CN115796056A - Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change - Google Patents

Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change Download PDF

Info

Publication number
CN115796056A
CN115796056A CN202310054892.2A CN202310054892A CN115796056A CN 115796056 A CN115796056 A CN 115796056A CN 202310054892 A CN202310054892 A CN 202310054892A CN 115796056 A CN115796056 A CN 115796056A
Authority
CN
China
Prior art keywords
blade
reynolds number
individual
segment
generation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310054892.2A
Other languages
Chinese (zh)
Other versions
CN115796056B (en
Inventor
卢剑伟
任远凯
邓高明
周定华
唐景春
吴勃夫
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN202310054892.2A priority Critical patent/CN115796056B/en
Publication of CN115796056A publication Critical patent/CN115796056A/en
Application granted granted Critical
Publication of CN115796056B publication Critical patent/CN115796056B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Structures Of Non-Positive Displacement Pumps (AREA)

Abstract

The invention discloses a torsion angle estimation method of a vehicle axial flow heat radiation fan blade considering Reynolds number change, which considers the Reynolds number of the blade along with the change of the spanwise length and the average inlet speed and comprises the following steps: 1, setting parameters such as output power of a motor shaft, rated rotating speed of a fan, chord length of a blade, blade profile, hub and fan blade radius and the like; 2, establishing a target function based on a phyllotactic theory; 3, establishing a parameter domain according to a lift curve and a polar diagram corresponding to the blade profile; 4, optimizing by using a genetic algorithm by taking the inlet flow rate as fitness, and considering the change of Reynolds number in the iterative process; and 5, finally, calculating the change rule of the blade torsion angle which enables the flow speed of the blade inlet to be the maximum along the spanwise direction of the blade. The invention considers the Reynolds number distribution of the blades, optimizes the inlet flow velocity as the fitness of the genetic algorithm, and thus can obtain the change rule of the torsional angle of the blades along the spanwise direction of the blades, which improves the flow of the axial flow heat dissipation fan for the vehicle.

