CN116227204A - Method for constructing simulation model of gas turbine - Google Patents

Method for constructing simulation model of gas turbine Download PDF

Info

Publication number
CN116227204A
CN116227204A CN202310216044.7A CN202310216044A CN116227204A CN 116227204 A CN116227204 A CN 116227204A CN 202310216044 A CN202310216044 A CN 202310216044A CN 116227204 A CN116227204 A CN 116227204A
Authority
CN
China
Prior art keywords
value range
gas turbine
intersection
argument
component
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.)
Pending
Application number
CN202310216044.7A
Other languages
Chinese (zh)
Inventor
赵文昆
贾琳渊
唐明智
任文成
张志舒
陈仲光
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 Shenyang Engine Research Institute
Original Assignee
AECC Shenyang Engine Research Institute
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 Shenyang Engine Research Institute filed Critical AECC Shenyang Engine Research Institute
Priority to CN202310216044.7A priority Critical patent/CN116227204A/en
Publication of CN116227204A publication Critical patent/CN116227204A/en
Pending legal-status Critical Current

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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application belongs to the technical field of gas turbines, and particularly relates to a method for constructing a simulation model of a gas turbine, wherein component models of all components of the gas turbine are constructed through different software, and the component models are put into the same computing cluster; respectively inputting corresponding input parameters into each component model, performing simulation calculation, and selecting the input parameter with the largest influence on the working point of each component model from the input parameters of each component as an independent variable; determining the association quantity with mutual association between every two parts in any parts with association relation, and setting the association quantity in the error of parameter values in the part models corresponding to the two parts; calculating the error and acquiring the independent variable after iteration; the novel method can truly and effectively reflect the three-dimensional flow field of the gas turbine and is suitable for engineering application.

