained by maximizing the variable acquisition function value;

[0023] Obtain the corresponding objective function value through simulation calculation;

[0024] Adding the hybrid design variable evaluation value and the corresponding objective function value as new data points to the sampling data set;

[0025] Update the analytical model based on the sampled data set;

[0026] When the absolute value of the difference between the variable acquisition function value in this iteration and the variable acquisition function value in the previous iteration is less than the set threshold, the iterative optimization ends, and the evaluation value of the hybrid design variable in this iteration is the optimal value of the hybrid design variable; otherwise, repeat the above steps to continue the iterative optimization.

[0027] Furthermore, in the design optimization method of the present invention, the fan design variables include the twist angle of the fan blade at 75% radius and the fan airfoil; the fan airfoil is selected from one or more of a set of predetermined mainstream fan airfoils.

[0028] Furthermore, in the design optimization method of the present invention, the duct design variables include CST parameters of the duct airfoil.

[0029] Furthermore, in the design optimization method of the present invention, the fan-duct interaction design variables include the chordwise distance from the duct leading edge to the fan disk, and the gap between the duct and the fan tip.

[0030] Furthermore, in the design optimization method of the present invention, the optimization objectives include minimizing fuel consumption and power of the vertical take-off and landing aircraft in a given mission profile; the mission profile includes specific operating conditions and a complete flight mission.

[0031] Furthermore, in the design optimization method of the present invention, the specific operating conditions include cruising and hovering.

[0032] Furthermore, in the design optimization method of the present invention, the constraints include thrust requirements and blade tip Mach number limitations.

[0033] Furthermore, in the design optimization method of the present invention, the simulation calculation includes obtaining the corresponding objective function value based on a set of mixed design variable values, combined with constraint conditions and mission profiles, through fan aerodynamic performance analysis and ducted fan performance analysis.

[0034] This invention divides the ducted fan design optimization process into two main target scenarios. The first involves optimizing for specific operating conditions (cruise or hover), with the goal of minimizing the fan's power consumption under these specific conditions. The second involves optimizing for the fan's entire mission profile, with the goal of minimizing the fan's energy consumption while completing the selected mission. This phased, multi-objective optimization approach not only meets the fan's performance requirements at different mission stages, but also improves the fan's overall energy efficiency.

[0035] The present invention further provides a vertical take-off and landing aircraft ducted fan design optimization system, which can implement the vertical take-off and landing aircraft ducted fan design optimization method of the present invention, including:

[0036] Variable design module: used to set the hybrid design variables and their value ranges of the ducted fan of the vertical take-off and landing aircraft;

[0037] Objective function building module, used to build objective functions based on mixed design variables, optimization objectives and constraints;

[0038] Sampling data set initialization module: used to obtain initial data points through simulation calculation and initialize the sampling data set;

[0039] Analysis model building module: used to build an analysis model based on the sampling data set and fit the objective function;

[0040] Iterative optimization module: used to iteratively optimize the analysis model using variable acquisition functions to obtain the optimal values of hybrid design variables;

[0041] Wherein, the hybrid design variables include fan design variables, duct design variables and fan-duct interaction design variables;

[0042] The sampling data set is composed of data points; the data points include a set of mixed design variable values and their corresponding objective function values;

[0043] The variable acquisition function includes a parameter exploration weight κ; the exploration weight κ linearly decays as the number of iterations increases.

[0044] The Bayesian optimization algorithm based on variable acquisition function provides an efficient and intelligent optimization tool for ducted fan design. By dynamically adjusting the acquisition function and adapting to complex variable types and multi-task working conditions, this method not only provides an innovative solution for the design of ducted fans for vertical take-off and landing aircraft, but also significantly improves design efficiency and provides strong support for achieving multi-stage performance optimization of vertical take-off and landing aircraft.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. Complex variable fusion optimization supports the fusion optimization of continuous and discrete design variables, adapts to the interactive relationship between complex variables, and provides an effective tool for solving multivariable optimization problems in actual engineering.

