Robust design optimization method for compressor airfoil under multi-working conditions considering mach number
By considering the Mach number, a robust design optimization method for compressor blades under multiple operating conditions is adopted. This method establishes a design sample space using blade design parameters and incoming flow operating parameters, constructs a weight function and an objective function, and uses an optimization algorithm to find the optimal blade. This solves the problems of blade instability and large computational load in traditional methods, and achieves efficient optimization design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHWESTERN POLYTECHNICAL UNIV
- Filing Date
- 2022-09-07
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional blade design methods optimize at a fixed Mach number, which leads to unstable blade operation under non-design conditions, and also results in high computational cost and low optimization efficiency.
By considering the Mach number, a robust design optimization method for compressor blades under multiple operating conditions is adopted. The design sample space is established using blade design parameters and incoming flow operating parameters. Weight functions and objective functions are constructed, and optimization algorithms are used to find the optimal blade profile, thereby reducing the amount of numerical calculation.
It improves the stability and optimization efficiency of the blade profile under multiple operating conditions, reduces calculation time and quantity, avoids redundant calculations, and expands the design space.
Smart Images
Figure CN116432324B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of aero-engine manufacturing technology, and in particular to a multi-condition robust design optimization method and blade for compressor blades considering Mach number. Background Technology
[0002] Currently, in the field of aero-engine technology, traditional blade design methods are often carried out at a fixed Mach number. However, during compressor operation, it often operates under non-design conditions, which leads to deviations in the incoming flow Mach number and thus affects the working stability of the blade.
[0003] Secondly, traditional airfoil design methods require calculating the airfoil's performance at multiple fixed angles of attack for a given incoming flow Mach number to perform optimization calculations. Therefore, this optimization method optimizes the airfoil by optimizing its performance at fixed angles of attack, qualitatively representing its performance across the entire angle-of-attack range using performance at a few fixed angles of attack. Consequently, this optimization method neglects airfoil performance at other incoming flow angles, leading to reduced optimization accuracy.
[0004] Furthermore, traditional optimization design methods typically require numerical simulations under multiple operating conditions for each design sample point. The number of numerical simulations required by this method equals the number of design sample points multiplied by the number of operating conditions, thus increasing the computational load of the optimization design process. Simultaneously, numerical simulations often require specifying total pressure inlet boundary conditions, necessitating multiple adjustments to the inlet total pressure or outlet static pressure to achieve the required inlet Mach number, further increasing the computational load. Therefore, traditional airfoil design optimization methods have relatively low optimization efficiency.
[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0006] The purpose of this disclosure is to provide a multi-condition robust design optimization method and blade for compressor airfoils that considers Mach number. This multi-condition robust design optimization method for compressor airfoils that considers Mach number can design airfoils with high stability "under design and non-design conditions", and the optimization design method has a small computational load and high optimization design efficiency.
[0007] This disclosure provides a multi-condition robust design optimization method for compressor blades considering Mach number, including:
[0008] The design sample space is established using the airfoil design parameters and incoming flow condition parameters as variables.
[0009] Multiple design sample points are selected in the design sample space, and each design sample point includes design parameters and incoming flow condition parameters;
[0010] Based on the design parameters of each design sample point, and combined with the leaf shape generation method, the leaf shape corresponding to each design sample point is generated.
[0011] Numerical calculations are performed on the airfoil based on the incoming flow operating parameters to obtain the loss coefficient of the airfoil under different incoming flow angles of attack.
[0012] Based on the loss coefficient and the incoming flow parameters, construct the weight function and objective function for the airfoil.
[0013] Based on the design parameters and objective function values corresponding to each blade shape, an approximate surrogate model is established, and an optimization algorithm is used to perform optimization calculations to obtain the optimal blade shape.
[0014] The incoming flow parameters include the incoming flow angle of attack and Mach number of the airfoil.
[0015] In an exemplary embodiment of this disclosure, establishing a design sample space using the airfoil's design parameters and incoming flow condition parameters as variables includes:
[0016] The design parameters and incoming flow conditions of the blade profile are selected as variables, and the value ranges of the design parameters and incoming flow conditions are set to form a design space;
[0017] The Design of Experiments (DOE) methodology is used to process different design parameters and different incoming flow condition parameters in the design space to form multiple design sample points.
[0018] The multiple design sample points are aggregated to form the design sample space.
