As a revolutionary paradigm of the next generation of aircraft,morphing aircraft features the real-time aero-dynamic shape reconstruction capability,which can break through the limitation of aerodynamic efficiency and mission adaptability of traditional fixed-wing aircraft.In order to solve the issue of limited morphing freedom and difficulty in achieving distributed continuous deformation of the mechanical metamaterial wings,this paper proposes a multi-modal morphing wing design scheme based on heterogeneous mechanical metamaterials.Firstly,by constructing a basic unit-cell system with gradient mechanical properties,combined with the sequential design of rigid load-bearing unit-cell and flexible deforming unit-cell,the coordinated control of structural lightweight and large morphing capacity is real-ized.Secondly,the active actuating unit-cell and the transition unit-cell element are further developed,and the para-metric space arrangement strategy for the aerodynamic contour was established to form a full-lattice wing structure with multi-modal collaborative morphing capacity.Finally,based on finite element simulation and morphing function experi-ment,the overall torsion and local thickness adjustment capacity of the wing surface are verified.The results show that the proposed gradient metamaterial wing can achieve continuous torsional deformation of 12°/-9° in the spanwise direction and thickness adjustment of 8%in the chordwise direction.The research results provide a new way to break through the optimization of cross-domain aerodynamic performance and multi-task adaptive control of aircraft,and have important engineering value for the development of intelligent morphing aircraft.
Addressing the frequent occurrence of landing gear shimmy under maneuvering conditions,this paper con-ducts research on high-fidelity modeling and parameter coupling effects.Based on a delay tire dynamic model,the coupled landing gear—tire shimmy dynamics equations are developed,which incorporate the time-varying tire lateral deformation modes.Utilizing Hopf bifurcation theory,we compute the global dynamic characteristics of the system through multi-dimensional projections,quantitatively revealing the influence patterns and mechanisms of key param-eters(such as lateral bending stiffness and torsional stiffness)on the stability of the landing gear system across vari-ous taxiing speeds.Comparative results with traditional tire models show consistency of both and limitations of tradi-tional tire models in capturing parameter coupling effects.By employing an enhanced Bayesian optimization method,we optimize the bifurcation characteristics of the landing gear system,resulting in a significant improvement in stability.Based on the parameter optimization results and combined with Sobol global sensitivity analysis,the comparative ef-fects of multi-dimensional parameters on system stability are quantitatively revealed,with the results complementing and validating those from the Hopf bifurcation analysis.The study identifies that when the torsional mode frequency of the landing gear approaches the lateral bending mode frequency,substantial modal coupling occurs,creating a bi-stable shimmy critical boundary that drastically reduces stability.Low lateral stiffness of the landing gear may trigger shimmy during low-speed taxiing,while greater lateral stiffness at high speeds necessitates matched damping en-hancement for shimmy mitigation.Increasing torsional stiffness and decreasing tire lateral stiffness yield notable shimmy suppression effects across the entire speed spectrum,indicating their potential as key control parameters for targeted design and optimization.
Aviation standards are characterized by complex data structures,rigorous semantics,and frequent cross-document references,which pose significant challenges for achieving efficient and accurate knowledge acquisition and question-answering applications.In response,an iterative retrieval-augmented generation(RAG)method for aviation standards based on large language models(LLMs)is proposed.A structure-aware vectorized knowledge base construction and retrieval mechanism is designed,which leverages the hierarchical structure and title chains of standard documents to support context-traceable knowledge representation.A hybrid retrieval strategy combining keyword matching and semantic similarity is further introduced.On this basis,an LLM-driven automatic iterative retrieval and generation mechanism is developed,enabling the model to autonomously determine whether sub-question decomposition and deep intent recognition are required.By integrating multi-turn retrieval with dynamic scheduling,the proposed framework forms a closed-loop process of problem decomposition,information acquisition,self-assessment,and content generation,effectively improving generation quality and semantic coverage for complex queries such as multi-point aggregation and semantic progression.Experiments are conducted on a knowledge base constructed from 7 459 aviation standard documents and a benchmark set of 500 expert-annotated question-answer pairs,evaluated across four mainstream open-source LLMs with varying parameter scales,model types,and bilingual capabilities.The results indicate that,for models with medium or large scales,the proposed method significantly outperforms traditional retrieval-augmented generation approaches in answer accuracy,coverage,and expression quality.Specifically,on DeepSeek-R1-70B,the bilingual evaluation understudy(BLEU)score improved by 27.97%and the simulated human preference score increased by 7.99%;on Qwen-2.5-32B,BLEU improved by 54.67%and the simulated score increased by 8.58%.The proposed method is applicable not only to aviation standards,but also to other structured document scenarios in the aviation field,including airworthiness regulations and maintenance manuals,as well as to domains such as law and healthcare that require high answer quality,reliability,and traceability.It offers a versatile technical framework and implementation approach for the construction of domain-specific question-answering systems.
To address the issues that the Kriging surrogate model using the learning function as the convergence cri-terion cannot reflect the accuracy of predicted failure probability directly and accurately and incur high computation-al cost in modeling,a reliability analysis method based on Indicator Error Controlled Kriging(IEC Kriging)is pro-posed in this paper.With Kriging as the basic model,IEC Kriging establishes a sequential transmission relationship among sample information,performance functions,and failure probability.It analyzes the error of predicted failure probability based on the probability of indicator sign misclassification by the Kriging model at each sample point,while considering the corrective effect of candidate sample points on prediction accuracy.A dynamic sample selec-tion strategy is designed with the upper bound of the overall sample indicator error as the criterion,and the sample point with the highest corrective effect is incorporated into the training set to optimize the surrogate model,ensuring the accuracy of prediction results while improving the efficiency of building the surrogate model.The proposed meth-od is applied to the wear reliability analysis of spherical plain bearings in an aircraft high-lift device as a case study.Compared with Kriging methods using the EGO learning function and U learning function,the IEC Kriging demon-strates higher computational efficiency for reliability estimation,which can provide a theoretical method reference for mechanism reliability analysis.
To fulfill the requirements for researching and validating the acoustic characteristics of aviation acoustic liners across a wide frequency range, a flow tube acoustic test bench was developed, employing the three-dimensional straightforward method (3D-SFM) and the pipe acoustic mode decoupling approach. In a grazing flow environment with a maximum Mach number of 0.14, the acoustic performances of single- and double-degree-of-freedom acoustic liners, including acoustic impedance and sound transmission loss, were tested. The maximum test frequency could reach 10 kHz, covering the main noise frequency bands of aeroengines and auxiliary power units. The experimental results demonstrate that the 3D-SFM can effectively educe the acoustic impedance of the acoustic liner in a multi-modal sound field. Furthermore, based on spanwise mode decomposition and the Prony method, the optimization principle and methodology were proposed to optimize acoustic impedance under multiple spanwise modes and scattering modes. The final acoustic impedance outcomes exhibited excellent consistency with the prediction of liner's impedance model. Sound transmission loss was capable of reflecting the variation law of the noise reduction capability of single/double-degree-of-freedom acoustic liners under different Mach numbers of grazing flow, as well as the advantages of double-degree-of-freedom acoustic liners in broadband noise reduction.