Description

Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change
Technical Field
The invention belongs to the field of design of cooling fans, and particularly relates to a method for estimating a fan blade installation angle of an axial flow cooling fan.
Background
Axial-flow type cooling fans are widely used in various products such as automobiles and computers, especially fuel cell vehicles, due to the characteristics of relatively low power consumption and compact structure. The flow rate per unit time, i.e. the mass flow rate or the volume flow rate, is a main parameter for measuring the heat dissipation performance of the fan. When the fan is sized, its flow rate is mainly affected by the average inlet airflow velocity, i.e. the larger the average inlet airflow velocity, the larger the volumetric flow rate of the fan. The blade twist angle is one of the main contributing factors to the average inlet airflow velocity, and determining the blade twist angle places higher demands on the engineer's experience, increasing the length of the experimental period. For the axial flow cooling fan for the vehicle, the requirement on the rotating speed is high, and the Reynolds numbers of the blades in the radial direction have certain difference. The fan inlet airflow velocity is also relatively high and is a non-negligible factor in calculating the reynolds number.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method for estimating the torsional angle of the blade of the axial-flow heat-radiating fan for the vehicle by considering the Reynolds number change, so that the torsional angle of the blade can be optimized under the condition of considering the Reynolds number distribution, the change rule of the torsional angle of the blade of the flow of the axial-flow heat-radiating fan for the vehicle along the spanwise direction of the blade is obtained, and the arrangement scheme of the torsional angle of the blade aiming at improving the flow of the axial-flow fan for the vehicle is accurately obtained.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a torsion angle estimation method of an automotive axial flow heat dissipation fan blade considering Reynolds number change, which is characterized by comprising the following steps of:
step 1: obtaining the output power of the motor shaft of the fanP w Rated speed of fanΩChord length of blade topc t Root chord lengthc 0 Radius of the blade and hubr 0 And radius of fan blader t Number of leavesN b
And 2, step: according to the length of blade, the blade is divided intoNSegment and calculated using equation (1)NReynolds number of segment bladeR 0 ~R N
Figure SMS_1
(1)
In the formula (1), the reaction mixture is,R 0 the root Reynolds number;R i is as followsiReynolds number of the segment blade;R N is the Reynolds number of the blade top,ρis the air density;r i c i are respectively the firstiThe radius of the segment blade and the blade chord length;μis an aerodynamic viscosity and has:
Figure SMS_2
(2)
Figure SMS_3
(3)
in the formulae (2) and (3),Nrepresenting the total number of the blade sections;
and step 3: using equation (4) to calculateiPower of sectional bladeP i
Figure SMS_4
(4)
In the formula (4), the reaction mixture is,Vin order to obtain the flow rate at the inlet of the fan,C Li andC Di are respectively the firstiThe airfoil lift coefficient and drag coefficient of the segment blade,rthe distance from any section of the blade to the blade root is represented;
and 4, step 4: calculating the output power of the motor shaft using equation (4)P w
Figure SMS_5
(5)
And 5: equations for inlet flow velocity, lift coefficient and drag coefficient were constructed using equation (5)f
f(V,C L1 ,C D1 ,C L2 ,C D2 ,,C LN ,C DN )-P w =0 (6)
Step 6: forming parameter domains with different Reynolds numbers according to the lift characteristic curve and the polar diagram of the blade profile;
by DeltaRIs a gap betweenR 0 ~2R N The range has a Reynolds number dispersion of [ [ alpha ] ]R 1 ,R 2 ,,R q ,,R Q ](ii) a Obtaining on the lift mapqReynolds number of segment bladeR q Corresponding lift curve and Reynolds numberR q On the corresponding lift curve, in deltaαFor interval, obtainJLift coefficient at each angle of attackC q Lj |j= 1, 2,,J},C q Lj Is shown asjAngle of attackα j Lift coefficient under;
then find out the first one according to the polar diagramjAngle of attackα j Coefficient of resistance ofC q Dj Thereby constituting a Reynolds numberR q Corresponding parameter setb q =[(α 1 ,C q L1 ,C q D1 ), (α 2 ,C q L2 ,C q D2 ),, (α j ,C q Lj ,C q Dj ),,(α J ,C q LJ ,C q DJ )](ii) a Wherein, the first and the second end of the pipe are connected with each other,α 1 ,α 2 , …,α J indicating the 1 st, 2 nd to 2 nd in the parameter setJAn angle of attack; further obtainQParameter domain composed of parameter groups corresponding to discrete Reynolds numbersB=[b 1 ,b 2 ,,b q ,,b Q ] T
And 7: define an inclusionMPopulation set of individualA=[a 1 ,a 2 ,,a m ,,a M ] T Wherein, in the step (A),a m is shown asmAn individual, anda m =[(C m L1 ,C m D1 ), (C m L2 ,C m D2 ),, (C m Li ,C m Di ),,(C m LN ,C m DN )];(C m Li ,C m Di ) Respectively representmIndividual onea m To middleiReynolds number of segment bladeR i Lift and drag coefficients in corresponding parameter domains;
defining the iteration number of the current population askAnd is initializedk=1, total number of iterationsK
The Reynolds numberR 0 ~R N As a firstkReynolds number of 1 iteration, noteR 1 k(-1) ,R 2 k(-1) ,,R i k(-1) ,,R N k( -1) Wherein, in the process,R i k(-1) is shown ask1 st iterationiReynolds number of the segment blade;
according to the firstkReynolds number of generation-1R 1 k(-1) ,R 2 k(-1) ,,R i k(-1) ,,R N k(-1) From the parameter domainBIn selectionMIndividual and constitute the firstk-1 generation populationA k(-1) Wherein, the firstk-1 generation populationA k(-1) To middlemIndividual recorda m k(-1)
