CN106015082B - A kind of optimum design method for the impeller that the core main pump coasting time can be improved - Google Patents
A kind of optimum design method for the impeller that the core main pump coasting time can be improved Download PDFInfo
- Publication number
- CN106015082B CN106015082B CN201610318493.2A CN201610318493A CN106015082B CN 106015082 B CN106015082 B CN 106015082B CN 201610318493 A CN201610318493 A CN 201610318493A CN 106015082 B CN106015082 B CN 106015082B
- Authority
- CN
- China
- Prior art keywords
- impeller
- point
- main pump
- optimization
- core main
- 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.)
- Expired - Fee Related
Links
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D29/00—Details, component parts, or accessories
- F04D29/18—Rotors
-
- 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
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- 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
- G06F2119/18—Manufacturability analysis or optimisation for manufacturability
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computational Mathematics (AREA)
- Mechanical Engineering (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Structures Of Non-Positive Displacement Pumps (AREA)
Abstract
The present invention relates to a kind of optimum design methods of impeller that the core main pump coasting time can be improved, initially set up the argument scalar functions that idle conditions running efficiency can be improved by optimization impeller major parameter, then major parameter is divided into three groups as design variable, in initializing constraint, complex method is utilized respectively to running down efficiency optimization to these three subhead scalar functions, respectively obtain the optimum point for making the highest impeller main geometric parameters of efficiency, then small range determines a new constraint near optimum point, major heading function optimization is solved in the new constraint interior feasible direction method of diminution, obtain final optimization pass result.Design of the present invention to core main pump impeller can improve the relatively low problem of impeller running down operational efficiency, extend the coasting time, improve nuclear safety.Meanwhile this method innovative usage partial objectives for reduces restriction range and major heading finds the step-by-step optimization design method of optimum point, improves the speed of optimization, ensure that optimum results validity.
Description
Technical field
According to the present invention is core main pump running down efficiency analysis and turbomachine optimization design field, and in particular to is used
Modern mechanical optimum design method carries out the design method of step-by-step optimization to impeller of pump parameter.
Background technique
Nuclear power is as important clean energy resource, it has also become the main power source of most developed countries.Core main pump full name core
Reactor coolant main circulation pump, is nuclear power station " heart ", and function is that high radioactivity high-temperature high pressure water follows in driving nuclear island
The thermal energy of reactor core nuclear fission is passed to steam generator and generates steam, pushing turbine power generation by ring.In compressed water reactor nuclear power
In standing, primary Ioops circulation working-medium water directly contacted with nuclear reaction, absorb and transmit a large amount of heat, make working medium operating temperature,
Operating pressure is very high, and working flow is very big, also has radioactivity, therefore the core master of matching driving coolant working medium circulation
The feature with high temperature and pressure big flow is pumped, this feature is exactly one of the design difficulty of core main pump.On the other hand, since core is anti-
The heat for answering heap is taken away by core main pump driving primary Ioops working-medium water circulation, so core main pump is most important pump in nuclear power station,
And the equipment uniquely to run at high speed in nuclear island, either to safety still to performance, its requirement is all higher than common water pump
Very much.
If core main pump is frequently shut down can cause huge economic loss to power plant, the catastrophic discontinuityfailure of core main pump even can
Immeasurable disaster can be brought, therefore, the requirement to core main pump safety coefficient is very high, generally requires it can safely without reason
Barrier operation year.In station blackout, core main pump continues running down by inertia, and the flow for keeping primary Ioops certain maintains heap
Core is cooling, guarantees that reactor is not up to nucleateboiling state, to ensure nuclear safety.Therefore in optimization design, it is desirable that core
Main pump running efficiency under the operating condition of power-off running down is high, to extend the coasting time, reduction core main pump as far as possible is because losing external power
When flow mutation, to ensure reactor core safety after station blackout.And in core main pump, impeller is most important flow passage components, leaf
Wheel design it is reasonable whether directly affect the properties of pump, therefore impeller was optimized to the raising coasting time have it is certain
Directive significance.
