CN112380794A - Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade - Google Patents

Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade Download PDF

Info

Publication number
CN112380794A
CN112380794A CN202011441870.4A CN202011441870A CN112380794A CN 112380794 A CN112380794 A CN 112380794A CN 202011441870 A CN202011441870 A CN 202011441870A CN 112380794 A CN112380794 A CN 112380794A
Authority
CN
China
Prior art keywords
blade
model
optimization
flow field
deformation
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.)
Granted
Application number
CN202011441870.4A
Other languages
Chinese (zh)
Other versions
CN112380794B (en
Inventor
李立州
张珺
原梅妮
路宽
陈鹏云
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.)
North University of China
Original Assignee
North University of China
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 North University of China filed Critical North University of China
Priority to CN202011441870.4A priority Critical patent/CN112380794B/en
Publication of CN112380794A publication Critical patent/CN112380794A/en
Application granted granted Critical
Publication of CN112380794B publication Critical patent/CN112380794B/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]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • 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
    • G06F2113/08—Fluids
    • 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
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Abstract

The invention relates to a multidisciplinary optimization design method for an aviation turbine engine blade, in particular to a multidisciplinary parallel cooperation optimization design method for an aviation turbine engine blade. The method solves the problems of large calculation amount, low optimization efficiency and difficult multidisciplinary integration when the traditional method is adopted to carry out multidisciplinary optimization design on the blades of the aviation turbine engine. The method is realized by adopting the following steps: the method comprises the following steps: generating a three-dimensional shape of the blade; step two: calculating aerodynamic force of the blade; calculating the deformation of the blade; step three: repeatedly executing the first step to the second step; step four: establishing a blade flow field reduced model; establishing a blade structure reduced model; step five: obtaining a fluid-solid coupling mixed model for blade structure analysis; obtaining a fluid-solid coupling mixed model for analyzing the blade flow field; step six: establishing a blade structure optimization subsystem; establishing a blade flow field optimization subsystem; step seven: and judging whether the calculation result meets the optimization target and the constraint condition.