And 8: will be firstk-1 generation populationA k(-1) Substitution equationfThe average inlet flow rate for each individual was calculated and recorded asV k(-1) =[V 1 k(-1) ,V 2 k(-1) ,,V m k(-1) ,,V M k(-1) ]And from the firstk1 cycle average inlet flow rate setV k(-1) Selecting the maximum inlet flow rateV max k-1() Then, the second calculation is performed by using the formula (7)kReynolds number of i-th blade in sub-iterationR i k()
Figure SMS_6
(7)
And step 9: according to the firstkReynolds number of sub-iterationR 1 k() ,R 2 k() ,,R i k() ,,R N k() From the parameter domainBIs selected tomThe individual isa m k() Thereby obtaining the firstkGeneration groupA k()
Step 10: if doesR i k() -R i k(-1) |>ΔRThen will bek+1 assignmentkThen, returning to the step 8 for sequential execution, otherwise, executing the step 11;
step 11, will bekGeneration groupA k() Substitution equationfCalculating the inlet flow rate of each individualV k() Taking the inlet velocity as the fitness of the individual, whereinmThe inlet flow rate of each individual is the fitnessV m k() . To the firstkGeneration groupA k() Performing crossover and mutation to obtain the updatedkGeneration group
Figure SMS_7
(ii) a And selecting the maximum inlet flow rate, i.e. the maximum fitnessV max k() The corresponding individual is taken as the firstkGeneration of optimal individualsa best k()
Step 12: if does not have magnetismV max k() -V max k-1() |>εAnd isk<KThen will bek+1 assignmentkThen, returning to step 9 for sequential execution, otherwise, stopping iteration and outputting the first outputkMaximum inlet flow rate of the sub-iterationV max k() And corresponding theretoR 1 k() ,R 2 k() ,,R i k() ,,R N k() The first stepkGeneration optimizationa best k() Corresponding as the optimum inlet flow rateV * max Optimum Reynolds numberR 1 * ,R 2 * ,,R i * ,,R N * Optimal individuala * best Wherein, in the step (A),R i * denotes the firstiThe final reynolds number of the segment blade,εindicating the set error;
step 13: according to the optimum Reynolds numberR i * And optimal individualsa * best Lift/drag coefficient of (1), from the parameter domainBIn order to obtainiAngle of attack corresponding to the profile of the sectionα i And calculating the second step according to the velocity geometryiThe torsion angles corresponding to the blade profiles are as follows:
θ i =α i +arctan(V * max /(Ωr i ));
step 14: obtaining a regular relation formula of the installation angle along with the change of the spanwise length of the blade by utilizing linear interpolation shown in the formula (8):
Figure SMS_8
(8)
in the formula (8), the reaction mixture is,r i-1 is as followsi-Radius of 1 segment of blade;θ i-1 is a firsti-The twist angle corresponding to the 1-segment blade profile,θ(r,i) Is shown asiThe twist angle of the segment blade at any cross section,i=1,2,,N
the method for estimating the torsional angle of the blade of the axial-flow heat-dissipation fan for the vehicle, which takes the Reynolds number change into consideration, is also characterized in that the crossing and the variation in the step 11 comprise the following steps:
step 11.1: will be firstkGeneration groupA k() All individuals in the system are arranged according to the descending mode of the average inlet flow velocity to obtain the ordered firstkThe generation group is marked asA' k()
Step 11.2: will be firstkGeneration groupA' k() Middle frontM(ii) a retention of 3 individuals, wherein,Mfor the total number of individuals in the population, will remainM/3~2MA/3 individualsPerforming a crossover operation to obtainM/3~2M(iii) 3 individuals after crossing;
step 11.3: will be firstkGeneration groupA' k() In 2M/3~MSubjecting individual to mutation to obtain 2M/3~M(ii) an individual after mutation;
step 11.3.1: will be firstkGeneration groupA' k() In 2M/3~MBefore replacing individualM(ii)/3 individuals;
step 11.3.2: random generationN2 pieces 1 toNIs a positive integer of (2)p 1 ,p 2 ,,p l ,,p N/2 ]Wherein, in the step (A),p l denotes the firstlA random number;
step 11.3.3: according to the firstp l Reynolds number of sectionR k() pl In the parameter domainBIn which a pair of lift and drag coefficients (C L ,C D ) And replace the current secondkGeneration groupA' k() To middlem' Individual corresponding to the secondp l Segment parameter (C m k'() L pl() ,C m k'() D pl() ) Wherein, in the step (A),m' is 2M/3~MA positive integer therebetween, thereby completingNUpdating the parameters of the positions corresponding to 2 random numbers;
step 11.4: from the firstkGeneration groupA' k() Middle frontM(3 individuals, the firstkGeneration groupA' k() Middle frontMA/3 individuals andkgeneration groupA' k() Middle frontMThe/3 individuals form the updatedkGeneration group
Figure SMS_9
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the lift characteristic curve and the polar diagram of the blade profile are converted into parameter domains with different Reynolds numbers, the problem is converted into the problem of finding the optimal parameter combination, and therefore the pneumatic data of the blade profile can be directly used for optimization calculation without fitting, and the calculation efficiency is improved.
2. The Reynolds number is updated in the iterative process in consideration of the change of the Reynolds number along with the span direction of the blades and the change of the average inlet flow velocity, so that parameter combinations which are more in line with actual conditions can be selected, and a more accurate torsion angle estimation rule is provided for the fan blades.
3. The invention aims at the maximum average inlet flow velocity, is favorable for improving the important performance index of high flow of the fan, and provides an initial torsion angle scheme favorable for improving the flow for the fan blade, thereby improving the heat radiation performance of the fan.
