CN107256297A - The optimization method of seam treated casing Parametric designing - Google Patents
The optimization method of seam treated casing Parametric designing Download PDFInfo
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- CN107256297A CN107256297A CN201710392863.1A CN201710392863A CN107256297A CN 107256297 A CN107256297 A CN 107256297A CN 201710392863 A CN201710392863 A CN 201710392863A CN 107256297 A CN107256297 A CN 107256297A
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- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
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Abstract
A kind of optimization method of seam treated casing Parametric designing, the geometric parameter variable and its corresponding change spatial dimension that need to be optimized by selected treated casing carry out experimental design and construct agent model, multiple-objection optimization solution is carried out using multi-objective genetic algorithm according to selected optimization aim for agent model, whether the optimum results as obtained by judging numerical simulation meet requirement, the numerical result for being unsatisfactory for requiring is added in Sample Storehouse and is iterated solution, the specific geometric parameter for meeting and requiring is finally given；The present invention is reasonable in design, improves the design efficiency of treated casing, shortens the design cycle and reduces cost, is adapted to secondary development.
Description
Technical field
The present invention relates to a kind of technology in aero-engine field, specifically a kind of seam treated casing parametrization is set
The optimization method of meter.
Background technology
Aero-engine is in broad operating mode during flight, and the compressor part of one of its core component is often by steady
Determine the not enough puzzlement of nargin.Stability margin is too small to cause compressor easily to enter unstable working condition, and then compressor
Energy degradation, or even engine damage.In the case of the load and efficiency requirements more and more higher of current aerospace compressor, seek
Ask the flow control method of improvement compressor stability margin, postponement air-flow generation stall further important.Treated casing is used as one kind
Conventional passive control technology, has the advantages that simple in construction, manufacturing cost is low, reliability is high and the steady effect of expansion is good, numerous
Engine model in obtain successful application.
The structure type of treated casing is more, by taking seam treated casing as an example, and it has more geometry design parameter,
When designing treated casing, influence of each geometric parameter to flowing control conditions each other, especially the crucial geometry of treated casing
Parameter has vital influence to expanding steady effect and extraneoas loss, it is difficult to which calculating just to find by substantial amounts of multi-scheme makes
Obtain the optimal design that compressor stability margin and efficiency are all improved.According to conventional experience, seam treated casing can be effectively improved
The stability margin of compressor, but cost can be dropped to compressor design point efficiency.
The content of the invention
The present invention is directed to deficiencies of the prior art, proposes a kind of optimization of seam treated casing Parametric designing
Method, by carrying out Parametric designing to treated casing, gives rational variable-value space, uses to design point efficiency and steady
The multiple target for determining nargin is optimized, and obtains the structure of optimal seam treated casing, in the case where ensuring design point efficiency substantially not
On the premise of drop, stability margin is improved.
The present invention is achieved by the following technical solutions：
The present invention is by selecting the geometric parameter variable and its progress of corresponding change spatial dimension that treated casing need to optimize
Experimental design simultaneously constructs agent model, is carried out for agent model according to selected optimization aim using multi-objective genetic algorithm many
Objective optimization is solved, and whether the optimum results as obtained by judging numerical simulation meet requirement, will be unsatisfactory for the numerical computations of requirement
As a result add in Sample Storehouse and be iterated solution, finally give the specific geometric parameter for meeting and requiring.
Described geometric parameter variable is：Leading edge axial location, trailing edge axial location, treated casing seam depth, inclination angle,
The knuckle position of treated casing and percent opening.
Described experimental design simultaneously constructs agent model and referred to：Sample is dispensed using the multiple-dimensional hierarchical methods of sampling, to dispensing
Sample carry out numerical simulation solve governing equation, according to sample point choose agent model constructed.
Described governing equation is：Wherein：For conservation form parameter vector, t sits for the time
Mark,The flux of vector is glued for nothing,For the sticky flux of vector, Q is source item.
Described optimization aim is stability margin and design point efficiency.
Described stability margin isWherein：π and m are respectively overall pressure tatio and flow, under
Mark NS represents closely to breathe heavily a little, and DE represents design efficiency point.
Described design point efficiency isWherein：πDEFor design point overall pressure tatio, eDEFor design point stagnation temperature
Than.
Technique effect
Compared with prior art, the present invention improves the design efficiency of aero-engine compressor treated casing, and shortening is set
In the meter cycle, development cost is reduced, program modularity processing, with good autgmentability, is adapted to secondary development；And optimize calculating
Example automatically generate operation, save cost of human resources.
Brief description of the drawings
Fig. 1 is flow chart of the present invention；
Fig. 2 is the geometric parameter schematic diagram of treated casing；
Fig. 3 is the Pareto forward positions angle distribution figure of the result optimized；
In figure：1 is treated casing seam, and 2 be casing, and 3 be blade.
Embodiment
As shown in figure 1, the present embodiment uses the axial broken line R-joining treated casing of typical axial flow compressor, specifically include
Following steps：
Step 1, the geometric parameter variable of the need optimization of selected treated casing and its corresponding spatial dimension carry out experiment and set
Count and construct agent model.
As shown in Fig. 2 described geometric parameter variable is：Leading edge axial location a, trailing edge axial location c, treated casing are deep
Spend d, treated casing inclination alpha, treated casing knuckle position (b-a)/(c-a) and treated casing percent opening W/ (W+G).
Described geometric parameter variable needs to refer to concrete structure and flow field is provided, and its corresponding spatial dimension is：
1) leading edge axial location a：Depending on the axial location residing for the axial coverage and seam of seam, according to engineering warp
Test, the axial coverage of seam 2 should be less than being equal to the axial chord length in the tip of blade 3, and the residing axial location of seam should be located at rotor point
The surface or upstream at end；
2) trailing edge axial location c：Given with reference to the axial coverage of leading edge axial location and treated casing seam 1；
3) the depth d of treated casing seam 1：Depth bounds, which is chosen, need to consider engine structure limiting factor；
4) inclination alpha of treated casing seam 1：Determine that seam inclines relative to meridional plane in a rotational direction according to the flow field of blade tip
Angular direction and angle；
5) knuckle position (b-a)/(c-a) of treated casing：Take into account the feasibility and optimization range during mess generation
Integrality；
6) the percent opening W/ (W+G) of treated casing：0.5~0.8, and structure limiting factor need to be considered.
The span of each geometric parameter variable is as shown in table 1 in the present embodiment.
The geometric parameter variable of table 1 optimizes span
Wherein：Leading edge axial location and trailing edge axial location are using compressor rotating vane leading edge as zero point, along flowing to direction for just,
S represents the axial chord length of the blade tip of rotor.
The axial chord length s of the blade tip of the rotor of the present embodiment value is 45mm.
Described experimental design simultaneously constructs agent model and comprised the following steps：
S1：Sample is dispensed using the multiple-dimensional hierarchical methods of sampling, numerical simulation solution governing equation is carried out to the sample of spreading.
The described multiple-dimensional hierarchical methods of sampling is Latin hypercube (LHS) method.
Described Latin hypercube method is taking out 60 samples in scope shown in table 1, given birth to using Parametric designing instrument
Into the corresponding grid of 60 chessboard sheets.
The governing equation numerical simulation that 60 samples selected carry out averaged Navier-Stokes equation one by one is asked
Solution, obtains the corresponding stability margin of 60 samples and design point efficiency.
Described governing equation is：Wherein：For conservation form parameter vector, t sits for the time
Mark,The flux of vector is glued for nothing,For the sticky flux of vector, Q is source item.
Effects of the described source item Q comprising coriolis force and centrifugal force.
Will be without the viscous flux of vectorWith the sticky flux of vectorDecompose to the component of cartesian coordinate system： Wherein：WithFor three reference axis in cartesian coordinate system
Unit vector.
Then the form of each variable is in governing equation： Wherein： For speed term, τijFor stress, ω is the angular velocity of rotation of relative coordinate system, and ρ is density,To be quiet
Pressure,For gross energy, qiFor heat flow density,For relative velocity, r is radius, and μ is viscosity, μtCaused by turbulence model
Viscosity amendment, x is space coordinate amount, and κ is heat transfer coefficient, κtThe heat transfer coefficient amendment caused by turbulence model, T is temperature
Degree, subscript "-" represent time-averaged amount, subscript "~" represent by the average time-averaged amount of density.
In view of the influence of tubulence energy, static pressureAnd gross energyForm be：
Wherein：K is tubulence energy, and e is interior energy.
S2：Agent model is chosen according to sample point to be constructed.
The agent model of the present embodiment is radial base neural net (Radial Basis Neural Network), is one
Local neural network is planted, basic function of different shapes is realized by adjusting the parameter of nonlinear function, passes through these basic functions
Weighted linear combination carrys out fit object function.
Radial base neural net is trained using effective sample, obtaining one can reflect that object function becomes with geometry
The agent model of quantitative change law.
Step 2, according to selected optimization aim many mesh are carried out using multi-objective genetic algorithm to the agent model in step 1
Mark Optimization Solution.
The design of compressor needs to take into account stability and efficiency, therefore the present embodiment selection stability margin and design point efficiency
It is used as optimization aim.
The multi-objective genetic algorithm of the present embodiment is NSGA-II multi-objective genetic algorithms, obtains the Pareto of agent model
Forward position is solved, as shown in figure 3, in figure：SM is stability margin, and DE is design point efficiency, and SW is light wall condition, and sample is sample
The result of point, the optimal solution that opt obtains for calculating, sample point and optimal solution constitutes final disaggregation.
Described stability margin isWherein：π and m are respectively overall pressure tatio and flow, under
Mark NS represents closely to breathe heavily a little, and DE represents design efficiency point.Using the maximum of stability margin as target in searching process, finally with steady
That determines nargin improves percentage as the criterion of optimizing result.
Described design point efficiency isWherein：πDEFor design point overall pressure tatio, eDEFor design point stagnation temperature
Than.To design the maximum of point efficiency as target in searching process, finally the raising amount using efficiency value is sentenced as optimizing result
Disconnected standard.
Stability margin and design point efficiency can be weighed from the solution of forward position, both is obtained and balances the optimal solution of raising, such as table 2
It is shown.
Whether step 3, the result obtained to step 2 are met on the premise of ensuring that design point efficiency does not decline substantially, are carried
The requirement of high stable nargin 10% carries out analysis and judges screening, and the result for being unsatisfactory for requiring is added to be iterated in Sample Storehouse and asked
Solution, finally gives the specific geometric parameter for meeting and requiring.
Stability margin and design point efficiency can be weighed from the forward position solution that step 2 is obtained, selection obtains both balances and carried
High optimal solution, as shown in table 2.
The specific geometric parameter of treated casing optimal solution of table 2 and optimization target values
The present embodiment can also be applied in the general turbomachine field in non-aero-engine field.
The application environment of above-mentioned embodiment is the process of optimization of seam treated casing Parametric designing, specifically
Involved parameter includes leading edge axial location, trailing edge axial location, depth, inclination angle, the knuckle of treated casing seam 1 in operation
Put and percent opening, being arranged on according to these in specific environment can obtain including stability margin and design point efficiency Bi-objective most
The detailed numerical value analog result of excellent solution.Contrasted with the existing a large amount of numerical simulation technologies for adjusting six design parameters one by one, this
Embodiment, which has, saves a large amount of manpowers and time cost and can find out the creative advantage of optimal solution.
Above-mentioned specific implementation can by those skilled in the art on the premise of without departing substantially from the principle of the invention and objective with difference
Mode local directed complete set is carried out to it, protection scope of the present invention is defined by claims and not by above-mentioned specific implementation institute
Limit, each implementation in the range of it is by the constraint of the present invention.
Claims (10)
1. a kind of optimization method of seam treated casing Parametric designing, it is characterised in that optimization is needed by selected treated casing
Geometric parameter variable and its corresponding change spatial dimension carry out experimental design and constructing agent model, for agent model root
Multiple-objection optimization solution is carried out using multi-objective genetic algorithm according to selected optimization aim, optimized as obtained by judging numerical simulation
As a result requirement whether is met, the numerical result for being unsatisfactory for requiring is added in Sample Storehouse and is iterated solution, is finally given
Meet desired specific geometric parameter.
2. optimization method according to claim 1, it is characterized in that, described geometric parameter variable is：Leading edge axial location,
Depth, inclination angle, the knuckle position of treated casing and the percent opening of trailing edge axial location, treated casing seam.
3. optimization method according to claim 2, it is characterized in that, the spatial dimension of described leading edge axial location for-
0.45~0s, the spatial dimension of trailing edge axial location is 0~0.6s, and the depth of treated casing is 3~16mm, inclination angle is 20~
70 °, knuckle position is 0.1~0.9, and percent opening is 0.5~0.8, wherein：Leading edge axial location and trailing edge axial location are to calm the anger
Machine rotating vane leading edge is zero point, is that just, s is the axial chord length of blade tip of rotor along direction is flowed to.
4. optimization method according to claim 1, it is characterized in that, described experimental design simultaneously constructs agent model and referred to：
Sample is dispensed using the multiple-dimensional hierarchical methods of sampling, numerical simulation solution governing equation is carried out to the sample of spreading, according to sample point
Agent model is chosen to be constructed.
5. optimization method according to claim 4, it is characterized in that, described governing equation is：Wherein：For conservation form parameter vector, t is time coordinate,The flux of vector is glued for nothing,It is viscous
The property flux of vector, Q is source item.
6. optimization method according to claim 4, it is characterized in that, described agent model is radial base neural net.
7. optimization method according to claim 1, it is characterized in that, described optimization aim is that stability margin and design point are imitated
Rate.
8. optimization method according to claim 7, it is characterized in that, described stability margin isWherein：π and m are respectively overall pressure tatio and flow, and subscript NS represents closely to breathe heavily a little, and DE represents to set
Count efficient point.
9. optimization method according to claim 7, it is characterized in that, described design point efficiency isIts
In：πDEFor design point overall pressure tatio, eDEFor design point stagnation temperature ratio.
10. a kind of seam casing, it is characterised in that obtained by the design of any of the above-described claim methods described.
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Cited By (2)
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| CN108664742A (en) * | 2018-05-15 | 2018-10-16 | 上海交通大学 | The Multipurpose Optimal Method of nacelle Parametric designing |
| CN110298051A (en) * | 2018-08-09 | 2019-10-01 | 上海交通大学 | Tandem Blades To An Aeroengine relative position design optimization method |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108664742A (en) * | 2018-05-15 | 2018-10-16 | 上海交通大学 | The Multipurpose Optimal Method of nacelle Parametric designing |
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| CN110298051A (en) * | 2018-08-09 | 2019-10-01 | 上海交通大学 | Tandem Blades To An Aeroengine relative position design optimization method |
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Application publication date: 20171017 |