Description

Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade
Technical Field
The invention relates to a multidisciplinary optimization design method for an aviation turbine engine blade, in particular to a multidisciplinary parallel cooperation optimization design method for an aviation turbine engine blade.
Background
When the multidisciplinary optimization design of the aviation turbine engine blade is carried out, how to calculate the optimal blade geometric parameters is a crucial problem. If the traditional method is adopted for the multidisciplinary optimization design of the blades of the aero-turbine engine, the traditional method is limited by the principle of the traditional method, the blade performance of each group of geometric parameters can be calculated only in an iterative coupling solution mode among multidisciplines, and accordingly the optimal blade parameters are searched, so that the calculation amount is large, the optimization efficiency is low, the multidisciplinary integration is difficult, and the multidisciplinary optimization design process of the blades of the aero-turbine engine is slow. Therefore, a multidisciplinary parallel collaborative optimization design method for the blades of the aero-turbine engine is needed to be invented, so that the problems of huge calculation amount, low optimization efficiency and difficult multidisciplinary integration when the traditional method is adopted to carry out multidisciplinary optimization design on the blades of the aero-turbine engine are solved.
Disclosure of Invention
The invention provides a multidisciplinary parallel cooperative optimization design method for an aviation turbine engine blade, which aims to solve the problems of huge calculation amount, low optimization efficiency and difficult multidisciplinary integration when a traditional method is adopted to carry out multidisciplinary optimization design on the aviation turbine engine blade.
The invention is realized by adopting the following technical scheme:
a multidisciplinary parallel cooperation optimization design method for an aviation turbine engine blade is realized by adopting the following steps:
the method comprises the following steps:
generating geometric parameters, deformation and aerodynamic force of the blades by using a random generator;
then, the coordinate (x) of the first point on the pressure surface of the blade is calculated according to the geometric parameters of the bladep1,yp1) Coordinate (x) of the last point on the pressure surface of the bladep2,yp2) Coordinate (x) of the first point on the suction surface of the blades1,ys1) Coordinate (x) of last point on suction surface of blades2,ys2)；
Then, substituting the calculation result into the profile equation set of the pressure surface of the blade and the profile equation set of the suction surface of the blade, thereby calculating the profile parameter [ a ] of the pressure surface of the blade0 a1 a2 a3 a4 a5]TProfile parameter of suction surface of blade0 b1 b2 b3b4 b5]T；
The set of profile equations for the pressure side of the blade is shown below:
Figure BDA0002822640650000021
the set of profile equations for the suction surface of the blade is shown below:
Figure BDA0002822640650000022
then, according to the profile parameter [ a ] of the pressure surface of the blade0 a1 a2 a3 a4 a5]TProfile parameter of suction surface of blade0 b1 b2 b3 b4 b5]TObtaining the molded lines of a plurality of blade sections, and generating a three-dimensional blade shape according to the molded line basic stack of each blade section;
then, inputting the three-dimensional modeling and deformation of the blade into a CFD model of the blade;
meanwhile, inputting the three-dimensional modeling of the blade and the aerodynamic force of the blade into a finite element model of the blade;
step two:
calculating blade aerodynamic force corresponding to blade geometric parameters and blade deformation by using a blade CFD model, and storing the calculation result in a database;
meanwhile, calculating blade geometric parameters and blade deformation corresponding to blade aerodynamic force by using a blade finite element model, and storing the calculation result into a database;
step three:
repeating the first step to the second step, thereby accumulating a certain amount of calculation results in the database;
step four:
establishing a blade flow field reduced model of the influence of blade geometric parameters and blade deformation on blade aerodynamic force, and identifying the blade flow field reduced model by using a calculation result in a database;
meanwhile, establishing a blade structure reduced model of which the blade geometric parameters and the blade aerodynamic force influence the blade deformation, and identifying the blade structure reduced model by using the calculation result in the database;
step five:
obtaining a fluid-solid coupling hybrid model of blade structure analysis by the following steps:
step a: a group of blade geometric parameters are given, blade aerodynamic force is calculated by utilizing a blade flow field reduced model, and the blade aerodynamic force is input into a blade finite element model;
step b: calculating blade deformation by using a blade finite element model, and inputting the blade deformation into a blade flow field reduced model;
step c: calculating blade aerodynamic force by using a blade flow field reduced model, and judging whether the blade aerodynamic force is matched with blade deformation; if not, executing step b; if the blade flow field reduced model and the blade finite element model are matched, the coupling of the blade flow field reduced model and the blade finite element model is realized, and thus a fluid-solid coupling mixed model for blade structure analysis is obtained;
meanwhile, a fluid-solid coupling hybrid model for blade flow field analysis is obtained through the following steps:
step a: a group of blade geometric parameters are given, blade deformation is calculated by utilizing a blade structure reduced model, and the blade deformation is input into a blade CFD model;
step b: calculating blade aerodynamic force by using a blade CFD model, and inputting the blade aerodynamic force into a blade structure reduced model;
step c: calculating the blade deformation by using the blade structure reduced model, and judging whether the blade deformation is matched with the blade aerodynamic force; if not, executing step b; if the blade structure reduced order model and the blade CFD model are matched, the blade structure reduced order model and the blade CFD model are coupled, and therefore a fluid-solid coupling mixed model for blade flow field analysis is obtained;
step six:
establishing a blade structure optimization subsystem based on a fluid-solid coupling hybrid model of blade structure analysis; in the blade structure optimization subsystem, a group of new blade geometric parameters are automatically generated by an optimization algorithm, then the current blade structure weight, the current blade service life, the current blade maximum equivalent stress and the current blade maximum deformation are calculated by utilizing a fluid-solid coupling hybrid model of blade structure analysis, and then the calculation result is uploaded to a collaborative optimization system;
meanwhile, a blade flow field optimization subsystem is established based on a fluid-solid coupling mixed model of blade flow field analysis; in the blade flow field optimization subsystem, a group of new blade geometric parameters are automatically generated by an optimization algorithm, then the current total blade pressure ratio, the current aerodynamic lift force of the blade airfoil profile and the current oscillation of the aerodynamic force of the blade are calculated by utilizing a fluid-solid coupling mixed model of the blade flow field analysis, and then the calculation result is uploaded to a cooperative optimization system;
step seven:
judging whether the calculation result meets an optimization target and a constraint condition by using a collaborative optimization system;
if the calculation result does not meet the optimization target and the constraint condition, executing a sixth step;
and if the calculation result meets the optimization target and the constraint condition, ending the optimization.
The blade geometry parameters include: radius r of leading edge of blade1Trailing edge radius r of the blade2Angle of attack i of blade, angle of structure beta of blade inlet1Blade outlet structure angle beta2Blade installation angle gamma, blade chord length b, blade cascade distance t and blade inlet leading edge wedge angle betaqBlade outlet leading edge wedge angle betahDiameter a of blade throat and maximum thickness C of blademax。
In the sixth step, a new blade geometric parameter is generated by using a global optimized MIGA algorithm and a local gradient optimized NLPQL algorithm.
In the seventh step, the optimization objective is: 1) the weight of the blade structure is minimum; 2) the blade total pressure ratio is maximum; the constraint conditions are as follows: 1) the service life of the blade is more than 2e9 cycles; the maximum equivalent stress of the blade and the maximum deformation of the blade do not exceed specified values; 2) the aerodynamic lift of the blade airfoil is greater than a specified value; the oscillation of the aerodynamic force of the blade is less than a specified amplitude; 3) and the geometric parameters of the blades in the blade structure optimization subsystem are consistent with those of the blades in the blade flow field optimization subsystem.
Compared with the traditional method, the multidisciplinary parallel collaborative optimization design method for the blades of the aviation turbine engine is based on a brand new principle, realizes the decoupling solution of each discipline of the multidisciplinary coupling problem, eliminates multidisciplinary coupling iteration, can consider the coupling effect among the disciplines, and further truly realizes the distributed parallel collaborative optimization of the multidisciplinary coupling problem, thereby remarkably reducing the calculation amount, remarkably improving the optimization efficiency, simplifying the multidisciplinary integration, and remarkably accelerating the multidisciplinary optimization design process of the blades of the aviation turbine engine.
The method effectively solves the problems of huge calculation amount, low optimization efficiency and difficult multidisciplinary integration when the traditional method is adopted to carry out multidisciplinary optimization design on the blades of the aero-turbine engine, and is suitable for the multidisciplinary optimization design of the blades of the aero-turbine engine.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
A multidisciplinary parallel cooperation optimization design method for an aviation turbine engine blade is realized by adopting the following steps:
the method comprises the following steps:
generating geometric parameters, deformation and aerodynamic force of the blades by using a random generator;
then, the coordinate (x) of the first point on the pressure surface of the blade is calculated according to the geometric parameters of the bladep1,yp1) Coordinate (x) of the last point on the pressure surface of the bladep2,yp2) Coordinate (x) of the first point on the suction surface of the blades1,ys1) Coordinate (x) of last point on suction surface of blades2,ys2)；
Then, substituting the calculation result into the profile equation set of the pressure surface of the blade and the profile equation set of the suction surface of the blade, thereby calculating the profile parameter [ a ] of the pressure surface of the blade0 a1 a2 a3 a4 a5]TProfile parameter of suction surface of blade0 b1 b2 b3b4 b5]T；
The set of profile equations for the pressure side of the blade is shown below:
Figure BDA0002822640650000061
the set of profile equations for the suction surface of the blade is shown below:
Figure BDA0002822640650000062
then, according to the profile parameter [ a ] of the pressure surface of the blade0 a1 a2 a3 a4 a5]TProfile parameter of suction surface of blade0 b1 b2 b3 b4 b5]TObtaining the molded lines of a plurality of blade sections, and generating a three-dimensional blade shape according to the molded line basic stack of each blade section;
then, inputting the three-dimensional modeling and deformation of the blade into a CFD model of the blade;
meanwhile, inputting the three-dimensional modeling of the blade and the aerodynamic force of the blade into a finite element model of the blade;
step two:
calculating blade aerodynamic force corresponding to blade geometric parameters and blade deformation by using a blade CFD model, and storing the calculation result in a database;
meanwhile, calculating blade geometric parameters and blade deformation corresponding to blade aerodynamic force by using a blade finite element model, and storing the calculation result into a database;
step three:
repeating the first step to the second step, thereby accumulating a certain amount of calculation results in the database;
step four:
establishing a blade flow field reduced model of the influence of blade geometric parameters and blade deformation on blade aerodynamic force, and identifying the blade flow field reduced model by using a calculation result in a database;
