CN106874588B - A kind of analysis of multilayer thermal protection system non-probabilistic uncertainty and optimum design method based on experimental design - Google Patents
A kind of analysis of multilayer thermal protection system non-probabilistic uncertainty and optimum design method based on experimental design Download PDFInfo
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Abstract
The analysis of multilayer thermal protection system non-probabilistic uncertainty and optimum design method that the invention discloses a kind of based on experimental design, its step are as follows：(1) thermal coefficient, density and specific heat capacity are chosen as uncertain parameter, realizes the section expression of each uncertain parameter；(2) it extracts each layer thickness and uncertain parameter is characterized parameter, realize Full Parameterized modeling, analysis and solve；(3) consider uncertain parameter correlation, propose correlation factors arbitrary sampling method, choose test sample；(4) approximate model is built according to sample, influence of the analysis uncertain parameter to response is simultaneously preferred, establishes multilayered structure temperature field Uncertainty Analysis Method；(5) the non-Probability mapping networks of multilayer thermal protection system are realized so that each layer thickness, each layer temperature are less than allowable value, quality is minimised as design variable, constraints and object function build Optimized model respectively.The present invention improves thermal protection system structure efficiency by efficient analysis of uncertainty and optimization.
Description
Technical field
The present invention relates to multilayer thermal protection system optimization design field, more particularly to a kind of Multi-layer thermals based on experimental design
Guard system non-probabilistic uncertainty is analyzed and optimum design method.
Background technology
Hypersonic aircraft is because its flying speed is fast, the reaction time is short, combat radius is big, good concealment, penetration ability
The advantages that strong, is future war is empty, status is outstanding in day main battle ground, it has also become falls over each other to seize in the arms race of big country of the world today
Commanding elevation.Hypersonic aircraft long service is in being leading multi- scenarios method extreme environment, thermal protection with Aerodynamic Heating, power
Technology has become the bottleneck for directly restricting its development.It, need to be in aircraft to ensure that pilot's safety and airborne equipment run well
Surface large area is laid with thermal protection structure, and aircraft is to the sensibility of construction weight in addition, thermal protection system from day of birth just
Face urgent safety issue and structural weight reduction problem.Therefore, it is based on thermal protection structure and carries out low cost, high reliability, height
The design optimizing research of anti-heat-proof quality develops high-performance hypersonic aircraft significant.
Typical multilayered structure, i.e. multilayer thermal protection system is presented in hypersonic aircraft thermal protection system more.Multi-layer thermal
Complex heat transfer mechanism under the heat analysis of guard system is related to harsh aero-thermal load, multilayered structure is coupled with more heat transfer types.Fly
During row device is on active service, first choice is that the aerothermal load that structure is subject to shows as nonlinearity variation at any time；Secondly, it is tying
Three kinds of heat transfer, heat radiation and thermal convection current heat transfer mechanisms exist simultaneously inside structure, and three influences each other, intercouples together,
Complex heat transfer feature is presented；Finally, for thermal protection structure itself, material property is mutual between the layers with structure function
It influences, in addition the hot physical property, including thermal coefficient, density, specific heat capacity etc. of layers of material, as the variation of pressure and temperature is in non-
Linear change, it is above-mentioned various temperature field Response Mechanism complicated difficult to be caused to be distinguished.Therefore it is the high anti-thermal insulation of realization thermal protection system
It can, it is necessary to further investigate the heat-transfer mechanism of multilayer thermal protection system.
Conventional multilayer thermal protection system design optimization probabilistic influence is uniformly included in empirical safety coefficient and
Without deeply investigating, often results in the certain layers of failure of certain layers of redundancy, runs counter to low cost and high safety reliability requirement
Design；Especially ignore in the case where harsh Aerodynamic Heating is pretended and used material thermal physical property parameter uncertainty will directly affect structure it is anti-every
Hot property, and then be on active service on the safety of aircraft and generate subversiveness influence.Therefore, simultaneously for raising aircraft military service security reliability
Cost is reduced, needs to account for probabilistic structure design to multilayer thermal protection system.But each layer structural thermal and storage
It intercouples between thermal efficiency difference and each layer, the influence that each material thermal physical property parameter responds temperature field is difficult to distinguish so that
Multilayer thermal protection structure analysis of uncertainty is more difficult.Therefore, the influence of all kinds of uncertain factors is furtherd investigate, development is high
Precision analysis of uncertainty and optimum design method, it is sound consider all kinds of probabilistic fining structure design systems at
For the following low cost, the inexorable trend of high reliability thermal protection system design.