[0019] In one exemplary embodiment of this disclosure, the use of the Design of Experiments (DOE) method to process different design parameters and different incoming flow condition parameters in the design space to form multiple design sample points includes:
[0020] Different design parameters and different incoming flow condition parameters in the design space are used as input variables for the DOE experimental design method;
[0021] The input variables are processed using the DOE experimental design method to form multiple design sample points.
[0022] In one exemplary embodiment of this disclosure, the step of numerically calculating the airfoil based on the incoming flow operating parameters to obtain the loss coefficient of the airfoil under different incoming flow angles of attack includes:
[0023] Based on the inlet total pressure, outlet total pressure, and inlet dynamic pressure of the blade profile, numerical simulations are performed on the blade profile under different incoming flow angles of attack to obtain the loss coefficient of the blade profile under different incoming flow angles of attack.
[0024] In one exemplary embodiment of this disclosure, constructing the weight function and objective function of the airfoil based on the loss coefficient and the incoming flow operating parameters includes:
[0025] The incoming flow angle of attack and Mach number of the blade are set as variables to construct the weight function of the blade.
[0026] The weighting function is multiplied by the loss coefficient to form the objective function of the leaf shape.
[0027] In one exemplary embodiment of this disclosure, the weighting function is:
[0028]
[0029] Where c(Ma,i) is a weighting coefficient with Mach number and incoming flow angle of attack as variables; Ma is the Mach number; i is the incoming flow angle of attack; Ma is the Mach number; and Ma0 is the design Mach number.
[0030] In one exemplary embodiment of this disclosure, the objective function is:
[0031] f = cω,
[0032] Where f is the objective function; c is the weighting function; and ω is the loss coefficient.
[0033] In one exemplary embodiment of this disclosure, the step of establishing an approximate surrogate model based on the design parameters and objective function values corresponding to each of the blade profiles, and employing an optimization algorithm to perform optimization calculations to obtain the optimal blade profile includes:
[0034] Substitute the incoming flow angle of attack, Mach number and loss coefficient under different incoming flow angles of attack for each blade type into the objective function to obtain the objective function value for each blade type.
[0035] Based on the design parameters and objective function values corresponding to each of the described airfoils, an approximate surrogate model is established;
[0036] Based on the approximate surrogate model, an optimization algorithm is used to obtain the optimal leaf shape through optimization calculation.
[0037] In one exemplary embodiment of this disclosure, the optimization algorithm is a multi-island genetic algorithm.
[0038] This disclosure also provides a blade that has obtained the optimal blade design through the compressor blade multi-condition robust design optimization method considering Mach number as described in any of the preceding claims.
[0039] The technical solution provided in this disclosure can achieve the following beneficial effects:
[0040] The compressor airfoil robustness design optimization method considering Mach number provided in this disclosure establishes a design sample space by using the airfoil design parameters and incoming flow condition parameters as variables. Therefore, compared to existing technologies, the design optimization method of this application expands the design space, thereby enabling the optimized airfoil to have a lower loss coefficient over a larger operating range, meaning that the optimized airfoil exhibits better stability under various operating conditions.
[0041] Furthermore, because the design optimization method of this application introduces incoming flow operating parameters as variables and constructs a weighting function with these parameters as variables, and establishes the objective function using the loss coefficient and weighting function of the airfoil under different incoming flow angles of attack, this application establishes an approximate surrogate model based on the design parameters and corresponding objective function values for each airfoil, and obtains the optimal airfoil through an optimization algorithm. Therefore, this application can significantly reduce the amount and time of numerical calculations during the design optimization process, improving optimization efficiency and accuracy, thereby solving the drawback of existing technologies that require aerodynamic performance calculations for multiple operating conditions for each design sample point. Simultaneously, the optimization design method of this application can also avoid the problem of extensive repetitive calculations in existing technologies to obtain a uniform incoming flow Mach number.
[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0044] Figure 1 A flowchart illustrating a multi-condition robust design optimization method for compressor blades considering Mach number according to an exemplary embodiment of the present disclosure is shown.
[0045] Figure 2A schematic diagram of forming a thickness distribution curve according to an exemplary embodiment of the present disclosure is shown;
[0046] Figure 3 A schematic diagram of an arc formed according to an exemplary embodiment of the present disclosure is shown;
[0047] Figure 4 A schematic diagram of forming a blade according to an exemplary embodiment of the present disclosure is shown.
[0048] Explanation of reference numerals in the attached figures:
[0049] 1. Mid-curve; 2. Thickness distribution curve; 3. Suction surface profile; 4. Pressure surface profile. Detailed Implementation
[0050] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.
