CN104991982A - Aircraft aeroelasticity inertia sensor layout method - Google Patents
Aircraft aeroelasticity inertia sensor layout method Download PDFInfo
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Abstract
The invention relates to an aircraft aeroelasticity inertia sensor layout method. According to the method, the aircraft structure dynamic response is solved by employing the finite element method, a finite element model is corrected via a fluid-solid interaction method, and a fluid-solid coupling analysis of all the flight states and disturbance factors is conducted; the relatively precise structure dynamic response is calculated, and the response of each node in the finite element model is obtained; standardization processing of a data set is performed, and an elasticity wing structure dynamic response data set is generated; and data clustering by employing a clustering method based on distance measurement in pattern recognition is finally performed, and the final positions of the sensors are optimized and obtained. According to the method, the maximization of measuring information of sensors is regarded as the overall goal, optimization balance between the quantity of the sensors and the optimal layout positions is performed, and valuable aeroelasticity characteristic values can be acquired at the arranging position of each sensor.
Description
Technical field:
The present invention relates to flight vehicle aerodynamic elastic lag sensor placement field, based on dynamic response analysis and the dynamic cluster method of Flight Vehicle Structure.
Background technology:
From at the end of last century, NASA starts the research about flexible flier intelligent structure control technology, project is intended to be optimized layout configurations for Embedded sensor and actuator, thus realizes the ACTIVE CONTROL of wing shapes, Flight Vehicle Structure noise and structural vibration.From large-scale spacecraft to minute vehicle, relevant achievement in research is applied in the aircraft of various scale, and this technology can be used for improving the security of aircraft, reliability and environmental suitability.The sensor placement's technical research optimized is a vital research contents of the research plan.
At the initial stage of sensor placement's technical research, domain experts often utilize artificial optimization's technology to carry out layout to sensor, carry out the layout of sensor and actuator with abundant engineering experience and intuition.Sensing station location problem can be described as: at given installation position selection M N number of possible location arrangements sensor, thus obtains optimum performance index.In general, the optimization for performance index is very complicated.In addition, placement scheme also can be subject to the geometric configuration restriction of structure, and placement scheme also can be subject to the physical restriction of sensor and control the restriction of energy.When performance index can not lean on experience intuition to obtain, when objective constraints is most important, or when the position M number that may install exceedes the quantity of desk checking, then more need a kind of optimization sensor placement technology of science.At present mostly sensor optimization placement scheme often rule of thumb examination gather and obtain, not yet retrieve general flight vehicle aerodynamic elastic lag sensor placement optimization method.
Summary of the invention:
The object of the invention is for above-mentioned the deficiencies in the prior art, in conjunction with flight vehicle aerodynamic elasticity own characteristic, a kind of dynamic clustering flight vehicle aerodynamic elastic lag sensor placement method is provided, it is a kind of general purpose transducer layout optimization criterion, overall goal is turned to so that sensor measurement information is maximum, balance is optimized between number of sensors and optimal location position, can guarantee that the position of each sensor can collect valuable aeroelasticity eigenwert, and support that domain expert manually specifies the quantity of placement sensor, can effectively reduce control system cost.
The present invention is achieved by the following technical solutions, specifically comprises the steps:
Step one: on the basis adopting finite element method Flight Vehicle Structure dynamic response, revised finite element model by fluid structurecoupling method, carries out the wind-structure interaction traveling through all flight conditions and disturbance factor;
Step 2: calculate relatively accurate structure dynamic response, obtain the response of each node in finite element model;
Step 3: carry out standardization to data set, generates elastic wing structure dynamic response data set;
Step 4: adopt the clustering method based on distance metric in pattern-recognition to carry out cluster to data, optimize and obtain final sensing station.
Described fluid structurecoupling method is fluid structurecoupling MPCCI method.
The method of direct integral or modal superposition is adopted to calculate described relatively accurate structure dynamic response.
Described standardization is carried out to data set, generate elastic wing structure dynamic response data set to be specially: repeated multiple times conversion flow field and disturbing load frequency, reproduce the various state of flights that may occur in practical flight as much as possible, comprise the deflection angle that all rudder faces of traversal may occur, the traversal each possible installation site of plug-in device, carry out field output after obtaining the response of each node, obtain structure dynamic response data set.
The clustering method based on distance metric in described employing pattern-recognition carries out cluster to data, and optimization obtains final sensing station and is specially: (1) determines rational cluster granularity, inter-object distance and between class distance by the mode of repeatedly debugging cluster threshold value; (2) the eigenwert means clustering algorithm based on improving Euclidean distance tolerance is adopted reasonably to be clustered; (3) numbering of each node and the position in finite element model thereof in obtaining clustering by finite element Knot Insertion fitting algorithm backwards calculation, carry out redundancy and clear up, obtain sensor final layout scheme.
