CN106015082A - Impeller optimized design method capable of prolonging running down time of reactor coolant pump - Google Patents

Abstract

The invention relates to an impeller optimized design method capable of prolonging running down time of a reactor coolant pump. According to the impeller optimized design method, firstly, a main objective function for improving running efficiency under a running down working condition by optimizing main parameters of an impeller is established; then the main parameters are divided into three groups which are taken as design variables respectively, three subordinate objective functions are optimized respectively with a complex method for running down efficiency under initial constraint conditions, and the optimum points, guaranteeing the highest efficiency, of main geometric parameters of the impeller are obtained respectively; and a new constraint is determined in a small range nearby the optimum points, optimized solution is performed on the main objective function under the reduced new constraint with a feasible direction method, and the final optimized result is obtained. By means of design of the impeller of the reactor coolant pump, the problem of lower running down efficiency of the impeller can be improved, the running down time is prolonged, and the nuclear safety is improved. Meanwhile, with innovative application of the step-by-step optimization design method adopting constraint range reduction for subordinate objectives and optimum point searching for the main objective, the optimization speed is increased, and the effectiveness of the optimized result is guaranteed.

Description

A kind of Optimization Design of the impeller improving the core main pump coasting time

Technical field

Involved in the present invention is core main pump running down efficiency analysis and turbomachine optimization design field, is specifically related to use Modern mechanical Optimization Design carries out the method for designing of step-by-step optimization to impeller of pump parameter.

Background technology

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 its function is that in driving nuclear island, high radioactivity high-temperature high pressure water follows Ring, passes to the heat energy of reactor core nuclear fission steam generator and produces steam, and pushing turbine generates electricity.At compressed water reactor nuclear power In standing, primary Ioops circulation working-medium water directly contact with nuclear reaction, absorb and transmit substantial amounts of heat, make working medium operating temperature, Operating pressure is the highest, and working flow is very big, also has radioactivity, the core master driving coolant working medium circulation therefore matched Pump has the feature of the big flow of High Temperature High Pressure, and this feature is exactly one of design difficulty of core main pump.On the other hand, anti-due to core The heat answering heap is to be driven the circulation of primary Ioops working-medium water to take away by core main pump, so core main pump is most important pump in nuclear power station, Also being the equipment uniquely run up in nuclear island, either to safety still to performance, its requirement is all high than common water pump A lot.

If the shutdown frequently of core main pump can cause huge economic loss, the catastrophic discontinuityfailure of core main pump even may be used to power plant Can bring immeasurable disaster, therefore, the requirement to core main pump safety coefficient is the highest, typically requires that it can the most without reason Barrier runs year.In the case of station blackout, core main pump relies on inertia to continue running down, keeps the flow that primary Ioops is certain, maintains heap Core cools down, it is ensured that reactor is not up to nucleateboiling state, thus ensures nuclear safety.Therefore when optimizing design, it is desirable to core Main pump running efficiency under the operating mode of power-off running down is high, thus extends the coasting time, reduces core main pump because losing external power as far as possible Time flow sudden change, to guarantee reactor core safety after station blackout.And in core main pump, impeller is most important flow passage components, leaf The whether reasonable properties directly affecting pump of wheel design, is therefore optimized impeller and has certain to the raising coasting time Directive significance.

Through retrieval, patent related to the present invention has: a kind of core main pump impeller method for designing (publication number: CN102691671A), relate to the method for designing of a kind of band core main pump impeller, the vane thickness of its design optimization impeller and cornerite, But only it is optimized from impeller blade thickness, other running down operation problems may be brought；Core main pump under a kind of loss of-coolant accident (LOCA) Greatly efficiency Hydraulic Design Method (CN104595232A), it is provided that a kind of Hydraulic Design Method determines impeller outer diameter, outlet width Degree, outlet laying angle, the number of blade and throat opening area, make there is maximum near the metered flow point that core main pump designs under loss of-coolant accident (LOCA) Efficient point, improves core main pump efficiency under loss of-coolant accident (LOCA), improves the cavitation resistive property of core main pump and reliable simultaneously Property, but the method relies on the experience of designer, and in the case of lacking experience can there is very large deviation in result.

Summary of the invention

For above existing problems, the present invention provides a kind of simpler, side of system for core main pump running down optimization design Method and thinking, with the performance parameter that optimizes impeller for target improve core main pump running down time running efficiency, it is thus achieved that longer running down Time, it is ensured that run safely and efficiently under the powering-off state of core main pump.

