Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.

This paper introduces the realization and wind tunnel testing of a novel variable camber wing equipped with compliant morphing trailing edges. Based on the aerodynamic shape and compliant mechanisms that were optimized in advance, a wind tunnel model called mTE4 was developed, in which the rigid leading edge, rigid wing box, and compliant trailing edge were manufactured by 3D printing technology using three different materials. Due to difficulties in the detailed design of a small-scale model, special attention is devoted to the implementation procedure. Additionally, the static and dynamic characteristics of the proposed wind tunnel model were evaluated by ground tests, and the aerodynamic characteristics were evaluated by numerical methods. Then, the aerodynamic performance and the static aeroelastic deformation of the compliant trailing edge were investigated in a low-speed wind tunnel. The load-bearing ability of the proposed compliant morphing trailing edge device was validated and the continuous outer mold surface was found to persist throughout the entire testing period. Notably, a maximum deflection range of 37.9° at the airspeed of 15 m/s was achieved. Additionally, stall mitigation was also achieved by periodically deflecting the morphing trailing edge, enabling a stall angle delay of approximately 1° and 13% increase in post-stall lift coefficient. Finally, the development procedure was validated by comparing the lift between numerical and experimental results.

Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

This article presents a gradient-based aerodynamic optimization framework and investigates optimum deformations for a transonic airfoil equipped with morphing leading and trailing edges. Specifically, the proposed optimization framework integrates an innovative morphing shape parameterization with a high fidelity Reynolds-averaged Navier–Stokes computational fluid dynamic solver, a hybrid mesh deformation algorithm, and an efficient gradient evaluation method based on continuous adjoint implementation. To achieve a feasible morphing shape, some structural properties of skin and wing-box constraints were introduced into the morphing shape parameterization, which offers skin length control and enables wing-box shape invariance. In this study, the optimum leading and trailing edge deformations with minimization of drag at this cruise stage were searched for using the adjoint-based optimization with a nested feasible morphing procedure, subject to the wing-box, skin length, and airfoil volume constraints. The numerical studies verified the effectiveness of the optimization strategy, and demonstrated the significant aerodynamic performance improvement achieved by using the morphing devices. A lambda shock pattern was observed for the optimized morphing leading edge. That result further indicates the importance of leading edge radius control.