4. The invention can calculate the change rule of the torsional angle of the blade only by using some basic parameters such as the output power of the motor shaft, the rated rotating speed of the fan, the chord length of the blade, the blade profile, the radius of the hub, the radius of the blade, the number of the blades and the like, reduces the dependence on the experience of engineering technicians and the times and the period of experiments, and improves the efficiency of the research and development work of the fan blade.
Drawings
FIG. 1 is a schematic view of an axial-flow cooling fan blade according to the present invention;
FIG. 2 is a schematic representation of the blade velocity geometry of the present invention;
FIG. 3a is a schematic representation of a profile lift characteristic and polar plot of the present invention;
FIG. 3b is a schematic illustration of a profile lift characteristic and polar line plot of the present invention;
FIG. 4 is a schematic illustration of the iterative error of the genetic algorithm of the present invention;
FIG. 5 is a schematic illustration of the output of the present invention;
fig. 6 is a flow chart of the method of the present invention.
Detailed Description
The following examples of the present invention will be described in detail, and the present invention is implemented on the premise of the technical solution of the present invention, but the scope of the present invention is not limited to the following examples.
In this embodiment, as shown in fig. 6, a method for estimating a torsional angle of a blade of an axial-flow heat-dissipating fan for a vehicle, which considers a change in a reynolds number, includes the following steps:
step 1: obtaining the output power of the motor shaft of the fanP w Rated speed of fanΩChord length of blade topc t Root chord lengthc 0 Blade profile, hub radiusr 0 And radius of fan blader t Number of bladesN b
Initial design parameters of a given heat dissipation fan are shown in table 1, and a schematic diagram of fan blades of the fan is shown in fig. 1;
table 1 initial design parameters of cooling fan
Figure SMS_10
Step 2: according to the length of blade, the blade is divided intoNSegment and calculated using equation (1)NReynolds number of segment bladeR 0 ~R N
Figure SMS_11
(1)
In the formula (1), the reaction mixture is,R 0 the root Reynolds number;R i is a firstiReynolds number of the segment blade;ρis the air density;R N the Reynolds number of the blade top;r i c i are respectively the firstiThe radius of the section blade and the chord length of the blade;μis an aerodynamic viscosity and has:
Figure SMS_12
(2)
Figure SMS_13
(3)
in the formulas (2) and (3), N represents the total number of blade stages;
in this example, N =10, and the data is substituted to obtain the initial valueThe initial Reynolds number is in the range of 1.193X 10 5 ~3.272 ×10 5
First, theiSegment radius ofr i =0.048+0.0105iChord length ofc i =0.086-0.0012i
First, theiSegment Reynolds number ofR i =2.89X10 7 ×(0.048+0.0105i)(0.086-0.0012i);
And 3, step 3: using equation (4) to calculateiPower of sectional bladesP i
Figure SMS_14
(4)
In the formula (4), the reaction mixture is,Vin order to obtain the flow rate at the inlet of the fan,C Li andC Di are respectively the firstiThe airfoil lift coefficient and drag coefficient of the segment blades,rrepresenting the distance from any section of the blade to the blade root;
the formula (4) is obtained according to the stress of the blade, the stress schematic diagram of the blade is shown in figure 2, and the stress schematic diagram can be calculated by numerical analysis software in an auxiliary mode after being substituted into data.
And 4, step 4: calculating the output power of the motor shaft using equation (4)P w
Figure SMS_15
(5)
And 5: equations for inlet flow velocity, lift coefficient and drag coefficient were constructed using equation (5)f
f(V,C L1 ,C D1 ,C L2 ,C D2 ,…,C LN ,C DN )-P w =0 (6)
The substitution of the data can be assisted by numerical analysis software.
Step 6: forming parameter domains with different Reynolds numbers according to the lift characteristic curve and the polar diagram of the blade profile;
by DeltaRIs a gap betweenR 0 ~2R N The range has a Reynolds number dispersion of [ [ alpha ] ]R 1 ,R 2 ,,R q ,,R Q ](ii) a Obtaining on the lift mapqReynolds number of segment bladeR q Corresponding lift curve and at Reynolds numberR q On the corresponding lift curve, in deltaαFor interval, obtainJLift coefficient at angle of attackC q Lj |j=1,2,,J},C q Lj Denotes the firstjAngle of attackα j Lift coefficient under;
then find the first one according to the polar diagramjAngle of attackα j Coefficient of resistance ofC q Dj Thereby constituting a Reynolds numberR q Corresponding parameter setb q =[(α 1 ,C q L1 ,C q D1 ), (α 2 ,C q L2 ,C q D2 ),, (α j ,C q Lj ,C q Dj ),,(α J ,C q LJ ,C q DJ )](ii) a Wherein the content of the first and second substances,α 1 ,α 2 , …,α J representing the 1 st, 2 nd to J th angles of attack in the parameter set; further obtainQParameter domain composed of parameter groups corresponding to discrete Reynolds numbersB=[b 1 ,b 2 ,,b q ,,b Q ] T
In this example, ΔR=1×10 4α=0.1˚,Q=40,J=200, the resulting parameter domain is a parametric combination of 40 × 200 data, with lift plots and pole plots as shown in fig. 3a and 3 b.
And 7: define an inclusionMPopulation set of individualsA=[a 1 ,a 2 ,,a m ,,a M ] T Wherein, in the step (A),a m is shown asmAn individual, anda m =[(C m L1 ,C m D1 ), (C m L2 ,C m D2 ),, (C m Li ,C m Di ),,(C m LN ,C m DN )];(C m Li ,C m Di ) Respectively representmIndividual onea m To middleiReynolds number of segment bladeR i Lift and drag coefficients in corresponding parameter domains;
defining the iteration number of the current population askAnd is initializedk=1, total number of iterationsK
The Reynolds numberR 0 ~R N As a firstkReynolds number of 1 iteration, noteR 1 k(-1) ,R 2 k(-1) ,,R i k(-1) ,,R N k( -1) Wherein, in the step (A),R i k(-1) is shown ask1 st iterationiReynolds number of the segment blade;