Invention content
The technical problem to be solved in the present invention is：It overcomes the deficiencies of the prior art and provide a kind of based on the more of experimental design
The analysis of layer thermal protection system non-probabilistic uncertainty and optimum design method can ensure multilayer thermal protection system anti-heat-insulated
Under the premise of performance safety is reliable, architecture quality is effectively reduced, improves the performance of thermal protection system.
The technical solution adopted by the present invention is：A kind of multilayer thermal protection system non-probabilistic uncertainty based on experimental design
Analysis and optimum design method, this method comprises the following steps：
Step (1), the performance determined according to the true Service Environment of thermal protection system and configuration demand, multilayer thermal protection system
By the different materials structure composition of n-layer different function；In view of the thermal physical property parameters such as material thermal conductivity, density and specific heat capacity are equal
It can vary with temperature, the material properties curve that each thermal physical property parameter varies with temperature need to pass through a series of corresponding heat of specific temperatures
Physical parameter value interpolation fitting obtains, and remembers that each layer thermal coefficient, density and specific heat capacity are respectively kij, ρijAnd cij, i=1,2 ...,
N, j=1,2 ..., m, wherein i and j numbers for variable, and n is the total number of plies of multilayer thermal protection structure, and m is the number for choosing specific temperature
Amount, the corresponding specific temperature of thermal coefficient, density, specific heat capacity are denoted as respectivelyWith
Step (2) considers layers of material thermal physical property parameter due to various not true caused by material scatter, measurement error etc.
It is qualitative, k in selecting step (1)ij、ρijAnd cijAs uncertain parameter；Each uncertain parameter distribution rule are obtained according to engineering test
Rule, obtains the distribution of each uncertain parameter, range format is used in combination to be quantified as kij∈[kij_min,kij_max], ρij∈
[ρij_min,ρij_max] and cij∈[cij_min,cij_max], wherein kij_min, ρij_minAnd cij_minRespectively each parameter distribution range is most
Small value, kij_max, ρij_maxAnd cij_maxRespectively each parameter distribution range maximum value；
Each layer thickness in step (3), selecting step (1) is denoted as X, X=(x as design variable1,x2,…,xn), n is
The involved multilayer thermal protection structure number of plies in step (1)；Each thickness is limited in given range, i.e. xi∈[xi_min,
xi_max], i=1,2 ..., n, wherein xi_minTo give xiThe minimum value of range, xi_maxTo give xiThe maximum value of range, generally
It is given by engineering experience and project cost condition；
Step (4), during Geometric Modeling, extract each design variable as control 3-D geometric model design feature
Parameter can realize geometry automatic modeling when each design variable arbitrarily changes in respective given range, to complete to be based on
The geometric parameterization of selected design variable models；
Step (5), on the basis of geometrical model, by the secondary development function of finite element software, extract each uncertain
Uncertain characteristic parameter of the parameter as the control hot physical property attribute of finite element model material, when each uncertain parameter is respectively dividing
When arbitrarily changing within the scope of cloth, the automatic assignment of the hot physical property attribute of finite element model material can be realized, be based on setting to establish
Count the multilayer thermal protection structure parameter finite element model of characteristic parameter and uncertain characteristic parameter；
Step (6), based on reentering process ballistic data, using radiation, convection current and the compound biography of a variety of heat transfer types of conduction
Heat analysis method considers influencing each other between Aerodynamic Heating and structural thermal, realizes the multilayer thermal protection system of overall trajectory process
Analysis On The Transient Temperature solves, and obtains the temperature history T that each bed boundary of multilayer thermal protection structure changes over times(t), extraction is each
Maximum temperature at bed boundary exports in response, is denoted asS=0,1 ..., n, wherein s increase to n from 0 and refer to multilayer thermal protection
Interface of the structure from outer surface to all layers of inner surface；
The rule varied with temperature that each thermal physical property parameter is presented under the conditions of step (7), consideration certainty is as sampling