[0051] Although relative terms such as "up" and "down" are used in this specification to describe the relative relationship of one component of an icon to another, these terms are used only for convenience, such as according to the orientation of the examples shown in the accompanying drawings. It is understood that if the device of the icon is flipped upside down, the component described as "up" will become the component described as "down." When a structure is "up" of another structure, it may mean that the structure is integrally formed on the other structure, or that the structure is "directly" mounted on the other structure, or that the structure is "indirectly" mounted on the other structure through another structure.
[0052] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended inclusion meaning and that there may be other elements / components / etc. in addition to the listed elements / components / etc.; the terms “first” and “second” are used only as markers and are not a limitation on the number of objects.
[0053] This disclosure first provides a robust multi-condition design optimization method for compressor airfoils considering Mach number. This method can be applied to the airfoil design of aero-engine compressor blades, but is not limited thereto. It can also be applied to other mechanical equipment using blades, all of which are within the scope of this disclosure. Airfoils designed using this robust multi-condition design optimization method exhibit high stability, and the computational load is relatively small, thus saving significant computation time and significantly improving the efficiency of airfoil design optimization.
[0054] The above-mentioned compressor blade robustness design optimization method considering Mach number can include:
[0055] Step S10: Establish a design sample space using the airfoil design parameters and incoming flow condition parameters as variables.
[0056] Step S20: Select multiple design sample points in the design sample space. Each design sample point includes design parameters and incoming flow condition parameters.
[0057] Step S30: Based on the design parameters of each design sample point and the blade shape generation method, generate the blade shape corresponding to each design sample point.
[0058] Step S40: Perform numerical calculations on the airfoil based on the incoming flow operating parameters to obtain the loss coefficient of the airfoil under different incoming flow angles of attack.
[0059] Step S50: Construct the weight function and objective function of the airfoil based on the loss coefficient and the incoming flow operating parameters.
[0060] Step S60: Based on the design parameters and objective function values corresponding to each blade type, establish an approximate surrogate model, use an optimization algorithm and perform optimization calculations to obtain the optimal blade type.
[0061] The following is a detailed explanation of each of the above steps:
[0062] In step S10 above, the design parameters of the airfoil and the incoming flow operating parameters can be used as variables to establish a design sample space. Specifically, the design parameters of the airfoil and the incoming flow operating parameters can be selected as variables, and a range of values can be set for the design parameters and the incoming flow operating parameters to form a design space.
[0063] In one embodiment of this disclosure, the design parameters of the airfoil may include one or more of the following: chord length, inlet geometry angle, gate pitch, bend angle, and maximum thickness, but are not limited thereto. The design parameters of the airfoil provided in this disclosure may also include other parameters, which can be selected according to actual needs, and all of these are within the protection scope of this disclosure.
[0064] The aforementioned incoming flow operating parameters may include the airfoil's incoming flow angle of attack and Mach number, but are not limited to these. The incoming flow operating parameters provided in this disclosure may also include other parameters, which can be selected according to actual needs, and all of these are within the protection scope of this disclosure.
[0065] Furthermore, the design parameters of the airfoil and the incoming flow operating parameters can be set with value ranges to form a design space. Thus, the optimization design method of this application can significantly expand the design space by introducing airfoil design parameters and incoming flow operating parameters, thereby enabling the optimized airfoil to have a lower loss coefficient over a larger operating range. In other words, the optimized airfoil of this application has better stability under various operating conditions.
[0066] In one embodiment of this disclosure, the Design of Experiments (DOE) method can be used to process different design parameters and different incoming flow condition parameters in the design space to form multiple design sample points. Specifically, the design space of this application can have multiple different design parameters and different incoming flow condition parameters. The different design parameters and different incoming flow condition parameters in the design space can be used as input variables for the DOE method. Furthermore, the DOE method can be used to process the input variables to form multiple design sample points.
[0067] Furthermore, multiple design sample points can be aggregated to form a design sample space. For example, each design sample point can be represented by x. n If we represent this, then the sample space can be designed as X = (x1, x2, ..., x...). n ).
[0068] In step S20 above, multiple design sample points can be selected in the design sample space. Each design sample point can include the airfoil design parameters and the incoming flow operating parameters. In one embodiment of this disclosure, all design sample points in the design sample space can be selected for subsequent calculations to obtain more accurate calculation optimization results, resulting in better stability of the optimized airfoil. However, this is not the only option; some design sample points in the design sample space can also be selected for subsequent calculations, thereby saving a significant amount of computation and improving computational efficiency. Those skilled in the art can make selections according to actual needs, all of which are within the scope of protection of this disclosure.