Description

Method for constructing simulation model of gas turbine
Technical Field
The application belongs to the technical field of gas turbines, and particularly relates to a method for constructing a simulation model of a gas turbine.
Background
In the design of the gas turbine, the simulation technology can forcefully push the gas turbine to study in advance, verify the design result, and reduce design defects and design process repetition. The three-dimensional simulation of the whole machine more accurately simulates the real working environment of each part of the gas turbine. Compared with the zero-dimensional simulation of the whole machine, the three-dimensional simulation can obtain the flow field distribution inside each part, the interface of the parts is the surface field distribution instead of the average value, the distortion degree of flow field information is small, and the boundary condition is closer to the real flow condition, so that the simulation accuracy of the whole machine is improved.
The three-dimensional simulation core technology of the whole machine is to realize the common work of all the components (namely, the rotation speed is equal, the flow is continuous, the power balance and the pressure balance are realized). In the current simulation method, the integral modeling of the gas turbine is carried out, and the calculation is carried out in the same software, so that the matching of working points of all parts is realized by solving a three-dimensional N-S equation, the calculation of all flow fields in the whole machine is completed once in each time step, and the iteration convergence is continuously carried out along with the increase of the time steps. And the other is the coupling of the three-dimensional calculation and the overall zero-dimensional calculation of each component, the calculation characteristics of each component under the full working condition are obtained through three-dimensional simulation, the component characteristics of the overall zero dimension are corrected, and the overall performance is obtained through overall zero-dimensional matching.
In the first method, the whole modeling of the gas turbine is performed, and the flow field calculation is performed in the same software, so that the defects are as follows:
(1) The calculation accuracy is low. Because all components in the whole machine are simulated under one piece of software, only a unified numerical calculation model and a turbulence model can be set. However, the flow conditions of the components are different, for example, the main combustion chamber of the gas turbine is generally constant-pressure combustion and can be regarded as non-compressible fluid; the fan/compressor, high/low pressure turbine, performs compression and expansion of the gas, mainly involving compressible fluid solutions, and therefore not applicable with the same computational model and turbulence model.
(2) The calculation efficiency is low. Different parts have different grid scales and grid quantities, and when the calculation is performed under the same software, the calculation iteration step sizes are consistent, so that for parts with less grid quantities, the smaller iteration step sizes are not applicable, and the calculation efficiency is reduced. For example, in the three-dimensional calculation of the whole machine, the flow of the main combustion chamber component is the most complex, and a denser grid and a smaller iteration step are needed, so that the iteration step is limited by the main combustion chamber during the integral modeling calculation of the whole machine, so that the calculation efficiency of other components is low, and the occupied calculation resources are larger.
(3) The calculation convergence is poor. The more complex the gas turbine complete machine calculation, the worse the iteration convergence, for example, the turbofan gas turbine has weaker convergence than the turbojet gas turbine, and the multiaxial gas turbine has weaker convergence than the monoaxial gas turbine. The calculation of a certain component of the integrated calculation of the gas turbine is not converged, which leads to the breakdown of the whole calculation process, so that the complexity of the system directly influences whether the calculation is converged or not.
The second method, coupling the three-dimensional computation of each component with the overall zero-dimensional computation, has the following drawbacks:
and the overall zero-dimensional calculation result is used as a component input condition, the calculation characteristic of the full working condition is obtained through the three-dimensional calculation of the component, the component characteristic diagram in the overall zero-dimensional program is corrected, and the corrected result is transmitted to the component again for calculation after the overall zero-dimensional matching calculation, so that the coupling iteration is performed repeatedly. However, the boundary condition of the components is an overall calculation average value, the influence among the components is not considered, the characteristic correction iteration between the overall and the components takes long time, and neither the calculation efficiency nor the accuracy are applicable to the design.
Disclosure of Invention
In order to solve the above problems, the present application provides a method for constructing a simulation model of a gas turbine, including:
step one: constructing part models of all parts of the gas turbine through different software, and placing the part models into the same computing cluster;
step two: respectively carrying out simulation calculation on input parameters corresponding to the input of each component model, and selecting the input parameter with the largest influence on the working point of each component model from the input parameters of each component as an independent variable A i Wherein i=1, 2,3 …;
step three: in any component with an association relationship, determining an association quantity with an association between every two components, and marking errors of parameter values of the association quantity in component models corresponding to the two components as a function relationship with the independent variables: e (E) j =f j （A 1 ，A 2 ，A 3 …A i ) Where j=1, 2,3 …;