Constraint works out sampling algorithm by data analyzing and processing software, compared to traditional completely random methods of sampling, realizes and considers constraint
The random sampling procedure of condition is for correlation factors arbitrary sampling method；Based on the method, from each uncertain ginseng of step (2)
Number kij, ρijAnd cijDistributed area in, select the sample point of the random combine of one group of consideration factor correlativity, be denoted as P, P=
(p1,p2,…,pu,…pr), pu=(k11,k12,…,kij,…,knm,ρ11,ρ12,…,ρij,…,ρnm,c11,c12,…,cij,…,
cnm)u, wherein r is sample point sum, puSome sample point of acute pyogenic infection of finger tip, kij, ρijAnd cijForm the factor in sample point, ()uFor
The specific level of certain sample point factor；
The sample point p of P in step (8), extraction step (7)u, u=1 ..., r are as the uncertain feature in step (5)
Parameter, repeats step (4) to (6) r times, obtains one group of discrete maximum temperature response of each bed boundary of thermal protection system,
It is denoted asFace method according to response is fitted sample set P and response setsThen each factor k in description P is constructedij, ρijAnd cijWithThe approximate function model of relationship
Step (9) passes through approximate model in step (8)Analyze each factor kij、ρij
And cijIt is exported with each bed boundary maximum temperature response of multilayer thermal protection structureBetween relationship and trend；Consider to calculate cost
With precision, by the distributed area [k in step (2)ij_min,kij_max], [ρij_min,ρij_max] and [cij_min,cij_max] equal normalizing
Change to same range, comparative analysis kij, ρijAnd cijTo each responsePercentage contribution, identification preferentially go out key parameter；In conjunction with
The k analyzedij, ρijAnd cijWithBetween relationship, obtain the uncertain response distributed area after sensitivity analysis, i.e.,WhereinWithRespectivelyThe lower bound of Uncertainty distribution range and upper
Boundary；
Step (10) is in summary analyzed, using each layer thickness X in step (3) as design variable, with multilayer in step (10)
Each bed boundary maximum temperature Uncertainty distribution upper bound of thermal protection structureLess than each bed boundary acceptability limit temperatureTo constrain, i.e.,To minimize architecture quality nominal value mass as optimization object function, build
The non-Probability mapping networks mathematical model of vertical multilayer thermal protection system, final realize consider that probabilistic multilayer thermal protection system is non-
Probability mapping networks.
Wherein, in the step (7), correlation factors arbitrary sampling method is suitable for each uncertain thermal physical property parameter
kij, ρijAnd cijThe corresponding adjacent factor of adjacent specific temperature has the case where correlation and Uncertainty distribution section are interfered, with
Specific heat capacity cijFor illustrate, wherein the corresponding adjacent factor of specific temperature be cijAnd cij+1, it is assumed that under the conditions of certainty
Has cij≤cij+1Vary with temperature rule, i.e., the adjacent factor has correlation, and consider it is uncertain under conditions of, it is adjacent
Factor cijAnd cij+1Uncertainty distribution section [cij_min,cij_max] and [cij+1_min,cij+1_max] there is interference, i.e. cij_max
>cij+1_min, in these cases, correlation factors arbitrary sampling method compares traditional arbitrary sampling method, it is contemplated that need to expire
Sufficient cij≤cij+1Rule as sampling restraint, avoid generating c to reachij>cij+1It is this to run counter to hot physical property and become with temperature
The effect of law result.
Wherein, in the step (10), the non-Probability mapping networks mathematical model of multilayer thermal protection system is shown below：
The advantages of the present invention over the prior art are that：The present invention provides the new of multilayer thermal protection system optimization design
Thinking fully considers Practical Project mismachining tolerance, test measurement error, material scatter etc. to the hot physical property of thermal protection system material
The affecting laws that each uncertain thermal physical property parameter of multi-layer-coupled labyrinth responds temperature field have been probed into the influence of performance parameter
And influence degree, realize the efficient analysis of uncertainty in multilayer thermal protection system temperature field.On this basis, it is excellent to introduce non-probability
Change thought, realizes multilayer thermal protection system and ensure minute design under the premise of anti-heat-proof quality, greatly improve thermal protection system
The security reliability of system, and effectively reduce the quality of thermal protection system.
