CN114297807A - Method for optimizing parameters of flute-shaped tube structure of anti-icing system - Google Patents

Method for optimizing parameters of flute-shaped tube structure of anti-icing system Download PDF

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CN114297807A
CN114297807A CN202210221884.8A CN202210221884A CN114297807A CN 114297807 A CN114297807 A CN 114297807A CN 202210221884 A CN202210221884 A CN 202210221884A CN 114297807 A CN114297807 A CN 114297807A
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hot gas
mass flow
gas mass
structural parameters
icing
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CN114297807B (en
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陈宁立
易贤
王强
柴得林
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Low Speed Aerodynamics Institute of China Aerodynamics Research and Development Center
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Abstract

The invention belongs to the technical field of anti-icing and deicing, and particularly relates to a flute-shaped pipe structure parameter optimization method of an anti-icing system. The method is characterized in that the minimum hot gas mass flow is taken as a target, the surface non-icing of an anti-icing system is taken as a limiting condition, a plurality of structural parameters, the maximum icing thickness and the minimum temperature are taken as input layers of a neural network, the hot gas mass flow is taken as an output layer of the neural network, neural network training is carried out to obtain a proxy model type, and the simulated annealing algorithm is adopted to carry out optimization design on the hot gas mass flow and the structural parameters of the flute-shaped pipe. According to the method, numerical simulation sample points required for establishing the proxy model are reduced by adopting a Latin hypercube method for sampling, the hot gas mass flow is quickly obtained by establishing the proxy model, the global optimization of the hot gas mass flow and structural parameters is realized by adopting a simulated annealing algorithm, and the whole optimization design method has the advantages of quickness and high efficiency.

Description

Method for optimizing parameters of flute-shaped tube structure of anti-icing system
Technical Field
The invention belongs to the technical field of anti-icing and deicing, and particularly relates to a flute-shaped pipe structure parameter optimization method of an anti-icing system.
Background
When the aircraft passes through the cloud layer containing the supercooled water drops, ice accumulation can occur on the windward surface of the aircraft, and the ice accumulation can cause great harm to the safety of the aircraft. Therefore, a corresponding anti-icing system needs to be designed on the windward surface of the aircraft, such as the front edge of the wing, the lip of an engine inlet and the like.
Hot gas anti-icing is one of the anti-icing means commonly used on the windward surfaces of the wing leading edge, the engine and the like at present. The method comprises the steps of leading out high-temperature and high-pressure gas from a high-pressure compressor of an engine, leading the gas to a position needing protection through a hot gas pipeline, and heating the inner part of a wall surface needing protection through a certain jet impact form, so that the temperature of supercooled water on the surface is increased, and the phenomenon of ice accumulation on the wall surface is prevented.
The hot gas anti-icing system of flute pipe jet impact has been widely applied to the front edge of an airfoil and the lip of an engine inlet. A large number of researches show that the structural parameters of the flute pipe are as follows: the aperture, the number of holes, the hole distance, the impact distance and the like have important influences on the working performance of the anti-icing system, so that the structural parameters of the flute-shaped pipe need to be optimally designed in the design process of the anti-icing system.
The traditional method is to carry out preliminary design according to the design experience of engineering personnel and carry out comparison optimization through a limited amount of numerical calculation so as to obtain a better design scheme. The final result obtained by the method is only a local optimal result and is not a global optimal solution, the requirement on the design experience of designers is high, meanwhile, a large amount of numerical simulation calculation needs to be carried out, the iteration speed is low, and the efficiency is low.
Disclosure of Invention
The invention provides a flute-shaped pipe structure parameter optimization method of an anti-icing system, which is used for improving the anti-icing performance of the anti-icing system by optimizing flute-shaped pipe structure parameters.
The invention is realized by the following technical scheme:
the invention provides a flute-shaped tube structure parameter optimization method of an anti-icing system, which comprises the following steps:
s100: determining a plurality of structural parameters and hot gas mass flow of the flute-shaped pipe, and simultaneously determining a plurality of structural parameters and a value interval of the hot gas mass flow of the flute-shaped pipe;
s200: sampling the structural parameters and the hot gas mass flow in the value interval to obtain a sample matrix, wherein the sample matrix comprises N groups of structural parameters and hot gas mass flow, and N is a natural number;
s300: calculating the maximum icing thickness of the surface of the airplane and the minimum temperature of the surface of the airplane acted by an anti-icing system corresponding to each group of structural parameters and hot gas mass flow in the sample matrix, and obtaining a mapping relation matrix between the structural parameters, the hot gas mass flow and the maximum icing thickness and the minimum temperature;
s400: taking the structural parameters, the maximum icing thickness and the minimum temperature as input layers of the neural network, and taking the hot gas mass flow as an output layer of the neural network to carry out neural network training to obtain a proxy model;
s500: and (3) optimizing the structural parameters of the flute-shaped pipe by combining the proxy model and adopting a simulated annealing algorithm by taking the minimum mass flow of hot gas as a target and the non-icing condition of the surface of the anti-icing system as a limiting condition.