The eigenwert means clustering algorithm of described improvement Euclidean distance tolerance is using maximum as measurement criterion to mean value error quadratic sum for each point, its distance to the center of clustering is measured to each example, when satisfied appointment threshold value, it is grouped into the class of barycenter, adjust through iterating in finally clustering simultaneously and there is distance in minimum class, there is between clustering maximum kind spacing, reach iteration ends after constraint condition.
Described finite element Knot Insertion fitting algorithm refers to and carries out interpolation to the result of calculation of modal superposition in finite element, obtains the aeroelasticity structure dynamic response of all nodes of level and smooth finite element model.
Classic method utilizes artificial optimization's technology to carry out layout to sensor, carries out the layout of sensor and actuator with abundant engineering experience and intuition, and when objective constraints is most important, the application of experience sensor placement just has certain limitation.
This method tool has the following advantages: adopt fluid structurecoupling method to obtain the maximum aeroelasticity response of each node, then obtain final sensing station by the method optimization of dynamic clustering; Repeated multiple times conversion flow field and disturbing load frequency, the various state of flights that may occur in reproduction practical flight as much as possible, comprise the deflection angle that all rudder faces of traversal may occur, the traversal each possible installation site of plug-in device, carry out field output after obtaining the response of each node, obtain structure dynamic response data set; By fluid structurecoupling MPCCI method or other fluid and structural simulation methods, finite element model is revised, carry out the wind-structure interaction traveling through all flight conditions and disturbance factor.
Compared with prior art, the present invention has following beneficial effect: can guarantee that the position of each sensor can collect valuable aeroelasticity eigenwert, and can the quantity of artificial specific arrangements sensor, reduces control system cost.By fluid structurecoupling MPCCI method or other fluid and structural simulation methods, finite element model is revised, the wind-structure interaction traveling through all flight conditions and disturbance factor can be carried out.Apply Flight Vehicle Structure kinetic sensors layout method of the present invention, regulate by careful parameter and approach optimum sensor placement.Utilize this method, the various state of flights that may be able to occur in reproduction practical flight as much as possible, comprise the deflection angle that all rudder faces of traversal may occur, the traversal each possible installation site of plug-in device, carry out field output after obtaining the response of each node, finally obtain structure dynamic response data set.
Accompanying drawing illustrates:
Fig. 1 is flight vehicle aerodynamic elastic lag sensor placement Method And Principle figure;
Fig. 2 analyzes granularity 4 figure;
Fig. 3 analyzes granularity 5 figure;
Fig. 4 analyzes granularity 6 figure;
Fig. 5 analyzes granularity 7 figure;
Fig. 6 analyzes granularity 8 figure;
Fig. 7 analyzes granularity 9 figure;
Fig. 8 analyzes granularity 10 figure;
Fig. 94 to cluster spacing figure;
Figure 10 5 to cluster spacing figure;
Figure 11 6 to cluster spacing figure;
Figure 12 7 to cluster spacing figure;
Figure 13 8 to cluster spacing figure;
Figure 14 9 to cluster spacing figure;
Figure 15 10 to cluster spacing figure;
Figure 16 is 5 point sensor layouts;
Figure 17 is 6 point sensor layouts;
Figure 18 is 9 point sensor layouts;
Embodiment:
Below in conjunction with drawings and Examples, the invention will be further described.
Flexible flier housing construction has larger flexibility, for measuring the needs such as flexural property, gust response of flexible wing fully, tackle each partial accession inertial sensor of full machine, multiple sensor forms sensor array and provides complementary information, thus more effectively can carry out ACTIVE CONTROL to aeroelasticity inertia.
The invention provides dynamic clustering flight vehicle aerodynamic elastic lag sensor placement method, system principle diagram is shown in accompanying drawing 1, can guarantee that placement position approaches optimum solution to greatest extent.Dynamic clustering is a kind of iteration cluster in essence, first given coarse initially clustering, and define the distance between clustering, then carry out iterative modifications by distance metric principle, relatively more reasonable until cluster, adopt the clustering method of this thought to be called dynamic state clustering.On the basis of initial clustering, first calculate the objective function of initial clustering, the individuality of adjustment initial classes, to new class, if objective function reduces, then realizes transposition; Otherwise individuality is stayed in former group.One repeating query loops bundle, then adjust intermediate individuality, until the reorganization transposition of any individuality all can not make objective function diminish, cluster terminates.Initial clustering defining method have by virtue of experience determine initial clustering, the number that will cluster is divided into N class at random, determine classification number M artificially.
