RESUMEN
The physics governing the fluid dynamics of bio-inspired flapping wings is effectively characterized by partial differential equations (PDEs). Nevertheless, the process of discretizing these equations at spatiotemporal scales is notably time consuming and resource intensive. Traditional PDE-based computations are constrained in their applicability, which is mainly due to the presence of numerous shape parameters and intricate flow patterns associated with bionic flapping wings. Consequently, there is a significant demand for a rapid and accurate solution to nonlinear PDEs, to facilitate the analysis of bionic flapping structures. Deep learning, especially physics-informed deep learning (PINN), offers an alternative due to its great nonlinear curve-fitting capability. In the present work, a hybrid coarse-data-driven physics-informed neural network model (HCDD-PINN) is proposed to improve the accuracy and reliability of predicting the time evolution of nonlinear PDEs solutions, by using an order-of-magnitude-coarser grid than traditional computational fluid dynamics (CFDs) require as internal training data. The architecture is devised to enforce the initial and boundary conditions, and incorporate the governing equations and the low-resolution spatiotemporal internal data into the loss function of the neural network, to drive the training. Compared to the original PINN with no internal data, the training and predicting dynamics of HCDD-PINN with different resolutions of coarse internal data are analyzed on the problem relevant to the two-dimensional unsteady flapping wing, which involves unsteady flow features and moving boundaries. Additionally, a hyper-parametrical study is conducted to obtain an optimal model for the problem under consideration, which is then utilized for investigating the effects of the snapshot and fraction of the coarse internal data on the HCDD-PINN's performances. The results show that the proposed framework has a sufficient stability and accuracy for solving the considered biomimetic flapping-wing problem, and its great potential means that it can be considered as an alternative to accelerate or replace traditional CFD solvers in the future. The interested variables of the flow field at any instant can be rapidly obtained by the trained HCDD-PINN model, which is superior to the traditional CFD method that usually needs to be re-run. For the three-dimensional and optimization problems of flapping wings, the advantages of the proposed method are supposedly even more apparent.
RESUMEN
In this study, two novel fluorine-functionalized crystalline covalent organic frameworks (COFs), namely DF-TAPB-COF and DF-TATB-COF, were synthesized, and their ordered structure, porosity, suitable pore size, and abundant fluorine groups were expected to serve as effective carriers in drug delivery. The excellent cell viability of DF-TAPB-COF and DF-TATB-COF was verified using MTT assays. Both COFs exhibited very high loading capacities in terms of drug loading performance, in particular the drug loading rate of DF-TAPB-COF for 5-fluorouracil (5-FU) was up to 69%. They also exhibited efficient drug release performance in a simulated body fluid environment. Cell endocytosis experiments demonstrated that DF-TAPB-COF and DF-TATB-COF could be effectively endocytosed by cells. Hence, this study offers new insight into the design and development of COF-based drug carrier systems.
RESUMEN
Noise reduction is an important development direction for aircrafts and wind turbines. Owl wings have three unique morphological characteristics (leading-edge serrations, trailing-edge serrations and velvet-like surfaces) that effectively suppress aerodynamic noise in low Reynolds numbers. Among them, trailing-edge serrations are widely considered the most effective noise-reduction method. Although different serrations have been studied, the quantitative relationship and influence mechanism between the serration shape, wavelength and amplitude are poorly understood. The acoustic characteristics of asymmetrical aerofoils with different trailing-edge serrations have not been fully studied. This work investigates the flow characteristics and acoustic scattering mechanisms of novel owl-based aerofoils with different trailing-edge serrations. A sensitivity analysis is utilized to quantitatively investigate the influence and interaction mechanisms of the shape, wavelength and amplitude in trailing-edge noise reduction. Numerical simulations of the transient flow over the aerofoil are performed via the large eddy simulation method, and the acoustic far-field is obtained by solving the Ffowcs Williams and Hawkings equation. The results indicate that the sawtooth and sinusoidal serrations provide the most significant noise reduction effects; the maximum noise reduction is 8.74 dB. The wavelength and amplitude play similar roles, but the amplitude has relatively greater influence. For the sawtooth and sinusoidal serrations, the large-scale vortex structures are broken into many small-scale spiral vortex structures due to the presence of the sharp serration tip. The serrations can effectively reduce the coherence of the turbulent fluctuations due to spanwise variations in the edge and may be the main reason for noise suppression. The original owl-based aerofoil generates more low-frequency noise and less high-frequency noise than aerofoils with trailing-edge serrations. The peak noise frequencies of all aerofoils are approximately 400 Hz; hence, low-frequency noise is a dominant influence in noise generation. Furthermore, the acoustic sources generated by transient pressure fluctuations are mainly located on the serration root.
