We investigate experimentally and theoretically the interactions between a cavitation bubble and a hemispherical pendant oil droplet immersed in water. In experiments, the cavitation bubble is generated by a focused laser pulse right below the pendant droplet with well-controlled bubble–wall distances and bubble–droplet size ratios. By high-speed imaging, four typical interactions are observed, namely: oil droplet rupture; water droplet entrapment; oil droplet large deformation; and oil droplet mild deformation. The bubble jetting at the end of collapse and the migration of the bubble centroid are particularly different in each bubble–droplet interaction. We propose theoretical models based on the method of images for calculating the Kelvin impulse and the anisotropy parameter which quantitatively reflects the migration of the bubble centroid at the end of the collapse. Finally, we explain that a combination of the Weber number and the anisotropy parameter determines the regimes of the bubble–droplet interactions.

Optimizing the structure of micromixers to improve the mixing efficiency is of great significance for chemical engineering and biology fields. In this study, an optimization of the microchannel in two liquids mixing is carried out based on computational fluid dynamics (CFD) and response surface methodology. Firstly, CFD simulations were performed to investigate the mixing flow field and mixing efficiency in the microchannel by considering different process and structure parameters (e.g., feed pressure p, microchannel width w). The response surface methodology was adopted to construct a fitting surface by CFD discrete working conditions. Then, an optimized microchannel width w was searched using the parallel particle swarm optimization (PPSO) algorithm from the response surface. Lastly, the searched optimum was validated by CFD simulation again, and the final result showed that the predicted mixing efficiency from the response surface model is well confirmed by CFD simulation. On average, the new optimized microchannel width of 1.634 mm performs higher flow flux and mixing efficiency than the original width of 1.5 mm, increasing 13.51% and 2.45%, respectively.

Advection–diffusion–Langmuir adsorption (ADLA) presents a complex problem in chemical engineering and biomedicine fields. This transport phenomenon can be described by the advection–diffusion–reaction (ADR) equations, which traditionally require intensive computational load at extreme conditions. In this paper, physics-informed neural networks (PINNs) are applied to solve the ADR equations due to their mesh-free and computationally efficient nature. Six cases are examined, including both diffusion-dominated and advection-dominated cases with varying Péclet numbers Pe and aspect ratios λ. To ensure stability and efficiency during training with the Adam optimizer, the gradients of the loss function are analyzed. Key gradient terms causing instability are identified, leading to recommendations for lower weights for these gradient terms. The validation results show that compared to the finite difference method, PINN achieves a concentration field error within 10% and an average adsorption amount error within 7.2% for diffusion-dominated cases. For advection-dominated cases, the errors are within 20% and 2.5%, respectively. In conclusion, PINNs can offer an efficient and accurate technique for solving ADR equations.

Invasive procedure is a prerequisite for studying coronary physiology. We established the measurement of non-invasive physiological parameters including coronary blood flow (CBF), flow velocity, and microvascular resistance using coronary computed tomography angiography (CCTA). Vessel-specific CBF was derived from transluminal attenuation flow encoding (TAFE) and then tested using three separate datasets consisted of computational simulation, human perfusion CT, and human CCTA. TAFE-derived CBF correlated well with measured vessel-specific myocardial blood flow and CBF. TAFE-derived CBF per myocardial mass consistently decreased with the progressive severity of stenosis, and it was found to better to detect significant stenosis than transluminal attenuation gradient (TAG). With the addition of vessel anatomy, TAFE-derived CBF could calculate flow velocity and microvascular resistance. The results of non-invasively acquired parameters according to the severity of stenosis were similar to those obtained through invasive physiology studies. Our study demonstrated that non-invasive comprehensive coronary physiology parameters can be derived from CCTA without any pre-specified condition or performing complex heavy computational processes. Our findings are expected to expand the clinical coverage of CCTA and coronary physiology.