CN106934074B - Global optimal turbofan engine air inlet channel noise reduction design method - Google Patents

Global optimal turbofan engine air inlet channel noise reduction design method Download PDF

Info

Publication number
CN106934074B
CN106934074B CN201511015576.6A CN201511015576A CN106934074B CN 106934074 B CN106934074 B CN 106934074B CN 201511015576 A CN201511015576 A CN 201511015576A CN 106934074 B CN106934074 B CN 106934074B
Authority
CN
China
Prior art keywords
design
noise
noise reduction
optimization
equation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201511015576.6A
Other languages
Chinese (zh)
Other versions
CN106934074A (en
Inventor
邱昇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
AECC Commercial Aircraft Engine Co Ltd
Original Assignee
AECC Commercial Aircraft Engine Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by AECC Commercial Aircraft Engine Co Ltd filed Critical AECC Commercial Aircraft Engine Co Ltd
Priority to CN201511015576.6A priority Critical patent/CN106934074B/en
Publication of CN106934074A publication Critical patent/CN106934074A/en
Application granted granted Critical
Publication of CN106934074B publication Critical patent/CN106934074B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/14—Pipes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/10—Noise analysis or noise optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention provides a global optimal turbofan engine air inlet noise reduction design method, which comprises the following steps: s1Taking a sample point in a design space for test design; s2Firstly, calculating a background flow field value of the sample point, and then calculating a noise value based on the background flow field to obtain a response value of the sample point; s3Obtaining a local optimization design point by adopting an adjoint method; s4Establishing a proxy model based on the sample points and the response values thereof; s5After the agent model is established, a cultural genetic algorithm is used for carrying out high-efficiency optimization design on the agent model. The global optimal turbofan engine air inlet channel noise reduction design method can find a global optimal solution, greatly saves calculation cost, and has great advantages compared with other optimization design methods in the situation of a large number of design variables, so that the low-noise optimization design of multi-parameter complex shapes is realized, and the method has great research value and wide application prospect.