Through retrieving, patent related to the present invention has: a seed nucleus main pump impeller design method (publication number: CN102691671
A), it is related to a kind of design method with core main pump impeller, the vane thickness and cornerite of design optimization impeller, but only from impeller
Vane thickness optimizes, and may bring other running down operation problems；The very big efficiency waterpower of core main pump under a kind of loss of-coolant accident (LOCA)
Design method (CN104595232A) provides a kind of Hydraulic Design Method and determines that impeller outer diameter, exit width, outlet are placed
Angle, the number of blade and throat opening area have pressure point of maximum efficiency near the metered flow point for designing core main pump under loss of-coolant accident (LOCA), improve
Efficiency of the core main pump under loss of-coolant accident (LOCA), while the cavitation resistive property and operational reliability of core main pump are improved, but the method
By the experience of designer, result can have very large deviation in the case where lacking experience.
Summary of the invention
For there are problem, the present invention provides a kind of simpler, system side for core main pump running down optimization design above
Method and thinking obtain longer running down to optimize running efficiency of the performance parameter of impeller as target raising core main pump running down when
Time guarantees safely and efficiently to run under the power blackout situation of core main pump.
The technical scheme is that
A kind of optimum design method for the impeller that the core main pump coasting time can be improved, specifically comprises the following steps:
S1: first looking for one can be by optimizing impeller major parameter: impeller inlet diameter D0, impeller outlet diameter D2、
Impeller outlet width b2, vane inlet angle beta1, blade exit laying angle β2, subtended angle of bladeNumber of blade Z, to improve idle conditions
The argument scalar functions of lower running efficiency；
S2: and then being divided into three groups of design variables for main geometric parameters, and establish three subhead scalar functions, then initially about
The optimal solution of three subhead scalar functions is respectively obtained to running down efficiency partial objectives for function optimization using complex method in beam condition,
S3: finally small range determines a new constraint condition near each impeller parameters of optimal solution, in new constraint condition
It is interior that major heading function optimization is solved with feasible direction method, obtain final optimization pass result.
In step S1, the design variable are as follows:
Argument scalar functions are as follows:
In formula, D0Impeller inlet diameter, unit rice；
D2Impeller outlet diameter, unit rice；
b2Impeller inlet width, unit rice；
β1Impeller inlet angle, unit degree；
β2Impeller outlet laying angle, unit degree；
Subtended angle of blade, unit degree；
The Z- number of blade.
In step S2, the three component design variables are as follows:
X1=[D0、D2]T；
X2=[b2、β1]T；
Subhead scalar functions are as follows:
In step S2, the associated design variables in the initializing constraint are calculated with initial reference to following design method:
1) impeller eye diameter D0Meet in the case where taking into account efficiency and cavitation,
In formula, Q- flow, cubic unit metre per second (m/s)；N- revolving speed, unit found revolutions per minute；
2) impeller outlet diameter D2Considering correction factorWhen constraint condition meet,
In formula, nsSpecific speed；
3) impeller outlet width b2It being obtained by experience and corresponding derivation formula, constraint condition meets,
b2Take median；
4) vane inlet angle beta1It being obtained by the parameter of empirical equation and pump, constraint condition meets,
In formula, ηvThe volumetric efficiency of pump；
The flow area of F- pump, unit square rice；
ψ-pump blade excretion coefficient；
The entrance of D- pump calculates spot diameter, unit rice；
M- empirical coefficient；
Δβ1The inlet incidence angle of pump；
5) blade exit angle beta2It being obtained by experience, constraint condition meets, and 22 °≤β2≤30°；
6) subtended angle of blade is determinedUsual value beIt takes
7) number of blade is selected as 4≤Z≤7；
After the completion of above-mentioned seven calculate, the initializing constraint is obtained are as follows:
Subhead scalar functions f (X1) initializing constraint be [0.9D0,1.1D0]、[0.9D2,1.1D2]；
Subhead scalar functions f (X2) initializing constraint be [0.9b2,1.1b2]、[0.9β1,1.1β1]；
Subhead scalar functions f (X3) initializing constraint be [22 °, 30 °],[4,7]。
In step S2, described optimizes running down efficiency with complex method, i.e., optimizes to subhead scalar functions
It solves, specifically comprises the following steps:
(1) apexes of complex number k is selected, n+1≤k≤2n is generally taken, is constituted in feasible zone and there was only the initial of k vertex
Complex；