meanwhile, establishing a blade structure reduced model of which the blade geometric parameters and the blade aerodynamic force influence the blade deformation, and identifying the blade structure reduced model by using the calculation result in the database;
step five:
obtaining a fluid-solid coupling hybrid model of blade structure analysis by the following steps:
step a: a group of blade geometric parameters are given, blade aerodynamic force is calculated by utilizing a blade flow field reduced model, and the blade aerodynamic force is input into a blade finite element model;
step b: calculating blade deformation by using a blade finite element model, and inputting the blade deformation into a blade flow field reduced model;
step c: calculating blade aerodynamic force by using a blade flow field reduced model, and judging whether the blade aerodynamic force is matched with blade deformation; if not, executing step b; if the blade flow field reduced model and the blade finite element model are matched, the coupling of the blade flow field reduced model and the blade finite element model is realized, and thus a fluid-solid coupling mixed model for blade structure analysis is obtained;
meanwhile, a fluid-solid coupling hybrid model for blade flow field analysis is obtained through the following steps:
step a: a group of blade geometric parameters are given, blade deformation is calculated by utilizing a blade structure reduced model, and the blade deformation is input into a blade CFD model;
step b: calculating blade aerodynamic force by using a blade CFD model, and inputting the blade aerodynamic force into a blade structure reduced model;
step c: calculating the blade deformation by using the blade structure reduced model, and judging whether the blade deformation is matched with the blade aerodynamic force; if not, executing step b; if the blade structure reduced order model and the blade CFD model are matched, the blade structure reduced order model and the blade CFD model are coupled, and therefore a fluid-solid coupling mixed model for blade flow field analysis is obtained;
step six:
establishing a blade structure optimization subsystem based on a fluid-solid coupling hybrid model of blade structure analysis; in the blade structure optimization subsystem, a group of new blade geometric parameters are automatically generated by an optimization algorithm, then the current blade structure weight, the current blade service life, the current blade maximum equivalent stress and the current blade maximum deformation are calculated by utilizing a fluid-solid coupling hybrid model of blade structure analysis, and then the calculation result is uploaded to a collaborative optimization system;
meanwhile, a blade flow field optimization subsystem is established based on a fluid-solid coupling mixed model of blade flow field analysis; in the blade flow field optimization subsystem, a group of new blade geometric parameters are automatically generated by an optimization algorithm, then the current total blade pressure ratio, the current aerodynamic lift force of the blade airfoil profile and the current oscillation of the aerodynamic force of the blade are calculated by utilizing a fluid-solid coupling mixed model of the blade flow field analysis, and then the calculation result is uploaded to a cooperative optimization system;
step seven:
judging whether the calculation result meets an optimization target and a constraint condition by using a collaborative optimization system;
if the calculation result does not meet the optimization target and the constraint condition, executing a sixth step;
and if the calculation result meets the optimization target and the constraint condition, ending the optimization.
The blade geometry parameters include: radius r of leading edge of blade1Trailing edge radius r of the blade2Angle of attack i of blade, angle of structure beta of blade inlet1Blade outlet structure angle beta2Blade installation angle gamma, blade chord length b, blade cascade distance t and blade inlet leading edge wedge angle betaqBlade outlet leading edge wedge angle betahDiameter a of blade throat and maximum thickness C of blademax。
In the sixth step, a new blade geometric parameter is generated by using a global optimized MIGA algorithm and a local gradient optimized NLPQL algorithm.
In the seventh step, the optimization objective is: 1) the weight of the blade structure is minimum; 2) the blade total pressure ratio is maximum; the constraint conditions are as follows: 1) the service life of the blade is more than 2e9 cycles; the maximum equivalent stress of the blade and the maximum deformation of the blade do not exceed specified values; 2) the aerodynamic lift of the blade airfoil is greater than a specified value; the oscillation of the aerodynamic force of the blade is less than a specified amplitude; 3) and the geometric parameters of the blades in the blade structure optimization subsystem are consistent with those of the blades in the blade flow field optimization subsystem.
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 multidisciplinary parallel collaborative optimization design method for an aviation turbine engine blade is characterized by comprising the following steps: the method is realized by adopting the following steps:
the method comprises the following steps:
generating geometric parameters, deformation and aerodynamic force of the blades by using a random generator;
then, the coordinate (x) of the first point on the pressure surface of the blade is calculated according to the geometric parameters of the bladep1,yp1) Coordinate (x) of the last point on the pressure surface of the bladep2,yp2) Coordinate (x) of the first point on the suction surface of the blades1,ys1) Coordinate (x) of last point on suction surface of blades2,ys2)；
Then, substituting the calculation result into the profile equation set of the pressure surface of the blade and the profile equation set of the suction surface of the blade, thereby calculating the profile parameter [ a ] of the pressure surface of the blade0 a1 a2 a3 a4 a5]TProfile parameter of suction surface of blade0 b1 b2 b3 b4b5]T；
The set of profile equations for the pressure side of the blade is shown below:
Figure FDA0002822640640000011
the set of profile equations for the suction surface of the blade is shown below:
Figure FDA0002822640640000012
then, according to the profile parameter [ a ] of the pressure surface of the blade0 a1 a2 a3 a4 a5]TProfile parameter of suction surface of blade0 b1b2 b3 b4 b5]TObtaining the molded lines of a plurality of blade sections, and generating a three-dimensional blade shape according to the molded line basic stack of each blade section;
then, inputting the three-dimensional modeling and deformation of the blade into a CFD model of the blade;