according to the firstkReynolds number of generation-1R 1 k(-1) ,R 2 k(-1) ,,R i k(-1) ,,R N k(-1) From the parameter domainBIn selectionMIndividual and constitute the firstk-1 generation populationA k(-1) Wherein, the firstk-1 generation populationA k(-1) To middlemIndividual recorda m k(-1)
In this embodiment, letM=20,K=300. Convergence error is set asε=0.2。
And 8: will be firstk-1 generation populationA k(-1) Substitution equationfThe average inlet flow rate for each individual was calculated and recorded asV k(-1) =[V 1 k(-1) ,V 2 k(-1) ,,V m k(-1) ,,V M k(-1) ]And from the firstk1 cycle average inlet flow Rate setV k(-1) To select the maximum inlet flow rateV max k-1() Then, the second calculation is performed by using the formula (7)kReynolds number of i-th blade in sub-iterationR i k()
Figure SMS_16
(7)
And step 9: according to the firstkReynolds number of sub-iterationR 1 k() ,R 2 k() ,,R i k() ,,R N k() From the parameter domainBIs selected tomThe individual isa m k() Thereby obtaining the firstkGeneration groupA k()
Step 10: if does not have magnetismR i k() -R i k(-1) |>ΔRThen will bek+1 assignmentkThen, returning to the step 8 for sequential execution, otherwise, executing the step 11;
step 11, will bekGeneration groupA k() Substitution equationfCalculating the inlet flow rate of each individualV k() Taking the inlet speed as the fitness of the individualWherein the firstmThe inlet flow rate of each individual is the fitnessV m k() . To the firstkGeneration groupA k() Performing crossover and mutation to obtain the updatedkGeneration group
Figure SMS_17
(ii) a And selecting the maximum inlet flow rate, i.e. the maximum fitnessV max k() The corresponding individual is taken as the firstkGeneration of optimal individualsa best k()
Step 11.1: will be firstkGeneration groupA k() All individuals in the system are arranged according to the descending mode of the average inlet flow velocity to obtain the ordered firstkThe generation group is marked asA' k()
Step 11.2: will be firstkGeneration groupA' k() Middle frontMA/3 individual reserve will remainM/3~2MPerforming cross operation on 3 individuals to obtainM/3~2M(iii) 3 individuals after crossing;
the first third of the most suitable data is retained to prevent the excellent individuals from being eliminated.
Step 11.3: will be firstkGeneration groupA' k() In 2M/3~MSubjecting individual to mutation to obtain 2M/3~M(ii) an individual after mutation;
step 11.3.1: will be firstkGeneration groupA' k() In (1)M/3~MBefore replacing individualM(ii)/3 individuals;
the step is to eliminate the batch with the worst fitness and carry out the next mutation operation on the individuals with the larger fitness to make the mutation direction go to the good direction as much as possible.
Step 11.3.2: random generationN2 pieces 1 toNIs a positive integer of (2)p 1 ,p 2 ,,p l ,,p N/2 ]Wherein, in the step (A),p l is shown aslA random number;
step 11.3.3: according to the firstp l Reynolds number of segmentR k() pl In the parameter domainBIn which a pair of lift and drag coefficients (C L ,C D ) And replace the current secondkGeneration groupA' k() To middlem' Individual corresponding to the secondp l Segment parameter (C m k'() L pl() ,C m k'() D pl() ),m' is 2M/3~MA positive integer therebetween, thereby completingNUpdating the parameters of the positions corresponding to 2 random numbers;
step 11.4: from the firstkGeneration groupA' k() Middle frontM(3 individuals, the firstkGeneration groupA' k() Middle frontMA/3 individuals andkgeneration groupA' k() Middle frontMThe 3 individuals form the updated thekGeneration group
Figure SMS_18
Step 12: if doesV max k() -V max k-1() |>εAnd isk<KThen will bek+1 assignmentkThen, returning to step 9 for sequential execution, otherwise, stopping iteration and outputting the first outputkMaximum inlet flow rate of the second iterationV max k() And corresponding theretoR 1 k() ,R 2 k() ,,R i k() ,,R N k() The first stepkGeneration optimizationa best k() Corresponding as the optimum inlet flow rateV * max Optimum Reynolds numberR 1 * ,R 2 * ,,R i * ,,R N * Optimal individuala * best Wherein, in the process,R i * is shown asiThe final reynolds number of the segment blade,εindicating the set error;
and the convergence error is smaller than the specified error or the iteration times reach the maximum iteration times, the population optimization is considered to reach the limit, the upward optimization cannot be continued, and the limitation to the dead cycle is prevented. The individual with the maximum fitness at this time is the individual with the maximum inlet airflow speed. The convergence error during the iteration of the genetic algorithm is shown in fig. 4.
Step 13: according to the optimum Reynolds numberR i * And optimal individualsa * best Lift/drag coefficient of (1), from the parameter domainBTo obtainiAngle of attack corresponding to the section airfoilα i And calculating the second step according to the velocity geometryiThe torsion angles corresponding to the blade profiles are as follows:
θ i =α i +arctan(V * max /(Ωr i ));
the relationship of twist angle to angle of attack is derived from the blade's velocity vector trigonometry, as shown in FIG. 2. The results of the calculations for this example are shown in Table 2.
TABLE 2 Final results of the parameter calculations for each segment
Figure SMS_19
Step 14: obtaining a regular relation formula of the installation angle along with the change of the spanwise length of the blade by utilizing linear interpolation shown in the formula (8):
Figure SMS_20
(8)
in the formula (8), the reaction mixture is,r i-1 is as followsi-Radius of 1 segment of blade;θ i-1 is a firsti-The twist angle corresponding to the 1-segment blade profile,θ(r,i) Is shown asiSegment bladeThe twist angle at any cross-section,i=1,2,,N
by substituting the data in step 13, a piecewise function of the change rule can be obtained, and the effect is shown in fig. 5.