(2) target function value for calculating k vertex of complex, selects wherein maximum value, i.e.,
Worst point X(H), f (X(H))=max { f (X(j)), j=1,2 ..., k }；
Good point X(G), f (X(G))=max { f (X(j), j=1,2 ..., k；But j ≠ H }；
Most better X(L), f (X(L))=min { f (X(j), j=1,2 ..., k }；；
(3) it calculates and removes worst point X(H)The central point X on remaining outer k-1 vertex(S), i.e.,(j=1,
2 ..., k, but j ≠ H), inspection center point X(S)Whether in feasible zone, if continuing to execute (4) step in feasible zone,
Otherwise (5) step is gone to；
(4) if X(S)It puts in feasible zone, then X(H)And X(S)Line direction on take mapping point X(R),X(R)=X(S)+α(X(S)-X(H)), in formula, α-mapping coefficient generally takes α=1.3；
If X(R)Feasible zone has been run off, then has needed to be retracted, i.e., halves mapping coefficient α, recalculate X(R)；If also not
Meet feasibility, continue to halve α, until mapping point X(R)Until feasible point；
(5) if X(S)Point is not in feasible zone, and feasible zone is non-convex set at this time；By above-mentioned (4) step calculate mapping point not
It may be feasible point, utilize central point X at this time(S)Most better X(L)Again a section is established, it is again random in this section
K vertex composition complex is generated, its boundary value of new section is,
If xi (L)< xi (S)(i=1,2 ..., n), then take
If xi (L)> xi (S)(i=1,2 ..., n), then take
Reconstitute complex and repeat (2), (3) step, until X(S)Until feasible point.
(6) mapping point target function value f (X is calculated(R))；
If f (X(R)) < f (X(H)), then use mapping point X(R)Instead of worst point X(H), new complex is constituted, an iteration is completed
It calculates, turns to (2) step, otherwise bear interest in next step.
(7) if f (X(R)) > f (X(H)), then mapping coefficient α is halved, recalculates mapping point；
If new mapping point X(R)Not only it is feasible point, but also meets f (X(R)) < f (X(H)), then with X(R)Instead of X(H), complete
Current iteration；Otherwise continue to halve α, until being less than (such as ξ=10 a previously given very little number ξ when α value-5) when；If
Objective function then turns to (4) step still without improvement, but uses time point X instead at this time(G)To replace previous worst point X(H)It is reflected
It penetrates；
(8) stop criterion: executing above-mentioned iterative process repeatedly, and complex gradually becomes smaller and approaches to optimum point, Zhi Daoman
FootWhen iterative calculation can terminate；Target function value is the smallest in complex at this time
Vertex is optimal solution, wherein X(C)For the point set center on all vertex of complex,
I.e.
It solves to obtain optimal solution X in subhead scalar functions1 *=[D0(1) *、D2(1) *]T、X2 *=[b2(2) *、β1(2) *]T、
In step S3, centered on each impeller parameters by optimal solution, new constraint condition is determined in a small range
It is as follows:
In step S3, argument scalar functions use the improved Topkis- of Zoutendijk method under new constraint condition
Veinott feasible direction method Optimization Solution, the specific steps are as follows:
(1) initial point X is given(1), determine initial feasible direction S(1)；
(2) label set E (X is determined(k))={ u | gu(X(k))=0,1≤u≤p } it whether is empty set, wherein k=1, gu(X)≥
0 is the constraint condition of main objective optimization function, and p is optimization constraint condition number, turns to (3) step if empty set, otherwise turns to
(4) step；
(3) judgeε 2 is iteration precision, directly obtains optimal solution X if setting up*=X(k)And it exports
As a result, otherwise enablingThen turn to (5) step；
(4) following Linear Program is solved,And judge Zk=0 whether at
It is vertical, optimal solution X is directly obtained if setting up*=X(k)And it exports as a result, otherwise turning to (5) step；
(5) one-dimensional search is solvedObtain λk, wherein
λmax=sup λ | gu(X(k))+λS(k)≤ 0, u=1,2 ..., p }, wherein sup indicates upper true in a set
Boundary, that is any element for belonging to the set is both less than equal to the value, guIt (X) >=0 is the constraint condition of majorized function, p is
Optimize constraint condition number；
(6) it enablesThen turn to (2) step；
After carrying out above-mentioned optimization design to argument scalar functions under new constraint condition, rounding is carried out to the optimal solution of solution
After verifying, a kind of final optimization pass result of the available optimum design method for meeting impeller that the core main pump coasting time can be improved
The beneficial effects of the present invention are:
(1) optimum design method of a kind of impeller that the core main pump coasting time can be improved proposed by the present invention, to core main pump
The design and manufacture of impeller have certain guidance meaning, can improve the problem that efficiency is relatively low in impeller operational process, extend core master
The coasting time under station blackout situation is pumped, the nuclear safety of core main pump is improved.
