To meet the requirements of high integration and multifunctionality, high-performance low-temperature co-fired ceramic (LTCC) materials were prepared from the CaO-B2O3 glass-ceramic system. The glass forming range of CaO-B2O3 glass is that the B2O3 content >= 62 mol%. With the increase of B2O3 content, the reduction of [BO3] chain structure and the increase of hexagonal ring structure in the parent glass cause the primary crystalline phase to transform from CaB2O4 to CaB4O7. After sintering at 730 degrees C for 5 h, the glass-ceramic with a B2O3 content of 64 mol% has a minimum crystallization activation energy of 676.61 kJ/mol, exhibiting a high content of CaB2O4 crystals. The precipitation of CaB2O4 crystals, and their fine, dense, uniform microstructure endow glass-ceramic with optimal microwave dielectric properties of epsilon(r) = 5.6, Q & times; f = 46,582 GHz, and tau(f) = -56 ppm/degrees C. Meanwhile, the CaO-B2O3 glass-ceramic has a good chemical compatibility with Al and Ag, making it a potential candidate for LTCC applications.
Due to the complexity of the operating environment and the diversity of components, fault maintenance and detection of aircraft engines have always been a huge challenge. To improve the reliability and maintenance efficiency of aircraft engines, a multi-channel, multi-scale, and graph relational meta learning intelligent detection method is proposed. By combining multi-channel data fusion, multi-scale feature extraction, graph neural networks, and meta learning strategies, a comprehensive understanding of fault characteristics and their propagation relationships is achieved. In the research results, the proposed multi-channel, multi-scale, and graph relation meta learning intelligent detection method has better performance, with a localization accuracy of 0.94 and an average localization error of 0.11. Meanwhile, its training time is 15.2 min, and inference time is 49.65 milliseconds, demonstrating high computational efficiency. In addition, the multi-channel multi-scale and graph relation meta learning intelligent detection method has shown good robustness under different testing conditions. The results show that this method not only has significant advantages in the accuracy and efficiency of fault detection, but also has excellent fault localization performance, significantly reducing the safety risks and economic losses caused by aircraft engine failures. The research provides a theoretical basis for real-time fault monitoring and maintenance decision-making of aircraft engines, which is beneficial for improving the efficiency and quality of maintenance work.
To address the issue of precise tracking of strong maneuvering targets, traditional Constant Velocity (CV) and Constant Acceleration (CA) models struggle to accurately describe their complex motion characteristics, leading to a sharp decline in the performance of Kalman Filtering (KF) algorithms based on these models. This paper first conducts an in-depth analysis of the algorithmic framework combining the "Current Statistical" model with standard Kalman Filtering (CS-KF). This model can adaptively adjust the process noise to reflect the target's maneuvering changes in real time. To further improve tracking accuracy and robustness, this paper explores a fusion strategy of the Interacting Multiple Model (IMM) algorithm with Unscented Kalman Filtering (IMM-UKF) to handle model uncertainty and nonlinearity. Through comparative analysis using Monte Carlo simulation experiments, the results demonstrate that in strong maneuvering scenarios, the CS-KF algorithm significantly outperforms the traditional KF. Moreover, the IMM-UKF algorithm, through multi-model interaction and more precise nonlinear processing, achieves higher tracking accuracy and faster convergence speed, establishing itself as an effective solution for tracking complex maneuvering targets.
Carbon fiber epoxy composites containing graphene oxide have high shape resilience but feature complex compositions and structures. Theoretical research on their mechanical properties and damage mechanisms is quite scarce. To this end, the current study generated the random fiber distribution corresponding to the composite material to represent its volume element. The macromechanical properties and microdamage modes of the material were predicted. The coupling matrix derived through micromechanical modeling and analysis was incorporated into the proposed mathematical model of the shape recovery force of the composite material. The experimental and theoretically predicted values of the shape recovery (restoring) force were 7.9 and 8.02 N, proving the proposed model’s feasibility and good accuracy. The results provide theoretical guidance for designing smart composite materials that require high shape resilience.
CorChina is rich in shale gas resources, and the Youjiang Basin in Guangxi is a major potential area and exploration region for shale gas. To determine the depth of the gas-rich layers in the Youjiang Basin of Guangxi and delineate prospective areas, potential field methods were used to explore the Youjiang Basin. Combined with geological and drilling data from the Youjiang Basin, the non-seismic geophysical data were processed and comprehensively studied. Gravity data were fitted quantitatively using 2.5-dimensional visualization forward modeling, and magnetotelluric data were inverted using two-dimensional nonlinear conjugate gradient (NLCG) inversion. This yielded the density and electrical resistivity distribution characteristics within the study area of the Youjiang Basin. The basement, structural features, spatial distribution of mudstone and shale, as well as the thickness and burial depth of the target layers, were evaluated. The results show that the top interface of the Luzhai Formation, the main shale gas reservoir, is buried at depths ranging from 0 to 2600 m, with an average depth of approximately 1000 to 1500 m. The top interface of the Luofu Formation is buried at depths between 550 and 3900 m, with an average depth of around 2000 to 2800 m. potential field methods have proven effective, reliable, and credible in detecting shale gas in the Youjiang Basin of Guangxi. These methods have revealed the geological structures of the shale gas reservoirs in the Youjiang Basin, providing important geophysical evidence for evaluating shale gas prospective areas in this area.