The technical scheme is that

The Optimization Design of a kind of impeller improving the core main pump coasting time, specifically includes 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 lay angle beta₂, subtended angle of bladeNumber of blade Z, improves idle conditions The major heading function of lower running efficiency；

S2: then main geometric parameters is divided into three groups of design variables, and sets up three partial objectives for functions, then the most about Utilize complex method to running down efficiency partial objectives for function optimization in bundle condition, respectively obtain the optimal solution of three partial objectives for functions,

S3: finally little scope determines a new constraints, in new constraints near each impeller parameters of optimal solution Major heading function optimization is solved by interior feasible direction method, obtains final optimization pass result.

In step S1, described design variable is:

Major heading function is:

In formula, D₀-impeller inlet diameter, unit rice；

D₂-impeller outlet diameter, unit rice；

b₂-impeller inlet width, unit rice；

β₁-impeller inlet angle, unit degree；

β₂-impeller outlet laying angle, unit degree；

-subtended angle of blade, unit degree；

The Z-number of blade.

In step S2, three described design of components variablees are:

X₁=[D₀、D₂]^T；

X₂=[b₂、β₁]^T；

Partial objectives for function is:

$f\left(X_1\right)=\frac{1}{\mathrm{\η}\left(X_1\right)}=\frac{1}{\mathrm{\η}(D_0,D_2)}\→min;$

$f\left(X_2\right)=\frac{1}{\mathrm{\η}\left(X_2\right)}=\frac{1}{\mathrm{\η}(b_2,{\mathrm{\β}}_1)}\→min;$

In step S2, the associated design variables in described initializing constraint calculates with initial reference to following method for designing:

1) impeller eye diameter D₀Meet in the case of taking into account efficiency and cavitation,

$D_0=4.5\×\sqrt[3]{Q/n};$

In formula, Q-flow, cubic unit metre per second (m/s)；N-rotating speed, unit is vertical turns every point；

2) impeller outlet diameter D₂Considering correction factorTime constraints meet,

$D_2=9.64\×(0.9742+0.0159\×\frac{n_s}{100})\·{\left(\frac{n_s}{100}\right)}^{-\frac{1}{2}}\·\sqrt[3]{Q/n};$

In formula, n_s-specific speed；

3) impeller outlet width b₂Being obtained by experience and corresponding derivation formula, its constraints meets,

b₂Take intermediate value；

4) vane inlet angle beta₁Being obtained by the parameter of empirical equation and pump, its constraints meets,

${\mathrm{\β}}_1=arctg\frac{\frac{Q}{{\mathrm{\η}}_{v}F\mathrm{\ψ}}}{\frac{\mathrm{\π}}{60}Dn-\frac{2m\sqrt[3]{Q^{2}n}}{D}}+{\mathrm{\Δ\β}}_1;$

In formula, η_vThe volumetric efficiency of-pump；

The flow area of F-pump, unit square rice；

The blade excretion coefficient of ψ-pump；

The import department of D-pump calculates spot diameter, unit rice；

M-empirical coefficient；

Δβ₁The inlet incidence angle of-pump；

5) blade exit angle beta₂Being obtained by experience, its constraints meets, 22 °≤β₂≤30°；

6) subtended angle of blade is determinedUsual value beTake

7) number of blade be chosen as 4≤Z≤7；

After above-mentioned seven calculating complete, described initializing constraint obtain for:

Partial objectives for function f (X₁) initializing constraint be [0.9D₀,1.1D₀]、[0.9D₂,1.1D₂]；

Partial objectives for function f (X₂) initializing constraint be [0.9b₂,1.1b₂]、[0.9β₁,1.1β₁]；

Partial objectives for function f (X₃) initializing constraint be [22 °, 30 °],[4,7]。

In step S2, running down efficiency is optimized by described utilization complex method, is i.e. optimized partial objectives for function Solve, specifically include following steps:

(1) select apexes of complex number k, typically take n+1≤k≤2n, in feasible zone, constitute the initial of only k summit Complex；

(2) calculate the target function value on k summit of complex, select wherein maximum, i.e.