(2) this method innovative usage partial objectives for reduces restriction range and major heading is found the step-by-step optimization of optimum point and set
Meter method, not only increases the speed of optimization, while ensure that the validity of optimum results.
Detailed description of the invention
Fig. 1 is complex method program chart.
Fig. 2 is feasible direction method program chart.
Specific embodiment
The present invention is further described with specific technical solution with reference to the accompanying drawing.
A kind of optimum design method for the impeller that the core main pump coasting time can be improved is established through optimization impeller major parameter
(impeller inlet diameter D0, impeller outlet diameter D2, impeller outlet width b2, vane inlet angle beta1, blade exit laying angle β2, leaf
Piece corneriteNumber of blade Z) improve the argument scalar functions of running efficiency under idle conditions, detailed process are as follows:
Design variable are as follows:Argument scalar functions are as follows:
In formula, D0Impeller inlet diameter, unit rice；
D2Impeller outlet diameter, unit rice；
b2Impeller inlet width, unit rice；
β1Impeller inlet angle, unit degree；
β2Impeller outlet laying angle, unit degree；
Subtended angle of blade, unit degree；
The Z- number of blade；
Then major design variable is divided into three component design variables are as follows:
X1=[D0、D2]T；
X2=[b2、β1]T；
Three subhead scalar functions of building are as follows:
It needs to be determined that the initial constraint condition that must satisfy during solving optimization, must be combined in specific implementation process
Relevant design parameter and experience:
1) impeller eye diameter D0Meet in the case where taking into account efficiency and cavitation,
In formula, Q- flow, cubic unit metre per second (m/s)；
N- revolving speed, unit found revolutions per minute；
2) impeller outlet diameter D2Considering correction factorWhen constraint condition meet,
In formula, nsSpecific speed；
3) impeller outlet width b2It being obtained by experience and corresponding derivation formula, constraint condition meets,
b2Take median；
4) vane inlet angle beta1It being obtained by the parameter of empirical equation and pump, constraint condition meets,
In formula, ηvThe volumetric efficiency of pump；
The flow area of F- pump, unit square rice；
ψ-pump blade excretion coefficient；
The entrance of D- pump calculates spot diameter, unit rice；
M- empirical coefficient；
Δβ1The inlet incidence angle of pump；
5) blade exit angle beta2It being obtained by experience, constraint condition meets, and 22 °≤β2≤30°；
6) subtended angle of blade is determinedUsual value beIt takes
7) number of blade is selected as 4≤Z≤7；
After the completion of above-mentioned seven calculate, the obtained initializing constraint are as follows:
Subhead scalar functions f (X1) initializing constraint be [0.9D0,1.1D0]、[0.9D2,1.1D2]；
Subhead scalar functions f (X2) initializing constraint be [0.9b2,1.1b2]、[0.9β1,1.1β1]；
Subhead scalar functions f (X3) initializing constraint be [22 °, 30 °],[4,7]。
Solution calculating is carried out with complex method to subhead scalar functions in initializing constraint, wherein complex method is exactly
In the feasible zone of n dimension design space, to complex (polyhedron being made of in n-dimensional space n+1≤k≤2n vertex)
The objective function on each vertex is compared one by one, constantly removes worst point (most for minimizing problem, i.e. objective function
A little louder), target function value can be instead made to be declined and the new point of institute's Prescribed Properties, gradually tuning optimum point.
Complex method program chart as shown in Fig. 1, the specific steps are as follows:
(1) apexes of complex number k is selected, n+1≤k≤2n is generally taken, is constituted in feasible zone and there was only the initial of k vertex
Complex.
(2) target function value for calculating k vertex of complex, selects wherein maximum value, i.e.,
Worst point X(H), f (X(H))=max { f (X(j)), j=1,2 ..., k }；
Good point X(G),
f(X(G))=max { f (X(j), j=1,2 ..., k；But j ≠ H }；
Most better X(L), f (X(L))=min { f (X(j), j=1,2 ..., k }；
(3) it calculates and removes worst point X(H)The central point X on remaining outer k-1 vertex(S), i.e.,(j=1,
2 ..., k, but j ≠ H), inspection center point X(S)Whether in feasible zone.If continuing to execute (4) step in feasible zone,
Otherwise (5) step is gone to.