[0047] 2. Optimization efficiency and performance improvement: By dynamically adjusting the exploration and development weights in the variable acquisition function, this method can quickly converge to a solution close to the global optimal solution within a relatively small number of iterations, effectively avoiding the optimization process from falling into a local optimal solution. This not only improves optimization efficiency but also significantly reduces computational costs. It is especially suitable for computationally complex problems such as ducted fan design, allowing optimization tasks to be completed efficiently even with limited computing resources.

[0048] 3. Multi-task and multi-operating condition adaptability. The present invention can comprehensively consider the aerodynamic performance requirements of vertical take-off and landing aircraft in multiple mission stages such as take-off, hovering, acceleration, and cruising, and meet the complex needs of multiple tasks and multiple operating conditions. This makes the ducted fan design not only perform well under a single operating condition, but also maintain high efficiency throughout the entire flight mission, thereby improving the overall applicability and flexibility of the aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0050] Figure 1 This is a flow chart for optimizing the design of a ducted fan under specific working conditions in an embodiment of the present invention.

[0051] Figure 2 Flowchart of ducted fan design optimization for a complete flight mission in an embodiment of the present invention.

[0052] Figure 3 Schematic diagram of a set of predetermined mainstream fan airfoils in an embodiment of the present invention.

[0053] Figure 4 Schematic diagram of the iteration of design variables and optimization objectives in the hovering optimization process in an embodiment of the present invention.

[0054] Figure 5 Schematic diagram of the iteration of design variables and optimization objectives in the cruise optimization process in an embodiment of the present invention.

[0055] Figure 6 Schematic diagram comparing the optimized front and rear ducted airfoils during cruising and hovering in an embodiment of the present invention.

[0056] Figure 7 Schematic diagram comparing the initial case and optimized case of cruising and hovering in an embodiment of the present invention.

[0057] Figure 8 This is a three-dimensional view of the NASA UAM tilt-ducted eVTOL aircraft in an embodiment of the present invention.

[0058] Figure 9 This is a cross-sectional diagram of a flight mission in an embodiment of the present invention.

[0059] Figure 10 Schematic diagram of the iteration of design variables and optimization objectives in the complete flight mission optimization process in an embodiment of the present invention.

[0060] Figure 11 2 is a comparison diagram of the ducted airfoil before and after optimization for a complete flight mission in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0062] Example 1

[0063] The Bayesian Optimization (BO) algorithm, based on a variable acquisition function, is an efficient algorithm for optimizing black-box functions (objective functions with unknown explicit expressions). It is suitable for complex optimization problems involving both continuous and discrete design variables. In the design of ducted fans for vertical takeoff and landing aircraft, this algorithm can quickly find the global optimal solution by dynamically adjusting the balance between exploration and exploitation.

[0064] In one embodiment of the present invention, Figures 1 and 2 illustrate the complete process of ducted fan design using a Bayesian optimization algorithm based on a variable acquisition function, as well as the specific implementation path for optimization under different operating conditions and flight missions:

[0065] First, the hybrid design variables and their value ranges of the ducted fan of the vertical take-off and landing aircraft are set, including fan design variables, duct design variables and fan-duct interaction design variables; the fan design variables include the fan airfoil, the torsion angle of the fan blade at 75% radius ( ), the fan airfoil is selected from 12 mainstream fan airfoils and represented by discrete digital variables; the duct design variables include the duct airfoil, and the Class Shape Transformation (CST) parameterization method is used to generate a variety of duct geometric shapes by changing the CST parameters to fully study the effect of duct shape on ducted fan performance; the fan-duct interaction design variables include the chordwise distance from the duct leading edge to the fan disk ( ), the gap between the duct and the fan tip ( ), used to describe the relationship between the fan and the duct.

[0066] Then, an objective function is constructed based on the hybrid design variables, optimization objectives and constraints. The optimization objectives and constraints are related to the given mission profile. First, the optimization is carried out for specific operating conditions (cruise or hover), with the goal of achieving the minimum power consumption of the ducted fan under these specific conditions; second, the optimization is carried out for the entire flight mission of the ducted fan, with the goal of minimizing the energy consumption of the ducted fan when completing the selected flight mission.