Worst point X^(H), F (X^(H))=max{F (X^(j)), j=1,2 ..., k}；

Good some X^(G), F (X^(G))=max{F (X (j), j=1,2 ..., k；But j ≠ H}；

The most better X^(L), F (X^(L))=min{F (X^(j)), j=1,2 ... k}；

(3) calculate except worst point X^(H)The central point X on remaining k-1 summit outer^(S), i.e.Inspection center point X^(S)Whether in feasible zone.If at feasible zone In, then continue executing with (4th) step, otherwise forward (5th) step to；

(4) if X^(S)Put 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, typically take α=1.3；

If X^(R)Run off feasible zone, then needed to be return, will halve by mapping coefficient α, recalculate X^(R)；If the most not Meet feasibility, continue to halve α, until mapping point X^(R)Till becoming feasible point；

(5) if X^(S)Point is not in feasible zone, and now feasible zone is non-convex set；By the mapping point of above-mentioned (4th) step calculating not It is probably feasible point, now utilizes central point X^(S)The most better X^(L)Again an interval is established, the most random in this interval Producing k summit and constitute complex, its boundary value of new interval is,

If x_i ^(L)<x_i ^(S)(i=1,2 ..., n), then take

If x_i ^(L)>x_i ^(S)(i=1,2 ..., n), then take

Reconstitute complex repeat (2nd), (3) step, until X^(S)Till becoming feasible point.

(6) mapping point target function value F (X is calculated^(R))；

If F is (X^(R))<F(X^(H)), then use mapping point X^(R)Replace worst point X^(H), constitute new complex, complete an iteration Calculate, turn to (2nd) step, otherwise bear interest next step.

(7) if F is (X^(R))>F(X^(H)), then mapping coefficient α is halved, recalculate mapping point；

If new mapping point X^(R)Both it had been feasible point, had met again F (X^(R))<F(X^(H)), then with X^(R)Replace X^(H), complete this Secondary iteration；Otherwise continue to halve α, until when a value is less than previously given very decimal ξ (such as ξ=10^-5) time；If mesh Scalar functions still without improving, then turns to (4th) step, but now uses time some X instead^(G)Replace previous worst point X^(H)Map；

(8) stop criterion: repeatedly perform above-mentioned iterative process, complex tapers into and approaches to optimum point, until full FootTime iterative computation can terminate；The summit that now in complex, target function value is minimum It is optimal solution, wherein, X^(C)For the point set center on all summits of complex, i.e.

Solve at partial objectives for function and obtain optimal solution X₁ ^*=[D₀₍₁₎ ^*、D₂₍₁₎ ^*]^T、X₂ ^*=[b₂₍₂₎ ^*、β₁₍₂₎ ^*]^T、

In step S3, centered by the described each impeller parameters by optimal solution, in little scope, determine new constraint bar Part is as follows:

In step S3, major heading function uses the Topkis-after the improvement of Zoutendijk method under new constraints Veinott feasible direction method Optimization Solution, specifically comprises the following steps that

(1) given initial point X⁽¹⁾, allowable error ε, determine initial feasible direction S⁽¹⁾, k=1 is set；

(2) label set E (X is determined^(k))={ j | g_j(X^(k))=0, whether 1≤j≤m} is empty set, if empty set turns to (3) step, otherwise turns to (4th) step, and wherein m is constraint conditional number；

(3) judgeIf setting up, directly obtain optimal solution X^*=X^(k)And export result, otherwise makeThen turn to (5th) step；

(4) following Linear Program is solved,And judge Z_kWhether=0 become Vertical, if setting up, directly obtain optimal solution X^*=X^(k)And export result, otherwise turn to (5th) step；

(5) one-dimensional search is solvedObtain λ_k, wherein

λ_max=sup{ λ | g_u(X^(k)+λS^(k)≤ 0, u=1,2 ..., m}；

(6) orderThen turn to (2nd) step；

After major heading function being carried out above-mentioned optimization design under new constraints, the optimal solution solved is carried out rounding After checking, the final optimization pass result of the Optimization Design of a kind of impeller improving the core main pump coasting time can be met

The beneficial effects of the present invention is:

(1) Optimization Design of a kind of impeller improving the core main pump coasting time that the present invention proposes, to core main pump Manufacturing and designing of impeller has certain guidance meaning, it is possible to improve the problem that in impeller running, efficiency is on the low side, extends core master Coasting time under pump station blackout situation, improve the nuclear safety of core main pump.