step four: calculating the error E j When all errors E j When the independent variables A are respectively smaller than the corresponding preset threshold values, outputting the independent variables A i When any error E j When the value is not smaller than the corresponding preset threshold value, iterating the independent variable A i Obtain all errors E j The independent variables A after iteration are respectively smaller than the corresponding preset threshold value i n N is the number of iterations.
Preferably, the argument A is iterated i The specific method of (2) comprises the following steps: based on the error E j And its corresponding argument a i Constructing a matrix [ delta E ] j /ΔA i ] j×i And solving the nonlinear equation set iteration to generate an independent variable A i n Wherein ΔE j =E n+1 -E n ，ΔA i =A n+1 -A n 。
Preferably, the argument A is iterated i The specific method of (2) comprises the following steps:
step s1: acquiring to make the error satisfy E 1 Argument A at < p 1 Value range a of (a) 1 1 ，A 2 Value range a of (a) 2 1 ，A 3 Value range a of (a) 3 1 …A i Value range a of (a) i 1 The method comprises the steps of carrying out a first treatment on the surface of the p is a preset value;
acquiring to make the error satisfy E 2 Argument A at < p 1 Value range a of (a) 1 2 ，A 2 Value range a of (a) 2 2 ，A 3 Value range a of (a) 3 2 …A i Value range a of (a) i 2 ；
Acquiring to make the error satisfy E 3 Argument A at < p 1 Value range a of (a) 1 3 ，A 2 Value range a of (a) 2 3 ，A 3 Value range a of (a) 3 3 …A i Value range a of (a) i 3 ；
…
Acquiring to make the error satisfy E j Argument A at < p 1 Value range a of (a) 1 j ，A 2 Value range a of (a) 2 j ，A 3 Value range a of (a) 3 j …A i Value range a of (a) i j ；
Step s2: when A is 1 1 ，A 1 2 ，A 1 3 …A 1 j When having an intersection, take intersection A 1 * The method comprises the steps of carrying out a first treatment on the surface of the When A is 2 1 ，A 2 2 ，A 2 3 …A 2 j When having an intersection, take intersection A 2 * The method comprises the steps of carrying out a first treatment on the surface of the When A is 3 1 ，A 3 2 ，A 3 3 ；A 3 j When having an intersection, take intersection A 3 * … when A i 1 ，A i 2 ，A i 3 …A i j When having an intersection, take intersection A i * The method comprises the steps of carrying out a first treatment on the surface of the Intersection A to be acquired 1 * ，A 2 * ，A 3 * …A i * As an independent variable A i The value range is A i Values in the range of values are taken as independent variables A i ；
When A is 1 * ，A 2 * ，A 3 * …A i * And if any one does not exist, the preset value p is increased, and the step s1 is returned.
Preferably, the amount of correlation between each two components includes: the power between the two components of the power coupling, the flow between the two components of the gas inlet and outlet flow coupling, the pressure between the two components under the same pressure environment.
Preferably, the software in the first step includes: nuteca, fluent, CFX, fluent.
Preferably, the gas turbine comprises an aeroengine having a fan, a compressor, a main combustion chamber, a high pressure turbine, a low pressure turbine, an outer duct, a nozzle.
Preferably, the argument A i Is also determined by the gas turbine component model and its corresponding software.
Preferably, the argument A i The selection of (2) further comprises: selecting a plurality of input parameters affecting the working points of the model of each part as independent variables A i 。
The advantages of the present application include: the invention aims to solve the technical problems of low matching iterative computation precision, low efficiency, poor convergence and the like generated in the process of a three-dimensional steady-state simulation test of a gas turbine or an aeroengine, and provides a new method which can truly and effectively reflect the three-dimensional flow field of the gas turbine and is suitable for engineering application.
Drawings
FIG. 1 is a flow chart of a method for constructing a simulation model of a gas turbine in accordance with a preferred embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the following describes the technical solutions in the embodiments of the present application in more detail with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present application. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application. All other embodiments, based on the embodiments herein, which would be apparent to one of ordinary skill in the art without undue burden are within the scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The application provides a method for constructing a simulation model of a gas turbine, as shown in fig. 1, comprising the following steps:
step one: constructing part models of all parts of the gas turbine through different software, and placing the part models into the same computing cluster; taking an aeroengine as an example, the various components include: the device comprises a fan, a gas compressor, a main combustion chamber, a high-pressure turbine, a low-pressure turbine, an outer duct, an afterburner and a spray pipe.