meanwhile, inputting the three-dimensional modeling of the blade and the aerodynamic force of the blade into a finite element model of the blade;
step two:
calculating blade aerodynamic force corresponding to blade geometric parameters and blade deformation by using a blade CFD model, and storing the calculation result in a database;
meanwhile, calculating blade geometric parameters and blade deformation corresponding to blade aerodynamic force by using a blade finite element model, and storing the calculation result into a database;
step three:
repeating the first step to the second step, thereby accumulating a certain amount of calculation results in the database;
step four:
establishing a blade flow field reduced model of the influence of blade geometric parameters and blade deformation on blade aerodynamic force, and identifying the blade flow field reduced model by using a calculation result in a database;
meanwhile, establishing a blade structure reduced model of which the blade geometric parameters and the blade aerodynamic force influence the blade deformation, and identifying the blade structure reduced model by using the calculation result in the database;
step five:
obtaining a fluid-solid coupling hybrid model of blade structure analysis by the following steps:
step a: a group of blade geometric parameters are given, blade aerodynamic force is calculated by utilizing a blade flow field reduced model, and the blade aerodynamic force is input into a blade finite element model;
step b: calculating blade deformation by using a blade finite element model, and inputting the blade deformation into a blade flow field reduced model;
step c: calculating blade aerodynamic force by using a blade flow field reduced model, and judging whether the blade aerodynamic force is matched with blade deformation; if not, executing step b; if the blade flow field reduced model and the blade finite element model are matched, the coupling of the blade flow field reduced model and the blade finite element model is realized, and thus a fluid-solid coupling mixed model for blade structure analysis is obtained;
meanwhile, a fluid-solid coupling hybrid model for blade flow field analysis is obtained through the following steps:
step a: a group of blade geometric parameters are given, blade deformation is calculated by utilizing a blade structure reduced model, and the blade deformation is input into a blade CFD model;
step b: calculating blade aerodynamic force by using a blade CFD model, and inputting the blade aerodynamic force into a blade structure reduced model;
step c: calculating the blade deformation by using the blade structure reduced model, and judging whether the blade deformation is matched with the blade aerodynamic force; if not, executing step b; if the blade structure reduced order model and the blade CFD model are matched, the blade structure reduced order model and the blade CFD model are coupled, and therefore a fluid-solid coupling mixed model for blade flow field analysis is obtained;
step six:
establishing a blade structure optimization subsystem based on a fluid-solid coupling hybrid model of blade structure analysis; in the blade structure optimization subsystem, a group of new blade geometric parameters are automatically generated by an optimization algorithm, then the current blade structure weight, the current blade service life, the current blade maximum equivalent stress and the current blade maximum deformation are calculated by utilizing a fluid-solid coupling hybrid model of blade structure analysis, and then the calculation result is uploaded to a collaborative optimization system;
meanwhile, a blade flow field optimization subsystem is established based on a fluid-solid coupling mixed model of blade flow field analysis; in the blade flow field optimization subsystem, a group of new blade geometric parameters are automatically generated by an optimization algorithm, then the current total blade pressure ratio, the current aerodynamic lift force of the blade airfoil profile and the current oscillation of the aerodynamic force of the blade are calculated by utilizing a fluid-solid coupling mixed model of the blade flow field analysis, and then the calculation result is uploaded to a cooperative optimization system;
step seven:
judging whether the calculation result meets an optimization target and a constraint condition by using a collaborative optimization system;
if the calculation result does not meet the optimization target and the constraint condition, executing a sixth step;
and if the calculation result meets the optimization target and the constraint condition, ending the optimization.
2. The multidisciplinary parallel collaborative optimization design method for an aircraft turbine engine blade according to claim 1, characterized in that: the blade geometry parameters include: radius r of leading edge of blade1Trailing edge radius r of the blade2Vane, and vaneAttack angle i, blade inlet structure angle beta1Blade outlet structure angle beta2Blade installation angle gamma, blade chord length b, blade cascade distance t and blade inlet leading edge wedge angle betaqBlade outlet leading edge wedge angle betahDiameter a of blade throat and maximum thickness C of blademax。
3. The multidisciplinary parallel collaborative optimization design method for an aircraft turbine engine blade according to claim 1, characterized in that: in the sixth step, a new blade geometric parameter is generated by using a global optimized MIGA algorithm and a local gradient optimized NLPQL algorithm.
4. The multidisciplinary parallel collaborative optimization design method for an aircraft turbine engine blade according to claim 1, characterized in that: in the seventh step, the optimization objective is: 1) the weight of the blade structure is minimum; 2) the blade total pressure ratio is maximum; the constraint conditions are as follows: 1) the service life of the blade is more than 2e9 cycles; the maximum equivalent stress of the blade and the maximum deformation of the blade do not exceed specified values; 2) the aerodynamic lift of the blade airfoil is greater than a specified value; the oscillation of the aerodynamic force of the blade is less than a specified amplitude; 3) and the geometric parameters of the blades in the blade structure optimization subsystem are consistent with those of the blades in the blade flow field optimization subsystem.
CN202011441870.4A 2020-12-08 2020-12-08 Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade Active CN112380794B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011441870.4A CN112380794B (en) 2020-12-08 2020-12-08 Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011441870.4A CN112380794B (en) 2020-12-08 2020-12-08 Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade

Publications (2)

Publication Number Publication Date
CN112380794A true CN112380794A (en) 2021-02-19
CN112380794B CN112380794B (en) 2022-11-08

Family

ID=74589718

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011441870.4A Active CN112380794B (en) 2020-12-08 2020-12-08 Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade

Country Status (1)

Country Link
CN (1) CN112380794B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114139298A (en) * 2021-10-20 2022-03-04 中国航发四川燃气涡轮研究院 Method for multidisciplinary coupling analysis of gas, heat and solid
CN114896699A (en) * 2022-05-23 2022-08-12 西安交通大学 Multidisciplinary optimization design method for centripetal turbine impeller in aircraft engine

Citations (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030125644A1 (en) * 2001-10-12 2003-07-03 Sharp Kabushiki Kaisha Method of preparing fluid-structure interactive numerical model and method of manufacturing fluttering robot using the same
US20030150962A1 (en) * 2002-02-12 2003-08-14 Bela Orban Method for controlling and delaying the separation of flow from a solid surface by suction coupling (controlling separation by suction coupling, CSSC)
EP1696356A1 (en) * 2005-02-24 2006-08-30 Siemens Aktiengesellschaft Flow acoustic simulation with the Lattice-Boltzmann method
JP2008276807A (en) * 2001-10-12 2008-11-13 Sharp Corp Method of manufacturing fluttering robot using method of preparing fluid-structure interactive numerical model
CN101882177A (en) * 2010-06-18 2010-11-10 北京航空航天大学 Aeroelastic stability fluid-structure interaction prediction method of turbo-machine changed interblade phase angles
WO2011042312A1 (en) * 2009-10-09 2011-04-14 Rolls-Royce Plc Rotor behaviour determination
US20120152007A1 (en) * 2007-01-12 2012-06-21 Richard Holmes Testing performance of a material for use in a jet engine
CN102799730A (en) * 2012-07-13 2012-11-28 北京航空航天大学 Method for estimating reverse twisting process of fan blade of gas turbine
CN102938003A (en) * 2012-10-17 2013-02-20 北京航空航天大学 Method for predicting aeroelasticity stability numerical value of turbomachinery with error frequency included
CN104331553A (en) * 2014-10-29 2015-02-04 浙江大学 Optimal design method of large turbo expander impeller blade structure with defect consideration
CN105843073A (en) * 2016-03-23 2016-08-10 北京航空航天大学 Method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction
CN106055791A (en) * 2016-05-31 2016-10-26 西北工业大学 Prediction-correction algorithm-based aircraft global pneumatic optimization method
CN106227967A (en) * 2016-08-01 2016-12-14 杭州汽轮机股份有限公司 Industrial steam turbine low-pressure stage group vane type line optimization method
CN106529072A (en) * 2016-11-25 2017-03-22 江苏大学 High-temperature and high-pressure centrifuging pump impeller comprehensive design method based on multidisciplinary optimization
CN106777526A (en) * 2016-11-25 2017-05-31 江苏大学 HTHP receded disk impeller multidisciplinary design optimization method based on genetic algorithm
CN108170972A (en) * 2018-01-10 2018-06-15 浙江吉润汽车有限公司 A kind of finite element method of equation motorcycle race vehicle frame
CN108182328A (en) * 2018-01-05 2018-06-19 北京航空航天大学 A kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter
CA2977245A1 (en) * 2016-12-21 2018-06-21 The Boeing Company Wing flap deflection control removal
CN108278180A (en) * 2018-03-09 2018-07-13 杭州培聚教育科技有限公司 On-bladed wind power generation plant based on vortex vibration and electromagnetic induction principle
CN207920774U (en) * 2018-03-09 2018-09-28 杭州科以才成科技有限公司 A kind of on-bladed wind power generation plant based on vortex vibration and electromagnetic induction principle
CN108710746A (en) * 2018-02-08 2018-10-26 哈尔滨广瀚燃气轮机有限公司 Take into account the anti-twisted design method that naval vessel combustion engine compressor blade and blade predeformation influences
US20180341850A1 (en) * 2017-05-23 2018-11-29 The Boeing Company System and method for predicting preliminary design requirements using artificial neural networks
CN109002680A (en) * 2018-10-28 2018-12-14 扬州大学 A kind of multidisciplinary automatic optimizing design method of axial-flow pump impeller
US20190128112A1 (en) * 2017-11-02 2019-05-02 Reveal Energy Services, Inc. Determining geometries of hydraulic fractures
CA3017382A1 (en) * 2017-12-07 2019-06-07 The Boeing Company Pre-deformed aircraft spoilers and droop panels designed to seal with flap in deflected state
US10415581B1 (en) * 2018-04-25 2019-09-17 Brien Aven Seeley Ultra-quiet propeller system
WO2019210330A1 (en) * 2018-04-28 2019-10-31 The Research Foundation For The State University Of New York Flexible wind turbine blade with actively variable twist distribution
CN111563339A (en) * 2020-04-27 2020-08-21 江苏理工学院 CFRP (carbon fiber reinforced plastics) air guide sleeve integrated design method based on variable coupling and software integration