[0069] In step S30 above, the blade shape corresponding to each design sample point can be generated based on the design parameters of each design sample point and the blade shape generation method.
[0070] Specifically, the leaf shape generation method may include: First, when generating the leaf shape, this application can utilize the chord length B of the leaf shape. t Import geometry angle β1, bend angle θ, grid pitch, and maximum thickness t max As a design parameter for the airfoil, the airfoil is formed by superimposing the thickness distribution on the mid-arc line 1. When determining the mid-arc line 1 of the airfoil, an initial installation angle value can be given first, and the leading arc can be determined using design parameters from known design sample points. Further, the coordinates of the maximum deflection point and the trailing edge point of the airfoil can be determined, thus initially determining the mid-arc line 1 of the airfoil. Then, this application can use the installation angle of the obtained mid-arc line 1 as the initial value to iterate back through the design process of the mid-arc line 1 until the error between the actual value and the initial value meets the minimum constraint condition. This error can then be used as the final installation angle value, thereby establishing the mid-arc line 1 formed by the tangency of two arc segments at the maximum deflection point.
[0071] Furthermore, the thickness distribution of the blade profile is obtained using a multi-circular arc tangency method. First, the leading edge point, maximum thickness point, and trailing edge point of the blade profile can be established based on the design parameters of the aforementioned design sample points. Then, based on the leading edge point, maximum thickness point, and trailing edge point, the leading edge circle, trailing edge circle, and the circles on the left and right sides of the maximum thickness are established. Next, the radii of each circle can be adjusted, and tangent arcs can be taken to form the leading edge arc, leading segment arc, trailing segment arc, and trailing edge arc. In this way, the blade profile thickness distribution curve composed of the leading edge arc, leading segment arc, trailing segment arc, and trailing edge arc can be established. When the thickness distribution curve of the blade profile is smoother, its surface velocity distribution is more continuous, and the blade profile loss is lower.
[0072] To further suppress excessive expansion of the airflow on the leading edge surface of the blade and reduce the peak velocity of the suction surface, an elliptical design method can be used when designing the leading edge of the blade profile. This method involves determining the leading edge arc, selecting a position L at a distance from the leading edge point on the blade profile thickness curve, and then drawing an elliptical arc at that position connected to the leading edge point, thus forming the leading edge of the blade profile.
[0073] Finally, after determining the thickness t distribution curve 2 and the mid-arc line 1 of the airfoil, the thickness t distribution curve 2 can be divided along the chord length to obtain a series of thickness t distribution values. Next, the mid-arc line 1 can be divided according to the coordinates of each division point on the chord as described above. The corresponding thickness t can be superimposed at each division point of the mid-arc line 1 to finally obtain the suction surface profile 3 and pressure surface profile 4 of the airfoil. Thus, the airfoil corresponding to each design sample point can be finally constructed using the suction surface profile 3 and pressure surface profile 4 of the airfoil.
[0074] In step S40 above, the blade profile can be numerically calculated based on the incoming flow operating parameters to obtain the loss coefficient of the blade profile under different incoming flow angles of attack.
[0075] Specifically, the incoming flow parameters here may also include: the total inlet pressure, total outlet pressure, and inlet dynamic pressure of the airfoil. A loss coefficient calculation function can be constructed based on the total inlet pressure, total outlet pressure, and inlet dynamic pressure of the airfoil, and the loss coefficient of the airfoil under different incoming flow angles of attack can be obtained through numerical calculation. Since the total inlet pressure, total outlet pressure, and inlet dynamic pressure are different for each airfoil, and other aerodynamic parameters of the airfoil, such as Mach number, can be calculated based on the total inlet pressure, total outlet pressure, and inlet dynamic pressure of the airfoil, this disclosure can calculate the loss coefficient of the airfoil under different incoming flow angles of attack based on the total inlet pressure, total outlet pressure, and inlet dynamic pressure of each airfoil.
[0076] It should be noted that the loss efficiency of the aforementioned airfoil and the incoming flow angle of attack can have a corresponding relationship, which can be a U-shaped curve. Since the incoming flow angle of attack is considered the effective angle of attack when the loss efficiency corresponding to the airfoil is below a certain value, the range of angles of attack below this specific loss efficiency value is the effective angle of attack range. Therefore, in airfoil design optimization, it is desirable to maximize the effective angle of attack range so that the designed airfoil can adapt to a wider range of operating conditions.
[0077] In one embodiment of this disclosure, the loss coefficient function described above can be:
[0078] w = (P t,in -P t,out ) / (P t,in -P in )
[0079] Where w is the loss coefficient; P t,in P is the total inlet pressure. t,out Total pressure at the outlet; (P) t,in -P in ) represents the inlet dynamic pressure; P in The inlet static pressure.
[0080] In step S50 above, the weight function and objective function of the airfoil can be constructed based on the loss coefficient and the incoming flow operating parameters.
[0081] Specifically, the incoming airflow angle of attack and Mach number of the blade shape can be set as variables to construct a weighting function.
[0082] In one embodiment of this disclosure, the weighting function can be:
[0083]
[0084] Where c(Ma,i) is a weighting coefficient with Mach number and incoming flow angle of attack as variables; Ma is the Mach number; i is the incoming flow angle of attack; Ma is the Mach number; and Ma0 is the design Mach number.
[0085] Therefore, this disclosure can obtain the weight function value for each airfoil by substituting the incoming flow angle of attack and Mach number corresponding to each airfoil generated from various design samples into the weight function formula described above. Since the weight function provided in this disclosure uses the incoming flow angle of attack and Mach number as variables, the optimized airfoil can adapt to a wider range of operating conditions. Furthermore, by introducing the incoming flow angle of attack and Mach number as variables, the number and time of numerical calculations during the design optimization process can be greatly reduced, improving optimization efficiency and accuracy, thus solving the drawback of existing technologies that require aerodynamic performance calculations for multiple operating conditions for each design sample point. Simultaneously, because the design optimization method of this disclosure uses the Mach number as a variable, this disclosure also avoids the problem of extensive repetitive calculations in existing technologies to obtain a uniform incoming flow Mach number.
[0086] In one embodiment of this disclosure, the weighting function can be multiplied by the loss coefficient to form the objective function of the leaf shape.
[0087] f=cω
[0088] Where f is the objective function; c is the weighting function; and ω is the loss coefficient.
[0089] In step S60 above, an approximate surrogate model can be established based on the design parameters and objective function values corresponding to each blade shape. An optimization algorithm is then used to perform optimization calculations to obtain the optimal blade shape.
[0090] Specifically, the incoming flow angle of attack, Mach number, and loss coefficients at different incoming flow angles of attack for each airfoil can be substituted into the objective function to obtain the objective function value for each airfoil. Furthermore, an approximate surrogate model can be established based on the design parameters and objective function values for each airfoil.
[0091] Furthermore, based on an approximate surrogate model, an optimization algorithm can be used to obtain the optimal airfoil through optimization calculation. This disclosure uses an optimization algorithm to find the minimum value of the objective function, and the airfoil corresponding to the minimum value of the objective function is the optimal airfoil. Since this optimal airfoil is obtained through an approximate surrogate model and an optimization algorithm, and the approximate surrogate model includes the variables of the incoming flow angle of attack and Mach number, the optimal airfoil obtained in this way has a wider operating range and higher stability. Consequently, blades designed with this airfoil can also have a wider operating range and higher stability.
[0092] In one embodiment of this disclosure, the optimization algorithm can be a multi-island genetic algorithm. However, it is not limited to this; other types of optimization algorithms can also be applied, and the choice can be made according to actual needs, all of which are within the scope of protection of this disclosure.
[0093] As described above, the multi-condition robust design optimization method for compressor airfoils considering Mach number provided in this disclosure establishes a design sample space by using the airfoil design parameters and incoming flow operating parameters as variables. Therefore, compared to existing technologies, this multi-condition robust design optimization method for compressor airfoils considering Mach number expands the design space, thereby enabling the optimized blades to have lower loss coefficients over a wider range of operating conditions. In other words, the optimized blades designed using this multi-condition robust design optimization method for compressor airfoils considering Mach number exhibit better stability under various operating conditions.
[0094] Furthermore, because the design optimization method of this disclosure introduces incoming flow condition parameters as variables and constructs a weighting function and an objective function with the incoming flow condition parameters as variables, and establishes an approximate surrogate model through the objective function, this disclosure can greatly reduce the amount and time of numerical calculations in the design optimization process, improve optimization efficiency and accuracy, and thus solve the drawback of the prior art that requires aerodynamic performance calculations for multiple conditions for each design sample point. At the same time, the design optimization method of this disclosure can also avoid the problem of extensive repetitive calculations in the prior art to obtain a uniform incoming flow Mach number.