Claims (3)

1. The torsion angle estimation method of the automotive axial flow cooling fan blade considering Reynolds number change is characterized by comprising the following steps of:
step 1: obtaining the output power of the motor shaft of the fanP w Rated speed of fanΩChord length of blade topc t Root chord lengthc 0 Blade profile, hub radiusr 0 And radius of fan blader t Number of bladesN b
Step 2: dividing the blades into groups according to their lengthsNSegment and calculated using equation (1)NReynolds number of segment bladeR 0 ~R N
Figure QLYQS_1
(1)
In the formula (1), the reaction mixture is,R 0 reynolds number of the blade root;R i is a firstiReynolds number of the segment blade;R N is the Reynolds number of the blade top,ρis the air density;r i c i are respectively the firstiThe radius of the segment blade and the blade chord length;μis an aerodynamic viscosity and has:
Figure QLYQS_2
(2)
Figure QLYQS_3
(3)
in the formulae (2) and (3),Nrepresenting the total number of the blade sections;
and step 3: using the formula (4) Calculate the firstiPower of sectional bladeP i
Figure QLYQS_4
(4)
In the formula (4), the reaction mixture is,Vis the flow speed of the inlet of the fan,C Li andC Di are respectively the firstiThe airfoil lift coefficient and drag coefficient of the segment blades,rthe distance from any section of the blade to the blade root is represented;
and 4, step 4: calculating the output power of the motor shaft using equation (4)P w
Figure QLYQS_5
(5)
And 5: equations for inlet flow velocity, lift coefficient and drag coefficient were constructed using equation (5)f
f(V, C L1 , C D1 , C L2 , C D2 ,…, C LN , C DN )- P w =0 (6)
Step 6: forming parameter domains with different Reynolds numbers according to the lift characteristic curve and the polar diagram of the blade profile;
by DeltaRIs a gap betweenR 0 ~2R N The range has a Reynolds number dispersion of [ [ alpha ] ]R 1 , R 2 ,…, R q ,…, R Q ](ii) a Obtaining on the lift mapqReynolds number of segment bladeR q Corresponding lift curve and Reynolds numberR q On the corresponding lift curve, in deltaαFor interval, obtainJLift coefficient at angle of attackC q Lj | j= 1, 2,…, J},C q Lj Is shown asjAngle of attackα j Lift coefficient under;
then find out the first one according to the polar diagramjAngle of attackα j Coefficient of resistance ofC q Dj Thereby constituting a Reynolds numberR q Corresponding parameter setb q =[(α 1 , C q L1 , C q D1 ), (α 2 , C q L2 , C q D2 ),…, (α j , C q Lj , C q Dj ), …,( α J , C q LJ , C q DJ )]Wherein, in the step (A),α 1 , α 2 , …, α J representing the 1 st, 2 nd to J th angles of attack in the parameter set; further obtainQParameter domain composed of parameter groups corresponding to discrete Reynolds numbersB=[b 1 ,b 2 ,…,b q , …,b Q ] T
And 7: define an inclusionMPopulation set of individualA=[a 1 ,a 2 ,…,a m ,…,a M ] T Wherein, in the step (A),a m is shown asmAn individual, anda m =[(C m L1 , C m D1 ), (C m L2 ,C m D2 ),…, (C m Li , C m Di ),…,(C m LN , C m DN )];(C m Li , C m Di ) Respectively representmIndividual onea m To middleiSegmental leafReynolds number of sheetR i Lift and drag coefficients in corresponding parameter domains;
defining the iteration number of the current population askAnd is initializedk=1, total number of iterationsK
The Reynolds numberR 0 ~R N As a firstkReynolds number of 1 iteration, noteR 1 k(-1) , R 2 k(-1) ,…, R i k(-1) ,…, R N k(-1) Wherein, in the step (A),R i k(-1) is shown ask1 st iterationiReynolds numbers of the segment blades;
according to the firstkReynolds number of generation-1R 1 k(-1) , R 2 k(-1) ,…, R i k(-1) ,…, R N k(-1) From the parameter domainBIn selectionMIndividual and constitute the firstk-1 generation populationA k(-1) Wherein, the firstk-1 generation populationA k(-1) To middlemThe individual is recorded asa m k(-1)
And step 8: will be firstk-1 generation populationA k(-1) Substitution equationfThe average inlet flow rate for each individual was calculated and recorded asV k(-1) =[V 1 k(-1) ,V 2 k(-1) ,…,V m k(-1) ,…,V M k(-1) ]And from the firstk1 cycle average inlet flow Rate setV k(-1) To select the maximum inlet flow rateV max k-(1) Then, the second calculation is performed by using the formula (7)kReynolds number of i-th blade in sub-iterationR i k()
Figure QLYQS_6
(7)
And step 9: according to the firstkReynolds number of sub-iterationR 1 k() , R 2 k() , …, R i k() , …, R N k() From the parameter domainBIs selected frommThe individual isa m k() Thereby obtaining the firstkGeneration groupA k()
