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Glutathione (GSH) plays a crucial role in various physiological processes and its imbalances are closely related to various pathological conditions. Probes for detection and imaging of GSH are not only useful for understanding GSH chemical biology but are also important for exploring potential theranostic agents. Herein, we report a fast intramolecular thiol-activated arylselenoamides (FITA)-based fluorescent probe using 2,4-dinitrophenyl alkylthioether as a sulfydryl-selective receptor for the first time. The fluorescence of the probe was low due to the double effects of PET, while the probe exhibits an 86-fold fluorescence enhancement at 460 nm after GSH activation and a detection limit of 0.95 μM. Furthermore, the probe is low-toxic and capable of imaging cellular GSH. This work further expands the design and applicability of the FITA-based platform, offering a new thiol-deprotection strategy for development of fluorescent probes. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed. [ABSTRACT FROM AUTHOR]

In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the test datasets, we conducted an in-depth analysis of the geometric attributes of the cascades reconstructed using our framework, considering adjustments made to the geometric information of the point cloud. We fine-tuned the input using 1603 data points and quantified the contribution of each point. The outcomes reveal that variations in the suction side of the cascade have a significantly more substantial influence on the field results compared to the pressure side and explain the way graph neural networks work for cascade flow-field prediction, enhancing the comprehension of graph-based flow-field prediction among developers and proves the potential of graph neural networks in flow-field prediction on large-scale point clouds and design. [ABSTRACT FROM AUTHOR]

As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles. [ABSTRACT FROM AUTHOR]

Due to the harsh operating environment of aero-engines, a surface structure that provides excellent aerodynamic performance is urgently required to save energy and reduce emissions. In this study, microgroove polyurethane coatings fabricated by chemical synthesis are investigated in terms of their effect on aerodynamic performance, which is a new attempt to investigate the impact on aerodynamic performance of compressor cascade at transonic speeds. This method reduces manufacturing and maintenance cost significantly compared with traditional laser machining. Wake measurements are conducted in the high-speed linear compressor cascade wind tunnel to evaluate the performance of cascade attached with different microgroove polyurethane coatings. Compared with the Blank case, the microgroove polyurethane coatings have the characteristic of reducing flow loss, with a maximum reducing rate of 5.87% in the area-averaged total pressure loss coefficient. The mechanism of flow loss control is discussed through analyzing the correlation between the total pressure distribution and turbulence intensity distribution. The results indicate that a large quantity of energy loss in the flow field due to turbulence dissipation and the reduction in viscous drag by microgroove polyurethane coatings relates to its effect on turbulence control. This paper demonstrates a great perspective on designing micro-nano surface structure for aero-engine applications. [ABSTRACT FROM AUTHOR]

The multi-objective optimization of compressor cascade rotor blade is important for aero engine design. Many conventional approaches are thus proposed; however, they lack a methodology for utilizing existing design data/experiences to guide actual design. Therefore, the conventional methods require and consume large computational resources due to their need for large numbers of stochastic cases for determining optimization direction in the design space of problem. This paper proposed a Reinforcement Learning method as a new approach for compressor blade multi-objective optimization. By using Deep Deterministic Policy Gradient (DDPG), the approach modifies the blade profile as an intelligent designer according to the design policy: it learns the design experience of cascade blade as accumulated knowledge from interaction with the computation-based environment; the design policy can thus be updated. The accumulated computational data is therefore transformed into design experience and policies, which are directly applied to the cascade optimization, and the good-performance profiles can be thus approached. In a case study provided in this paper, the proposed approach is applied on a blade profile, which is thus optimized in terms of total pressure loss and laminar flow area. Compared with the initial profile, the total pressure loss coefficient is reduced by 3.59 % , and the relative laminar flow area at the suction surface is improved by 25.4 % . [ABSTRACT FROM AUTHOR]