An Analysis of Two Fluid Layers Enclosed Between Two Non-Porous Surfaces

The stability of a two-phase interface is a crucial occurrence that involves the design of many engineering applications. It correlates the spatial and droplet size-distributions of many fluid spraying applications and has a great effect on the estimations of the critical heat flux of systems that involves phase change or evaporation. However, the existing hydrodynamic models are only able to predict the stability of a plane fluid sheet, surrounded by an infinite pool of liquid. The case of a thin sheet of liquid surrounding a vapor sheet and enclosed between two walls has not been studied yet. The present paper solves this problem using a linearized stability analysis. Velocity potentials satisfying these conditions are introduced and a complete analysis is presented.

Fluid Restriction Negatively Influences Perceived Morning Alertness and Visuomotor Ability

The purpose of this study was to assess the effect of two fluid intake protocols on alertness and reaction time before and after fluid intake. Healthy college-age males (n = 12) followed two fluid intake protocols on separate occasions: (1) prescribed fluid (PF) and fluid restricted (FR). In PF, participants were instructed to consume 500 mL of fluid the night prior to and the morning of data collection. In FR, participants were instructed to refrain from the consumption of fluid for 12 h. To assess hydration status, urine specific gravity and urine color were measured. Participants perceived level of thirst and alertness were also recorded. Participants then completed visuomotor reaction time tests using the Dynavision LED board, using both a central visuomotor test and a peripheral visuomotor test (PVRT) prior to (1) and following (2) the ingestion of 100 mL of water. Participants displayed significantly improved PVRT in PF state as compared to FR (PF1 = 1.13 ± 0.16, PF2 = 1.04 ± 0.14; FR1 = 1.27 ± 0.27, FR2 = 1.18 ± 0.20; p = 0.038, ηp2 = 0.363). Both CVRT and PVRT improved over time, following the ingestion of 100 mL of fluid. Participants in the PF state were also significantly more alert than participants in the FR state (PF = 4 ± 2, FR = 5 ± 2; p = 0.019, ES = 0.839). Collectively, perceived alertness and PVRT were negatively impacted by FR.

Machine Learning Augmented Two-Fluid Model for Segregated Flow

Segregated flow, including stratified and annular flows, is commonly encountered in several practical applications such as chemical, nuclear, refrigeration, and oil and gas industries. Accurate prediction of liquid holdup and the pressure gradient is of great importance in terms of system design and optimization. The current most widely accepted model for segregated flow is a physics-based two-fluid model that treats gas and liquid phases separately by incorporating mass and momentum conservation equations. It requires empirically derived closure relationships that have the limitation of being applicable only under a narrow range of input parameters under which they were developed. In this paper, we proposed a more generalized machine learning augmented two-fluid model, using a database that spans the range of various flowing conditions and fluid properties. Machine learning algorithms such as random forest, neural networks, and gradient boosting were tested for the best performing data-driven predictive model. The new model proposed in this work successfully captures the complex, dynamic, and non-linear relationships between the friction factor and flowing conditions. A comprehensive model evaluation against nineteen existing correlations shows the best results from the proposed model.