(2) this method innovative usage partial objectives for reduces restriction range and major heading finds the setting of step-by-step optimization of optimum point Meter method, not only increases the speed of optimization, ensure that the effectiveness of optimum results simultaneously.

Accompanying drawing explanation

Fig. 1 is complex method flow chart.

Fig. 2 is feasible direction method flow chart.

Detailed description of the invention

The present invention is further described with concrete technical scheme below in conjunction with the accompanying drawings.

The Optimization Design of a kind of impeller improving the core main pump coasting time, sets up by optimizing impeller major parameter (impeller inlet diameter D₀, impeller outlet diameter D₂, impeller outlet width b₂, vane inlet angle beta₁, blade exit lay angle beta₂, leaf Sheet corneriteNumber of blade Z) improve the major heading function of running efficiency under idle conditions, detailed process is:

Design variable is:Major heading function is:

In formula, D₀-impeller inlet diameter, unit rice；

D₂-impeller outlet diameter, unit rice；

b₂-impeller inlet width, unit rice；

β₁-impeller inlet angle, unit degree；

β₂-impeller outlet laying angle, unit degree；

-subtended angle of blade, unit degree；

The Z-number of blade；

Then major design variable be divided into three design of components variablees be:

X₁=[D₀、D₂]^T；

X₂=[b₂、β₁]^T；

Three the partial objectives for functions built are:

$f\left(X_1\right)=\frac{1}{\mathrm{\&eta;}\left(X_1\right)}=\frac{1}{\mathrm{\&eta;}(D_0,D_2)}\&RightArrow;min;$

$f\left(X_2\right)=\frac{1}{\mathrm{\&eta;}\left(X_2\right)}=\frac{1}{\mathrm{\&eta;}(b_2,{\mathrm{\&beta;}}_1)}\&RightArrow;min;$

It needs to be determined that the initial constraints must being fulfilled for during solving-optimizing, must be in conjunction with in its specific implementation process Relevant design parameter and experience:

1) impeller eye diameter D₀Meet in the case of taking into account efficiency and cavitation,

$D_0=4.5\&times;\sqrt[3]{Q/n};$

In formula, Q-flow, cubic unit metre per second (m/s)；

N-rotating speed, unit is vertical turns every point；

2) impeller outlet diameter D₂Considering correction factorTime constraints meet,

$D_2=9.64\&times;(0.9742+0.0159\&times;\frac{n_s}{100})\&CenterDot;{\left(\frac{n_s}{100}\right)}^{-\frac{1}{2}}\&CenterDot;\sqrt[3]{Q/n};$

In formula, n_s-specific speed；

3) impeller outlet width b₂Being obtained by experience and corresponding derivation formula, its constraints meets,

b₂Take intermediate value；

4) vane inlet angle beta₁Being obtained by the parameter of empirical equation and pump, its constraints meets,

${\mathrm{\&beta;}}_1=arctg\frac{\frac{Q}{{\mathrm{\&eta;}}_{v}F\mathrm{\&psi;}}}{\frac{\mathrm{\&pi;}}{60}Dn-\frac{2m\sqrt[3]{Q^{2}n}}{D}}+{\mathrm{\&Delta;\&beta;}}_1;$

In formula, η_vThe volumetric efficiency of-pump；

The flow area of F-pump, unit square rice；

The blade excretion coefficient of ψ-pump；

The import department of D-pump calculates spot diameter, unit rice；

M-empirical coefficient；

Δβ₁The inlet incidence angle of-pump；

5) blade exit angle beta₂Being obtained by experience, its constraints meets, 22 °≤β₂≤30°；

6) subtended angle of blade is determinedUsual value beTake

7) number of blade be chosen as 4≤Z≤7；

After above-mentioned seven calculating complete, the described initializing constraint obtained is:

Partial objectives for function f (X₁) initializing constraint be [0.9D₀,1.1D₀]、[0.9D₂,1.1D₂]；

Partial objectives for function f (X₂) initializing constraint be [0.9b₂,1.1b₂]、[0.9β₁,1.1β₁]；

Partial objectives for function f (X₃) initializing constraint be [22 °, 30 °],[4,7]。

Using complex method to solve calculating partial objectives for function in initializing constraint, wherein complex method is exactly In the feasible zone of n dimension design space, to complex (polyhedron being i.e. made up of 2n summit of n+1 k in n-dimensional space) The object function on each summit compares one by one, constantly removes worst point (for minimizing problem, i.e. object function A little louder), target function value instead can be made to have declined, meet again the new point of institute's Prescribed Properties, progressively tuning optimum point.