Description

Global optimal turbofan engine air inlet channel noise reduction design method
Technical Field
The invention relates to the field of turbofan engine pneumatic noise control, in particular to a global optimal turbofan engine air inlet channel noise reduction design method.
Background
In the field of aircraft, aircraft noise issues pose an important issue for the continuously growing air transportation industry. The magnitude of the noise level is directly related to the acquisition of the airworthiness visa of the airplane. This is undoubtedly a huge challenge for civil airliners being developed in our country.
Engine noise, which is one of the main sources of aircraft noise, has changed greatly in its noise form (directivity, radiated sound power level). The velocity of the air flow exhausted by the traditional low bypass ratio engine is very high, and jet flow and shock wave noise are main noise sources. Nowadays, in pursuit of high efficiency, the bypass ratio of the civil turbofan engine is made larger, even more than 10, and the exhaust gas flow speed is greatly reduced. Fan noise in the nacelle, relative to compressor, jet and turbine noise, becomes a major source of noise in modern high bypass ratio engines. In addition, for modern civilian aircraft, fan noise is a major source of noise when the aircraft is hovering and flying sideways.
Conventional approaches in engine nacelle noise reduction optimization typically only consider one to two few design variables, and are mostly based on simple one or two design variables. This method has too little design space and the resulting optimized shape is deformed too little and not smooth enough to represent well the various possible situations of the geometric shape. These optimization efforts typically employ conventional optimization methods that are search inefficient. However, for practical engineering problems, the design variables involved are often large.
The current civil passenger plane development has higher and higher requirements on noise, economy, safety and range. At present, the design of the aircraft begins to develop from the traditional multi-turn iteration between single disciplines to the multidisciplinary comprehensive design, and at the moment, the design problem of the engine nacelle is a multi-objective and multi-constraint problem. In the multi-point design and multidisciplinary optimization design problem, the design parameters of the nacelle of the turbofan engine not only comprise appearance parameters, but also comprise aerodynamic performance parameters (lift coefficient, drag coefficient, lift-drag ratio and the like), so that the noise reduction optimization problem of the fan noise of the nacelle of the turbofan engine comprises a large number of design parameters. Due to more design variables and higher single computation cost of the abnormal nacelle fan noise, the whole noise reduction optimization computation workload is huge. This is difficult to achieve with existing optimization methods and computational conditions.
In this regard, many approaches have been proposed to solve the complex optimization problem. Such as genetic algorithms, simulated annealing algorithms, particle swarm algorithms, ant colony algorithms, and the like. Compared with the traditional optimization method based on the gradient, the algorithms have the characteristics of good robustness, globality, high parallelism and the like. The method is successfully applied to the multi-peak nonlinear function optimization problem. However, the biggest disadvantage of these algorithms is the slow convergence speed. To overcome this drawback of global optimization algorithms, a method called proxy model (surrogate model) has been used instead of time-consuming accurate model evaluation. The calculated amount of the proxy model is much smaller than that of an accurate model, the accuracy can be guaranteed, and the calculated amount in the optimization process can be greatly reduced by adopting the proxy model.
The concomitant method proposed by Jameson in 1988 has incomparable advantages with other optimization methods. The method is based on the mathematical theory of optimization control of a system defined by partial differential equations, takes the boundary shape of an object as a control equation, a flowing control equation as a constraint condition of the system, and a design target is expressed by an objective function. Thus, the design problem translates into a search for an optimal control problem that satisfies the constraints. The gradient calculation only needs about twice the calculation amount of the flow field calculation, and is independent of the number of design variables, so the calculation amount is greatly reduced.
The companion method is a great advantage in terms of computational overhead as an alternative to other methods in dealing with design problems that contain a large number of design variables. All the sensitivity derivatives can be obtained only by once flow field calculation and once control theory equation solution. The calculation cost for solving the control theory equation is similar to the cost magnitude for solving the flow field equation, so that the gradient value of any design parameter is equivalent to the calculation amount of the flow field equation in two times. The method has the obvious advantage that the gradient calculation is almost independent of the number of design variables, overcomes the problem of high calculation cost in pneumatic noise optimization, enables multi-parameter low-noise optimization design to be possible, and has great research value and wide application prospect. Therefore, the contour optimization method based on the adjoint method is more economical than the classical gradient-based optimization method, especially when the design problem involves a large number of design parameters.
However, little research has been done on the current aerodynamic noise optimization problem, particularly engine duct noise optimization, and genetic optimization algorithms are generally used that are computationally expensive. The technical problems and difficulties of small number of design variables and high calculation cost exist. It is crucial to develop inlet noise reduction design theories and tools that can handle a large number of design parameters. However, there is no theory or methodology that is easy to operate and that is efficient in performing noise reduction designs that contain a large number of design parameters. In a word, the development of the high-efficiency multi-parameter noise reduction optimization method based on the continuous adjoint method, the improved culture gene algorithm and the agent model has important significance.
The adjoint method has disadvantages in the overall convergence and global optimal solution problem, and generally converges to the local optimal solution. This is a limitation of most gradient-based non-linear optimization methods.
In summary, the existing air inlet design method has the following problems:
one, the traditional method only can consider one or two or other few design variables in the noise reduction optimization of the engine nacelle, and mostly bases on one or two simple design variables, the design space is too small, the obtained optimized shape is too small in deformation and not smooth enough, and various possible situations of the geometric shape cannot be well expressed. However, for practical engineering problems, the design variables involved are often large.
Second, conventional methods usually employ conventional optimization methods, such as genetic algorithms, which are inefficient or time-consuming to search. The optimization results are obtained by consuming a large amount of calculation time, which is not favorable for the design of engine noise reduction.