[0067] Initial data points are obtained through simulation calculation to initialize the sampling data set; the aerodynamic performance of the fan is obtained through XFOIL calculation; the overall performance of the ducted fan is obtained through DFDC calculation method.

[0068] An analytical model is constructed based on a sampled data set to fit the objective function. Bayesian optimization mainly uses the Gaussian Process (GP) model. Without knowing the explicit expression of the objective function, the objective function is modeled using existing sample data points. Gaussian Process Regression (GP) is used to calculate the posterior probability distribution of the existing data points, thereby providing predictions for the mean (μ) and variance (σ²) of the objective function in the unsampled area.

[0069] The analytical model is iteratively optimized using a variable acquisition function to obtain the optimal values of the hybrid design variables. The acquisition function balances exploration and exploitation to determine the location of the next sampling point. Exploration attempts to select points far from known points as reference points for the next iteration, attempting to explore the unknown region, where the distribution of points is as uniform as possible. Exploitation selects points as close as possible to known points as reference points for the next iteration, attempting to explore points around the known points. This results in a dense distribution of points, which is more likely to reach local maxima. Because the choice of exploration and exploitation approach significantly influences the acquisition function and the optimization process, the exploration and exploitation weights are appropriately adjusted as the optimization progresses to quickly find the global optimal solution. Therefore, embodiments of the present invention utilize a variable acquisition function to dynamically adjust the exploration and exploitation weights during the optimization process. In the early stages of optimization, a larger exploration weight (a larger κ) is preferred to fully search the design space and cover unexplored areas. In the later stages of optimization, the exploration weight is gradually reduced (a smaller κ) to focus more on exploitation and explore high-potential solutions near known points, thereby rapidly converging to the global optimal solution.

[0070] In another embodiment of the present invention, the variable acquisition function calculation formula is:

[0071]

[0072] Among them, κ is the exploration weight, which is used to adjust the exploration intensity. The larger the κ value, the more inclined to explore unknown areas.

[0073] In another embodiment of the present invention, a linear decay function is used to describe the change of the exploration weight κ with the number of iterations t. The mathematical expression is as follows:

[0074]

[0075] Exploration phase: For the first 100 times, κ = 10, extensive exploration is performed.

[0076] Transition stage: from 100 to 400 times, as the number of iterations increases, κ linearly decays from 10 to 0.5, gradually shifting from exploration to exploitation.

[0077] Exploitation phase: 400 to 500 times, κ = 0.5, and fine exploration to find the optimal solution.

[0078] By dynamically adjusting the acquisition function between exploration and exploitation, the Bayesian optimization (BO) method enables efficient search and optimization of complex design variable combinations, effectively balancing the weights of global search and local optimization. Initially, the method tends to explore understudied areas, prioritizing the uncertainty of sampling points. Later in the optimization process, the method gradually shifts toward exploring areas with known potential good solutions, prioritizing the expected benefits of sampling points. By designing a variable acquisition function, the BO method avoids being trapped in local optima, allowing it to rapidly converge to the global optimum while exploring the entire design space.

[0079] Example 2

[0080] In another embodiment of the present invention, specific parameters are used to optimize specific operating conditions and complete flight missions respectively. Table 1 lists the complete optimization formulas. β 0.75 、 X disκ 、 T gap , fan airfoil number and ducted airfoil CST parameters are used as design variables, thrust demand and blade tip Mach number are used as design constraints, and the two optimization objectives are the minimum power of the ducted fan working thrust (i.e., maximum efficiency) and the minimum fuel consumption for a given flight mission.

[0081] Table 1 Formulation of the ducted fan optimal problem

[0082]

[0083] Twelve classic airfoils were selected for fan optimization, such as Figure 3 As shown, candidate airfoils include NACA 0012, 0015, 0018, the NACA 64 series airfoils used on the tiltrotor XV-15, as well as the NACA 24 and 44 series, ARAD10, CLARK-Y, E851, HQ07, and RAF6.