Further, the structural parameters in S100 include at least one of the following parameters: pore diameter
Figure 962835DEST_PATH_IMAGE001
Number of holes
Figure 69331DEST_PATH_IMAGE002
Hole pitch h.
Further, the obtaining process of the sample matrix in S200 is as follows:
s210: determining the number of samples to be N;
s220: sampling the structural parameters and the hot gas mass flow by adopting a Latin hypercube method to obtain a sample matrix:
the value intervals of the structural parameters and the hot gas mass flow are evenly divided into N layers, and one structural parameter is randomly extracted from each layer
Figure 608897DEST_PATH_IMAGE003
And mass flow of hot gas
Figure 255779DEST_PATH_IMAGE004
The following sample matrix is obtained:
Figure 564401DEST_PATH_IMAGE005
；
wherein:
Figure 638536DEST_PATH_IMAGE006
is indicative of at least one structural parameter,
Figure 665398DEST_PATH_IMAGE003
obtained for sampling
Figure 319233DEST_PATH_IMAGE007
The parameters of the group structure are set up,
Figure 872574DEST_PATH_IMAGE004
for the nth hot gas mass flow sampled,
Figure 338451DEST_PATH_IMAGE008
。
further, in step S300, each set of structural parameters of the sample matrix is calculated
Figure 852609DEST_PATH_IMAGE003
And mass flow of hot gas
Figure 106873DEST_PATH_IMAGE004
Maximum thickness of icing of the corresponding aircraft surface
Figure 124507DEST_PATH_IMAGE009
Aircraft surface minimum temperature acting with anti-icing system
Figure 806024DEST_PATH_IMAGE010
And obtaining a mapping relation matrix:
Figure 869795DEST_PATH_IMAGE011
。
further, the obtaining process of the proxy model in S400 is as follows:
s410: establishing a new mapping matrix based on the mapping relation matrix:
Figure 537537DEST_PATH_IMAGE012
；
s420: based on the new mapping matrix, the structure parameter
Figure 799891DEST_PATH_IMAGE003
Maximum thickness of ice formation
Figure 855572DEST_PATH_IMAGE009
Minimum temperature of
Figure 344322DEST_PATH_IMAGE010
Is the input layer of the neural network, and has hot gas mass flow
Figure 674809DEST_PATH_IMAGE004
And carrying out neural network training for the output layer to obtain the proxy model.
Further, a layer is arranged in the middle layer of the neural network training, and the number of neurons in the middle layer is
Figure 667036DEST_PATH_IMAGE013
，
Figure 690356DEST_PATH_IMAGE014
；
Wherein:
Figure 463140DEST_PATH_IMAGE015
for the input layer of the number of neurons,
Figure 472684DEST_PATH_IMAGE016
the number of neurons in the output layer is,
Figure 975209DEST_PATH_IMAGE017
。
further, the optimization design of the mass flow and the structural parameters of the hot gas of the flute-shaped pipe by adopting the simulated annealing algorithm in the step S500 comprises the following steps:
s510: setting the maximum thickness of ice to zero, i.e.
Figure 779217DEST_PATH_IMAGE018
(ii) a Minimum temperature
Figure 101614DEST_PATH_IMAGE019
At least the minimum non-icing temperature, and randomly generating a group of structural parameters in a value range
Figure 977166DEST_PATH_IMAGE020
And obtaining a parameter combination:
Figure 209565DEST_PATH_IMAGE021
inputting the parameter combination into the proxy model to obtain the corresponding hot gas mass flow
Figure 43528DEST_PATH_IMAGE022
；
S520: obtaining a second parameter combination by adopting the method of the step S510:
Figure 728588DEST_PATH_IMAGE023
and corresponding hot gas mass flow
Figure 204568DEST_PATH_IMAGE024
；
Wherein:
Figure 291473DEST_PATH_IMAGE025
，
Figure 827497DEST_PATH_IMAGE026
representing the number of iterations;
S530：
Figure 468694DEST_PATH_IMAGE022
and
Figure 482786DEST_PATH_IMAGE024
the comparison is according to the following formula:
Figure 424197DEST_PATH_IMAGE027
；
（1）
Figure 136981DEST_PATH_IMAGE028
at the time, reserve
Figure 531054DEST_PATH_IMAGE024
Kicking and removing device
Figure 348837DEST_PATH_IMAGE022
；
（2）
Figure 144755DEST_PATH_IMAGE029
Time, probability
Figure 22581DEST_PATH_IMAGE030
(ii) a When in use
Figure 903949DEST_PATH_IMAGE031
Retention of
Figure 259844DEST_PATH_IMAGE024
Kicking and removing device
Figure 910268DEST_PATH_IMAGE022
(ii) a When in use
Figure 958996DEST_PATH_IMAGE032
Retention of
Figure 327660DEST_PATH_IMAGE022
Kicking and removing device
Figure 487246DEST_PATH_IMAGE024
；
Wherein:
Figure 992177DEST_PATH_IMAGE033
in order to set the threshold value(s),
Figure 211805DEST_PATH_IMAGE034
(ii) a e is a natural constant;
s540: repeating steps S520-S530 until remaining
Figure 67766DEST_PATH_IMAGE022
Or
Figure 765464DEST_PATH_IMAGE024
Are retained for M consecutive times, then
Figure 390480DEST_PATH_IMAGE022
Or
Figure 781010DEST_PATH_IMAGE024
The optimal hot gas mass flow is achieved; optimum hot gas mass flow
Figure 858687DEST_PATH_IMAGE022
The corresponding structural parameters are the optimal structural parameters.
Further, the air conditioner is provided with a fan,
Figure 625655DEST_PATH_IMAGE035
。
by adopting the technical scheme, the invention has the following advantages:
according to the method, numerical simulation sample points required for establishing the proxy model are reduced by adopting a Latin hypercube method for sampling, the hot gas mass flow is quickly obtained by establishing the proxy model, the global optimization of the hot gas mass flow and structural parameters is realized by adopting a simulated annealing algorithm, and the whole optimization design method has the advantages of quickness and high efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention or the prior art will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for optimizing parameters of a flute-shaped tube structure of an anti-icing system according to the present invention;
FIG. 2 is a neural network proxy model of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.
As shown in fig. 1, the present embodiment provides a method for optimizing parameters of a flute-shaped tube structure of an anti-icing system, including the following steps:
s100: determining a plurality of structural parameters and hot gas mass flow of the flute-shaped pipe, and simultaneously determining a plurality of structural parameters and a value interval of the hot gas mass flow of the flute-shaped pipe; the structural parameters of the flute-shaped pipe comprise aperture, number of holes, pitch of holes, impact distance and the like, and a plurality of structural parameters represent that 1 or more structural parameters can be selected; of course, specific structural parameters of the flute tube include, but are not limited to, the above list.
S200: sampling structural parameters and hot gas mass flow in a value interval to obtain a sample matrix, wherein the sample matrix comprises N groups of structural parameters and hot gas mass flow, each group of structural parameters and hot gas mass flow comprises a plurality of structural parameters and hot gas mass flow, and N is a natural number.
Further, sampling a value interval of sampling a plurality of structural parameters and a value interval of hot gas mass flow by adopting a Latin hypercube method to obtain a sample matrix.
S210: determining the number of samples to be N;
s220: sampling the structural parameters and the hot gas mass flow by adopting a Latin hypercube method to obtain a sample matrix:
the value intervals of the structural parameters and the hot gas mass flow are evenly divided into N layers, and one structural parameter is randomly extracted from each layer
Figure 105178DEST_PATH_IMAGE003
And mass flow of hot gas
Figure 666609DEST_PATH_IMAGE004
The following sample matrix is obtained:
Figure 231583DEST_PATH_IMAGE036
；
wherein:
Figure 271083DEST_PATH_IMAGE006
is indicative of at least one structural parameter,
Figure 870692DEST_PATH_IMAGE003
obtained for sampling
Figure 603024DEST_PATH_IMAGE037
The parameters of the group structure are set up,
Figure 655294DEST_PATH_IMAGE004
for the nth hot gas mass flow sampled,
Figure 498485DEST_PATH_IMAGE008
。
the N groups of structural parameters and the hot gas mass flow are specifically as follows:
Figure 952600DEST_PATH_IMAGE038
，
Figure 855834DEST_PATH_IMAGE039
，...，
Figure 395400DEST_PATH_IMAGE040
，
Figure 42282DEST_PATH_IMAGE041
。
it should be noted that the latin hypercube method is known in the art, and the specific sampling process will not be described in detail here.
In certain embodiments, further, the structural parameters are selected: pore diameter
Figure 350903DEST_PATH_IMAGE001
Number of holes
Figure 419179DEST_PATH_IMAGE002
，
Figure 180462DEST_PATH_IMAGE006
The structural parameter aperture is expressed
Figure 631035DEST_PATH_IMAGE001
Number of holes
Figure 794163DEST_PATH_IMAGE002
The sampled sample matrix is:
Figure 39199DEST_PATH_IMAGE042
(ii) a If the number of samples is determined to be 40, the sample matrix is:
Figure 553357DEST_PATH_IMAGE043
。
the 40 groups of structural parameters and hot gas mass flow are specifically as follows:
Figure 542042DEST_PATH_IMAGE044
，
Figure 559677DEST_PATH_IMAGE045
，...，
Figure 241194DEST_PATH_IMAGE046
。
further, the aperture
Figure 977068DEST_PATH_IMAGE001
The value interval is as follows:
Figure 503865DEST_PATH_IMAGE047
number of holes
Figure 641585DEST_PATH_IMAGE048
The value interval is as follows:
Figure 494003DEST_PATH_IMAGE049
(ii) a Mass flow of hot gas
Figure 451595DEST_PATH_IMAGE050
The value interval is as follows:
Figure 47662DEST_PATH_IMAGE051
。
s300: calculating each set of structural parameters of the sample matrix
Figure 39888DEST_PATH_IMAGE003
And mass flow of hot gas
Figure 797629DEST_PATH_IMAGE004
Maximum thickness of icing of the corresponding aircraft surface
Figure 508096DEST_PATH_IMAGE052
Aircraft surface minimum temperature acting with anti-icing system
Figure 642274DEST_PATH_IMAGE010
And obtaining a mapping relation matrix between the structural parameters, the mass flow of hot gas and the maximum thickness and the minimum temperature of the ice:
Figure 489007DEST_PATH_IMAGE053
。
for those skilled in the art, the calculation of the maximum thickness and the minimum temperature of the ice is prior art and will not be described herein.
S400: taking the structural parameters, the maximum icing thickness and the minimum temperature as input layers of the neural network, and taking the hot gas mass flow as an output layer of the neural network to carry out neural network training to obtain a proxy model;
further, the obtaining process of the proxy model in S400 is as follows:
s410: establishing a new mapping matrix based on the mapping relation matrix:
Figure 683228DEST_PATH_IMAGE054
；
s420: based on the new mapping matrix, the structure parameter
Figure 615412DEST_PATH_IMAGE003
Maximum thickness of ice formation
Figure 553281DEST_PATH_IMAGE009
Minimum temperature of
Figure 520100DEST_PATH_IMAGE010
Is the input layer of the neural network, and has hot gas mass flow
Figure 619643DEST_PATH_IMAGE004
And carrying out neural network training for the output layer to obtain the proxy model.
Further, a layer is arranged in the middle layer of the neural network training, and the number of neurons in the middle layer is
Figure 304702DEST_PATH_IMAGE013
，
Figure 780683DEST_PATH_IMAGE014
。
Wherein:
Figure 602009DEST_PATH_IMAGE015
for the input layer of the number of neurons,
Figure 872453DEST_PATH_IMAGE016
the number of neurons in the output layer is,
Figure 779229DEST_PATH_IMAGE017
。
as shown in fig. 2, in some embodiments, is an aperture
Figure 58901DEST_PATH_IMAGE001
Number of holes
Figure 734733DEST_PATH_IMAGE002
Maximum thickness of ice formation
Figure 447517DEST_PATH_IMAGE055
Minimum temperature of
Figure 841589DEST_PATH_IMAGE056
Is the input layer of the neural network, and has hot gas mass flow
Figure 659372DEST_PATH_IMAGE050
A proxy model obtained by training a neural network for an output layer, wherein
Figure 455290DEST_PATH_IMAGE057
。
S500: and (3) optimizing the structural parameters of the flute-shaped pipe by combining the proxy model and adopting a simulated annealing algorithm by taking the minimum mass flow of hot gas as a target and the non-icing condition of the surface of the anti-icing system as a limiting condition.
Further, the structural parameters are selected: pore diameter
Figure 333116DEST_PATH_IMAGE001
Number of holes
Figure 948905DEST_PATH_IMAGE002
For example, the optimization design of the hot gas mass flow and the structural parameters of the flute-shaped pipe by adopting a simulated annealing algorithm comprises the following steps:
randomly generating a set of combinations of parameters of pore diameter, pore number, maximum thickness of ice formation and minimum temperature:
Figure 570379DEST_PATH_IMAGE058
obtaining hot gas mass flow by using parameter combination and adopting the proxy model
Figure 220804DEST_PATH_IMAGE059
(ii) a At this time, no iteration is performed, the number of iterations is 0,
Figure 269531DEST_PATH_IMAGE060
the parameter combination is
Figure 638196DEST_PATH_IMAGE061
。
Wherein:
Figure 797781DEST_PATH_IMAGE062
the value interval is as follows:
Figure 302712DEST_PATH_IMAGE063
，
Figure 522341DEST_PATH_IMAGE064
the value interval is as follows:
Figure 112722DEST_PATH_IMAGE065
；
Figure 75999DEST_PATH_IMAGE066
，
Figure 701015DEST_PATH_IMAGE067
(ii) a The minimum freezing-free temperature is 273.15K,
Figure 91545DEST_PATH_IMAGE067
ensure thatAnd designing a margin.
Randomly generating a group of parameter combinations II of aperture, number of holes, icing maximum thickness and minimum temperature around the parameter combination I:
Figure 169223DEST_PATH_IMAGE068
using said proxy model to obtain hot gas mass flow for parameter combination two
Figure 670611DEST_PATH_IMAGE069
(ii) a At this time, no iteration is performed, the number of iterations is 0,
Figure 150134DEST_PATH_IMAGE070
the parameter combination is
Figure 711566DEST_PATH_IMAGE071
；
Wherein:
Figure 276539DEST_PATH_IMAGE072
the value interval is as follows:
Figure 581619DEST_PATH_IMAGE073
，
Figure 181227DEST_PATH_IMAGE074
the value interval is as follows:
Figure 913560DEST_PATH_IMAGE075
。
Figure 700250DEST_PATH_IMAGE076
and
Figure 809021DEST_PATH_IMAGE077
the comparison is according to the following formula:
Figure 263136DEST_PATH_IMAGE078
；
（1）
Figure 166370DEST_PATH_IMAGE079
at the time, reserve
Figure 440356DEST_PATH_IMAGE077
Kicking and removing device
Figure 87238DEST_PATH_IMAGE076
；
（2）
Figure 395860DEST_PATH_IMAGE080
Time, probability
Figure 487573DEST_PATH_IMAGE081
(ii) a When in use
Figure 514435DEST_PATH_IMAGE082
Retention of
Figure 699429DEST_PATH_IMAGE077
Kicking and removing device
Figure 128136DEST_PATH_IMAGE076
(ii) a When in use
Figure 373172DEST_PATH_IMAGE083
Retention of
Figure 621751DEST_PATH_IMAGE076
Kicking and removing device
Figure 610436DEST_PATH_IMAGE077
。
Wherein:
Figure 893650DEST_PATH_IMAGE084
。
if it is reserved
Figure 309587DEST_PATH_IMAGE077
Randomly generating a group of parameter sets of aperture, number of holes, icing maximum thickness and minimum temperature around the second parameter combinationCombining three:
Figure 45462DEST_PATH_IMAGE085
obtaining the mass flow of hot gas by using the proxy model by using the parameter combination III
Figure 837838DEST_PATH_IMAGE069
(ii) a In this case 1 iteration, the number of iterations is 1,
Figure 975558DEST_PATH_IMAGE086
the parameter combination III is
Figure 562397DEST_PATH_IMAGE087
。
Figure 785568DEST_PATH_IMAGE077
And
Figure 381634DEST_PATH_IMAGE088
the comparison is according to the following formula:
Figure 373861DEST_PATH_IMAGE089
；
（1）
Figure 131602DEST_PATH_IMAGE090
at the time, reserve
Figure 169965DEST_PATH_IMAGE088
Kicking and removing device
Figure 179509DEST_PATH_IMAGE077
；
（2）
Figure 150876DEST_PATH_IMAGE091
Time, probability
Figure 220463DEST_PATH_IMAGE092
(ii) a When in use
Figure 277281DEST_PATH_IMAGE082
Retention of
Figure 90516DEST_PATH_IMAGE088
Kicking and removing device
Figure 916390DEST_PATH_IMAGE077
(ii) a When in use
Figure 156878DEST_PATH_IMAGE083
Retention of
Figure 700992DEST_PATH_IMAGE077
Kicking and removing device
Figure 52339DEST_PATH_IMAGE088
。
Wherein:
Figure 998298DEST_PATH_IMAGE084
。
if the reservation is still
Figure 675267DEST_PATH_IMAGE077
And randomly generating a group of parameter combinations IV of aperture, number of holes, maximum thickness of ice and minimum temperature around the parameter combination III:
Figure 706677DEST_PATH_IMAGE093
obtaining the mass flow of hot gas by using the proxy model by using the parameter combination III
Figure 195471DEST_PATH_IMAGE069
(ii) a In this case 1 iteration, the number of iterations is 2,
Figure 871303DEST_PATH_IMAGE094
the parameter combination is four
Figure 578227DEST_PATH_IMAGE095
(ii) a According to the method described above
Figure 972300DEST_PATH_IMAGE077
And
Figure 790083DEST_PATH_IMAGE096
and (6) comparing.
If the reservation is still
Figure 586001DEST_PATH_IMAGE077
Obtaining the parameter combination again according to the method, and comparing again until
Figure 463827DEST_PATH_IMAGE077
Continuously retaining for 11 times, then
Figure 345195DEST_PATH_IMAGE077
For the purpose of an optimum hot gas mass flow,
Figure 966669DEST_PATH_IMAGE077
corresponding to
Figure 351514DEST_PATH_IMAGE097
Then the optimum aperture is obtained for the desired aperture,
Figure 665821DEST_PATH_IMAGE077
corresponding to
Figure 768906DEST_PATH_IMAGE074
The optimal number of holes is obtained; in this case, the value of M is 11, and it should be noted that the value of M includes, but is not limited to, 11.
Of course
Figure 928492DEST_PATH_IMAGE077
By way of specific example only, the optimum hot gas mass flow may be
Figure 699002DEST_PATH_IMAGE077
，
Figure 918631DEST_PATH_IMAGE088
，
Figure 509012DEST_PATH_IMAGE096
，
Figure 472289DEST_PATH_IMAGE098
，...。
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principles of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A flute-shaped tube structure parameter optimization method for an anti-icing system is characterized by comprising the following steps: the method comprises the following steps:
s100: determining a plurality of structural parameters and hot gas mass flow of the flute-shaped pipe, and simultaneously determining a plurality of structural parameters and a value interval of the hot gas mass flow of the flute-shaped pipe;
s200: sampling the structural parameters and the hot gas mass flow in the value interval to obtain a sample matrix, wherein the sample matrix comprises N groups of structural parameters and hot gas mass flow, and N is a natural number;
s300: calculating the maximum icing thickness of the surface of the airplane and the minimum temperature of the surface of the airplane acted by an anti-icing system corresponding to each group of structural parameters and hot gas mass flow in the sample matrix, and obtaining a mapping relation matrix between the structural parameters, the hot gas mass flow and the maximum icing thickness and the minimum temperature;
s400: taking the structural parameters, the maximum icing thickness and the minimum temperature as input layers of the neural network, and taking the hot gas mass flow as an output layer of the neural network to carry out neural network training to obtain a proxy model;
s500: and (3) optimizing the structural parameters of the flute-shaped pipe by combining the proxy model and adopting a simulated annealing algorithm by taking the minimum mass flow of hot gas as a target and the non-icing condition of the surface of the anti-icing system as a limiting condition.
2. The method for optimizing the parameters of the flute structure of the anti-icing system according to claim 1, wherein the method comprises the following steps: the structural parameters in S100 include at least one of the following parameters: pore diameter
Figure 734997DEST_PATH_IMAGE001
Number of holes
Figure 386558DEST_PATH_IMAGE002
Hole pitch h.