Embodiment one: the present embodiment comprises the steps:
1. on the basis adopting finite element method Flight Vehicle Structure dynamic response, by fluid structurecoupling MPCCI or other fluid structurecoupling methods, finite element model is revised, carry out the wind-structure interaction traveling through all flight conditions and disturbance factor.The analysis of flight vehicle aerodynamic elastic lag comprises harmonic responding analysis, random vibration and instantaneous response analysis, and carries out corresponding Harmony response loading, PSD response, transient response experiment.
2. calculate relatively accurate structure dynamic response, obtain the response of each node in finite element model: because computational resource is limited in practical engineering calculation, carry out the response analysis data that fluid and structural simulation obtains often comprehensive not, but the aeroelasticity response of aircraft under typical disturbance can be reflected substantially.Derivation algorithm can adopt the method such as direct integral, modal superposition to calculate and obtain.
3. pair data set carries out standardization, generate elastic wing structure dynamic response data set: repeated multiple times conversion flow field and disturbing load frequency, the various state of flights that may occur in reproduction practical flight as much as possible, comprise the deflection angle that all rudder faces of traversal may occur, the traversal each possible installation site of plug-in device, carry out field output after obtaining the response of each node, obtain structure dynamic response data set
4. adopt the clustering method based on distance metric in pattern-recognition to carry out cluster to data: artificial appointment cluster granularity, inter-object distance and between class distance.After obtaining the characteristic value data collection of typical response, determining the number that rationally clusters further, obtaining the response of accurate node by specifying reasonably analysis granularity; Clear up redundancy by specifying reasonable class spacing to cluster; Optimal sensor placement position is approached by specifying rational inter-object distance.Adopt the eigenwert means clustering algorithm based on improving Euclidean distance tolerance, after rationally being clustered, each node numbering in obtaining clustering by finite element Knot Insertion fitting algorithm backwards calculation, and the position in finite element model, again and carry out redundancy and clear up, this process data amount is little, and model is directly perceived, fully can adopt Flight Vehicle Structure dynamics field expertise, corresponding actual physical meaning.
The eigenwert means clustering algorithm improving Euclidean distance tolerance is using maximum as measurement criterion to mean value error quadratic sum for each point, its distance to the center of clustering is measured to each example, when satisfied appointment threshold value, it is grouped into the class of barycenter, adjust through iterating in finally clustering simultaneously and there is distance in minimum class, there is between clustering maximum kind spacing, reach iteration ends after constraint condition.
Finite element Knot Insertion fitting algorithm refers to and carries out interpolation to the result of calculation of modal superposition in finite element, obtains the aeroelasticity structure dynamic response of all nodes of level and smooth finite element model.
Such as, export structure dynamic response calculated field data, obtain wing finite element node 1457 after carrying out standardization, and the Y-axis shift value data set of each node structure dynamic response.Adopt Dynamic Clustering Algorithm to carry out cluster analysis to data set, specify cluster analysis granularity from 4 to 10, specify the interior distance metric threshold value that clusters to be 0.9, stochastic generation clusters spacing.Cluster result is as shown in accompanying drawing 2-8.
Cluster spacing as shown in Fig. 9-15 under different analysis granularity.
Can obtain according to above analysis, the method that the spacing that clusters is specified at random can meet algorithm requirements substantially, can be effectively isolated between clustering, if possess the correlation parameter of elastic wing structure, can artificially specify cluster spacing, make Clustering Effect more excellent.
Each node numbering in clustering by finite element Knot Insertion fitting algorithm backwards calculation, obtains sensor final layout scheme, selects representative configuration scheme as shown in figs. 16-18.
Claims (7)
1. a flight vehicle aerodynamic elastic lag sensor placement method, is characterized in that: described method comprises the steps:
Step one: on the basis adopting finite element method Flight Vehicle Structure dynamic response, revised finite element model by fluid structurecoupling method, carries out the wind-structure interaction traveling through all flight conditions and disturbance factor;
Step 2: calculate relatively accurate structure dynamic response, obtain the response of each node in finite element model;
Step 3: carry out standardization to data set, generates elastic wing structure dynamic response data set;
Step 4: adopt the clustering method based on distance metric in pattern-recognition to carry out cluster to data, optimize and obtain final sensing station.
2. flight vehicle aerodynamic elastic lag sensor placement according to claim 1 method, is characterized in that: described fluid structurecoupling method is fluid structurecoupling MPCCI method.