Complex method flow chart as shown in Figure 1, specifically comprises the following steps that

(1) select apexes of complex number k, typically take n+1≤k≤2n, in feasible zone, constitute the initial of only k summit Complex.

(2) calculate the target function value on k summit of complex, select wherein maximum, i.e.

Worst point X^(H), F (X^(H))=max{F (X^(j)), j=1,2 ..., k}；

Good some X^(G), F (X^(G))=max{F (X (j), j=1,2 ..., k；But j ≠ H}；

The most better X^(L), F (X^(L))=min{F (X^(j)), j=1,2 ... k}.

(3) calculate except worst point X^(H)The central point X on remaining k-1 summit outer^(S), i.e.Inspection center point X^(S)Whether in feasible zone.If at feasible zone In, then continue executing with (4th) step, otherwise forward (5th) step to.

(4) if X^(S)Put 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 in formula, typically takes α=1.3.

If X^(R)Run off feasible zone, then needed to be return, will halve by mapping coefficient α, recalculate X^(R)；If the most not Meet feasibility, continue to halve α, until mapping point X^(R)Till becoming feasible point.

(5) if X^(S)Point is not in feasible zone, and now feasible zone is non-convex set.By the mapping point of above-mentioned (4th) step calculating not It is probably feasible point, now utilizes central point X^(S)The most better X^(L)Again an interval is established, the most random in this interval Produce k summit and constitute complex.Its boundary value of new interval is,

If x_i ^(L)<x_i ^(S)(i=1,2 ..., n), then take

If x_i ^(L)>x_i ^(S)(i=1,2 ..., n), then take

Reconstitute complex repeat (2nd), (3) step, until X^(S)Till becoming feasible point.

(6) mapping point target function value F (X is calculated^(R)).If F is (X^(R))<F(X^(H)), then use mapping point X^(R)Replace worst point X^(H), constitute new complex, complete an iteration and calculate, turn to (2nd) step, otherwise bear interest next step.

(7) if F is (X^(R))>F(X^(H)), then mapping coefficient α is halved, recalculate mapping point.If new mapping point X^(R) Both it had been feasible point, had met again F (X^(R))<F(X^(H)), then with X^(R)Replace X^(H), complete current iteration.Otherwise continue to halve, directly α To when α value is less than previously given very decimal ξ (such as ξ=10^-5) time.If object function still without improving, then turns to (4th) Step, but now use time some X instead^(G)Replace previous worst point X^(H)Map.

(8) stop criterion: repeatedly perform above-mentioned iterative process, complex tapers into and approaches to optimum point, until full FootTime iterative computation can terminate.The top that now in complex, target function value is minimum Point is optimal solution, wherein, and X^(C)For the point set center on all summits of complex, i.e.

Partial objectives for function solves and obtains optimal solution and be:

X₁ ^*=[D₀₍₁₎ ^*、D₂₍₁₎ ^*]^T、X₂ ^*=[b₂₍₂₎ ^*、β₁₍₂₎ ^*]^T、

We determine that in little scope new constraints is as follows centered by optimal solution each stator parameter:

Feasible direction method flow chart as shown in Figure 2, specifically comprises the following steps that

(1) given initial point X⁽¹⁾, allowable error ε, determine initial feasible direction S⁽¹⁾, k=1 is set.

(2) label set E (X is determined^(k))={ j | g_j(X^(k))=0, whether 1≤j≤m} is empty set,

If empty set turns to (3rd) step, otherwise turning to (4th) step, wherein m is constraint conditional number.

(3) judgeIf setting up, directly obtain optimal solution X^*=X^(k)And export result, otherwise makeThen turn to (5th) step.

(4) following Linear Program is solved,And judge Z_kWhether=0 set up, If

Set up and then directly obtain optimal solution X^*=X^(k)And export result, otherwise turn to (5th) step.

(5) one-dimensional search is solvedObtain λ_k, wherein

λ_max=sup{ λ | g_u(X^(k)+λS^(k)≤ 0, u=1,2 ..., m}.

(6) orderThen turn to (2nd) step.