Step 10: if doesR i k() - R i k(-1) |>ΔRThen will bek+1 assignmentkThen, returning to the step 8 for sequential execution, otherwise, executing the step 11;
step 11, will bekGeneration groupA k() Substitution equationfCalculating the inlet flow rate of each individualV k() Taking the inlet velocity as the fitness of the individual, whereinmThe inlet flow rate of each individual is the fitnessV m k() To the secondkGeneration groupA k() Performing crossover and mutation to obtain the updatedkGeneration group
Figure QLYQS_7
(ii) a And selecting the maximum inlet flow rate, i.e. the maximum fitnessV max k() The corresponding individual is taken as the firstkGeneration of optimal individualsa best k()
Step 12: if does V max k() - V max k-1() |>εAnd isk<KThen will bek+1 assignmentkThen, returning to step 9 for sequential execution, otherwise, stopping iteration and outputting the first outputkMaximum inlet flow rate of the sub-iterationV max k() And corresponding theretoR 1 k() , R 2 k() ,…, R i k() , …, R N k() First, akGeneration optimizationa best k() Corresponding as the optimum inlet flow rateV * max Optimum Reynolds numberR 1 * , R 2 * , …, R i * , …, R N * Optimal individuala * best Wherein, in the step (A),R i * is shown asiThe final reynolds number of the segment blade,εindicating the set error;
step 13: according to the optimum Reynolds numberR i * And optimal individualsa * best The lift/drag coefficient of (1), the second one is obtained from the parameter domain BiAngle of attack corresponding to the profile of the sectionα i And calculating the second step according to the velocity geometryiThe torsion angles corresponding to the segment blade profiles are as follows:
θ i =α i +arctan(V * max /(Ωr i ));
step 14: obtaining a regular relation formula of the installation angle along with the change of the spanwise length of the blade by utilizing linear interpolation shown in the formula (8):
Figure QLYQS_8
(8)
in the formula (8), the reaction mixture is,r i-1 is as followsi-Radius of 1 segment of blade;θ i-1 is as followsi-The twist angle corresponding to the 1-segment blade profile,θ(r, i) Is shown asiThe twist angle of the segment blade at any cross section,i=1,2, …,N
2. the method for estimating the twist angle of the blade of the axial-flow heat-dissipation fan for the vehicle, which takes the reynolds number variation into consideration, according to claim 1, wherein the crossing and the variation in the step 11 include:
step 11.1: will be firstkGeneration groupA k() All individuals in the system are arranged according to the descending mode of the average inlet flow velocity to obtain the ordered firstkThe generation group is marked asA' k()
Step 11.2: will be firstkGeneration groupA' k() Middle frontM(ii) individual retention of 3, whereinMFor the total number of individuals in the population, will remainM/3~2MPerforming cross operation on 3 individuals to obtainM/3~2M(iii) 3 individuals after crossing;
step 11.3: will be firstkGeneration groupA' k() In 2M/3~MSubjecting individual to mutation to obtain 2M/3~M(ii) an individual after the variation;
step 11.4: from the firstkGeneration groupA' k() Middle frontM(3 individuals, the firstkGeneration groupA' k() Middle frontMA/3 individuals andkgeneration groupA' k() Middle frontMThe 3 individuals form the updated thekGeneration group
Figure QLYQS_9
3. The method for estimating the torsion angle of the blade of the axial flow heat dissipation fan for the vehicle, considering the reynolds number change, according to claim 2, wherein the step 11.3 includes:
step 11.3.1: will be firstkGeneration groupA' k() In (1)M/3~MBefore replacing individualM(ii)/3 individuals;
step 11.3.2: random generationN2 pieces 1 toNIs a positive integer of (2)p 1 , p 2 ,…, p l ,…, p N/2 ]Wherein, in the step (A),p l is shown aslA random number;
step 11.3.3: according to the firstp l Reynolds number of sectionR k() pl In the parameter domainBIn which a pair of lift and drag coefficients (C L ,C D ) And replace the current secondkGeneration groupA' k() To middlem' Individual corresponding to the secondp l Segment parameter (C m k'() L pl() , C m k'() D pl() ), m' is 2M/3~MA positive integer therebetween, thereby completingNAnd/2 updating the parameters of the positions corresponding to the random numbers.
CN202310054892.2A 2023-02-03 2023-02-03 Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change Active CN115796056B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310054892.2A CN115796056B (en) 2023-02-03 2023-02-03 Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310054892.2A CN115796056B (en) 2023-02-03 2023-02-03 Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change