3. flight vehicle aerodynamic elastic lag sensor placement according to claim 1 method, is characterized in that: adopt the method for direct integral or modal superposition to calculate described relatively accurate structure dynamic response.
4. flight vehicle aerodynamic elastic lag sensor placement according to claim 1 method, it is characterized in that: described standardization is carried out to data set, generate elastic wing structure dynamic response data set to be specially: repeated multiple times conversion flow field and disturbing load frequency, reproduce the various state of flights that may occur in practical flight as much as possible, comprise the deflection angle that all rudder faces of traversal may occur, the traversal each possible installation site of plug-in device, carry out field output after obtaining the response of each node, obtain structure dynamic response data set.
5. flight vehicle aerodynamic elastic lag sensor placement according to claim 1 method, it is characterized in that: the clustering method based on distance metric in described employing pattern-recognition carries out cluster to data, optimization obtains final sensing station and is specially: (1) determines rational cluster granularity, inter-object distance and between class distance by the mode of repeatedly debugging cluster threshold value; (2) the eigenwert means clustering algorithm based on improving Euclidean distance tolerance is adopted reasonably to be clustered; (3) numbering of each node and the position in finite element model thereof in obtaining clustering by finite element Knot Insertion fitting algorithm backwards calculation, carry out redundancy and clear up, obtain sensor final layout scheme.
6. flight vehicle aerodynamic elastic lag sensor placement according to claim 5 method, it is characterized in that: the eigenwert means clustering algorithm of described improvement Euclidean distance tolerance is using maximum as measurement criterion to mean value error quadratic sum for each point, its distance to the center of clustering is measured to each example, when satisfied appointment threshold value, it is grouped into the class of barycenter, adjust through iterating in finally clustering simultaneously and there is distance in minimum class, there is between clustering maximum kind spacing, reach iteration ends after constraint condition.
7. flight vehicle aerodynamic elastic lag sensor placement according to claim 5 method, its feature is being: described finite element Knot Insertion fitting algorithm refers to that the result of calculation of modal superposition in finite element carries out interpolation, obtains the aeroelasticity structure dynamic response of all nodes of level and smooth finite element model.
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| CN106919584A (en) * | 2015-12-26 | 2017-07-04 | 华为技术有限公司 | The layout method and device of a kind of topological diagram |
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| CN108092751A (en) * | 2016-11-22 | 2018-05-29 | 北京空间技术研制试验中心 | Pneumatic gauging parameter information processing method |
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| CN106919584A (en) * | 2015-12-26 | 2017-07-04 | 华为技术有限公司 | The layout method and device of a kind of topological diagram |
| CN105843076A (en) * | 2016-03-31 | 2016-08-10 | 北京理工大学 | Flexible aircraft aeroelasticity modeling and controlling method |
| CN108092751A (en) * | 2016-11-22 | 2018-05-29 | 北京空间技术研制试验中心 | Pneumatic gauging parameter information processing method |
| CN106557633A (en) * | 2016-11-29 | 2017-04-05 | 上海卫星工程研究所 | Satellite sun wing sensor placement method is realized based on EI methods |
| CN107330133A (en) * | 2017-04-01 | 2017-11-07 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | A kind of optimizing layout method based on virtual test |
| CN107330133B (en) * | 2017-04-01 | 2019-03-29 | 中国商用飞机有限责任公司北京民用飞机技术研究中心 | A kind of optimizing layout method based on virtual test |
| CN109711036A (en) * | 2018-12-24 | 2019-05-03 | 中国航空工业集团公司西安飞机设计研究所 | The appraisal procedure of flight control system test result |
| CN109444350A (en) * | 2018-12-27 | 2019-03-08 | 中山大学 | A kind of layout method of the atmosphere pollution monitoring sensor based on unmanned plane |
| CN111595433A (en) * | 2019-02-20 | 2020-08-28 | 中国航发商用航空发动机有限责任公司 | Position determination method and system for vibration sensor of whole aircraft engine |
| CN111595433B (en) * | 2019-02-20 | 2022-07-08 | 中国航发商用航空发动机有限责任公司 | Position determination method and system for vibration sensor of whole aircraft engine |
| CN110532607A (en) * | 2019-07-24 | 2019-12-03 | 北京航空航天大学 | The sensor placement method of hypersonic aircraft rudder face structure distribution load identification |
| CN117169900A (en) * | 2023-11-03 | 2023-12-05 | 威科电子模块（深圳）有限公司 | Accurate sensing system, method, equipment and medium based on thick film circuit |
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