Patent Citations (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030125644A1 (en) * 2001-10-12 2003-07-03 Sharp Kabushiki Kaisha Method of preparing fluid-structure interactive numerical model and method of manufacturing fluttering robot using the same
JP2008276807A (en) * 2001-10-12 2008-11-13 Sharp Corp Method of manufacturing fluttering robot using method of preparing fluid-structure interactive numerical model
US20030150962A1 (en) * 2002-02-12 2003-08-14 Bela Orban Method for controlling and delaying the separation of flow from a solid surface by suction coupling (controlling separation by suction coupling, CSSC)
EP1696356A1 (en) * 2005-02-24 2006-08-30 Siemens Aktiengesellschaft Flow acoustic simulation with the Lattice-Boltzmann method
US20120152007A1 (en) * 2007-01-12 2012-06-21 Richard Holmes Testing performance of a material for use in a jet engine
WO2011042312A1 (en) * 2009-10-09 2011-04-14 Rolls-Royce Plc Rotor behaviour determination
CN101882177A (en) * 2010-06-18 2010-11-10 北京航空航天大学 Aeroelastic stability fluid-structure interaction prediction method of turbo-machine changed interblade phase angles
CN102799730A (en) * 2012-07-13 2012-11-28 北京航空航天大学 Method for estimating reverse twisting process of fan blade of gas turbine
CN102938003A (en) * 2012-10-17 2013-02-20 北京航空航天大学 Method for predicting aeroelasticity stability numerical value of turbomachinery with error frequency included
CN104331553A (en) * 2014-10-29 2015-02-04 浙江大学 Optimal design method of large turbo expander impeller blade structure with defect consideration
CN105843073A (en) * 2016-03-23 2016-08-10 北京航空航天大学 Method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction
CN106055791A (en) * 2016-05-31 2016-10-26 西北工业大学 Prediction-correction algorithm-based aircraft global pneumatic optimization method
CN106227967A (en) * 2016-08-01 2016-12-14 杭州汽轮机股份有限公司 Industrial steam turbine low-pressure stage group vane type line optimization method
CN106777526A (en) * 2016-11-25 2017-05-31 江苏大学 HTHP receded disk impeller multidisciplinary design optimization method based on genetic algorithm
CN106529072A (en) * 2016-11-25 2017-03-22 江苏大学 High-temperature and high-pressure centrifuging pump impeller comprehensive design method based on multidisciplinary optimization
CA2977245A1 (en) * 2016-12-21 2018-06-21 The Boeing Company Wing flap deflection control removal
US20180341850A1 (en) * 2017-05-23 2018-11-29 The Boeing Company System and method for predicting preliminary design requirements using artificial neural networks
US20190128112A1 (en) * 2017-11-02 2019-05-02 Reveal Energy Services, Inc. Determining geometries of hydraulic fractures
CA3017382A1 (en) * 2017-12-07 2019-06-07 The Boeing Company Pre-deformed aircraft spoilers and droop panels designed to seal with flap in deflected state
CN108182328A (en) * 2018-01-05 2018-06-19 北京航空航天大学 A kind of big angle of attack Nonlinear Aerodynamic reduced-order model suitable for stall flutter
CN108170972A (en) * 2018-01-10 2018-06-15 浙江吉润汽车有限公司 A kind of finite element method of equation motorcycle race vehicle frame
CN108710746A (en) * 2018-02-08 2018-10-26 哈尔滨广瀚燃气轮机有限公司 Take into account the anti-twisted design method that naval vessel combustion engine compressor blade and blade predeformation influences
CN108278180A (en) * 2018-03-09 2018-07-13 杭州培聚教育科技有限公司 On-bladed wind power generation plant based on vortex vibration and electromagnetic induction principle
CN207920774U (en) * 2018-03-09 2018-09-28 杭州科以才成科技有限公司 A kind of on-bladed wind power generation plant based on vortex vibration and electromagnetic induction principle
US10415581B1 (en) * 2018-04-25 2019-09-17 Brien Aven Seeley Ultra-quiet propeller system
WO2019210330A1 (en) * 2018-04-28 2019-10-31 The Research Foundation For The State University Of New York Flexible wind turbine blade with actively variable twist distribution
CN109002680A (en) * 2018-10-28 2018-12-14 扬州大学 A kind of multidisciplinary automatic optimizing design method of axial-flow pump impeller
CN111563339A (en) * 2020-04-27 2020-08-21 江苏理工学院 CFRP (carbon fiber reinforced plastics) air guide sleeve integrated design method based on variable coupling and software integration