Publications (2)

Publication Number Publication Date
CN115796056A true CN115796056A (en) 2023-03-14
CN115796056B CN115796056B (en) 2023-04-18

Family

ID=85429696

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310054892.2A Active CN115796056B (en) 2023-02-03 2023-02-03 Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change

Country Status (1)

Country Link
CN (1) CN115796056B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106919749A (en) * 2017-02-24 2017-07-04 湖北工业大学 A kind of low noise vane design of wind turbines method and low noise pneumatic equipment bladess
US20180135593A1 (en) * 2015-04-29 2018-05-17 Universiti Brunei Darussalam Low Reynolds Number Airfoil for a Wind Turbine Blade and Method Thereof
CN109340042A (en) * 2018-11-19 2019-02-15 内蒙古工业大学 Turbine blade and its design method and solar chimney electricity generation system
CN111859651A (en) * 2020-07-10 2020-10-30 浙江运达风电股份有限公司 Method for optimizing power generation performance of wind turbine generator set under low air density
WO2021045931A2 (en) * 2019-08-26 2021-03-11 Massachusetts Institute Of Technology Propeller design systems and methods
CN114169088A (en) * 2021-11-12 2022-03-11 华能华家岭风力发电有限公司 Fan blade optimization design method and system based on Wilson model and genetic algorithm
CN114676529A (en) * 2022-04-11 2022-06-28 哈尔滨工程大学 Material distribution optimization method of blade model
CN115344963A (en) * 2022-08-08 2022-11-15 合肥工业大学 Efficient pump blade design method capable of meeting requirements of multiple working conditions

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180135593A1 (en) * 2015-04-29 2018-05-17 Universiti Brunei Darussalam Low Reynolds Number Airfoil for a Wind Turbine Blade and Method Thereof
CN106919749A (en) * 2017-02-24 2017-07-04 湖北工业大学 A kind of low noise vane design of wind turbines method and low noise pneumatic equipment bladess
CN109340042A (en) * 2018-11-19 2019-02-15 内蒙古工业大学 Turbine blade and its design method and solar chimney electricity generation system
WO2021045931A2 (en) * 2019-08-26 2021-03-11 Massachusetts Institute Of Technology Propeller design systems and methods
CN111859651A (en) * 2020-07-10 2020-10-30 浙江运达风电股份有限公司 Method for optimizing power generation performance of wind turbine generator set under low air density
CN114169088A (en) * 2021-11-12 2022-03-11 华能华家岭风力发电有限公司 Fan blade optimization design method and system based on Wilson model and genetic algorithm
CN114676529A (en) * 2022-04-11 2022-06-28 哈尔滨工程大学 Material distribution optimization method of blade model
CN115344963A (en) * 2022-08-08 2022-11-15 合肥工业大学 Efficient pump blade design method capable of meeting requirements of multiple working conditions

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JIANWEI REN ET AL.: "Frequency Lock-in Phenomenon of Vortex Induced Vibration of a Roating Blade considering Bending-Torsion Coupling Effect" *
王树杰;王宽宽;袁鹏;于晓丽;王旭超;: "潮流能水平轴水轮机叶片扭角与弦长分布研究" *

Also Published As

Publication number Publication date
CN115796056B (en) 2023-04-18

Similar Documents

Publication Publication Date Title
CN107944175B (en) Method for acquiring real power curve of fan by considering wind turbulence intensity
CN111859746B (en) Method for predicting variable working condition performance of turbomachinery based on flow field reconstruction
CN112685855A (en) Axial flow compressor blade type attack angle and drop relief angle calculation method
CN106677979B (en) A kind of pneumatic equipment bladess aerodynamic configuration couples optimization method with main frame operation characteristic
CN114444331A (en) Stage characteristic matching method of multistage axial flow compressor
CN112270139B (en) Pneumatic optimization design method for centrifugal compressor of fuel cell based on mother type library
CN111368373A (en) Iterative analysis method for cold-state blade profile and hot-state blade profile
CN112685968A (en) Axial flow compressor pneumatic design method based on space load customization thought
CN113297705A (en) Method for predicting characteristics of gas compressor according to design indexes
CN115796056B (en) Torsion angle estimation method of automotive axial flow cooling fan blade considering Reynolds number change
Bashiri et al. Design optimization of a centrifugal pump using particle swarm optimization algorithm
CN109902382B (en) Statistical calculation method for specific rotating speed of large and medium-sized mixed-flow water turbine
Wu et al. Optimization design of axial fan blade
CN117592388A (en) CFD-based wind farm multi-machine wake simulation method
CN113742861B (en) Blade model optimization design method suitable for wind tunnel test of wind driven generator
CN116595874A (en) Impeller mechanical performance prediction model parameter optimization method and device and storage medium
CN116822417A (en) Wind turbine blade surface wind pressure analysis method
Pang et al. Intelligence algorithm for optimization design of the low wind speed airfoil for wind turbine
CN113591419B (en) Radial turbine machinery one-dimensional design evaluation and optimization method
CN113294297B (en) Variable weight adjusting method for wind turbine generator nonlinear model prediction torque control
CN115712976A (en) Heat energy and electric load optimization method based on multi-element heat supply unit and related equipment
CN114065621A (en) Gas compressor characteristic equation coefficient fitting method based on genetic algorithm
CN111079367A (en) Axial flow pump spoke optimization design method suitable for different water inlet taper pipes
CN115186401B (en) Method for determining key angle parameters of subsonic cascade modeling of axial flow compressor
CN114462159B (en) Multi-working-condition blade dehumidification optimization design method of marine wet steam turbine

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant