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CN-122024943-A - Thermoplastic composite material front edge structural design allowable value probability representation method and system under small sample condition

CN122024943ACN 122024943 ACN122024943 ACN 122024943ACN-122024943-A

Abstract

The invention belongs to the technical field of uncertainty probability characterization analysis, and discloses a thermoplastic composite material front edge structure design allowable value probability characterization method and system under the condition of a small sample, wherein the method comprises the steps of defining a plurality of candidate probability distribution models; fitting each model based on original sample data, calculating an AIC value and a BIC value, calculating a dynamic weight factor alpha according to a sample size n, further calculating a mixed information criterion HIC value, generating a plurality of sample sets through Bootstrap self-service sampling, recalculating the HIC value on each sample set, and counting the frequency of each model selected as optimal, and determining an optimal probability distribution model according to the frequency, wherein the optimal probability distribution model is used for representing a design allowable value. According to the invention, by introducing the dynamic weight factor alpha, AIC and BIC criteria are effectively unified, the optimal balance of prediction precision and model complexity is realized under the condition of a small sample, and engineering practicability and robustness are remarkably improved.

Inventors

  • WANG XINNIAN
  • YANG FENRONG
  • JIN AN
  • CHEN LI
  • YANG HUALUN

Assignees

  • 中航西飞民用飞机有限责任公司

Dates

Publication Date
20260512
Application Date
20251224

Claims (10)

  1. 1. The allowable value probability characterization method for the thermoplastic composite material front edge structural design under the condition of a small sample is characterized by comprising the following steps of: S1, defining a plurality of candidate probability distribution models for fitting thermoplastic composite material performance data; s2, calculating a mixed information criterion HIC value of each candidate probability distribution model based on an original performance sample data set, wherein the HIC value is calculated by a formula HIC=alpha×AIC+ (1-alpha) x BIC, alpha is a weight parameter, the calculation formula is alpha=1/(1+log (n)), n is the sample size of the original performance sample data set, AIC is an Akaike information criterion, and BIC is a Bayesian information criterion; s3, carrying out B times of self-service sampling with replacement on the original performance sample data set to generate B self-service sample sets; S4, recalculating the HIC value of each candidate probability distribution model according to each self-service sample set generated in the step S3, and recording a model with the minimum HIC value as an optimal distribution model under the self-service sample set; s5, counting the frequency of each candidate probability distribution model selected as an optimal distribution model in B times of self-help sampling; S6, determining a final probability distribution model for representing the thermoplastic composite material front edge structure design allowable value from the candidate probability distribution models according to the frequency.
  2. 2. The method of claim 1, wherein the candidate probability distribution model in step S1 includes at least three of a normal distribution, a lognormal distribution, a weber distribution, a gamma distribution, and an exponential distribution.
  3. 3. The method according to claim 1, characterized in that in step S2, AIC values and BIC values of each candidate probability distribution model are calculated, in particular: fitting each candidate probability distribution model by a maximum likelihood estimation method based on the original performance sample data set, and obtaining a maximum likelihood value L of the model; Calculating an AIC value according to the formula aic=2k-2 ln (L), wherein k is the number of parameters of the candidate probability distribution model; the BIC value is calculated according to the formula bic=k×ln (n) -2ln (L).
  4. 4. The method according to claim 1, wherein the step S6 of determining a final probability distribution model based on the frequency is performed by selecting a candidate probability distribution model with the highest frequency selected as an optimal distribution model as the final probability distribution model.
  5. 5. The method according to claim 1, wherein the number B of self-service samples in step S3 is not less than 1000.
  6. 6. The method according to claim 1, further comprising the step, after determining the final probability distribution model: and S7, calculating a mechanical property allowable value of the thermoplastic composite material front edge structure under the design required confidence level based on the final probability distribution model.
  7. 7. The thermoplastic composite material leading edge structural design allowable value probability characterization system under the condition of a small sample is characterized by comprising: the model definition module is used for defining a plurality of candidate probability distribution models; The fitting calculation module is used for fitting each candidate distribution model based on the original sample data and calculating an AIC value and a BIC value; the weight calculation module is used for calculating a dynamic weight factor alpha according to the sample size n; a HIC calculation module for calculating an HIC value of each model based on the AIC value, the BIC value, and the weight factor α; the self-help sampling module is used for executing Bootstrap self-help sampling and generating a plurality of self-help sample sets; the frequency statistics module is used for repeatedly calculating the HIC value on the self-service sample set and counting the frequency of each model selected as the optimal model; And the model determining module is used for determining an optimal probability distribution model according to the frequency.
  8. 8. The system of claim 7, wherein the fit calculation module is further configured to calculate a maximum likelihood value L for each candidate distribution model.
  9. 9. The system of claim 7, wherein the Bootstrap module performs a Bootstrap sampling number B of not less than 1000.
  10. 10. The system of claim 7, wherein the model determination module selects a distribution model with the highest selected frequency as the optimal probability distribution model.

