Timely and effective maintenance of aircraft structures is essential for ensuring operational safety, reducing operating costs, and extending service life. However, in current engineering practice, aircraft structural maintenance is often accompanied by a lack of complete and reliable load data, which leads to inaccurate strength and fatigue assessments of in-service aircraft structures and severely restricts subsequent structural modification and redesign. To address this inverse problem of load prediction, this paper proposes an inversion method for the extreme value of aircraft structural equivalent fatigue load based on historical maintenance data. First, historical maintenance records of aircraft structures are statistically analyzed, and the statistical fatigue life is determined by incorporating confidence and reliability assessment methods. Then, a numerical simulation model of the aircraft structure is established to obtain the predicted fatigue life under given loading conditions. Finally, an optimization framework is constructed in which the absolute difference between the predicted fatigue life and the statistical fatigue life is minimized, with the extreme value of the structural equivalent fatigue load treated as the design variable. Through iterative optimization, the structural equivalent fatigue load extreme value that best matches the actual service loading condition is identified. To verify the effectiveness of the proposed method, an aircraft kicker plate angle is selected as a case study, and the inversion results are compared with experimental data obtained from component-level fatigue tests. The results show that the prediction error of the proposed method is within 10%, demonstrating higher accuracy than conventional life prediction approaches based directly on strain data. These results indicate that the proposed method enables accurate inversion of the extreme value of aircraft structural equivalent fatigue load and provides useful guidance for aircraft structural modification design.

Aimed at the urgent needs of high-precision real-time life prediction and intelligent operation and maintenance in aircraft structural health management, this paper proposes an aircraft structural health management approach based on flight parameter-load-life digital twin models. First, measured data of flight parameters and loads in structural key parts are used to train flight parameter-load digital twin models based on the incremental learning eXtreme Gradient Boosting (XGBoost), so as to realize high-precision dynamic mapping of the loads of the key parts. Second, simulated data of parametric models are used to train load-stress field digital twin models based on the non-intrusive reduced-order technique to realize high-precision dynamic reconstruction of the stress field of the key parts. Furthermore, the fatigue life estimation model is constructed based on the Detail Fatigue Rating (DFR) method. The parameters of the fatigue life estimation model are calculated from the prediction results of the above digital twin models, and the life consumption is estimated to realize the high-accuracy dynamic prediction of the remaining life, forming a flight parameter-load-life digital twin model. On this basis, fleet maintenance and flight task multi-objective planning models are constructed respectively, and solution sets of historical planning problems are formed by intelligent optimization algorithm. Then, based on clustering center distance and coefficient of determination metrics, similarity between historical planning (source-domain) problems and new planning (target-domain) problem in terms of solution set distribution and feature space is quantified. Finally, the highly similar source-domain problem solutions are transferred to the initial population of the target-domain problems to form an intelligent planning method for fleet maintenance and flight task based on transfer learning of historical information, which realizes the intelligent and efficient management of fleet life. The results of a typical flight-testing example show that this method can dynamically predict the loads and remaining life of the structural key parts with a load prediction error of 5.30% and a life prediction error of −7.19%. Meanwhile, for the complex maintenance and flight task planning problems of 20 aircraft, the efficiency of the proposed method is improved by 33.9% and 14.5%, respectively, compared with the direct optimization method, which verifies the effectiveness of the proposed method.

The topology optimization density field results of curved stiffened structures have problems such as discontinuity and too small structural features, which make them difficult to be directly applied to subsequent fine design and manufacturing. Meanwhile, feature extraction and model reconstruction based on artificial experience have problems such as cumbersome operation and long reconstruction period. To address the above problems, a parametric reconstruction for topology features of curved stiffened structures method is proposed. Firstly, a mapping relationship between three-dimensional surface space and two-dimensional planar space is established for the results of surface stiffened topology optimization, and the transformation from surface optimization results to planar optimization results is realized based on forward mesh mapping. Then, for the planar optimization results, the contour feature parameters are extracted based on the image morphology method, and the geometric model is reconstructed by spline curve interpolation to obtain the parametric model of the planar optimization results. Finally, based on the inverse mapping mesh mapping method, the parametric model of the surface optimization results is obtained, and the parametric reconstruction of the topology optimization results of the surface stiffened structure is realized. Based on the proposed method, three typical curved stiffened structures of bearing cylinder, cabin door and sealed cabin are taken as examples, and the reconstruction results are compared with the optimization results. The results show that the error between the structural response of the parametric reconstruction model and the topology optimization result is within 5%, indicating that the proposed method has excellent reconstruction accuracy.