[0084] In the optimization design of ducted fans, the geometric parameter combinations of the fan and the duct are extensively explored in the early stage of the design to ensure that the global search space is covered. A large κ is selected to explore the entire design space (iteration number t≤100, κ=10). In the middle stage of the design, κ decreases linearly with the increase of the iteration number (100 <t≤400, ), in the later stages of the design, we will focus on optimizing high-potential areas to improve the aerodynamic performance and energy efficiency of the ducted fan. κ will be adjusted to a smaller value (iterations t>400, κ=0.05) to find the optimal solution.

[0085] 1) Optimization results of ducted fans under specific working conditions

[0086] The results shown in Table 2 demonstrate that a total ducted fan efficiency exceeding 70% can be achieved in both cruise and hover scenarios, with ducted power decreasing by 24.4% and 12.2% in cruise and hover conditions, respectively. None of the optimized design variables reached their boundaries, indicating that the choice of optimization boundaries was reasonable.

[0087] Table 2 Results of ducted fan optimization during cruise and hover

[0088]

[0089] Figure 4 and Figure 5 The iterations of design parameters and design objectives for the hover and cruise optimization cases are shown, respectively. In the early stages of optimization, making BO more exploratory allows the calculation points to better fill the design space and effectively avoid the results from falling into local optimality. In the later stages of optimization, BO becomes more exploitative, exploring the region where the global optimality may occur in more detail and comprehensively investigating the performance of the ducted fan.

[0090] Since the free-fall speeds in the cruise and hover states are quite different, the optimized ducted airfoils in the two cases are also quite different. Figure 6 A comparison of the ducted airfoil before and after optimization is given.

[0091] Figure 7 DFDC was used to plot a comparison of the pressure vectors on the ducted fan surface under cruise and hover conditions before and after optimization, providing a more intuitive understanding of the changes in ducted fan performance. During the hover and cruise phases, although the optimized duct lip curvature radius is smaller, resulting in a partial loss of pressure differential between the upper and lower surfaces, the thicker duct airfoil increases the pressure differential between the upper and lower surfaces of the rear three-quarters of the duct, increasing the total duct thrust. Due to the changes in design parameters, the air velocity within the duct changes, directly affecting the pressure distribution on the ducted fan surface. The optimization of the ducted airfoil results in an increase in duct thrust.

[0092] 2) Overall optimization of vertical take-off and landing aircraft considering flight missions

[0093] The NASA UAM tilt-ducted eVTOL aircraft is used as a reference aircraft to complete the benchmark vertical take-off and landing for flight mission optimization. Figure 8 The geometric layout of the reference tilt-ducted vertical take-off and landing aircraft is shown, and Table 3 gives the top-level aircraft parameters of the reference VTOL aircraft.

[0094] Table 3 Top-level parameters of NASA UAM tilt-ducted eVTOL vertical take-off and landing reference aircraft

[0095]

[0096] The mission profile of the tilt-ducted vertical take-off and landing aircraft is based on the data of the NASA UAM tilt-ducted eVTOL reference aircraft. The aircraft takes off at the maximum take-off weight. The mission profile is shown in Figure 9 The mission profile consists of two identical takeoff and landing segments (1-9 and 10-18) and a 20-minute cruise reserve segment. Table 4 calculates the ducted fan power and energy consumption according to the aircraft's flight status.

[0097] Table 4 Task segment attributes

[0098]

[0099] Table 5 shows the optimization results of the ducted fan considering the entire flight mission profile. When minimizing fuel consumption for the entire flight mission is set as the design goal, the optimized ducted fan efficiency in cruise and hovering is improved to 77.7% and 81.7%, respectively. The power of the ducted fan is significantly reduced, energy consumption is reduced by 18.1%, and the total energy used is reduced from 2763.9 MJ to 2263.0 MJ.

