CN114491417A - CDFS modal variation performance-based one-dimensional input correction method - Google Patents

CDFS modal variation performance-based one-dimensional input correction method Download PDF

Info

Publication number
CN114491417A
CN114491417A CN202210357425.2A CN202210357425A CN114491417A CN 114491417 A CN114491417 A CN 114491417A CN 202210357425 A CN202210357425 A CN 202210357425A CN 114491417 A CN114491417 A CN 114491417A
Authority
CN
China
Prior art keywords
modal
correction
cdfs
mutation
ratio
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
CN202210357425.2A
Other languages
Chinese (zh)
Other versions
CN114491417B (en
Inventor
徐林
王永明
李清华
张军
郝玉扬
米攀
罗璇
郭昶宏
黄顺洲
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 Sichuan Gas Turbine Research Institute
Original Assignee
AECC Sichuan Gas Turbine 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 Sichuan Gas Turbine Research Institute filed Critical AECC Sichuan Gas Turbine Research Institute
Priority to CN202210357425.2A priority Critical patent/CN114491417B/en
Publication of CN114491417A publication Critical patent/CN114491417A/en
Application granted granted Critical
Publication of CN114491417B publication Critical patent/CN114491417B/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
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • 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
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • Data Mining & Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Mathematical Physics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Algebra (AREA)
  • Evolutionary Biology (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Control Of Positive-Displacement Air Blowers (AREA)

Abstract

The application provides a CDFS modal variation performance-based one-dimensional input correction method which comprises the steps of constructing a CDFS one-dimensional characteristic evaluation model containing modal variation, bypass ratio variation and mutation angles; determining a core machine driving fan mode, and each rotating speed, corresponding bypass ratio parameters and mutation angles of the core machine driving fan under the mode, and corresponding flow, pressure ratio and efficiency characteristics; determining initial one-dimensional design input parameters including dimensionless rotation speed, angle rule and correction coefficient; constructing a flow, pressure ratio and efficiency relational expression containing the correction coefficient; constructing a relational expression comprising modal mutation parameters, bypass ratio parameters and correction coefficients of mutation angles; and calculating to obtain each correction coefficient according to the relational expression constructed in the last step and the mode, the adjusting angle and the bypass ratio to be evaluated, and predicting the one-dimensional characteristics of the variable cycle compression component with the nonlinear mutation. The method improves the accuracy of performance prediction of the compression component of the variable-cycle engine.