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DAN SU等: "A reduced order model for uncoupled and coupled cascade flutter analysis", 《JOURNAL OF FLUIDS AND STRUCTURES》 *
罗骁: "基于降阶模型的尾流激励下的叶片气动弹性快速分析方法", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114139298A (en) * 2021-10-20 2022-03-04 中国航发四川燃气涡轮研究院 Method for multidisciplinary coupling analysis of gas, heat and solid
CN114139298B (en) * 2021-10-20 2023-10-03 中国航发四川燃气涡轮研究院 Multi-disciplinary coupling analysis method for gas, heat and solid
CN114896699A (en) * 2022-05-23 2022-08-12 西安交通大学 Multidisciplinary optimization design method for centripetal turbine impeller in aircraft engine
CN114896699B (en) * 2022-05-23 2024-03-19 西安交通大学 Multidisciplinary optimization design method for centripetal turbine impeller in aero-engine

Also Published As

Publication number Publication date
CN112380794B (en) 2022-11-08

Similar Documents

Publication Publication Date Title
Oyama et al. Transonic axial-flow blade optimization: Evolutionary algorithms/three-dimensional Navier-Stokes solver
Benini Three-dimensional multi-objective design optimization of a transonic compressor rotor
CN112380794B (en) Multi-disciplinary parallel cooperation optimization design method for aviation turbine engine blade
Jeong et al. Optimization of thick wind turbine airfoils using a genetic algorithm
CN109578085B (en) Method for weakening unsteady acting force of turbine movable blade through guide blade inclination
CN112597709B (en) Turbine blade cascade flow channel CFD calculation method considering transition disturbance factor
Liou et al. Challenges and progress in aerodynamic design of hybrid wingbody aircraft with embedded engines
CN114186513A (en) Modeling design method for axial flow compressor blade with reverse S-shaped front edge
Tran et al. Aerodynamic performance optimization of a single-stage axial compressor using circumferential bleeding airflow
Zhang et al. Effect of boundary layer suction on aerodynamic performance of high-turning compressor cascade
Chen Optimization of wind turbine airfoils/blades and wind farm layouts
Ling et al. Relationship between optimum curved blade generate line and cascade parameters in subsonic axial compressor
CN114165477B (en) Axial ultrasonic through-flow fan serial configuration and serial configuration optimization method
CN114065423B (en) Method for rapidly evaluating flutter of fan blade of aircraft engine
CN112883575B (en) Impeller mechanical boundary layer transition model correction method considering surface roughness
CN112632728B (en) Turbine mechanical blade profile design and performance prediction method based on deep learning
Teichel et al. Optimized preliminary design of compact axial compressors: A comparison of two design tools
Chahine et al. Multidisciplinary design optimization of an aero-engine fan blade with consideration of bypass and core performance
Shao et al. Aerodynamic optimization of the radial inflow turbine for a 100kW-class micro gas turbine based on metamodel-semi-assisted method
CN105822510A (en) Blade tip serialization segmentation blade and design method thereof
Jinxin et al. Aerodynamic optimization design of compressor blades based on improved artificial bee colony algorithm
Lei et al. Three-Dimensional Multi-Objective Design Optimization of a 6.5-Stage Axial Flow Compressor Blades With Lean and Twist
Rehman et al. Non-Axisymmetric Endwall Profiling of a Stator Row in the Presence of the Rotor in a High Pressure Turbine
Becker et al. Multi-objective optimization in axial compressor design using a linked cfd-solver
Xi et al. Aerodynamic optimization design of vaned diffusers for the 100 kW micro gas turbine’s centrifugal compressor

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
GR01 Patent grant
GR01 Patent grant