[0100] Table 5 Ducted fan optimization results considering mission profile

[0101]

[0102] Figure 10 The iteration of design parameters and optimization design goals during the optimization process is shown. Figure 11 The ducted airfoil shapes before and after optimization are shown. While the ducted fan optimization results are less efficient than those for cruise and hover alone, overall performance improvements are achieved in both cruise and hover scenarios across the entire mission, with improvements achieved in both hover and cruise. In the mission considered, the cruise phase constitutes the majority of the mission, and the optimization results actually favor cruise optimization.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A design optimization method for a ducted fan of a vertical take-off and landing aircraft, characterized in that: The Bayesian optimization algorithm based on variable acquisition function is used to solve the mixed design variables in ducted fan design, including: Set the hybrid design variables and their value ranges for the ducted fan of a vertical take-off and landing aircraft; Construct an objective function based on the mixed design variables, optimization objectives, and constraints; Obtain initial data points through simulation calculation and initialize the sampling data set; Build an analytical model based on the sampled data set to fit the objective function; Iteratively optimize the analytical model using variable acquisition functions to obtain the optimal values of hybrid design variables; Wherein, the hybrid design variables include fan design variables, duct design variables and fan-duct interaction design variables; The sampling data set is composed of data points; the data points include a set of mixed design variable values and their corresponding objective function values; The variable acquisition function includes a parameter exploration weight κ; the exploration weight κ linearly decays as the number of iterations increases.

2. The design optimization method according to claim 1, characterized in that: The analysis model is iteratively optimized using variable acquisition functions to obtain the optimal values of the hybrid design variables, including: Construct variable acquisition functions based on analytical models; A set of mixed design variable evaluation values are obtained by maximizing the variable acquisition function value; Obtain the corresponding objective function value through simulation calculation; Adding the hybrid design variable evaluation value and the corresponding objective function value as new data points to the sampling data set; Update the analytical model based on the sampled data set; When the absolute value of the difference between the variable acquisition function value in this iteration and the variable acquisition function value in the previous iteration is less than the set threshold, the iterative optimization ends, and the evaluation value of the hybrid design variable in this iteration is the optimal value of the hybrid design variable; otherwise, repeat the above steps to continue the iterative optimization.

3. The design optimization method according to claim 1, characterized in that: The fan design variables include a twist angle of the fan blade at 75% radius and a fan airfoil; the fan airfoil is selected from one or more of a set of predetermined mainstream fan airfoils.

4. The design optimization method according to claim 1, characterized in that: The duct design variables include CST parameters of the duct airfoil.

5. The design optimization method according to claim 1, characterized in that: The fan-duct interaction design variables include the chordwise distance from the duct leading edge to the fan disk and the gap between the duct and the fan tip.

6. The design optimization method according to claim 1, characterized in that: The optimization goal is to minimize the fuel consumption or power of the vertical take-off and landing aircraft in a given mission profile; the mission profile includes specific operating conditions and a complete flight mission.

7. The design optimization method according to claim 6, characterized in that: The specific operating conditions include cruising and hovering.

8. The design optimization method according to claim 1, characterized in that: The constraints include thrust requirements and blade tip Mach number limits.

9. The design optimization method according to claim 1, characterized in that: The simulation calculation includes obtaining a corresponding objective function value based on a set of mixed design variable values, combined with constraint conditions and mission profiles, through fan aerodynamic performance analysis and ducted fan performance analysis.

10. A vertical take-off and landing aircraft ducted fan design optimization system, which can implement the vertical take-off and landing aircraft ducted fan design optimization method according to any one of claims 1 to 9, characterized in that: include: Variable design module: used to set the hybrid design variables and their value ranges of the ducted fan of the vertical take-off and landing aircraft; Objective function building module, used to build objective functions based on mixed design variables, optimization objectives and constraints; Sampling data set initialization module: used to obtain initial data points through simulation calculation and initialize the sampling data set; Analysis model building module: used to build an analysis model based on the sampling data set and fit the objective function; Iterative optimization module: used to iteratively optimize the analysis model using variable acquisition functions to obtain the optimal values of hybrid design variables; Wherein, the hybrid design variables include fan design variables, duct design variables and fan-duct interaction design variables; The sampling data set is composed of data points; the data points include a set of mixed design variable values and their corresponding objective function values; The variable acquisition function includes a parameter exploration weight κ; the exploration weight κ linearly decays as the number of iterations increases.

Images