And thirdly, the traditional method only can consider the influence of no-flow or uniform background flow on the sound field.
And fourthly, when the air inlet channel is optimized, the used noise calculation method, the optimized objective function and the optimized search method are different.
Disclosure of Invention
The invention provides a global optimal turbofan engine air inlet channel noise reduction design method, aiming at overcoming the defect that the problem of noise reduction optimization of a turbofan engine air inlet channel involving a large number of design parameters in the prior art cannot be well solved.
The invention solves the technical problems through the following technical scheme: a global optimal turbofan engine air inlet noise reduction design method is characterized by comprising the following steps:
S1taking a sample point in a design space for test design;
S2firstly, calculating a background flow field value of the sample point, and then calculating a noise value based on the background flow field to obtain a response value of the sample point;
S3obtaining a local optimization design point by adopting an adjoint method;
S4establishing a proxy model based on the sample points and the response values thereof;
S5after the agent model is established, a cultural genetic algorithm is used for carrying out high-efficiency optimization design on the agent model.
Preferably, the step S5Then the following step S is included6: checking whether the convergence criterion is met, and if the convergence criterion meets the requirement, obtaining the optimal solution of the object problem; otherwise, adding the optimal design into the sample point, and returning to the step S4。
Preferably, the cultural genetic algorithm adds the current optimal design point into the sample for the next modeling optimization until the convergence criterion is met.
Preferably, the step S2Wherein the response value of the sample point is obtained by a numerical analysis program.
Preferably, the step S5The culture genetic algorithm sequentially performs a culture genetic algorithm with high search efficiency and a noise reduction optimization design method based on an adjoint method and a noise control equation.
Preferably, the cultural genetic algorithm with high search efficiency comprises the following steps:
S51initializing a population;
S52calculating the fitness of the particles, and recording the individual extreme value and the group three-dimensional extreme value of the particles;
S53carrying out global search on the particles;
S54performing local search on the particles;
S55updating the particles and keeping individual extremum and group extremum;
S56judging whether the termination condition is met, if so, entering the step S57(ii) a If not, returning to the step S52；
S57Performing crossover and mutation operations;
S58judging whether the termination condition is met, if so, ending; if it isIf not, returning to the step S53。
Preferably, the noise reduction optimization design method based on the adjoint method and the noise control equation comprises the following steps:
S511the nacelle of the turbofan engine is pneumatic;
S512calculating average background flow;
S513calculating a noise adjoint equation through the target function definition and the adjoint boundary condition;
S514calculating a target function and a gradient value through a large number of design variables and network disturbance;
S515judging whether the convergence criterion is met; if so, performing local optimal design; if not, an optimization algorithm is carried out, a new pneumatic appearance of the nacelle is designed, and the step S is returned512。
Preferably, the step S513The method specifically comprises the following steps:
firstly, derivation of an air inlet passage noise propagation control equation under a curve coordinate system is carried out, and the expression of a 2.5-D L EE equation in a matrix form under a cylindrical coordinate system is as follows:
Figure BDA0000893999800000041
wherein:
Figure BDA0000893999800000042
then, the noise adjoint equation is solved, and the variation of the state vector is dividedwThe coefficient term of the front is 0, and a differential control theory equation of psi is obtained:
Figure BDA0000893999800000051
wherein:
Figure BDA0000893999800000052
thus, the control principle on the fixed wall is obtained by making psi satisfy the condition that the coefficient term including p' front is 0The theoretical boundary conditions are as follows:
Figure BDA0000893999800000053
by discretizing the above equation, the theoretical boundary conditions for upper surface control are obtained:
Figure BDA0000893999800000054
the control theory boundary conditions on the integral line are respectively as follows:
Figure BDA0000893999800000055
an integral line l can also be obtained1Boundary condition of
Figure BDA0000893999800000056
Finally, design variable gradient solving is carried out, and the gradient expression is finally simplified into the following form
Figure BDA0000893999800000057
Simultaneously solving the gradient:
Figure BDA0000893999800000058
wherein a isiAre the coefficients of the shape function.
The positive progress effects of the invention are as follows:
the global optimal turbofan engine air inlet channel noise reduction design method can find a global optimal solution, greatly saves calculation cost, and has great advantages compared with other optimization design methods in the situation of a large number of design variables, so that the low-noise optimization design of multi-parameter complex shapes is realized, and the method has great research value and wide application prospect. The design method for reducing the noise of the air inlet passage of the turbofan engine can quickly design the low-noise air inlet passage of the nacelle meeting the pneumatic requirement. The optimized air inlet passage shape has the advantages that the intensity of sound wave diffraction is smaller, less energy is radiated outwards, and the low-noise characteristic is achieved. In addition, the design method has the advantages of low calculation cost, wide application range, high calculation precision and easy realization, and can be applied to noise reduction optimization design of a large number of design parameters of all parts of the turbofan engine.
Drawings
The above and other features, properties and advantages of the present invention will become more apparent from the following description of the embodiments with reference to the accompanying drawings in which like reference numerals denote like features throughout the several views, wherein:
FIG. 1 is a flow chart of a global optimal turbofan engine intake duct noise reduction design method of the present invention.
FIG. 2 is a flow chart of a cultural genetic algorithm with high search efficiency in the global optimal turbofan engine air inlet noise reduction design method of the present invention.
FIG. 3 is a flow chart of a noise reduction optimization design method based on an adjoint method and a noise control equation in the global optimal turbofan engine air inlet channel noise reduction design method.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and thus the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 is a flow chart of a global optimal turbofan engine intake duct noise reduction design method of the present invention.
As shown in FIG. 1, in one embodiment of the present invention, a global optimal turbofan engine inlet noise reduction design method is provided, which comprises the following steps:
step one, taking a sample point in a design space to carry out test design.
And secondly, calculating a background flow field value of the sample point, and then calculating a noise value based on the background flow field to obtain a response value of the sample point. Here, the response value of the sample point can be obtained by a numerical analysis program.