After major heading function being carried out above-mentioned optimization design under new constraints, the optimal solution solved is carried out rounding After checking, the final optimization pass result of the Optimization Design of a kind of impeller improving the core main pump coasting time can be met

The invention is not restricted to above-described embodiment, also comprise other embodiments and variation in the range of present inventive concept.

Claims (8)

1. the Optimization Design of the impeller that can improve the core main pump coasting time, it is characterised in that comprise the steps:

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 lay angle beta₂, subtended angle of bladeNumber of blade Z, improves and transports under idle conditions The major heading function of transfer efficient；

S2: then main geometric parameters is divided into three groups of design variables, and sets up three partial objectives for functions, more initially retraining bar Utilize complex method to running down efficiency partial objectives for function optimization in part, respectively obtain the optimal solution of three partial objectives for functions,

S3: finally little scope determines a new constraints near each impeller parameters of optimal solution, uses in new constraints Major heading function optimization is solved by feasible direction method, obtains final optimization pass result.

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 1, its feature Being, in step S1, described design variable is:Major heading function is:

In formula, D₀-impeller inlet diameter, unit rice；

D₂-impeller outlet diameter, unit rice；

b₂-impeller inlet width, unit rice；

β₁-impeller inlet angle, unit degree；

β₂-impeller outlet laying angle, unit degree；

-subtended angle of blade, unit degree；

The Z-number of blade.

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 1, its feature Being, in step S2, three described design of components variablees are:

X₁=[D₀、D₂]^T；

X₂=[b₂、β₁]^T；

Partial objectives for function is:

$f\left(X_1\right)=\frac{1}{\mathrm{\&eta;}\left(X_1\right)}=\frac{1}{\mathrm{\&eta;}(D_0,D_2)}\&RightArrow;min;$

$f\left(X_2\right)=\frac{1}{\mathrm{\&eta;}\left(X_2\right)}=\frac{1}{\mathrm{\&eta;}(b_2,{\mathrm{\&beta;}}_1)}\&RightArrow;min;$

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 1, its feature Being, in step S2, described initializing constraint is:

Partial objectives for function f (X₁) initializing constraint be [0.9D₀,1.1D₀]、[0.9D₂,1.1D₂]；

Partial objectives for function f (X₂) initializing constraint be [0.9b₂,1.1b₂]、[0.9β₁,1.1β₁]；

Partial objectives for function f (X₃) initializing constraint be

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 4, its feature Being, in step S2, the associated design variables in described initializing constraint calculates with initial reference to following method for designing:

1) impeller eye diameter D₀Meet in the case of taking into account efficiency and cavitation,

$D_0=4.5\&times;\sqrt[3]{Q/n};$

In formula, Q-flow, cubic unit metre per second (m/s)；N-rotating speed, unit is vertical turns every point；

2) impeller outlet diameter D₂Considering correction factorTime constraints meet,

$D_2=9.64\&times;(0.9742+0.0159\&times;\frac{n_s}{100})\&CenterDot;{\left(\frac{n_s}{100}\right)}^{\frac{1}{2}}\&CenterDot;\sqrt[3]{Q/n};$

In formula, n_s-specific speed；

3) impeller outlet width b₂Being obtained by experience and corresponding derivation formula, its constraints meets,

b₂Take intermediate value；

4) vane inlet angle beta₁Being obtained by the parameter of empirical equation and pump, its constraints meets,

${\mathrm{\&beta;}}_1=arctg\frac{\frac{Q}{{\mathrm{\&eta;}}_{v}F\mathrm{\&psi;}}}{\frac{\mathrm{\&pi;}}{60}Dn-\frac{2m\sqrt[3]{Q^{2}n}}{D}}+{\mathrm{\&Delta;\&beta;}}_1;$

In formula, η_vThe volumetric efficiency of-pump；

The flow area of F-pump, unit square rice；

The blade excretion coefficient of ψ-pump；

The import department of D-pump calculates spot diameter, unit rice；

M-empirical coefficient；

Δβ₁The inlet incidence angle of-pump；

5) blade exit angle beta₂Being obtained by experience, its constraints meets, 22 °≤β₂≤30°；

6) subtended angle of blade is determinedUsual value beTake

7) number of blade be chosen as 4≤Z≤7.

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 1, its feature Being, in step S2, running down efficiency is optimized by described utilization complex method, is i.e. optimized partial objectives for function and asks Solve, specifically include following steps:

(1) select apexes of complex number k, typically take n+1≤k≤2n, in feasible zone, constitute the initial composite on only k summit Shape；

(2) calculate the target function value on k summit of complex, select wherein maximum, i.e.