Description of the drawings
Fig. 1 is the method implementation flow chart of the present invention；
Fig. 2 is the targeted multilayer thermal protection system schematic layout pattern of the present invention；
Fig. 3 is the targeted multilayer thermal protection system FEM model schematic diagram of the present invention；
Fig. 4 is the targeted each bed boundary transient temperature course curve of multilayer thermal protection system of the present invention；
Fig. 5 is correlation factors arbitrary sampling method superiority schematic diagram proposed by the invention；
Fig. 6 is the targeted multilayer thermal protection system uncertain parameter importance ranking schematic diagram of the present invention；
Fig. 7 is the targeted multilayer thermal protection system uncertainty optimization design iteration course curve of the present invention.
Specific implementation mode
The present invention proposes a kind of analysis of multilayer thermal protection system non-probabilistic uncertainty and optimization based on experimental design
Design method, the characteristics of in order to more fully understand the invention and its to the actual applicability of engineering, according to scheme as shown in Figure 1
Flow realizes the optimization design to multilayer thermal protection system, includes the following steps：
Step (1), the performance determined according to the true Service Environment of thermal protection system and configuration demand, multilayer thermal protection system
By the different materials structure composition of 6 layers of different function, as shown in Fig. 2, from outer surface to inner surface successively include enhancing C/C layers,
Room temperature curing glue-line, high temperature insulating layer, low temperature thermal insulation layer, strain isolating bed course and covering air-cooled structure layer；In view of material conducts heat
The thermal physical property parameters such as coefficient, density and specific heat capacity can vary with temperature, the material properties that each thermal physical property parameter varies with temperature
Curve need to be obtained by a series of corresponding thermal physical property parameter value interpolation fitting of specific temperatures, remember each layer thermal coefficient, density and
Specific heat capacity is respectively kij, ρijAnd cij, i=1,2 ..., n, j=1,2 ..., m, wherein i and j numbers for variable, and n is Multi-layer thermal
The total number of plies of safeguard structure, m are the quantity for choosing specific temperature, and the corresponding specific temperature of thermal coefficient, density, specific heat capacity is remembered respectively
ForWithIt is directed to structure shown in Fig. 2, the specific temperature sum of all layers of thermal physical property parameter is
Step (2), consider each thermal physical property parameter of layers of material due to various caused by material scatter, measurement error etc.
Uncertainty, k in selecting step (1)ij, ρijAnd cijAs uncertain parameter；Each uncertain parameter point is obtained according to engineering test
Cloth rule obtains the distribution of each uncertain parameter, range format is used in combination to be quantified as kij∈[kij_min,kij_max], ρij∈
[ρij_min,ρij_max] and cij∈[cij_min,cij_max], wherein kij_min, ρij_minAnd cij_minRespectively each parameter distribution range is most
Small value, kij_max, ρij_maxAnd cij_maxRespectively each parameter distribution range maximum value；
Each layer thickness in step (3), selecting step (1) is denoted as X, X=(x as design variable1,x2,…,x6)；Respectively
Thickness is limited in given range, i.e. xi∈[xi_min,xi_max], i=1,2 ..., 6, wherein xi_minTo give xiRange is most
Small value, xi_maxTo give xiThe maximum value of range, generally relies on engineering experience and project cost condition is given；
Step (4), during Geometric Modeling, by the secondary development function of Geometric Modeling software, it is anti-to establish Multi-layer thermal
The 3-D geometric model of protection structure, and design feature parameter of each design variable as control 3-D geometric model is extracted, when each
When design variable arbitrarily changes in respective given range, geometry automatic modeling can be realized, to complete to be based on selected design
The geometric parameterization of variable models；
Step (5), on the basis of geometrical model, by the secondary development function of finite element software, set gradually radiation,
The heat analysis cell type of convection current and conduction, the nonlinear material attribute for being defined as temperature funtion and temperature independent linear material
Expect attribute, divide finite element grid etc., convert the geometrical model that step (4) obtains to finite element model；It extracts each uncertain
Uncertain characteristic parameter of the parameter as the control hot physical property attribute of finite element model material, when each uncertain parameter is respectively dividing
When arbitrarily changing within the scope of cloth, the automatic assignment of the hot physical property attribute of finite element model material can be realized, be based on setting to establish
Count the multilayer thermal protection structure parameter finite element model of characteristic parameter and uncertain characteristic parameter；
Step (6), based on reentering process ballistic data, using radiation, convection current and the compound biography of a variety of heat transfer types of conduction
Heat analysis method considers influencing each other between Aerodynamic Heating and structural thermal, is applied by setting gradually outer surface and inner surface
With the hot load of time change, complete insulation remaining surface, apply multistep load and solve the operations such as multi-load step, realizes
The multilayer thermal protection system Analysis On The Transient Temperature of overall trajectory process solves, and obtains each bed boundary of multilayer thermal protection structure at any time
The temperature history T of variations(t), it as shown in figure 4, the maximum temperature extracted at each bed boundary exports in response, is denoted ass
=0,1 ..., 6, wherein s increase to interface of the 6 finger multilayer thermal protection structures from outer surface to all layers of inner surface from 0；
The rule varied with temperature that each thermal physical property parameter is presented under the conditions of step (7), consideration certainty is as sampling
Constraint works out sampling algorithm by data analyzing and processing software, compared to traditional completely random methods of sampling, realizes and considers constraint
The random sampling procedure of condition is for correlation factors arbitrary sampling method；Based on the method, from each uncertain ginseng of step (2)
Number kij, ρijAnd cijDistributed area in, select the sample point of the random combine of one group of consideration factor correlativity, be denoted as P, P=
(p1,p2,…,pu,…p2500), pu=(k11,k12,…,kij,…,knm,ρ11,ρ12,…,ρij,…,ρnm,c11,c12,…,
cij,…,cnm)u, wherein 2500 be sample point sum, puSome sample point of acute pyogenic infection of finger tip, kij, ρijAnd cijForm sample point in because
Son, ()uFor the specific level of certain sample point factor；
The sample point p of P in step (8), extraction step (7)u, u=1,2 ..., 2500 as uncertain in step (5)
Property characteristic parameter, repeats step (4) to (6) 2500 times, obtains one group of discrete highest of each bed boundary of thermal protection system
Temperature-responsive is denoted asFace method according to response, fitting sample set P
With response setsThen each factor k in description P is constructedij, ρijAnd cijWithThe polynary quadratic regression of relationship is approximate
Function modelIt shows as：
Step (9) passes through approximate model in step (8)Analyze each factor kij、ρij
And cijIt is exported with each bed boundary maximum temperature response of multilayer thermal protection structureBetween relationship and trend；Consider to calculate cost
With precision, by the distributed area [k in step (2)ij_min,kij_max], [ρij_min,ρij_max] and [cij_min,cij_max] equal normalizing
Change to same range [- 1 ,+1], passes through normalized approximate function model coefficient φvComparative analysis kij, ρijAnd cijTo each responsePercentage contribution, pass throughV=1,2 ..., 903, by φvIt is converted into contribution rate percentage, and is passed through
Pareto chart sorts contribution rate by order of magnitude, as shown in fig. 6, identification preferentially goes out key parameter；The k that binding analysis obtainsij,
ρijAnd cijWithBetween relationship, obtain the uncertain response distributed area after sensitivity analysis, i.e.,WhereinWithRespectivelyThe lower bound of Uncertainty distribution range and upper
Boundary；
Step (10) is in summary analyzed, using each layer thickness X in step (3) as design variable, with multilayer in step (10)
Each bed boundary maximum temperature Uncertainty distribution upper bound of thermal protection structureLess than each bed boundary acceptability limit temperatureTo constrain, i.e.,To minimize architecture quality nominal value mass as optimization object function, build
The non-Probability mapping networks mathematical model of Liru multilayer thermal protection system shown in following formula：
Wherein, f (ρij,xi) it is architecture quality name value function；Optimized Iterative course curve is as shown in fig. 7, final realize
Consider probabilistic non-Probability mapping networks of multilayer thermal protection system.
In conclusion the present invention proposes a kind of multilayer thermal protection system non-probabilistic uncertainty based on experimental design point
Analysis and optimum design method, this method are uncertain to consider using each layer thickness size of thermal protection system as optimization design variable
Property when to be less than each layer acceptability limit temperature be constraints, thermal protection system quality name in each layer military service maximum temperature distribution upper bound
Value is optimization object function, and the minimum of architecture quality is realized on the basis of ensureing anti-heat-proof quality.In view of as constraint
Each layer military service maximum temperature distribution upper bound of condition must be safe and reliable, therefore present invention introduces the non-probability for considering uncertain boundary
Interval theory quantifies the uncertainty of layers of material thermal physical property parameter, is based on correlation factors random experiment design method, realizes
The bounded-but-unknown uncertainty analysis of multilayer thermal protection system each bed boundary military service maximum temperature and the knot based on analysis of uncertainty
Structure optimization design.Wherein, the correlation factors random experiment design method in the present invention refers to random in carried correlation factors
On the basis of the methods of sampling realizes that test sample is chosen, continues to complete approximate model structure, analysis factor and the relationship of response, distinguishes
Know the complete set Uncertainty Analysis Method of preferential key parameter；Compared to other Uncertainty Analysis Methods, this method was both
There is compatibility suitable between uncertain parameter the case where correlation, and with other methods, physical significance definitely, subsequently
The analysis obtained based on the extracting method and optimum results more have confidence level.
The specific steps that the above is only the present invention, are not limited in any way protection scope of the present invention；Its is expansible to answer
For multilayered structure Heat Transfer Optimization design field, any technical scheme formed by adopting equivalent transformation or equivalent replacement, falls
Within that scope of the present invention.
Part of that present invention that are not described in detail belong to the well-known technology of those skilled in the art.
Claims (3)
1. a kind of analysis of multilayer thermal protection system non-probabilistic uncertainty and optimum design method based on experimental design, feature
It is, this method realizes that steps are as follows：
Step (1), the performance determined according to the true Service Environment of thermal protection system and configuration demand, multilayer thermal protection system is by n
The different materials structure composition of layer different function；In view of the thermal physical property parameters such as material thermal conductivity, density and specific heat capacity can
It varies with temperature, the material properties curve that each thermal physical property parameter varies with temperature need to pass through a series of corresponding hot object of specific temperatures
Property parameter value interpolation fitting obtains, and remembers that each layer thermal coefficient, density and specific heat capacity are respectively kij, ρijAnd cij, i=1,2 ..., n,
J=1,2 ..., m, wherein i and j numbers for variable, and n is the total number of plies of multilayer thermal protection structure, and m is the number for choosing specific temperature
Amount, the corresponding specific temperature of thermal coefficient, density, specific heat capacity are denoted as respectively With
Step (2) considers layers of material thermal physical property parameter due to various uncertain caused by material scatter, measurement error etc.
Property, k in selecting step (1)ij、ρijAnd cijAs uncertain parameter；Each uncertain parameter distribution rule are obtained according to engineering test
Rule, obtains the distribution of each uncertain parameter, range format is used in combination to be quantified as kij∈[kij_min,kij_max], ρij∈
[ρij_min,ρij_max] and cij∈[cij_min,cij_max], wherein kij_min, ρij_minAnd cij_minRespectively each parameter distribution range is most
Small value, kij_max, ρij_maxAnd cij_maxRespectively each parameter distribution range maximum value；
Each layer thickness in step (3), selecting step (1) is denoted as X, X=(x as design variable1,x2,…,xn), n is step
(1) the involved multilayer thermal protection structure number of plies in；Each thickness is limited in given range, i.e. xi∈[xi_min,xi_max], i
=1,2 ..., n, wherein xi_minTo give xiThe minimum value of range, xi_maxTo give xiThe maximum value of range, generally relies on engineering
Experience and project cost condition are given；
Step (4), during Geometric Modeling, extract each design variable as control 3-D geometric model design feature ginseng
Number, when each design variable arbitrarily changes in respective given range, can realize geometry automatic modeling, to complete to be based on institute
The geometric parameterization of design variable is selected to model；
Step (5), on the basis of geometrical model, by the secondary development function of finite element software, extract each uncertain parameter
As the uncertain characteristic parameter of the control hot physical property attribute of finite element model material, when each uncertain parameter is in respectively distribution model