And step three, obtaining a local optimization design point by adopting an adjoint method.
And fourthly, establishing a proxy model based on the sample points and the response values thereof.
And fifthly, after the agent model is established, performing high-efficiency optimization design on the agent model by using a cultural gene algorithm. And adding the current optimal design point into the sample by the culture gene algorithm for next modeling optimization until the convergence criterion is met.
Step six, checking whether the convergence criterion is met, and if the convergence criterion meets the requirement, obtaining the optimal solution of the object problem; otherwise, adding the optimal design into the sample point, and returning to the step four.
Particularly, the cultural genetic algorithm in the fifth step sequentially performs a cultural genetic algorithm with high search efficiency and a noise reduction optimization design method based on an adjoint method and a noise control equation.
FIG. 2 is a flow chart of a cultural genetic algorithm with high search efficiency in the global optimal turbofan engine air inlet noise reduction design method of the present invention.
As shown in fig. 2, the cultural genetic algorithm with high search efficiency includes the following steps:
first, a population is initialized.
Secondly, calculating the fitness of the particles, and recording the individual extreme value and the group three-dimensional extreme value of the particles.
And thirdly, carrying out global search on the particles.
Fourthly, local searching is carried out on the particles.
And fifthly, updating the particles and keeping the individual extremum and the group extremum.
Sixthly, judging whether a termination condition is met, if so, entering the following steps; if not, returning to the second step.
And seventhly, performing crossover and mutation operations.
Eighthly, judging whether the termination condition is met, if so, ending; if not, returning to the third step.
FIG. 3 is a flow chart of a noise reduction optimization design method based on an adjoint method and a noise control equation in the global optimal turbofan engine air inlet channel noise reduction design method.
As shown in fig. 3, the noise reduction optimization design method based on the adjoint method and the noise control equation includes the following steps:
first, the turbofan engine nacelle is aerodynamic.
Second, average background flow calculation.
Thirdly, noise adjoint equation calculation is carried out through the definition of an objective function and adjoint boundary conditions.
And fourthly, calculating an objective function and a gradient value through a large number of design variables and network disturbance.
Fifthly, judging whether the convergence criterion is met; if so, performing local optimal design, thereby finishing a cycle; if not, an optimization algorithm is carried out, a new pneumatic appearance of the nacelle is designed, and the second step is returned.
For the turbofan engine noise reduction design based on the accompanying method, the third step is derived as follows:
firstly, derivation of an air inlet passage noise propagation control equation under a curve coordinate system is carried out.
The expression of the 2.5-D L EE equation in matrix form in a cylindrical coordinate system is as follows:
equation 1:
Figure BDA0000893999800000081
wherein:
Figure BDA0000893999800000082
next, the noise accompaniment equation is solved.
By making the coefficient term before the variation w of the state vector 0, a differential control theory equation of ψ is obtained:
equation 2:
Figure BDA0000893999800000083
wherein:
Figure BDA0000893999800000084
therefore, by making ψ satisfy a condition that the coefficient term including p' front is 0, the control theory boundary condition on the solid wall is obtained.
Equation 3:
Figure BDA0000893999800000085
dispersing the upper expression to obtain the theoretical boundary condition of the upper surface control:
equation 4:
Figure BDA0000893999800000086
the control theory boundary conditions on the integral line are respectively as follows:
equation 5:
Figure BDA0000893999800000091
an integral line l can also be obtained1Boundary condition of
Equation 6:
Figure BDA0000893999800000092
and finally, solving the gradient of the design variable.
Finally, the gradient expression is simplified into the following form
Equation 7:
Figure BDA0000893999800000093
the value of the gradient can be determined by solving the governing theory equations in inverse time based on solving the acoustic field and governing theory boundary conditions at 2.5D L EE equation 7 above is a cost function variation for optimization that provides direction for reducing the objective function.
Based on this, the gradient is solved by the following equation 8:
equation 8:
Figure BDA0000893999800000094
wherein a isiAre the coefficients of the shape function, i.e. the design variables.
In the actual implementation of the adjoint method process, the design code is divided into a plurality of module parts, including flow field solution, sound field solution, shape and grid deformation algorithm, BFGS optimization algorithm and the like. The further design flow is described as follows:
first, the inlet profile is parameterized and a noise cost function is defined using 50 Hicks-Henne methods.
The stationary RANS flow field solution is then calculated by using the commercial software F L UENT.A 2.5D L EE and FW-H codes are then used to calculate the intake near-field noise propagation and far-field radiation based on this stationary background flow result.
Secondly, solving a noise control theory equation under the constraint of a control theory boundary condition.
And thirdly, calculating the gradient value of the cost function for each design variable by using a variable gradient expression.
Next, the optimum values of all design variables are found based on the BFGS optimization algorithm, and then the shape is updated based on this optimum value.
Finally, the entire design flow is repeated until convergence is reached or acceptable design results are obtained.
According to the above description, the global optimal turbofan engine air inlet noise reduction design method can process noise reduction design of any number of design variables. The gradient calculation of the algorithm only needs about twice of the calculated amount of the flow field calculation, and the design method for reducing the noise of the air inlet passage of the turbofan engine is independent of the number of design variables.
The turbofan engine air inlet noise reduction design adopts a cultural gene algorithm to find a global optimum value. The combination mechanism of the global search and the local search enables the search efficiency to be faster by several orders of magnitude than that of the traditional genetic algorithm in some problem areas. The noise calculation adopts a linear Euler equation, the influence of any background flow field on a sound field can be considered, and a multi-frequency multi-mode engine noise source can be processed.
The global optimal turbofan engine air inlet channel noise reduction design method adopts an adjoint method to process the noise reduction design problem containing a large number of design variables, then adopts a mixed method of the adjoint method, a cultural genetic algorithm and a Kriging method to enable the local optimization to jump out under the condition of a small number of initial samples, and finally finds a global optimal point, thereby greatly reducing the noise reduction design time and being capable of processing real engineering problems.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (4)

