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Though single droplet impact dynamics was extensively investigated, the complex hydrodynamics involved in the simultaneous interaction of droplets with a loosely held dense solid particle on a hydrophobic substrate has received less attention. In this paper, the authors report the impingement response of a water droplet colliding on a non-fixed spherical steel particle placed on a hydrophobic surface and the subsequent creation of a particle-laden compound droplet. Utilizing the kinetic energy of the water drop, the heavy metal bead is picked up from the surface during the droplet bouncing. Here, a dense metal spherical mass is located on a hydrophobic substrate having contact angle, θ c = 140 ° and drop collides with a Weber number range of 8.20 ≤ We ≤ 38.07. During the droplet spreading, a thin film is developed between the particle and the hydrophobic plate due to capillary action and the particle is engulfed inside the droplet while it recoils, thereby a compound droplet is created. For instance, during the rebound of a composite drop, the metal bead, having one-third the mass of the droplet, is elevated to a height of 2.5 times its diameter at We = 24.5. Phenomenological models are developed for the prediction of compound droplet rebound height and the minimum Weber number required for the composite drop creation, and it exhibited good accord with experimental observations. These results shed more light on the self-cleaning mechanism involving dense particles and provided a promising strategy for the production of a solid–liquid composite droplet. [ABSTRACT FROM AUTHOR]

The design of low-frequency sound absorbers with broadband absorption characteristics and optimized dimensions is a pressing research problem in engineering acoustics. In this work, a deep neural network based inverse prediction mechanism is proposed to geometrically design a Helmholtz resonator (HR) based acoustic absorber for low-frequency absorption. Analytically obtained frequency response from electro-acoustic theory is deployed to create the large dataset required for training and testing the deep neural network. The trained convolutional neural network inversely speculates optimum design parameters corresponding to the desired absorption characteristics with high fidelity. To validate, the inverse design procedure is initially implemented on a standard HR based sound absorber model with high accuracy. Thereafter, the inverse design strategy is extended to forecast the optimum geometric parameters of an absorber with complex features, which is realized using HRs and a micro-perforated panel. Subsequently, a quasi-perfect low-frequency acoustic absorber having minimum thickness and broadband characteristics is deduced. Importantly, it is demonstrated that the proposed absorber, comprising four parallel HRs and a microperforated panel, absorbed more than 90% sound in the frequency band of 347–630 Hz. The introduced design process reveals a wide variety of applications in engineering acoustics as it is suitable for tailoring any sound absorber model with desirable features. [ABSTRACT FROM AUTHOR]

Real-time object identification and classification are essential in many microfluidic applications especially in the droplet microfluidics. This paper discusses the application of convolutional neural networks to detect the merged microdroplet in the flow field and classify them in an on-the-go manner based on the extent of mixing. The droplets are generated in PMMA microfluidic devices employing flow-focusing and cross-flow configurations. The visualization of binary coalescence of droplets is performed by a CCD camera attached to a microscope, and the sequence of images is recorded. Different real-time object localization and classification networks such as You Only Look Once and Singleshot Multibox Detector are deployed for droplet detection and characterization. A custom dataset to train these deep neural networks to detect and classify is created from the captured images and labeled manually. The merged droplets are segregated based on the degree of mixing into three categories: low mixing, intermediate mixing, and high mixing. The trained model is tested against images taken at different ambient conditions, droplet shapes, droplet sizes, and binary-fluid combinations, which indeed exhibited high accuracy and precision in predictions. In addition, it is demonstrated that these schemes are efficient in localization of coalesced binary droplets from the recorded video or image and classify them based on grade of mixing irrespective of experimental conditions in real time. [ABSTRACT FROM AUTHOR]