When enclosing interior arbitrary change, the automatic assignment of the hot physical property attribute of finite element model material can be realized, it is special based on design to establish
Levy the multilayer thermal protection structure parameter finite element model of parameter and uncertain characteristic parameter；
Step (6), based on reentering process ballistic data, using radiation, convection current and the compound heat transfer point of a variety of heat transfer types of conduction
Analysis method considers influencing each other between Aerodynamic Heating and structural thermal, realizes the multilayer thermal protection system transient state of overall trajectory process
Temperature field analysis solves, and obtains the temperature history T that each bed boundary of multilayer thermal protection structure changes over times(t), each stratum boundary is extracted
Maximum temperature at face exports in response, is denoted asS=0,1 ..., n, wherein s increase to n from 0 and refer to multilayer thermal protection structure
From outer surface to the interface of all layers of inner surface；
Step (7) considers under the conditions of certainty the rule varied with temperature that is presented of each thermal physical property parameter as sampling restraint,
Sampling algorithm is worked out by data analyzing and processing software, compared to traditional completely random methods of sampling, realizes and considers constraints
Random sampling procedure, be for correlation factors arbitrary sampling method；Based on the method, from each uncertain parameter k of step (2)ij,
ρijAnd cijDistributed area in, select the sample point of the random combine of one group of consideration factor correlativity, be denoted as P, P=(p1,
p2,…,pu,…pr), pu=(k11,k12,…,kij,…,knm,ρ11,ρ12,…,ρij,…,ρnm,c11,c12,…,cij,…,cnm
)u, wherein r is sample point sum, puSome sample point of acute pyogenic infection of finger tip, kij, ρijAnd cijForm the factor in sample point, ()uFor certain
The specific level of the sample point factor；
The sample point p of P in step (8), extraction step (7)u, u=1 ..., r as the uncertain characteristic parameter in step (5),
It repeats step (4) to (6) r times, obtains one group of discrete maximum temperature response of each bed boundary of thermal protection system, be denoted as Face method according to response is fitted sample set P and response setsThen each factor k in description P is constructedij, ρijAnd cijWithThe approximate function model of relationship
Step (9) passes through approximate model in step (8)Analyze each factor kij、ρijAnd cij
It is exported with each bed boundary maximum temperature response of multilayer thermal protection structureBetween relationship and trend；Consider to calculate cost and essence
Degree, by the distributed area [k in step (2)ij_min,kij_max], [ρij_min,ρij_max] and [cij_min,cij_max] normalize to
Same range, comparative analysis kij, ρijAnd cijTo each responsePercentage contribution, identification preferentially go out key parameter；Binding analysis
The k obtainedij, ρijAnd cijWithBetween relationship, obtain the uncertain response distributed area after sensitivity analysis, i.e.,WhereinWithRespectivelyThe lower bound of Uncertainty distribution range and upper
Boundary；
Step (10) is in summary analyzed, anti-with Multi-layer thermal in step (10) using each layer thickness X in step (3) as design variable
Each bed boundary maximum temperature Uncertainty distribution upper bound of protection structureLess than each bed boundary acceptability limit temperature
To constrain, i.e.,To minimize architecture quality nominal value mass as optimization object function, Multi-layer thermal is established
The non-Probability mapping networks mathematical model of guard system, final realize consider probabilistic non-probability optimization of multilayer thermal protection system
Design.
2. the analysis of multilayer thermal protection system non-probabilistic uncertainty and optimization according to claim 1 based on experimental design
Design method, it is characterised in that：In the step (7), correlation factors arbitrary sampling method is suitable for each hot object of uncertainty
Property parameter kij, ρijAnd cijThere is the corresponding adjacent factor of adjacent specific temperature correlation and Uncertainty distribution section to interfere
Situation, with specific heat capacity cijFor illustrate, wherein the corresponding adjacent factor of specific temperature be cijAnd cij+1, it is assumed that in certainty
Under the conditions of have cij≤cij+1Vary with temperature rule, i.e., the adjacent factor has correlation, and considers probabilistic condition
Under, adjacent factor cijAnd cij+1Uncertainty distribution section [cij_min,cij_max] and [cij+1_min,cij+1_max] there is interference,
That is cij_max>cij+1_min, in these cases, correlation factors arbitrary sampling method compares traditional arbitrary sampling method, considers
It need to meet cij≤cij+1Rule as sampling restraint, avoid generating c to reachij>cij+1It is this run counter to hot physical property with
The effect of temperature changing regularity result.
3. the analysis of multilayer thermal protection system non-probabilistic uncertainty and optimization according to claim 1 based on experimental design
Design method, it is characterised in that：In the step (10), the non-Probability mapping networks mathematical model such as following formula of multilayer thermal protection system
It is shown：
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