(4) if X(S)It puts in feasible zone, then X(H)And X(S)Line direction on take mapping point X(R),X(R)=X(S)+α(X(S)-X(H)),
α-mapping coefficient, generally takes α=1.3 in formula.
If X(R)Feasible zone has been run off, then has needed to be retracted, i.e., halves mapping coefficient α, recalculate X(R)；If also not
Meet feasibility, continue to halve α, until mapping point X(R)Until feasible point.
(5) if X(S)Point is not in feasible zone, and feasible zone is non-convex set at this time.By above-mentioned (4) step calculate mapping point not
It may be feasible point, utilize central point X at this time(S)Most better X(L)Again a section is established, it is again random in this section
It generates k vertex and constitutes complex.Its boundary value of new section is,
If xi (L)< xi (S)(i=1,2 ..., n), then take
If xi (L)> xi (S)(i=1,2 ..., n), then take
Reconstitute complex and repeat (2), (3) step, until X(S)Until feasible point.
(6) mapping point target function value F (X is calculated(R)).If f (X(R)) < f (X(H)), then use mapping point X(R)Instead of the worst
Point X(H), new complex is constituted, an iteration is completed and calculates, turn to (2) step, is otherwise born interest in next step.
(7) if f (X(R)) > f (X(H)), then mapping coefficient α is halved, recalculates mapping point.If new mapping point X(R)Not only it is feasible point, but also meets f (X(R)) < f (X(H)), then with X(R)Instead of X(H), complete current iteration.Otherwise continue to subtract α
Half, until being less than (such as ξ=10 a previously given very little number ξ when α value-5) when.If objective function still without improvement, turns to
(4) step, but use time point X instead at this time(G)To replace previous worst point X(H)It is mapped.
(8) stop criterion: executing above-mentioned iterative process repeatedly, and complex gradually becomes smaller and approaches to optimum point, Zhi Daoman
FootWhen iterative calculation can terminate.Target function value is the smallest in complex at this time
Vertex is optimal solution, wherein X(C)For the point set center on all vertex of complex,
I.e.
Subhead scalar functions solve to obtain optimal solution are as follows:
X1 *=[D0(1) *、D2(1) *]T、X2 *=[b2(2) *、β1(2) *]T、
We determine that new constraint condition is as follows centered on each guide vane parameter of optimal solution, in a small range:
Feasible direction method program chart as shown in Fig. 2, the specific steps are as follows:
(1) initial point X is given(1), determine initial feasible direction S(1)。
(2) label set E (X is determined(k))={ u | gu(X(k))=0,1≤u≤p } it whether is empty set, wherein k=1, gu(X)≥
0 is the constraint condition of main objective optimization function, and p is optimization constraint condition number,
(3) step is turned to if empty set, otherwise turns to (4) step.
(3) judgeOptimal solution X is directly obtained if setting up*=X(k)And it exports as a result, otherwise enablingThen turn to (5) step.
(4) following Linear Program is solved,And judge ZkWhether=0 is true,
Optimal solution X is directly obtained if setting up*=X(k)And it exports as a result, otherwise turning to (5) step.
(5) one-dimensional search is solvedObtain λk, wherein
λmax=sup λ | gu(X(k)+λS(k)≤ 0, u=1,2 ..., m }.Wherein, sup indicates upper true in a set
Boundary, that is any element for belonging to the set is both less than equal to the value, guIt (X) >=0 is the constraint condition of majorized function, p is
Optimize constraint condition number；
(6) it enablesThen turn to (2) step.
After carrying out above-mentioned optimization design to argument scalar functions under new constraint condition, rounding is carried out to the optimal solution of solution
After verifying, a kind of final optimization pass result of the available optimum design method for meeting impeller that the core main pump coasting time can be improved
The present invention is not limited to the above embodiments, also comprising other embodiments and variation within the scope of present inventive concept.
