Precise measurement is the basis for the precise assembly of aeroengine rotors. However, due to the limitations of data application in the measurement process and the insufficient digitization of auxiliary assembly, the guided assembly after precise measurement heavily relies on manual analysis and operation. Moreover, due to the difficulty of fully utilizing measurement data of aeroengine rotors to guide assembly in existing assembly methods, it is hard to unify measurement and assembly processes, which limits the efficiency of aeroengine rotors assembly. According to the real-time response and high-fidelity interaction capability of the digital twin (DT) system, a coaxiality prediction model of aeroengine multistage rotors assembly and a cloud-based measurement data rapid interaction are established. Based on the large coaxiality measuring instrument, a DT system with measurement and real-time guidance for assembly is designed. Augmented reality device is used as the “sixth dimension” of the DT system to realize the digital measurement and assembly of aeroengine rotors. The experiment uses a certain type of engine three-stage rotors for measurement and assembly. The experimental results show that the large-scale coaxiality measuring instrument DT system can carry out accurate measurement and real-time assembly guidance for the engine, and achieve breakthroughs in engine assembly quality in multiple metrics, which significantly broadens the application dimension of precision measuring instruments.

Thin-film diffractive optical elements (DOEs) have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency (DIE) was introduced into the model to remove the nondesign level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a DIE of less than 60% was found to significantly impact the final image products.

The safety monitoring of transportation infrastructure foundation is crucial for the sustainable service of transportation systems. In recent years, the Ground Penetrating Radar (GPR) has become a powerful tool to identify and locate the subgrade distresses according to the different responses of wave characteristics, preliminarily realizing an intelligent nondestructive detection. To solve the problems like small sample size and unbalanced dataset, this study used a deep data augmentation method, e.g. WGAN-GP network, to augment the original limited B-Scan GPR data of subgrade, and then carried out supervised learning for classification task. The detailed computation steps include the image processing, data augmentation and intelligent analysis. First, the dataset was initially enlarged through the traditional methods after noise filtering, gamma transform and other processing methods. Then, the WGAN-GP network was adopted to generate new high-quality B-Scan images. Finally, the intelligent classification of subgrade distresses was realized by ResNet50 model with a satisfactory accuracy of 90.85%.

Diffractive membrane imaging systems have been an important development trend for high-orbit satellite cameras owing to their advantages of large aperture, light weight, rapid manufacture, and low cost. However, caused by the cross-coupling effects of diffraction imaging, membrane properties, subaperture stitching, on-orbit disturbances, and other physical factors, lager-aperture space diffractive membrane imaging systems have specific and complex degradation characteristics: the modulation transfer function (MTF) and signal-to-noise ratio (SNR) have more prominent degradation and serious space-variant characteristics over fields of view, with obvious background radiation properties that seriously affect the application of imaging products. To address this problem, this study established a global information transmission model by characterizing the PSF and background radiation in a full field of view to represent the imaging law of an on-orbit system. Aiming at the inverse problem of the information transmission model, we also propose a novel image inversion restoration method for the special degradation characteristics. In particular, the effect of diffraction efficiency is introduced into the inversion restoration method to solve the background radiation problem. Moreover, we innovatively designed matrix regularization parameters to further improve the correction ability of spatial variation. When the diffraction efficiency was experimentally higher than 60% and the mean measured spatial variability was less than 0.2, the proposed method exhibited a satisfactory processing performance, and could improve multiobjective comprehensive processing, such as transfer function compensation, spatial variation correction, and background radiation removal.

Borehole Heat Exchanger (BHE) can be installed in different infrastructures to store or extract energy, such as pavement cooling in summer and de-icing in winter. Although there are approaches to estimating the performance of a BHE based on analytical, semi-analytical or numerical simulation methods, predicting the performance of a BHE in the perspective of Machine Learning (ML) is still necessary. In this investigation, four ML algorithms including Linear Regression (LR), Polynomial Regression (PR), Artificial Neural Network (ANN) and Random Forest (RF) were used to explore the underlying relationship between the Heat Extraction Rate (HER) and the influencing factors. In total, the annual HERs of 400 Thermal Performance Tests (TPTs) covering 12 major factors were computed in a validated numerical simulation framework. After providing necessary database, the 4 ML approaches were trained and evaluated. The results showed that the trained PR approach had the best performance. Specifically, the trained PR approach had a Root-Mean-Square Error (RMSE) lower than 1.74$\text{W}{}\cdot {}\text{m}$−1, with the Coefficient of Determination (R2) higher than 0.99. Additionally, the trained approach was compared to an in-situ TPT in Shijiazhuang (China) and two numerical simulated scenarios in Alsace (France). The results showed that the trained PR approach predicted well the in-situ TPT by having 3$\text{W}{}\cdot {}\text{m}$−1 of RMSE, and it predicted correctly the numerical simulated HER of a BHE with 0.09 and 0.72$\text{W}{}\cdot {}\text{m}$−1 of RMSEs for two scenarios with 5 and 1 °C of inlet fluid temperatures. The proposed prediction model can be applied to estimate the performance of a BHE, providing another option to fast evaluate the BHE performance.