3. The method for optimizing the parameters of the flute structure of the anti-icing system according to claim 1, wherein the method comprises the following steps: the obtaining process of the sample matrix in the S200 is as follows:
s210: determining the number of samples to be N;
s220: sampling the structural parameters and the hot gas mass flow by adopting a Latin hypercube method to obtain a sample matrix:
the value intervals of the structural parameters and the hot gas mass flow are evenly divided into N layers, and one structural parameter is randomly extracted from each layer
Figure 759771DEST_PATH_IMAGE003
And mass flow of hot gas
Figure 623821DEST_PATH_IMAGE004
The following sample matrix is obtained:
Figure 31669DEST_PATH_IMAGE005
；
wherein:
Figure 322973DEST_PATH_IMAGE006
is indicative of at least one structural parameter,
Figure 183482DEST_PATH_IMAGE003
obtained for sampling
Figure 851223DEST_PATH_IMAGE007
The parameters of the group structure are set up,
Figure 113578DEST_PATH_IMAGE004
for the nth hot gas mass flow sampled,
Figure 575783DEST_PATH_IMAGE008
。
4. a method as claimed in claim 3, wherein the method comprises the following steps: in step S300, each set of structural parameters of the sample matrix is calculated
Figure 923588DEST_PATH_IMAGE003
And mass flow of hot gas
Figure 129441DEST_PATH_IMAGE004
Maximum thickness of icing of the corresponding aircraft surface
Figure 246302DEST_PATH_IMAGE009
Aircraft surface minimum temperature acting with anti-icing system
Figure 144987DEST_PATH_IMAGE010
And obtaining a mapping relation matrix:
Figure 980088DEST_PATH_IMAGE011
。
5. the method for optimizing the parameters of the flute structure of the anti-icing system according to claim 4, wherein the method comprises the following steps: the process of obtaining the proxy model in S400 is as follows:
s410: establishing a new mapping matrix based on the mapping relation matrix:
Figure 724053DEST_PATH_IMAGE012
；
s420: based on the new mapping matrix, taking structural parametersNumber of
Figure 695420DEST_PATH_IMAGE003
Maximum thickness of ice formation
Figure 499428DEST_PATH_IMAGE009
Minimum temperature of
Figure 821825DEST_PATH_IMAGE010
Is the input layer of the neural network, and has hot gas mass flow
Figure 635061DEST_PATH_IMAGE004
And carrying out neural network training for the output layer to obtain the proxy model.
6. The method for optimizing the parameters of the flute structure of the anti-icing system according to claim 5, wherein the method comprises the following steps: the middle layer of the neural network training is provided with a layer, and the number of neurons in the middle layer is
Figure 726513DEST_PATH_IMAGE013
，
Figure 967002DEST_PATH_IMAGE014
；
Wherein:
Figure 511116DEST_PATH_IMAGE015
for the input layer of the number of neurons,
Figure 596883DEST_PATH_IMAGE016
the number of neurons in the output layer is,
Figure 814281DEST_PATH_IMAGE017
。
7. the method for optimizing the parameters of the flute structure of the anti-icing system according to claim 5, wherein the method comprises the following steps: s500, the optimization design of the mass flow and the structural parameters of the hot gas of the flute-shaped pipe by adopting a simulated annealing algorithm comprises the following steps:
s510: setting the maximum thickness of ice to zero, i.e.
Figure 225671DEST_PATH_IMAGE018
(ii) a Minimum temperature
Figure 991502DEST_PATH_IMAGE019
At least the minimum non-icing temperature, and randomly generating a group of structural parameters in a value range
Figure 880960DEST_PATH_IMAGE020
And obtaining a parameter combination:
Figure 947005DEST_PATH_IMAGE021
inputting the parameter combination into the proxy model to obtain the corresponding hot gas mass flow
Figure 529296DEST_PATH_IMAGE022
；
S520: obtaining a second parameter combination by adopting the method of the step S510:
Figure 48002DEST_PATH_IMAGE023
and corresponding hot gas mass flow
Figure 741152DEST_PATH_IMAGE024
；
Wherein:
Figure 396124DEST_PATH_IMAGE025
，
Figure 149316DEST_PATH_IMAGE026
representing the number of iterations;
s530: comparison
Figure 155319DEST_PATH_IMAGE022
And
Figure 652159DEST_PATH_IMAGE024
：
Figure 427217DEST_PATH_IMAGE027
；
（1）
Figure 85731DEST_PATH_IMAGE028
at the time, reserve
Figure 579030DEST_PATH_IMAGE024
Kicking and removing device
Figure 613982DEST_PATH_IMAGE022
；
（2）
Figure 181229DEST_PATH_IMAGE029
Time, probability
Figure 338541DEST_PATH_IMAGE030
(ii) a When in use
Figure 319135DEST_PATH_IMAGE031
Retention of
Figure 157779DEST_PATH_IMAGE024
Kicking and removing device
Figure 641849DEST_PATH_IMAGE022
(ii) a When in use
Figure 704483DEST_PATH_IMAGE032
Retention of
Figure 110057DEST_PATH_IMAGE022
Kicking and removing device
Figure 549129DEST_PATH_IMAGE024
；
Wherein:
Figure 356548DEST_PATH_IMAGE033
in order to set the threshold value(s),
Figure 590083DEST_PATH_IMAGE034
(ii) a e is a natural constant;
s540: repeating steps S520-S530 until remaining
Figure 155056DEST_PATH_IMAGE022
Or
Figure 663398DEST_PATH_IMAGE024
Are retained for M consecutive times, then
Figure 122061DEST_PATH_IMAGE022
Or
Figure 729760DEST_PATH_IMAGE024
The optimal hot gas mass flow is achieved; optimum hot gas mass flow
Figure 578767DEST_PATH_IMAGE022
The corresponding structural parameters are the optimal structural parameters.