[0095] This disclosure also provides a blade that can be optimized by the multi-condition robust design optimization method for compressor blades that takes into account Mach number as described above.
[0096] The blade designed with the optimal blade profile obtained by the above-mentioned compressor blade robustness design optimization method considering Mach number can comprehensively consider the influence of incoming flow angle of attack and Mach number on blade performance. This optimizes the design method and expands the design space, so that the blade designed by the method adopted in this disclosure can have a lower loss coefficient in a larger operating range. That is, the blade generated by the design optimization of this disclosure has better stability under various operating conditions.
[0097] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.
Claims
1. A robust design optimization method for compressor blades considering Mach number under multiple operating conditions, characterized in that, include: The design sample space is established using the airfoil design parameters and incoming flow condition parameters as variables. Multiple design sample points are selected in the design sample space, and each design sample point includes design parameters and incoming flow condition parameters; Based on the design parameters of each design sample point, and combined with the leaf shape generation method, the leaf shape corresponding to each design sample point is generated. Numerical calculations are performed on the airfoil based on the incoming flow operating parameters to obtain the loss coefficient of the airfoil under different incoming flow angles of attack. Based on the loss coefficient and the incoming flow parameters, construct the weight function and objective function for the airfoil. Based on the design parameters and objective function values corresponding to each blade shape, an approximate surrogate model is established, and an optimization algorithm is used to perform optimization calculations to obtain the optimal blade shape. The incoming flow operating parameters include the incoming flow angle of attack and Mach number of the airfoil; the step of constructing the weight function and objective function of the airfoil based on the loss coefficient and the incoming flow operating parameters includes: setting the incoming flow angle of attack and Mach number of the airfoil as variables to construct the weight function of the airfoil; and multiplying the weight function with the loss coefficient to form the objective function of the airfoil.
2. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 1, characterized in that, The step of establishing a design sample space using the airfoil's design parameters and incoming flow condition parameters as variables includes: The design parameters and incoming flow conditions of the blade profile are selected as variables, and the value ranges of the design parameters and incoming flow conditions are set to form a design space; The Design of Experiments (DOE) methodology is used to process different design parameters and different incoming flow condition parameters in the design space to form multiple design sample points. The multiple design sample points are aggregated to form the design sample space.
3. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 2, characterized in that, The DOE (Design of Experiments) experimental design method is used to process different design parameters and different incoming flow condition parameters in the design space to form multiple design sample points, including: Different design parameters and different incoming flow condition parameters in the design space are used as input variables for the DOE experimental design method; The input variables are processed using the DOE experimental design method to form multiple design sample points.
4. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 1, characterized in that, The step of performing numerical calculations on the airfoil based on the incoming flow operating parameters to obtain the loss coefficient of the airfoil under different incoming flow angles of attack includes: Based on the inlet total pressure, outlet total pressure, and inlet dynamic pressure of the blade profile, numerical simulations are performed on the blade profile under different incoming flow angles of attack to obtain the loss coefficient of the blade profile under different incoming flow angles of attack.
5. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 1, characterized in that, The weighting function is: ， in, These are weighting coefficients with Mach number and incoming angle of attack as variables; It is the Mach number; For the angle of attack of the incoming flow; To design the Mach number.
6. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 5, characterized in that, The objective function is: ， in, The objective function is... The weighting function is... The loss coefficient is denoted as .
7. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 1, characterized in that, The step of establishing an approximate surrogate model based on the design parameters and objective function values corresponding to each blade shape, and using an optimization algorithm to perform optimization calculations to obtain the optimal blade shape includes: Substitute the incoming flow angle of attack, Mach number and loss coefficient under different incoming flow angles of attack for each blade type into the objective function to obtain the objective function value for each blade type. Based on the design parameters and objective function values corresponding to each of the described airfoils, an approximate surrogate model is established; Based on the approximate surrogate model, an optimization algorithm is used to obtain the optimal leaf shape through optimization calculation.
8. The compressor blade robustness design optimization method considering Mach number under multiple operating conditions according to claim 7, characterized in that, The optimization algorithm is a multi-island genetic algorithm.
9. A blade, characterized in that, The blade is the optimal blade design obtained by the compressor blade multi-condition robust design optimization method considering Mach number as described in any one of claims 1 to 8.