Step two: respectively carrying out simulation calculation on input parameters corresponding to the input of each component model, and selecting the input parameter with the largest influence on the working point of each component model from the input parameters of each component as an independent variable A i Wherein i=1, 2,3 …; in connection with the above embodiments of the aeroengine: the associated components may be: independent variable A i The device comprises a fan outer culvert outlet static pressure, a fan inner culvert outlet static pressure, a high-pressure physical rotating speed, a compressor outlet static pressure and a mixing chamber static pressure; the input parameters comprise an inlet total temperature/total pressure and an outlet static pressure, and in addition, the input parameters of the fan also comprise the rotating speed and the corresponding iteration variable; the input parameters of the air compressor further comprise fan outlet parameters, the rotating speed and the corresponding iteration variables calculated by the three-dimensional calculation software of the fan; the input parameters of the main combustion chamber are the fuel flow, the corresponding iteration variable and the gas compressor outlet parameters calculated by three-dimensional calculation software of the gas compressor; the input parameters of the high-pressure turbine and the low-pressure turbine also comprise the outlet parameter of the main combustion chamber, the corresponding iteration variable and the rotating speed, which are calculated by the three-dimensional calculation software of the main combustion chamber; the culvert input parameters further comprise fan outlet parameters calculated by three-dimensional calculation software of the fan; the afterburner input parameters further comprise outlet parameters of the main combustion chamber, outlet parameters of the high-pressure turbine and the low-pressure turbine and corresponding iteration variables, wherein the outlet parameters are calculated by three-dimensional calculation software of the culvert, and the outlet parameters are calculated by three-dimensional calculation software of the high-pressure turbine and the low-pressure turbine; the lance input parameters also comprise the outlet parameters of the afterburner calculated by three-dimensional calculation software of the afterburner, and the corresponding iteration variables.
Step three: calculating an error E between the correlation amounts between each two components based on the simulation calculation result and the independent variable j =f j （A 1 ，A 2 ，A 3 …A i ) Where j=1, 2,3 …; in connection with the above embodiments of the aeroengine: the two components include: fan connotation and connotation, fan connotation and compressor, main combustion chamber and high pressure turbine, compressor and high pressure turbine, fan and low pressure turbine; fan connotation and air compressor; a main combustion chamber and a high pressure turbine; a compressor and a high pressure turbine; the parameters of the fan and the low-pressure turbine include pressure, power, rotating speed, flow rate and the like.
Step four: calculating the error E j When the error satisfies E j ＜ε j Outputting the independent variable A when epsilon is infinitesimal i When the error is different from that of E j ＜ε j When iterating the argument A i Acquiring the error E j Simultaneous converged post-iteration argument A i n N is the number of iterations.
In some alternative embodiments, the argument A is iterated i The specific method of (2) comprises the following steps: based on said error E of non-convergence j And its corresponding argument a i Constructing a matrix [ delta E ] j /ΔA i ] j×i And solving the nonlinear equation set iteration to generate an independent variable A i n Wherein ΔE j =E n+1 -E n ，ΔA i =A n+1 -A n 。
In some alternative embodiments, the argument A is iterated i The specific method of (2) comprises the following steps:
step s1: acquiring to make the error satisfy E 1 Argument A at < p 1 Value range a of (a) 1 1 ，A 2 Value range a of (a) 2 1 ，A 3 Value range a of (a) 3 1 …A i Value range a of (a) i 1 The method comprises the steps of carrying out a first treatment on the surface of the p is a preset value;
acquiring to make the error satisfy E 2 Self-variation when < pQuantity A 1 Value range a of (a) 1 2 ，A 2 Value range a of (a) 2 2 ，A 3 Value range a of (a) 3 2 …A i Value range a of (a) i 2 ；
Acquiring to make the error satisfy E 3 Argument A at < p 1 Value range a of (a) 1 3 ，A 2 Value range a of (a) 2 3 ，A 3 Value range a of (a) 3 3 …A i Value range a of (a) i 3 ；
…
Acquiring to make the error satisfy E j Argument A at < p 1 Value range a of (a) 1 j ，A 2 Value range a of (a) 2 j ，A 3 Value range a of (a) 3 j …A i Value range a of (a) i j ；
Step s2: when A is 1 1 ，A 1 2 ，A 1 3 …A 1 j When having an intersection, take intersection A 1 * The method comprises the steps of carrying out a first treatment on the surface of the When A is 2 1 ，A 2 2 ，A 2 3 …A 2 j When having an intersection, take intersection A 2 * The method comprises the steps of carrying out a first treatment on the surface of the When A is 3 1 ，A 3 2 ，A 3 3 …A 3 j When having an intersection, take intersection A 3 * The method comprises the steps of carrying out a first treatment on the surface of the When A is i 1 ，A i 2 ，A i 3 …A i j When having an intersection, take intersection A i * The method comprises the steps of carrying out a first treatment on the surface of the Intersection A to be acquired 1 * ，A 2 * ，A 3 * …A i * As an independent variable A i Iterative value range;
when A is 1 * ，A 2 * ，A 3 * …A i * When any one does not exist, changing the preset value p, which includes increasing the value of p according to the preset value, and returning to the step s1.
In some alternative embodiments, the amount of association between each two components includes: the power between the two parts of the power coupling, the flow between the two parts of the gas inlet and outlet flow coupling, and the pressure between the two parts under the same pressure environment.
In some alternative embodiments, the different software in step one includes: nuteca, fluent, CFX, fluent.
In some alternative embodiments, the gas turbine includes an aeroengine having a fan, a compressor, a main combustor, a high pressure turbine, a low pressure turbine, an outer duct, and a nozzle.
In some alternative embodiments, argument A i Is also determined by the gas turbine component model and its corresponding software.
In some alternative embodiments, a method of solving a system of nonlinear equations includes: newton-raphson or Broyden theory.
In some alternative embodiments, the matrix constructed comprises a jacobian matrix.
In some alternative embodiments, argument A i The selection of (2) further comprises: selecting a plurality of input parameters affecting the working points of the component model as independent variables A i 。
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (8)