8. The method for optimizing the parameters of the flute structure of the anti-icing system according to claim 7, wherein the method comprises the following steps:
Figure 421958DEST_PATH_IMAGE035
。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114757109A (en) * 2022-06-14 2022-07-15 中国空气动力研究与发展中心低速空气动力研究所 Method and system for testing relation of parameters of icing inside and outside air inlet channel and application

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101982765A (en) * 2010-09-21 2011-03-02 南京航空航天大学 Method and device for on-line detecting iced and damaged transmission line based on elastic wave
CN105139274A (en) * 2015-08-16 2015-12-09 东北石油大学 Power transmission line icing prediction method based on quantum particle swarm and wavelet nerve network
CN106867455A (en) * 2017-04-06 2017-06-20 中国民用航空总局第二研究所 The quick preparation system of pipeline and method of civil aviaton's ice-removing and ice-preventing liquid
CN109117951A (en) * 2018-01-15 2019-01-01 重庆大学 Probabilistic Load Flow on-line calculation method based on BP neural network
CN111738481A (en) * 2020-04-01 2020-10-02 南京航空航天大学 Airplane icing meteorological parameter MVD prediction method based on BP neural network
US20200391871A1 (en) * 2019-06-14 2020-12-17 Rosemount Aerospace Inc. Health monitoring of an electrical heater of an air data probe

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101982765A (en) * 2010-09-21 2011-03-02 南京航空航天大学 Method and device for on-line detecting iced and damaged transmission line based on elastic wave
CN105139274A (en) * 2015-08-16 2015-12-09 东北石油大学 Power transmission line icing prediction method based on quantum particle swarm and wavelet nerve network
CN106867455A (en) * 2017-04-06 2017-06-20 中国民用航空总局第二研究所 The quick preparation system of pipeline and method of civil aviaton's ice-removing and ice-preventing liquid
CN109117951A (en) * 2018-01-15 2019-01-01 重庆大学 Probabilistic Load Flow on-line calculation method based on BP neural network
US20200391871A1 (en) * 2019-06-14 2020-12-17 Rosemount Aerospace Inc. Health monitoring of an electrical heater of an air data probe
CN111738481A (en) * 2020-04-01 2020-10-02 南京航空航天大学 Airplane icing meteorological parameter MVD prediction method based on BP neural network

Non-Patent Citations (10)

* Cited by examiner, † Cited by third party
Title
YI XIAN等: "Investigation on heat transfer characteristics of aircraft icing including runback water", 《INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER》 *
YI XIAN等: "Investigation on heat transfer characteristics of aircraft icing including runback water", 《INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER》, vol. 53, no. 19, 30 September 2010 (2010-09-30), pages 3702 - 3707 *
张镇等: "基于神经网络的主动式红外结冰分类方法研究", 《自动化与仪表》 *
张镇等: "基于神经网络的主动式红外结冰分类方法研究", 《自动化与仪表》, no. 02, 15 February 2010 (2010-02-15), pages 1 - 4 *
易贤等: "民用航空发动机进气道防冰系统设计方法研究", 《航空工程进展》 *
易贤等: "民用航空发动机进气道防冰系统设计方法研究", 《航空工程进展》, vol. 8, no. 3, 28 August 2017 (2017-08-28), pages 335 - 341 *
朱永峰等: "某型飞机发动机短舱防冰系统设计计算", 《航空动力学报 》 *
朱永峰等: "某型飞机发动机短舱防冰系统设计计算", 《航空动力学报 》, vol. 27, no. 6, 15 June 2012 (2012-06-15), pages 1326 - 1331 *
肖晓阳: "基于RBF神经网络的波音737NG飞机引气系统故障诊断模型", 《航空维修与工程》 *
肖晓阳: "基于RBF神经网络的波音737NG飞机引气系统故障诊断模型", 《航空维修与工程》, no. 07, 20 July 2020 (2020-07-20), pages 81 - 84 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114757109A (en) * 2022-06-14 2022-07-15 中国空气动力研究与发展中心低速空气动力研究所 Method and system for testing relation of parameters of icing inside and outside air inlet channel and application
CN114757109B (en) * 2022-06-14 2022-08-23 中国空气动力研究与发展中心低速空气动力研究所 Method and system for testing relation between internal and external icing parameters of air inlet channel

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