Claims (6)
1. the optimum design method that one kind can extend the impeller of core main pump coasting time, which comprises the steps of:
S1: first looking for one can be by optimizing impeller major parameter: impeller inlet diameter D0, impeller outlet diameter D2, impeller
Exit width b2, vane inlet angle beta1, blade exit laying angle β2, subtended angle of bladeNumber of blade Z, to improve core main pump efficiency eta,
Running down process loss is reduced, extends the argument scalar functions f (X) of core main pump coasting time under identical primary power；
Design variable are as follows:
Argument scalar functions are as follows:
In formula, D0Impeller inlet diameter, unit rice；
D2Impeller outlet diameter, unit rice；
b2Impeller outlet width, unit rice；
β1Inlet blade angle, unit degree；
β2Blade exit laying angle, unit degree；
Subtended angle of blade, unit degree；
The Z- number of blade；
The region min- minimum value；
S2: and then major parameter is divided into three component design variables, and establish three subhead scalar functions, then in initializing constraint
It is interior to utilize complex method to running down efficiency partial objectives for function optimization, the optimal solution of three subhead scalar functions is respectively obtained,
The three component design variables are as follows:
X1=[D0、D2]T；
X2=[b2、β1]T；
Subhead scalar functions are as follows:
The initializing constraint are as follows:
Subhead scalar functions f (X1) initializing constraint be [0.9D0,1.1D0]、[0.9D2,1.1D2]；
Subhead scalar functions f (X2) initializing constraint be [0.9b2,1.1b2]、[0.9β1,1.1β1]；
Subhead scalar functions f (X3) initializing constraint be
S3: finally small range determines a new constraint condition near each impeller parameters optimal solution, uses in new constraint condition
Feasible direction method solves major heading function optimization, obtains final optimization pass result.
2. one kind according to claim 1 can extend the optimum design method of the impeller of core main pump coasting time, feature
It is, in step S2, the associated design variables in the initializing constraint are calculated referring to following design method:
1) impeller inlet diameter D0Meet in the case where taking into account efficiency and cavitation,
In formula, Q- flow, cubic unit metre per second (m/s)；N- revolving speed, unit revolutions per minute；
2) impeller outlet diameter D2Considering correction factorWhen constraint condition meet,
In formula, nsSpecific speed；
3) impeller outlet width b2It being obtained by experience and corresponding derivation formula, constraint condition meets,
Take median；
4) blade exit laying angle β2It being obtained by experience, constraint condition meets, and 22 °≤β2≤30°；
5) subtended angle of blade is determinedValue be
6) number of blade is selected as 4≤Z≤7.
3. one kind according to claim 2 can extend the optimum design method of the impeller of core main pump coasting time, feature
It is, it is described in step 5)
4. one kind according to claim 1 can extend the optimum design method of the impeller of core main pump coasting time, feature
It is, in step S2, running down efficiency is optimized with complex method, i.e., subhead scalar functions are optimized, specifically
Include the following steps:
(1) n value, range of variables [a are giveni、bi] and iteration precision ε 1, apexes of complex number k is selected, n+1≤k≤2n is taken,
The intial compound form for there was only k vertex is constituted in feasible zone；
(2) target function value for calculating k vertex of complex, selects wherein maximum value, i.e.,
Worst point X(H), f (X(H))=max { f (X(j)), j=1,2 ..., k }；
Good point X(G), f (X(G))=max { f (X(j), j=1,2 ..., k；But j ≠ H }；
Most better X(L), f (X(L))=min { f (X(j), j=1,2 ..., k }；
(3) it calculates and removes worst point X(H)The central point X on remaining outer k-1 vertex(S),
I.e.Inspection center point X(S)Whether in feasible zone, if can
In row domain, then (4) step is continued to execute, (5) step is otherwise gone to；
(4) if X(S)It puts in feasible zone, then X(H)And X(S)Line direction on take mapping point X(R),X(R)=X(S)+α(X(S)-X(H)), in formula, α-mapping coefficient takes α=1.3；
If X(R)Feasible zone has been run off, then has needed to be retracted, i.e., halves mapping coefficient α, recalculate X(R)；If had not been met
Feasibility continues to halve α, until mapping point X(R)Until feasible point；
(5) if X(S)Point is not in feasible zone, and feasible zone is non-convex set at this time；It can not by the mapping point that above-mentioned (4) step calculates
It is feasible point, utilizes central point X at this time(S)Most better X(L)Again a section is established, k is randomly generated again in this section
A vertex constitutes complex, its boundary value of new section is,
If xi (L)< xi (S)(i=1,2 ..., n), then take
If xi (L)> xi (S)(i=1,2 ..., n), then take
Reconstitute complex and repeat (2), (3) step, until X(S)Until feasible point；
(6) mapping point target function value f (X is calculated(R))；
If f (X(R)) < f (X(H)), then use mapping point X(R)Instead of worst point X(H), new complex is constituted, an iteration meter is completed
It calculates, turns to (2) step, otherwise carry out in next step；
(7) if f (X(R)) > f (X(H)), then mapping coefficient α is halved, recalculates mapping point；
If new mapping point X(R)Not only it is feasible point, but also meets f (X(R)) < f (X(H)), then with X(R)Instead of X(H), complete this
Iteration；Otherwise continue to halve α, until being less than previously given iteration precision 10 when α value-5When；If objective function still without improvement,
(4) step is then turned to, but has used secondary point X instead at this time(G)To replace previous worst point X(H)It is mapped；
(8) stop criterion: executing above-mentioned iterative process repeatedly, and complex gradually becomes smaller and approaches to optimum point, until meetingWhen iterative calculation can terminate；The smallest top of target function value in complex at this time
Point is optimal solution, wherein X(C)For the point set center on all vertex of complex, i.e.,
It solves to obtain optimal solution X in subhead scalar functions1 *=[D0(1) *、D2(1) *]T、X2 *=[b2(2) *、β1(2) *]T、
5. one kind according to claim 4 can extend the optimum design method of the impeller of core main pump coasting time, feature
It is, in step S3, centered on each impeller parameters of optimal solution, determines that new constraint condition is as follows in a small range:
6. one kind according to claim 1 can extend the optimum design method of the impeller of core main pump coasting time, feature
It is, in step S3, argument scalar functions use the improved Topkis- of Zoutendijk method under new constraint condition
Veinott feasible direction method Optimization Solution, the specific steps are as follows:
(1) initial point X is given(1), determine initial feasible direction S(1)；
(2) label set E (X is determined(k))={ u | gu(X(k))=0,1≤u≤p } it whether is empty set, wherein k=1, gu(X) >=0 it is
The constraint condition of major heading majorized function, p are optimization constraint condition number, turn to (3) step if empty set, otherwise turn to the
(4) step；
(3) judgeε2For iteration precision, optimal solution X is directly obtained if setting up*=X(k)And export as a result,
Otherwise it enablesThen turn to (5) step；
(4) following linear programming is solved,WhereinAnd
Judge ZkWhether=0 is true, directly obtains optimal solution X if setting up*=X(k)And it exports as a result, otherwise turning to (5) step；
(5) one-dimensional search is solvedObtain λk,
λmax=sup λ | gu(X(k))+λS(k)≤ 0, u=1,2 ..., p }, wherein as soon as sup indicates the supremum in a set,
It is to say that any element for belonging to the set is both less than equal to the value, guIt (X) >=0 is the constraint condition of majorized function, p is to optimize about
Beam condition number；
(6) it enablesThen turn to (2) step；
After carrying out above-mentioned optimization design to argument scalar functions under new constraint condition, rounding verifying is carried out to the optimal solution of solution
Afterwards, the final optimization pass result of the available optimum design method for meeting a kind of impeller that can extend the core main pump coasting time
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610318493.2A CN106015082B (en) | 2016-05-13 | 2016-05-13 | A kind of optimum design method for the impeller that the core main pump coasting time can be improved |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610318493.2A CN106015082B (en) | 2016-05-13 | 2016-05-13 | A kind of optimum design method for the impeller that the core main pump coasting time can be improved |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106015082A CN106015082A (en) | 2016-10-12 |
| CN106015082B true CN106015082B (en) | 2019-02-05 |
Family
ID=57099985
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610318493.2A Expired - Fee Related CN106015082B (en) | 2016-05-13 | 2016-05-13 | A kind of optimum design method for the impeller that the core main pump coasting time can be improved |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106015082B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106762820B (en) * | 2016-12-25 | 2018-11-23 | 宁波至高点工业设计有限公司 | A kind of design method of prismatic blade radial fan impeller |
| CN107313970B (en) * | 2017-08-07 | 2018-09-21 | 哈尔滨电气动力装备有限公司 | Reactor coolant pump impeller integrity analysis method |
| CN110414171B (en) * | 2019-08-05 | 2023-09-08 | 南京工程学院 | Group coordination adjusting method for plate-shaped actuating mechanism |
| CN110598316B (en) * | 2019-09-10 | 2021-07-27 | 大连理工大学 | Method for constructing shielded nuclear main pump digital prototype based on beam finite element model |
| CN111079367B (en) * | 2019-11-28 | 2022-02-01 | 河海大学 | Axial flow pump spoke optimization design method suitable for different water inlet taper pipes |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IT1272019B (en) * | 1992-03-21 | 1997-06-10 | Barmag Barmer Maschf | HYDRAULIC CONTROL DEVICE |
| CN101833607B (en) * | 2010-05-11 | 2012-05-23 | 天津大学 | Multi-objective hybrid particle swam optimization design method for double-fed wind power generator |
| CN102004836A (en) * | 2010-12-01 | 2011-04-06 | 河海大学 | Method for optimizing improved particle swarm of nonlinear water power set control parameters |
| CN102054099B (en) * | 2010-12-16 | 2012-11-14 | 清华大学 | Method and device for designing three-dimensional closed guide blade with tubular type conical space |
| CN103020345A (en) * | 2012-12-05 | 2013-04-03 | 华南理工大学 | Centrifugal pump design method based on matrix laboratory (MATLAB) genetic algorithm |
| CN104408260B (en) * | 2014-12-04 | 2017-06-06 | 湖南大学 | A kind of tidal current energy water turbine vane airfoil profile method for designing |
-
2016
- 2016-05-13 CN CN201610318493.2A patent/CN106015082B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN106015082A (en) | 2016-10-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106015082B (en) | A kind of optimum design method for the impeller that the core main pump coasting time can be improved | |
| CN102207101B (en) | CFD (Computational Fluid Dynamics)-based modeling design method for nuclear main pump and designed million-kilowatt-grade nuclear main pump impeller | |
| US8433450B2 (en) | Optimized control of power plants having air cooled condensers | |
| CN106936627B (en) | Thermal power equipment performance monitoring method based on big data analysis and mining | |
| KR101664906B1 (en) | A counter-rotating type pump-turbine and a self generating system having the same and an optimal design method of a counter-rotating type pump-turbine | |
| CN103440528A (en) | Thermal power generating unit operation optimization method and device based on consumption difference analysis | |
| CN107330543A (en) | A kind of coal consumption method for optimization analysis based on load characteristics clustering and Controlling UEP | |
| CN105864097B (en) | The multi objective fuzzy Hydraulic Optimizing Design method of one seed nucleus main pump impeller | |
| Barbarelli et al. | Using a statistical-numerical procedure for the selection of pumps running as turbines to be applied in water pipelines: Study cases | |
| Kler et al. | An effective approach to optimizing the parameters of complex thermal power plants | |
| Pilev et al. | Multiobjective optimal design of runner blade using efficiency and draft tube pulsation criteria | |
| Ferreira et al. | Efficiency of biradial impulse turbines concerning rotor blade angle, guide-vane deflection and blockage | |
| CN108767855A (en) | A kind of electric system random production analog method that sequential persistently mixes | |
| CN106015102B (en) | A kind of optimum design method for the guide vane extending the core main pump coasting time | |
| CN116595874A (en) | Impeller mechanical performance prediction model parameter optimization method and device and storage medium | |
| Guédez et al. | Reducing the number of turbine starts in concentrating solar power plants through the integration of thermal energy storage | |
| Ohira et al. | Benchmark analyses of sodium natural convection in the upper plenum of the MONJU reactor vessel | |
| CN106599392B (en) | Multidisciplinary optimization design method of heavy metal axial flow pump | |
| Han et al. | Improved thermodynamic and aerodynamic design method and off‐design performance analysis of a radial inflow turbine for ORC system | |
| CN105468832B (en) | A kind of supercritical water reactor reactor core nuclear heat couples alternative manner | |
| Malik et al. | Fractional order modelling and robust multi-model intelligent controllers' synthesis for ACP1000 nuclear power plant | |
| Wang et al. | Aerodynamic prediction on the off-design performance of a S-CO2 turbine based on deep learning | |
| CN113297807B (en) | Method for designing circulation parameters of supercritical carbon dioxide power generation system | |
| Kassanos et al. | Numerical optimization of a centrifugal pump impeller with splitter blades running in reverse mode | |
| Cuturi et al. | Design and CFD analysis of an industrial fan for a concentrating high-temperature solar powerplant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190205 Termination date: 20190513 |