1. A method of constructing a simulation model of a gas turbine, comprising:
step one: constructing part models of all parts of the gas turbine through different software, and placing the part models into the same computing cluster;
step two: respectively carrying out simulation calculation on input parameters corresponding to the input of each component model, and selecting each component modelThe input parameter with the largest influence on the working point of each part model among the input parameters of the parts is taken as an independent variable A i Wherein i=1, 2,3 …;
step three: in any component with an association relationship, determining an association quantity with an association between every two components, and marking errors of parameter values of the association quantity in component models corresponding to the two components as a function relationship with the independent variables: e (E) j =f j （A 1 ，A 2 ，A 3 …A i ) Where j=1, 2,3 …;
step four: calculating the error E j When all errors E j When the independent variables A are respectively smaller than the corresponding preset threshold values, outputting the independent variables A i When any error E j When the value is not smaller than the corresponding preset threshold value, iterating the independent variable A i Obtain all errors E j The independent variables A after iteration are respectively smaller than the corresponding preset threshold value i n N is the number of iterations.
2. The gas turbine simulation model design method of claim 1, wherein the independent variable a is iterated i The specific method of (2) comprises the following steps: based on the error E j And its corresponding argument a i Constructing a matrix [ delta E ] j /ΔA i ] j×i And solving the nonlinear equation set iteration to generate an independent variable A i n Wherein ΔE j =E n+1 -E n ，ΔA i =A n+1 -A n 。
3. The gas turbine simulation model design method of claim 1, wherein the independent variable a is iterated i The specific method of (2) comprises the following steps:
step s1: acquiring to make the error satisfy E 1 Argument A at < p 1 Value range a of (a) 1 1 ，A 2 Value range a of (a) 2 1 ，A 3 Value range a of (a) 3 1 …A i Value range a of (a) i 1 The method comprises the steps of carrying out a first treatment on the surface of the p is a preset value;
acquiring to make the error satisfy E 2 Argument A at < p 1 Value range a of (a) 1 2 ，A 2 Value range a of (a) 2 2 ，A 3 Value range a of (a) 3 2 …A i Value range a of (a) i 2 ；
Acquiring to make the error satisfy E 3 Argument A at < p 1 Value range a of (a) 1 3 ，A 2 Value range a of (a) 2 3 ，A 3 Value range a of (a) 3 3 …A i Value range a of (a) i 3 ；
…；
Acquiring to make the error satisfy E j Argument A at < p 1 Value range a of (a) 1 j ，A 2 Value range a of (a) 2 j ，A 3 Value range a of (a) 3 j …A i Value range a of (a) i j ；
Step s2: when A is 1 1 ，A 1 2 ，A 1 3 …A 1 j When having an intersection, take intersection A 1 * The method comprises the steps of carrying out a first treatment on the surface of the When A is 2 1 ，A 2 2 ，A 2 3 …A 2 j When having an intersection, take intersection A 2 * The method comprises the steps of carrying out a first treatment on the surface of the When A is 3 1 ，A 3 2 ，A 3 3 ；A 3 j When having an intersection, take intersection A 3 * … when A i 1 ，A i 2 ，A i 3 …A i j When having an intersection, take intersection A i * The method comprises the steps of carrying out a first treatment on the surface of the Intersection A to be acquired 1 * ，A 2 * ，A 3 * …A i * As an independent variable A i The value range is A i Values in the range of values are taken as independent variables A i ；
When A is 1 * ，A 2 * ，A 3 * …A i * And if any one does not exist, the preset value p is increased, and the step s1 is returned.
4. The gas turbine simulation model design method of claim 1, wherein the correlation amount having a correlation between each two components comprises: the power between the two components of the power coupling, the flow between the two components of the gas inlet and outlet flow coupling, the pressure between the two components under the same pressure environment.
5. The gas turbine simulation model design method of claim 1, wherein the software in the step one includes: nuteca, fluent, CFX, fluent.
6. The gas turbine simulation model design method of claim 1, wherein the gas turbine comprises an aeroengine with a fan, a compressor, a main combustion chamber, a high pressure turbine, a low pressure turbine, an external duct, and a nozzle.
7. The gas turbine simulation model design method as set forth in claim 1, wherein the argument a i Is also determined by the gas turbine component model and its corresponding software.
8. The gas turbine simulation model design method as set forth in claim 1, wherein the argument a i The selection of (2) further comprises: selecting a plurality of input parameters affecting the working points of the model of each part as independent variables A i 。
CN202310216044.7A 2023-03-08 2023-03-08 Method for constructing simulation model of gas turbine Pending CN116227204A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310216044.7A CN116227204A (en) 2023-03-08 2023-03-08 Method for constructing simulation model of gas turbine