1. A global optimal turbofan engine air inlet noise reduction design method is characterized by comprising the following steps:
S1taking an initial sample point in a design space to carry out test design;
S2firstly, calculating a background flow field value of the initial sample point, and then calculating a noise value based on the background flow field to obtain a response value of the initial sample point;
S3obtaining a local optimization design point by adopting an adjoint method;
S4establishing an initial proxy model based on the initial sample points and the response values thereof;
S5after the initial agent model is established, a cultural genetic algorithm formed by a global and local combined search strategy is used for the initial agent modelPerforming high-efficiency optimization design on the initial agent model to obtain a global optimal design point;
S6: checking whether the convergence criterion is met, and if the convergence criterion meets the requirement, obtaining the optimal solution of the object problem; otherwise, adding the local optimization design point and the global optimization design point into the initial sample point, and returning to the step S4；
Adding a current local optimal design point and a global optimal design point into an initial sample by the culture gene algorithm to perform next modeling optimization until a convergence criterion is met;
said step S5The culture genetic algorithm sequentially carries out a culture genetic algorithm with high search efficiency and a noise reduction optimization design method based on an adjoint method and a noise control equation;
the cultural gene algorithm with high search efficiency comprises the following steps:
S51initializing a population;
S52calculating the fitness of the particles, and recording the individual extreme value and the group three-dimensional extreme value of the particles;
S53carrying out global search on the particles;
S54performing local search on the particles;
S55updating the particles and keeping individual extremum and group extremum;
S56judging whether the termination condition is met, if so, entering the step S57(ii) a If not, returning to the step S52；
S57Performing crossover and mutation operations;
S58judging whether the termination condition is met, if so, ending; if not, returning to the step S53。
2. The method of claim 1, wherein the step S is performed by a global optimum turbofan engine inlet noise reduction design method2Wherein the response value of the sample point is obtained by a numerical analysis program.
3. The global optimum turbofan engine intake duct noise reduction design method of claim 1 wherein the noise reduction optimization design method based on the companion method and noise control equation comprises the steps of:
S511the nacelle of the turbofan engine is pneumatic;
S512calculating average background flow;
S513calculating a noise adjoint equation through the target function definition and the adjoint boundary condition;
S514calculating a target function and a gradient value through a large number of design variables and network disturbance;
S515judging whether the convergence criterion is met; if so, performing local optimal design; if not, an optimization algorithm is carried out, a new pneumatic appearance of the nacelle is designed, and the step S is returned512。
4. The globally optimal turbofan engine inlet noise reduction design method of claim 3 wherein step S513The method specifically comprises the following steps:
firstly, derivation of an air inlet passage noise propagation control equation under a curve coordinate system is carried out, and the expression of a 2.5-D L EE equation in a matrix form under a cylindrical coordinate system is as follows:
Figure FDA0002512852290000021
wherein:
Figure FDA0002512852290000022
next, the noise-accompanying equation is solved to obtain a differential control theory equation of ψ by making the coefficient term before the variation w of the state vector 0:
Figure FDA0002512852290000023
wherein:
Figure FDA0002512852290000024
the control theory boundary condition on the solid wall is thus obtained by making ψ satisfy the condition that the coefficient term including p' front is 0:
Figure FDA0002512852290000025
by discretizing the above equation, the theoretical boundary conditions for upper surface control are obtained:
Figure FDA0002512852290000031
the control theory boundary conditions on the integral line are respectively as follows:
Figure FDA0002512852290000032
an integral line l can also be obtained1Boundary condition of
Figure FDA0002512852290000033
Finally, design variable gradient solving is carried out, and the gradient expression is finally simplified into the following form
Figure FDA0002512852290000034
Simultaneously solving the gradient:
Figure FDA0002512852290000035
wherein a isiAre the coefficients of the shape function.
CN201511015576.6A 2015-12-29 2015-12-29 Global optimal turbofan engine air inlet channel noise reduction design method Active CN106934074B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201511015576.6A CN106934074B (en) 2015-12-29 2015-12-29 Global optimal turbofan engine air inlet channel noise reduction design method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201511015576.6A CN106934074B (en) 2015-12-29 2015-12-29 Global optimal turbofan engine air inlet channel noise reduction design method