Worst point X^(H), F (X^(H))=max{F (X^(j)), j=1,2 ..., k}；

Good some X^(G), F (X^(G))=max{F (X (j), j=1,2 ..., k；But j ≠ H}；

The most better X^(L), F (X^(L))=min{F (X^(j)), j=1,2 ... k}；

(3) calculate except worst point X^(H)The central point X on remaining k-1 summit outer^(S), i.e. Inspection center point X^(S)Whether in feasible zone.If in feasible zone, then continue executing with (4th) step, otherwise forward to (5th) step；

(4) if X^(S)Put 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, typically take α=1.3；

If X^(R)Run off feasible zone, then needed to be return, will halve by mapping coefficient α, recalculate X^(R)；If had not been met Feasibility, continues to halve α, until mapping point X^(R)Till becoming feasible point；

(5) if X^(S)Point is not in feasible zone, and now feasible zone is non-convex set；The mapping point calculated by above-mentioned (4th) step is impossible It is feasible point, now utilizes central point X^(S)The most better X^(L)Again establish an interval, in this interval, again randomly generate k Individual summit constitutes complex, and its boundary value of new interval is,

If x_i ^(L)＜ x_i ^(S)(i=1,2 ..., n), then take

If x_i ^(L)＞ x_i ^(S)(i=1,2 ..., n), then take

Reconstitute complex repeat (2nd), (3) step, until X^(S)Till becoming feasible point.

(6) mapping point target function value F (X is calculated^(R))；

If F is (X^(R)) ＜ F (X^(H)), then use mapping point X^(R)Replace worst point X^(H), constitute new complex, complete an iteration meter Calculate, turn to (2nd) step, otherwise bear interest next step.

(7) if F is (X^(R)) ＞ F (X^(H)), then mapping coefficient α is halved, recalculate mapping point；

If new mapping point X^(R)Both it had been feasible point, had met again F (X^(R)) ＜ F (X^(H)), then with X^(R)Replace X^(H), complete this Iteration；Otherwise continue to halve α, until when α value is less than previously given very decimal ξ (such as ξ=10^-5) time；If target Function still without improving, then turns to (4th) step, but now uses time some X instead^(G)Replace previous worst point X^(H)Map；

(8) stop criterion: repeatedly perform above-mentioned iterative process, complex tapers into and approaches to optimum point, until meetingTime iterative computation can terminate；The top that now in complex, target function value is minimum Point is optimal solution, wherein, and X^(C)For the point set center on all summits of complex, i.e.

Solve at partial objectives for function and obtain optimal solution X₁ ^*=[D₀₍₁₎ ^*、D₂₍₁₎ ^*]^T、X₂ ^*=[b₂₍₂₎ ^*、β₁₍₂₎ ^*]^T、

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 1, its feature It is, in step S3, centered by the described each impeller parameters by optimal solution, in little scope, determines new constraints such as Under:

The Optimization Design of a kind of impeller improving the core main pump coasting time the most according to claim 1, its feature Being, in step S3, major heading function uses the Topkis-after the improvement of Zoutendijk method under new constraints Veinott feasible direction method Optimization Solution, specifically comprises the following steps that

(1) given initial point X⁽¹⁾, allowable error ε, determine initial feasible direction S⁽¹⁾, k=1 is set；

(2) label set E (X is determined^(k))={ j | g_j(X^(k))=0, whether 1≤j≤m} is empty set, if empty set turns to (3rd) step, Otherwise turning to (4th) step, wherein m is constraint conditional number；

(3) judgeIf setting up, directly obtain optimal solution X^*=X^(k)And export result, otherwise makeThen turn to (5th) step；

(4) following Linear Program is solved,And judge Z_kWhether=0 set up, if Set up and then directly obtain optimal solution X^*=X^(k)And export result, otherwise turn to (5th) step；

(5) one-dimensional search is solvedObtain λ_k, wherein

λ_max=sup{ λ | g_u(X^(k)+λS^(k)≤ 0, u=1,2 ..., m}；

(6) orderThen turn to (2nd) step；

After major heading function being carried out above-mentioned optimization design under new constraints, the optimal solution solved is carried out rounding checking After, the final optimization pass result of the Optimization Design of a kind of impeller improving the core main pump coasting time can be met