Description

CDFS modal variation performance-based one-dimensional input correction method
Technical Field
The application relates to the technical field of aerodynamic performance design prediction of aero-engines, in particular to a one-dimensional input correction method based on CDFS modal variation performance.
Background
Compared with the conventional Fan layout, the Core Driven Fan Stage (CDFS) is a Fan Stage that is divided into a front part and a rear part in a double-duct manner, wherein the front part is still Driven by the low-pressure turbine, and the rear part is Driven by the Core, i.e., the high-pressure turbine, so that the Fan Stage that is Driven by the Core is called the Core Driven Fan Stage.
The core compression system with the variable cycle characteristic is characterized in that the core machine drives the fan to be in strong coupling work with the mode converter, the inlet guide vane and the duct ejector, and large performance change can be presented in multiple modes. Through the change of the variable circulation mechanisms, the inlet flow and the outlet flow and the pressure of the core machine driving fan are changed in a large range, so that the variable circulation function of the compression part in a wide working range is realized. However, the function of variable cycling is to establish a sudden change in the mode selection valve closing/opening and large angle changes of the guide vanes and the bypass eductor inlet-outlet pressure, which creates a significant challenge to the design/experimental characterization of the compression element performance.
The traditional method for correcting the characteristics of the core machine driven fan generally corrects the characteristics by direct scaling translation and the like according to the existing test characteristics, corrects the result, and is difficult to reflect the rule that the mode mutation, the bypass ratio and the nonlinear change of the guide vane are far beyond the range of the conventional correction coefficient.
The characteristic correction method of the optimization algorithm or artificial intelligence has the advantages of being fast and wide in adaptation, still scaling and correcting the characteristic result, only providing an optimization coefficient or a neuron black box weight coefficient of the correction result, being difficult to reflect the influence of modal mutation, duct ratio and guide vane nonlinear change rule on the characteristic, and being difficult to provide one-dimensional design input and prediction.
The method is rarely used for correcting the design input of the prediction of the one-dimensional characteristic of the mode change compression component of the variable-cycle engine.
Disclosure of Invention
In view of this, the embodiments of the present application provide a one-dimensional input modification method based on CDFS modal variability, which at least partially solves the problem of inaccurate prediction of the modal variability in the prior art.
The embodiment of the application provides a one-dimensional input correction method based on CDFS modal variability performance, which comprises the following steps:
step one, constructing a CDFS one-dimensional characteristic evaluation model containing modal change, bypass ratio change and mutation angles;
determining a core machine driving fan mode, and each rotating speed, corresponding bypass ratio parameters and mutation angles of the core machine driving fan in the mode, and corresponding flow, pressure ratio and efficiency characteristics;
determining initial one-dimensional design input parameters including dimensionless rotation speed, angle rule and correction coefficient, wherein the correction coefficient comprises flow correction coefficient KGAnd a correction coefficient K of a falling angleδHead correction factor KHAnd efficiency correction factor
Figure 113005DEST_PATH_IMAGE001
；
Step four, taking the flow, the pressure ratio and the efficiency characteristic as target values, and constructing a flow, pressure ratio and efficiency relational expression containing the correction coefficient;
constructing a relational expression of the correction coefficients comprising modal mutation parameters, bypass ratio parameters and mutation angles;
and step six, calculating to obtain each correction coefficient according to the relational expression constructed in the step five and the mode, the adjusting angle and the bypass ratio to be evaluated, and predicting the one-dimensional characteristics of the variable-cycle compression component with the nonlinear mutation in the existing or new state.
According to a specific implementation manner of the embodiment of the present application, a flow calculation formula required in the CDFS one-dimensional characteristic evaluation model is:
Figure 560167DEST_PATH_IMAGE002
，
wherein,
Figure DEST_PATH_IMAGE003
，mifor inlet flow, pi *And Ti *Respectively stagnation pressure and temperature, q (lambda), at the inlet characteristic cross sectioni) Is a flow function over a characteristic cross-section of the inlet, Ai being the area of the characteristic cross-section of the inlet through which the gas flows, alphaiIs the included angle between the airflow and the axial direction, R is the gas constant 287.023J/(Kg.K), K is the gas specific heat ratio, and K is the gas specific heat ratiomAbbreviated as a function of gas specific heat ratio and gas constant;
the pressure ratio calculation formula required in the CDFS one-dimensional characteristic evaluation model is as follows:
Figure 280998DEST_PATH_IMAGE004
，
wherein, pi* st,1Compression ratio of n stages of compression elements, HZ1Is the total enthalpy;
the required efficiency calculation formula in the CDFS one-dimensional characteristic evaluation model is as follows:
Figure DEST_PATH_IMAGE005
，
wherein eta is* ad,kAdiabatic efficiency for n-stage compression elements, Lad,kThe actual required rim work for the n-stage compression unit, LuIs a unit mass of gasWheel rim work, Lad,iThe actual required rim work to compress the parts, Lu,iFor actually consuming rim work, CPIs the isobaric specific heat of the gas, T* 1,iTotal inlet temperature, eta, of the i-th stage compressor* st,iEfficiency of the i-th stage, T1 *Total inlet temperature of the compression element, pi* stTo compress the component pressure ratio,. pi* st,iIs the compression component ith stage pressure ratio.
According to a specific implementation manner of the embodiment of the application, the pressure head correction coefficient KHDefined as the load factor
Figure 977559DEST_PATH_IMAGE006
The expression of the reduction coefficient is:
Figure DEST_PATH_IMAGE007
the expression of the efficiency correction coefficient is:
Figure 74828DEST_PATH_IMAGE008
，
wherein eta is* ad,maxMaximum adiabatic efficiency of order (η)* ad,max)kyonFor the maximum adiabatic efficiency of a theoretical stage,
Figure DEST_PATH_IMAGE009
is the theoretical load factor.
According to a specific implementation manner of the embodiment of the present application, the flow rate M including the correction coefficient in step 4iPressure ratio of pi* st,1Efficiency eta* ad,kThe relation is as follows:
Mi,n=KG*mi;
π* st1,n=KH*π* st,1；
Figure 907654DEST_PATH_IMAGE010
；
Kδ,n=Kδ*f(δ)；
in the formula, MiN is the corrected inlet flow, pi* st1N is the corrected compression ratio of the n stages of compression parts,
Figure DEST_PATH_IMAGE011
for corrected adiabatic efficiency of n-stage compression elements, KδN is the corrected drop angle, and f (delta) is a drop angle correction function.
According to a specific implementation manner of the embodiment of the present application, the relationship including the modal sudden change parameter, the bypass ratio parameter, and the correction coefficient of the sudden change angle in step five includes:
head correction factor KH=f1(α1*Md,α2*BPR,α3*δ)；
Flow correction factor KG=f2(β1*Md,β2*BPR,β3*δ);
Coefficient of efficiency correction
Figure 799387DEST_PATH_IMAGE001
=f3(γ1*Md,γ2*BPR,γ3*δ)；
Wherein Md is a modal mutation parameter, BPR is a bypass ratio parameter, δ is a mutation angle, f represents a functional relation, and α, β and γ are fitting weight coefficients.
Advantageous effects
Compared with the traditional method only for correcting the characteristic result, the CDFS modal variation performance-based one-dimensional input correction method constructs a two-step correction method for the characteristic result and the input parameter associated with the modal variation, the guide vane rule and the bypass ratio variation parameter, can predict the one-dimensional design performance of the CDFS with the modal variation, the guide vane rule and the bypass ejector back pressure variation, can flexibly correct the one-dimensional input parameter according to the special configuration characteristic rule, and can be used for predicting the design performance of the one-dimensional characteristic which is not limited to the nonlinear variation of a compression component in a variable cycle engine.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic view of a core engine driven fan variable circulation system according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a one-dimensional input modification method based on CDFS modal variability performance, according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a CDFS modal-varying characteristic variation according to an embodiment of the present invention.
Detailed Description
The embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present application, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be further noted that the drawings provided in the following embodiments are only schematic illustrations of the basic concepts of the present application, and the drawings only show the components related to the present application rather than the numbers, shapes and dimensions of the components in actual implementation, and the types, the numbers and the proportions of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The configuration of the CDFS modal variation constructed in the present application is illustrated as the configuration of the core driven fan variable cycle system shown in fig. 1. The operating characteristics of the core machine driving fan are influenced by the working state of the core machine driving fan, and strong coupling relevance is generated by modal change, a guide vane rule and a duct ratio change generated by a modal converter, an inlet guide vane and a duct ejector, so that a characteristic correction method comprising three parameters of the modal change, the guide vane rule and the duct ratio change is required to be established, and the characteristics after the CDFS modal change can be accurately estimated.
Therefore, an embodiment of the present application provides a one-dimensional input correction method based on CDFS modal performance, which is described in detail below with reference to fig. 2 to 3, and specifically includes the following steps:
step one, constructing a CDFS one-dimensional characteristic evaluation model containing modal change, bypass ratio change and mutation angle (angle change), wherein a flow calculation formula required for establishing the CDFS one-dimensional characteristic evaluation model is as follows:
Figure 452085DEST_PATH_IMAGE012
，
wherein,
Figure DEST_PATH_IMAGE013
，mifor inlet flow, pi *And Ti *Respectively stagnation pressure and temperature, q (lambda), at the inlet characteristic cross sectioni) Is a flow function over a characteristic cross-section of the inlet, Ai being the area of the characteristic cross-section of the inlet through which the gas flows, alphaiIs the included angle between the airflow and the axial direction, R is a gas constant which is equal to 287.023J/(Kg.K), K is the ratio of specific heat of the gas, and K ismAbbreviated as a function of gas specific heat ratio and gas constant;
the pressure ratio calculation formula required in the CDFS one-dimensional characteristic evaluation model is established as follows:
Figure 353045DEST_PATH_IMAGE014
，
wherein, pi* st,1Compression ratio of n stages of compression elements, HZ1Is the total enthalpy;
the efficiency calculation formula required in the CDFS one-dimensional characteristic evaluation model is established as follows:
Figure 509220DEST_PATH_IMAGE015
，
wherein eta is* ad,kAdiabatic efficiency for n-stage compression elements, Lad,kThe actual required rim work for the n-stage compression unit, LuIs the unit mass of the gas rim work, Lad,iThe actual required rim work to compress the parts, Lu,iFor actually consuming rim work, CPIs the isobaric specific heat of the gas, T* 1,iTotal inlet temperature, eta, of the i-th stage compressor* st,iEfficiency of the i-th stage, T1 *For the total inlet temperature of the compression element, pi* stTo compress the component pressure ratio,. pi* st,iIs the compression component ith stage pressure ratio.
And step two, determining the mode of the core machine driving fan, the rotating speed of the core machine driving fan in the mode, corresponding bypass ratio parameters, corresponding mutation angles (corresponding to the change parameter rule of the adjustment angle and the reference angle), and corresponding flow, pressure ratio and efficiency characteristics, and inputting parameters required by the one-dimensional pre-estimation model, wherein the bypass ratio parameters are defined as BPR, the mode mutation parameters are defined as Md, and the mutation angles are defined as delta.
Determining initial one-dimensional design input parameters including dimensionless rotation speed, angle rule and correction coefficient, wherein the correction coefficient comprises flow correction coefficient KGAnd a correction coefficient K of a falling angleδHead correction factor KHAnd efficiency correction factor
Figure 571854DEST_PATH_IMAGE001
。
Specifically, the indenter correction coefficient KHDefined as the load factor
Figure 446269DEST_PATH_IMAGE016
The expression of the reduction coefficient is:
Figure 150920DEST_PATH_IMAGE017
；
the expression of the efficiency correction coefficient is as follows:
Figure 427180DEST_PATH_IMAGE018
wherein η* ad,maxMaximum adiabatic efficiency of order (η)* ad,max)kyonFor the maximum adiabatic efficiency of a theoretical stage,
Figure 660716DEST_PATH_IMAGE009
is the theoretical load factor.
And step four, taking the flow rate, the pressure ratio and the efficiency characteristic as target values, and constructing a flow rate, pressure ratio and efficiency relational expression containing the correction coefficient.
Specifically, the flow rate M including the correction coefficientiPressure ratio of pi* st,1Efficiency eta* ad,kThe relational expressions are respectivelyComprises the following steps:
Mi,n=KG*mi;
π* st1,n=KH*π* st,1；
Figure 22427DEST_PATH_IMAGE019
；
Kδ,n=Kδ*f(δ)；
in the formula, MiN is the corrected inlet flow, pi* st1N is the corrected compression ratio of the n stages of compression parts,
Figure 999610DEST_PATH_IMAGE020
for corrected adiabatic efficiency of the compression element of n stages, KδN is the corrected drop angle, and f (delta) is a drop angle correction function.
And fifthly, constructing a relational expression of the correction coefficient comprising modal jump parameters, bypass ratio parameters and adjustment rule jump angles before and after modal change. The specific relation is as follows:
head correction factor KH=f1(α1*Md,α2*BPR,α3*δ)；
Flow correction factor KG=f2(β1*Md,β2*BPR,β3*δ);
Coefficient of efficiency correction
Figure 130377DEST_PATH_IMAGE001
=f3(γ1*Md,γ2*BPR,γ3*δ)；
Wherein Md is a modal mutation parameter, BPR is a bypass ratio parameter, δ is a mutation angle, f represents a functional relation, and α, β and γ are fitting weight coefficients.
And step six, calculating to obtain each correction coefficient according to the modal mutation, the bypass ratio and the mutation angle change rule relation constructed in the step five and the modal, the adjusting angle and the bypass ratio to be evaluated, and predicting the one-dimensional characteristics of the variable-cycle compression component with the nonlinear mutation in the existing or new state, wherein the characteristics after the modal change are shown in figure 3.
The correction method provided by the invention not only corrects the characteristic result, but also performs correlation correction on the one-dimensional design input, realizes the prediction of the one-dimensional design performance of the CDFS with mode mutation, guide vane rule and duct ratio mutation, can flexibly correct the input parameters, is used for the prediction of the one-dimensional characteristic of the variable-cycle compression component with nonlinear mutation, and provides quick and accurate prediction for the performance prediction of the variable-cycle engine compression component.
The method is used for constructing a two-step correction method for characteristic results and input parameters associated with parameters including modal change, guide vane regularity and bypass ratio change, and the application field is not limited to CDFS (compact disc sizing system) and includes protection of one-dimensional correction methods for characteristics of other aero-engine compression components including modal change characteristics.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within 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 (5)

1. A one-dimensional input correction method based on CDFS modal variability performance is characterized by comprising the following steps:
step one, constructing a CDFS one-dimensional characteristic evaluation model containing modal change, bypass ratio change and mutation angles;
determining a core machine driving fan mode, and each rotating speed, corresponding bypass ratio parameters and mutation angles of the core machine driving fan in the mode, and corresponding flow, pressure ratio and efficiency characteristics;
determining initial one-dimensional design input parameters including dimensionless rotation speed, angle rule and correction coefficient, wherein the correction coefficient comprises flow correction coefficient KGAnd a correction coefficient K of a falling angleδHead correction factor KHSynergistic effectCoefficient of rate correction
Figure 115926DEST_PATH_IMAGE001
；
Step four, taking the flow, the pressure ratio and the efficiency characteristic as target values, and constructing a flow, pressure ratio and efficiency relational expression containing the correction coefficient;
constructing a relational expression of the correction coefficients comprising modal mutation parameters, bypass ratio parameters and mutation angles;
and step six, calculating to obtain each correction coefficient according to the relational expression constructed in the step five and the mode, the adjusting angle and the bypass ratio to be evaluated, and predicting the one-dimensional characteristics of the variable-cycle compression component with the nonlinear mutation in the existing or new state.
2. The one-dimensional input modification method based on CDFS modal variability performance according to claim 1, wherein the flow calculation formula required in the CDFS one-dimensional characteristic evaluation model is:
Figure 999569DEST_PATH_IMAGE002
，
wherein,
Figure 942117DEST_PATH_IMAGE003
，mifor inlet flow, pi *And Ti *Respectively stagnation pressure and temperature, q (lambda), at the inlet characteristic cross sectioni) Is a flow function over a characteristic cross-section of the inlet, Ai being the area of the characteristic cross-section of the inlet through which the gas flows, alphaiIs the included angle between the airflow and the axial direction, R is the gas constant 287.023J/(Kg.K), K is the gas specific heat ratio, and K is the gas specific heat ratiomAbbreviated as a function of gas specific heat ratio and gas constant;
the pressure ratio calculation formula required in the CDFS one-dimensional characteristic evaluation model is as follows:
Figure 911210DEST_PATH_IMAGE004
，
wherein, pi* st,1Compression ratio of n stages of compression elements, HZ1Is the total enthalpy;
the required efficiency calculation formula in the CDFS one-dimensional characteristic evaluation model is as follows:
Figure 597406DEST_PATH_IMAGE005
，
wherein eta is* ad,kAdiabatic efficiency for n-stage compression elements, Lad,kThe actual required rim work for the n-stage compression unit, LuIs the unit mass of the gas rim work, Lad,iThe actual required rim work to compress the parts, Lu,iFor actually consuming rim work, CPIs the isobaric specific heat of the gas, T* 1,iTotal inlet temperature, eta, of the i-th stage compressor* st,iEfficiency of the i-th stage, T1 *For the total inlet temperature of the compression element, pi* stTo compress the part pressure ratio,. pi* st,iIs the compression component ith stage pressure ratio.
3. The CDFS modal variability performance based one dimensional input correction method of claim 2, wherein the indenter correction factor KHDefined as the load factor
Figure 335555DEST_PATH_IMAGE006
The expression of the reduction coefficient is:
Figure 714584DEST_PATH_IMAGE007
the expression of the efficiency correction coefficient is:
Figure 905394DEST_PATH_IMAGE008
wherein η* ad,maxMaximum adiabatic efficiency of order (η)* ad,max)kyonFor the maximum adiabatic efficiency of a theoretical stage,
Figure 395281DEST_PATH_IMAGE009
is the theoretical load factor.
4. The CDFS modal variability performance based one-dimensional input correction method of claim 3, wherein the flow M containing the correction coefficient in step 4iPressure ratio of pi* st,1Efficiency eta* ad,kThe relation is as follows:
Mi,n=KG*mi;
π* st1,n=KH*π* st,1；
Figure 722357DEST_PATH_IMAGE010
；
Kδ,n=Kδ*f(δ)；
in the formula, MiN is the corrected inlet flow, pi* st1N is the corrected compression ratio of the n stages of compression parts,
Figure 537866DEST_PATH_IMAGE011
for corrected adiabatic efficiency of the compression element of n stages, KδN is the corrected drop angle, and f (delta) is a drop angle correction function.
5. The one-dimensional input modification method based on CDFS modal variability performance according to claim 1, wherein the relationship of the modification coefficients including modal sudden change parameter, bypass ratio parameter, sudden change angle in step five comprises:
head correction factor KH=f1(α1*Md,α2*BPR,α3*δ)；
Flow correction factor KG=f2(β1*Md,β2*BPR,β3*δ);
Coefficient of efficiency correction
Figure 215972DEST_PATH_IMAGE001
=f3(γ1*Md,γ2*BPR,γ3*δ)；
Wherein Md is a modal mutation parameter, BPR is a bypass ratio parameter, δ is a mutation angle, f represents a functional relation, and α, β and γ are fitting weight coefficients.
CN202210357425.2A 2022-04-07 2022-04-07 CDFS (compact disc library) modal variation performance-based one-dimensional input correction method Active CN114491417B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210357425.2A CN114491417B (en) 2022-04-07 2022-04-07 CDFS (compact disc library) modal variation performance-based one-dimensional input correction method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210357425.2A CN114491417B (en) 2022-04-07 2022-04-07 CDFS (compact disc library) modal variation performance-based one-dimensional input correction method

Publications (2)

Publication Number Publication Date
CN114491417A true CN114491417A (en) 2022-05-13
CN114491417B CN114491417B (en) 2022-07-26

Family

ID=81489195

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210357425.2A Active CN114491417B (en) 2022-04-07 2022-04-07 CDFS (compact disc library) modal variation performance-based one-dimensional input correction method

Country Status (1)

Country Link
CN (1) CN114491417B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115680932A (en) * 2022-10-13 2023-02-03 中国航发四川燃气涡轮研究院 Adaptive engine binary adaptive ejector nozzle mathematical modeling method

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109376498A (en) * 2018-12-20 2019-02-22 清华大学 The modeling method of fanjet
CN109657341A (en) * 2018-12-17 2019-04-19 北京清软创想信息技术有限责任公司 A kind of real-time computing technique suitable for the conversion of variable cycle engine flow path
CN110472311A (en) * 2019-07-29 2019-11-19 中国航发沈阳发动机研究所 A kind of high-pressure compressor performance estimating method for change circulation core compression system
CN110647052A (en) * 2019-08-16 2020-01-03 南京航空航天大学 Variable cycle engine mode switching self-adaptive identity card model construction method
CN111914365A (en) * 2020-08-04 2020-11-10 南京航空航天大学 Variable-cycle engine modeling method and variable-cycle engine component level model
CN111914362A (en) * 2020-07-22 2020-11-10 中国航发沈阳发动机研究所 Self-adaptive method for turbofan engine model in research and development stage
CN112067304A (en) * 2020-11-11 2020-12-11 中国航发上海商用航空发动机制造有限责任公司 Method for measuring inlet flow of compressor in engine whole machine test
CN112284752A (en) * 2020-11-05 2021-01-29 南京航空航天大学 Variable cycle engine resolution redundancy estimation method based on improved state tracking filter
CN113297705A (en) * 2021-04-22 2021-08-24 西北工业大学 Method for predicting characteristics of gas compressor according to design indexes
CN113513373A (en) * 2021-07-08 2021-10-19 哈尔滨工程大学 Variable geometry turbine one-dimensional pneumatic design method
CN114169084A (en) * 2021-10-20 2022-03-11 中国航发四川燃气涡轮研究院 Method for correcting performance mathematical model of core machine based on characteristic test data of core machine
CN114444331A (en) * 2022-04-07 2022-05-06 中国航发四川燃气涡轮研究院 Stage characteristic matching method of multistage axial flow compressor

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109657341A (en) * 2018-12-17 2019-04-19 北京清软创想信息技术有限责任公司 A kind of real-time computing technique suitable for the conversion of variable cycle engine flow path
CN109376498A (en) * 2018-12-20 2019-02-22 清华大学 The modeling method of fanjet
CN110472311A (en) * 2019-07-29 2019-11-19 中国航发沈阳发动机研究所 A kind of high-pressure compressor performance estimating method for change circulation core compression system
CN110647052A (en) * 2019-08-16 2020-01-03 南京航空航天大学 Variable cycle engine mode switching self-adaptive identity card model construction method
CN111914362A (en) * 2020-07-22 2020-11-10 中国航发沈阳发动机研究所 Self-adaptive method for turbofan engine model in research and development stage
CN111914365A (en) * 2020-08-04 2020-11-10 南京航空航天大学 Variable-cycle engine modeling method and variable-cycle engine component level model
CN112284752A (en) * 2020-11-05 2021-01-29 南京航空航天大学 Variable cycle engine resolution redundancy estimation method based on improved state tracking filter
CN112067304A (en) * 2020-11-11 2020-12-11 中国航发上海商用航空发动机制造有限责任公司 Method for measuring inlet flow of compressor in engine whole machine test
CN113297705A (en) * 2021-04-22 2021-08-24 西北工业大学 Method for predicting characteristics of gas compressor according to design indexes
CN113513373A (en) * 2021-07-08 2021-10-19 哈尔滨工程大学 Variable geometry turbine one-dimensional pneumatic design method
CN114169084A (en) * 2021-10-20 2022-03-11 中国航发四川燃气涡轮研究院 Method for correcting performance mathematical model of core machine based on characteristic test data of core machine
CN114444331A (en) * 2022-04-07 2022-05-06 中国航发四川燃气涡轮研究院 Stage characteristic matching method of multistage axial flow compressor

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
CHANGYOU MA等: "Matching Performance Prediction Between Core Driven Fan Stage and High Pressure Compressor", 《INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES》 *
余秋霞等: "带CDFS的核心机过渡态数值模拟方法研究", 《燃气涡轮试验与研究》 *
周红: "变循环发动机特性分析及其与飞机一体化设计研究", 《中国博士学位论文全文数据库 (工程科技Ⅱ辑)》 *
宋甫等: "部件三维仿真模型与发动机循环参数分析的耦合方法研究", 《HTTPS://KNS.CNKI.NET/KCMS/DETAIL/11.1813.V.20220330.1055.007.HTML》 *
谷彬等: "变循环发动机核心机稳态性能计算模型修正方法", 《燃气涡轮试验与研究》 *
贾琳渊等: "变循环发动机过渡态性能直接模拟方法", 《HTTPS://KNS.CNKI.NET/KCMS/DETAIL/11.1929.V.20200818.0901.002.HTML》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115680932A (en) * 2022-10-13 2023-02-03 中国航发四川燃气涡轮研究院 Adaptive engine binary adaptive ejector nozzle mathematical modeling method
CN115680932B (en) * 2022-10-13 2024-05-24 中国航发四川燃气涡轮研究院 Mathematical modeling method for binary self-adaptive jet nozzle of self-adaptive engine

Also Published As

Publication number Publication date
CN114491417B (en) 2022-07-26

Similar Documents

Publication Publication Date Title
CN111914362B (en) Self-adaptive method for turbofan engine model in research and development stage
US6466858B1 (en) Methods and apparatus for monitoring gas turbine engine operation
CN108108528B (en) One-dimensional matching design method for power turbine of split-shaft type aeroderivative
CN110647052B (en) Variable cycle engine mode switching self-adaptive identity card model construction method
CN108223140A (en) A kind of three shaft gas turbine power turbine control of inlet guide vanes rule optimization method
KR20130098179A (en) Turbo-machinery stage families tuning/calibration system and method
CN114491417B (en) CDFS (compact disc library) modal variation performance-based one-dimensional input correction method
CN112253515A (en) State adjusting method for performance test of double-duct combined type gas compressor
CN115903484A (en) Cooperative game-based optimization method for multivariable robust controller of aircraft engine
CN109829238B (en) Method for identifying aero-engine system based on reduced balance manifold expansion model
EP3714143B1 (en) Method of designing and producing a turbine
CN114970364A (en) Method and device for determining component characteristics of gas turbine and electronic equipment
CN114486277A (en) Dynamic mode conversion verification method based on variable cycle engine core platform
JP5845705B2 (en) Gas turbine performance estimation device
CN116702380A (en) Aeroengine performance degradation monitoring and model correction method based on digital twin
CN111523276B (en) Gas turbine engine fuel flow correction method based on network model
CN111473976B (en) Correction method for fuel flow of gas turbine engine
Riegler et al. Some aspects of modelling compressor behavior in gas turbine performance calculations
CN111611743A (en) Axial-flow compressor characteristic line self-adaption method
Ma et al. Multi-stage axial flow compressors characteristics estimation based on system identification
Ziegler et al. Development of a Novel Axial Compressor Generation for Industrial Applications: Part 1—Compressor Design and Performance
Tomita et al. A model for numerical simulation of variable stator axial flow compressors
Zhang et al. Operational data-based adaptive improvement method of gas turbine component characteristics for performance simulation
Indarti et al. Integration of Technology Capability for Performance Diagnostics of MS7001EA Using PYTHIA
Anton et al. Exhaust volume dependency of turbocharger turbine design for a heavy duty otto cycle engine

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