Publications (2)

Publication Number Publication Date
CN106934074A CN106934074A (en) 2017-07-07
CN106934074B true CN106934074B (en) 2020-07-31

Family

ID=59442188

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201511015576.6A Active CN106934074B (en) 2015-12-29 2015-12-29 Global optimal turbofan engine air inlet channel noise reduction design method

Country Status (1)

Country Link
CN (1) CN106934074B (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107679294B (en) * 2017-09-18 2019-04-12 西安交通大学 A kind of board-like heat exchanger inlet and outlet design method of multichannel
CN108509722B (en) * 2018-04-02 2019-02-05 西北工业大学 Aircraft sensibility based on support vector machines weighs optimization method
CN108959741B (en) * 2018-06-20 2023-04-18 天津大学 Parameter optimization method based on marine physical ecological coupling model
CN109214056B (en) * 2018-08-07 2023-02-17 浙江大学 Pneumatic optimization design variable selection method based on flow physics
CN111079325B (en) * 2019-11-19 2022-05-31 南京航空航天大学 Turbofan engine jet noise real-time calculation and prediction method based on proxy model
CN110991017B (en) * 2019-11-19 2022-05-20 南京航空航天大学 Modeling method for flight and propulsion system and jet flow noise comprehensive real-time model
CN113297677B (en) * 2021-06-15 2023-09-22 中国航发沈阳发动机研究所 Aircraft engine air system probability analysis method based on Kriging model