Description

Thermoplastic composite material front edge structural design allowable value probability representation method and system under small sample condition Technical Field The invention belongs to the technical field of uncertainty probability characterization analysis, and particularly relates to a thermoplastic composite material leading edge structure design allowable value probability characterization method and system under the condition of a small sample, which are used for mainly obtaining the design allowable value of a wing leading edge structure. Background Thermoplastic composite leading edge structures exhibit great potential for application. The anti-impact anti-collision device is excellent in anti-impact toughness, can effectively resist impact loads such as bird strike and hail in flight, and improves flight safety. Compared with the traditional thermosetting composite material, the composite material has unique weldability and reworkability, can realize efficient low-cost connection and repair, is beneficial to recovery, and accords with the green manufacturing trend. In addition, through the thermoforming process, the complex curved surface structure can be rapidly formed within a few minutes, the production period is short, and the method is suitable for large-scale manufacturing. However, this structure also faces challenges in engineering applications. The raw material cost is high, the thermoforming process needs accurate temperature and pressure control, the requirements on die design and process stability are severe, and initial investment and production cost are increased. Meanwhile, long-term performance data (long-term creep and aging performance) under severe environments such as high humidity, high temperature and the like are still insufficient, so that the dispersion of design allowable values is large, and the accurate setting of the safety boundary is restricted. In order to reliably apply the structure in the development of new aircraft, a set of efficient design allowable probability characterization methods based on limited test data is needed to be established. Traditional probability characterization methods (such as Markov random fields, monte Carlo sampling and the like) generally require massive sample support, have high test cost and long period, and are difficult to meet the development requirements of rapid iteration. For this reason, the probability characterization method in the case of introducing a small sample is important. For example, model selection and self-sampling techniques based on AIC criteria can efficiently perform robust assessment of the overall probability distribution of material properties using limited experimental data, providing a quantitative and reliable uncertainty analysis for determination of core design allowable values. In summary, in order to promote the mature application of the thermoplastic composite material front edge structure in the aviation field, a small sample probability characterization method suitable for the characteristics of the thermoplastic composite material front edge structure is proposed and discussed herein, and the purpose of providing a solid theoretical basis and data support for damage tolerance design, reliability evaluation and final successful application of the structure is provided. Disclosure of Invention Aiming at the problems of high production cost and large dispersion of design allowable values of the existing thermoplastic composite material, the invention provides a method and a system for representing probability of the design allowable values of a thermoplastic composite material front edge structure under the condition of a small sample, and solves the problems of large sample requirement, difficult model selection, insufficient engineering practicability and the like in the prior art. The technical scheme of the invention is realized as follows: in a first aspect, the invention provides a thermoplastic composite material leading edge structural design allowable value probability characterization method under the condition of a small sample, which comprises the following steps: S1, defining a plurality of candidate probability distribution models for fitting thermoplastic composite material performance data; s2, calculating a mixed information criterion HIC value of each candidate probability distribution model based on an original performance sample data set, wherein the HIC value is calculated by a formula HIC=alpha×AIC+ (1-alpha) x BIC, alpha is a weight parameter, the calculation formula is alpha=1/(1+log (n)), n is the sample size of the original performance sample data set, AIC is an Akaike information criterion, and BIC is a Bayesian information criterion; s3, carrying out B times of self-service sampling with replacement on the original performance sample data set to generate B self-service sample sets; S4, recalculating the HIC value of each candidate probability distribution model acco