To address the issue that traditional direct and indirect deformation monitoring methods are difficult to meet the requirements of high-precision real-time deformation monitoring of structures, a digital twin-driven high-precision reconstruction method for full-field deformation of structure is proposed. Firstly, a multi-directional digital twin strain field is constructed by integrating simulation and measured strain data, which reduces the influence of load deviation and other factors on the simulation reliability, and ensures the strain accuracy of the deformation reconstruction. Secondly, a modal coordinate solving and deformation reconstruction method considering multi-directional strain is proposed. By incorporating multi-directional strain in the modal coordinate solving process, this method solves the problem of insufficient stability of the reconstruction results when single-directional strain is used, and improves the stability of the full-field deformation reconstruction of the structure. Finally, based on the proposed method, the experimental validation is carried out with the wing structure, and the results are compared with the simulation and the deformation reconstruction results of the traditional modal superposition method. The result shows that the proposed method has a higher advantage of reconstruction accuracy in the place of large deformation. The relative error between the reconstruction results of the proposed method and the measured deformation is less than 0.8%, and the absolute error is less than 0.09 mm, which is 7% and 12% lower than that of the traditional modal method and the simulation method, respectively. At the same time, the average relative error of the proposed method for deformation reconstruction at multiple measurement points is 1.2%, which is 5.7% and 9.3% lower than that of the traditional modal method and simulation analysis method, respectively. Moreover, when the number of strain gauges is small, the deformation reconstruction accuracy of the proposed method is improved by more than one order of magnitude compared with the traditional modal superposition method, which indicates that the proposed method has higher deformation reconstruction accuracy and stability.

Experimental validation and numerical simulation are two typical methods for evaluating the structural strength. However, the experimental validation method based on sparse sensors is difficult to ensure the coverage of the structural stress monitoring, and the numerical simulation method may lead to the insufficient accuracy of stress results due to the simplification and idealization of physical entities. Therefore, it is a challenging issue to comprehensively utilize the advantages of these two methods of strength evaluation and carry out data fusion to achieve the full-field structural stress monitoring. In this study, the Digital Twin for Structural Static Test Monitoring (DT-SSTM) method is proposed, which can obtain a high-precision digital twin model of structural static test to realize the real-time monitoring of the structural stress fields and the structural strength evaluation. The DT-SSTM method includes two stages: offline and online stages. In the offline stage, the Gradient Boosting Decision Tree (GBDT) algorithm is used to train the simulation data and build a pre-trained model. In the online stage, based on the ensemble learning concept, the Stacking algorithm is used to train the residuals between the response values of the experimental data and the response values of the pre-training model, and then the residual model is established. Multi-source data fusion is carried out by combining the pre-trained model with the residual model to establish a high-precision digital twin model. Finally, the open-hole rectangular plate under axial tension is tested to validate the effectiveness of the DT-SSTM method.Results show that the DT-SSTM method can establish a high-precision digital twin model of the structural static test with higher global prediction accuracy, local prediction accuracy and data fusion efficiency compared with the similar data fusion methods, providing a novel solution for the real-time monitoring of structural stress fields.

Due to the extreme lightweight requirements of surface components in compact design space, this paper proposed a data-driven shape-topology optimization method of curved shells, which consists of three stages, namely the offline stage, the online stage and the update stage. First, in the offline stage, the Latin hypercube sampling method is used to extract the sample points from the design space, and the mesh deformation technique is used for modeling to obtain the mesh model corresponding to the sample points. Then, topology optimization was carried out on the mesh models to obtain the optimized strain energy. Based on the sample data obtained in the above steps, the radial basis function surrogate model is trained, where the shape design variable is the input and the strain energy after topology optimization is the output. In the online stage, optimization is carried out based on the surrogate model obtained in the offline stage, and the covariance matrix adaptive evolution strategy is adopted to improve the optimization efficiency. In the update stage, the real response of optimization results of the surrogate model is calculated and added to the sample dataset to update the surrogate model. Finally, the algorithm is verified by a simply supported beam and a spacecraft cabin door. The results show that compared with the topology optimization with the fixed shape, the strain energy obtained by the proposed method can be reduced by 20.08% and 37.93%, respectively, indicating that the proposed method has better design capability.