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN2396421Y (en) * 1999-08-02 2000-09-13 海尔集团公司 Noise reducing device for power supply of computer
FR2969122B1 (en) * 2010-12-20 2012-12-28 Aircelle Sa INTERNAL STRUCTURE FOR A NACELLE FOR A DOUBLE TURBOREACTOR FLOW OF AN AIRCRAFT
EP2798183B8 (en) * 2011-12-30 2021-01-20 Raytheon Technologies Corporation Gas turbine engine with low fan pressure ratio
FR2999150B1 (en) * 2012-12-10 2015-10-09 Bermond Gerome Maurice Paul CONVERTIBLE AIRCRAFT COMPRISING TWO CAREN ROTORS AT THE END OF A WING AND A HORIZONTAL FAN IN FUSELAGE
CN103473424B (en) * 2013-09-23 2016-05-18 北京理工大学 Based on the aerocraft system Optimization Design of sequence radial basic function agent model
US9546618B2 (en) * 2013-10-24 2017-01-17 The Boeing Company Methods and apparatus for passive thrust vectoring and plume deflection
CN105134409B (en) * 2015-07-28 2018-09-25 南京航空航天大学 The big bypass ratio fan propeller Pneumatic design method of the ultralow rotating speed of superelevation load

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于GPU/CPU多级并行CFD优化策略的研究;孟伟超;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20120715(第7期);第4章 *
基于伴随方法的涡扇发动机涵道的气动噪声优化设计研究;邱昇;《中国博士学位论文全文数据库 工程科技II辑》;20141215(第12期);第3-4章 *

Also Published As

Publication number Publication date
CN106934074A (en) 2017-07-07

Similar Documents

Publication Publication Date Title
CN106934074B (en) Global optimal turbofan engine air inlet channel noise reduction design method
CN107391891B (en) Large-aspect-ratio wing optimization design method based on model fusion method
CN106055791B (en) Aircraft overall situation Aerodynamic optimization method based on Predictor-Correcting Algorithm
CN102682172B (en) Numerous-parameter optimization design method based on parameter classification for supercritical aerofoil
Sasaki et al. Aerodynamic optimization of an over-the-wing-nacelle-mount configuration
Li et al. Aeroelastic global structural optimization using an efficient CFD-based reduced order model
Yanhui et al. Performance improvement of optimization solutions by POD-based data mining
CN106919724B (en) Turbofan engine outer duct noise reduction design method
Chao et al. Multi-infill strategy for kriging models used in variable fidelity optimization
Tejero et al. Aerodynamic optimisation of civil aero-engine nacelles by dimensionality reduction and multi-fidelity techniques
Song et al. Aerodynamic and structural multidisciplinary optimization design method of fan rotors based on blade curvature constraints
Sánchez Moreno et al. Robustness of optimisation algorithms for transonic aerodynamic design
Ellbrant et al. CFD optimization of a transonic compressor using multiobjective GA and metamodels
Raul et al. Aerodynamic shape optimization for delaying dynamic stall of airfoils using cokriging regression
Gaiddon et al. Multicriteria design optimization of integrated three-dimensional supersonic inlets
Peigin et al. Multiconstrained aerodynamic design of business jet by CFD driven optimization tool
CN105160108A (en) Determining method of sounding rocket aerodynamic configuration optimization objective function
Zhang et al. Supercritical wing design and optimization for transonic civil airplane
Lyu et al. Strategies for solving high-fidelity aerodynamic shape optimization problems
Zhu et al. Multi-objective optimization design of airfoil and wing
Epstein et al. Accurate CFD driven optimization of lifting surfaces for wing-body configuration
Epstein et al. Efficient approach for multipoint aerodynamic wing design of business jet aircraft
Tao et al. Multi-fidelity and multi-objective aerodynamic short nacelle shape optimisation under different flight conditions
Kai et al. Aerodynamic shape optimization of wing-body-tail configuration via efficient surrogate-based optimization
Epstein et al. Aerodynamic Shape Optimization by Automatic Hybrid Genetic Tool OPTIMENGA_AERO

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information

Address after: 200241 Minhang District Lianhua Road, Shanghai, No. 3998

Applicant after: China Hangfa commercial aviation engine limited liability company

Address before: 200241 Minhang District Lianhua Road, Shanghai, No. 3998

Applicant before: AVIC Commercial Aircraft Engine Co.,Ltd.

CB02 Change of applicant information
GR01 Patent grant
GR01 Patent grant