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310216044.7A CN116227204A (en) 2023-03-08 2023-03-08 Method for constructing simulation model of gas turbine

Publications (1)

Publication Number Publication Date
CN116227204A true CN116227204A (en) 2023-06-06

Family

ID=86572914

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310216044.7A Pending CN116227204A (en) 2023-03-08 2023-03-08 Method for constructing simulation model of gas turbine

Country Status (1)

Country Link
CN (1) CN116227204A (en)

Similar Documents

Publication Publication Date Title
CN111914362B (en) Self-adaptive method for turbofan engine model in research and development stage
CN108647428B (en) Turbofan engine adaptive component level simulation model construction method
CN108829928B (en) Turboshaft engine adaptive component-level simulation model construction method
CN108828947B (en) Modeling method for time-lag-containing uncertain fuzzy dynamic model of aircraft engine
CN112487734B (en) CFD numerical simulation method for air system structure
CN111079290B (en) Engine model correction method based on improved multivariate cosmic algorithm
CN112131670B (en) Aero-engine model iterative algorithm based on hybrid adaptive differential evolution
CN115203983B (en) Main combustion chamber simulation method based on upstream and downstream limits
Zinnecker et al. Development of a twin-spool turbofan engine simulation using the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS)
CN110321586B (en) Value taking method for iterative solution of working state of aircraft engine deviating from design point
CN109783882B (en) Modeling simulation method for gas turbine fuel system combining matlab and flowmaster
CN113656907B (en) Three-dimensional steady-state simulation matching iteration method for aircraft engine
CN114048554A (en) Three-dimensional matching iteration method for aircraft engine
CN116227204A (en) Method for constructing simulation model of gas turbine
Pachidis et al. A comparison of component zooming simulation strategies using streamline curvature
Zhang et al. An integrated modeling approach for variable cycle engine performance analysis
Mist’e et al. Improvements in off design aeroengine performance prediction using analytic compressor map interpolation
Moroz et al. Methods and tools for multidisciplinary optimization of axial turbine stages with relatively long blades
CN114781153B (en) Complete machine dimension-variable simulation performance simulation flow control method
CN112668162A (en) Aero-engine modeling method based on inertia sliding mode
Liu et al. Research on Verification and Validation of Turbofan Engine Performance Model
CN111797580B (en) CFX software-based three-dimensional CFD automatic calculation method for turbine characteristics
CN115859536B (en) Method for simulating asynchronous vibration frequency locking value of rotor blade of air compressor
CN114528657B (en) Energy loss dimension-reducing coupling method and system
CN114781153A (en) Whole machine dimension-variable simulation performance simulation flow control method

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