Dr Pouria Amani

Surfactant-stabilized foams have been at the centre of scientific research for over a century due to their ubiquitous applications in different industries. Many of these applications involve inorganic salts either due to their natural presence (e.g. use of seawater in froth floatation) or their addition (e.g. in cosmetics) to manipulate foam characteristics for the best outcomes. This paper provides a clear understanding of the effect of salts on surfactant-stabilized foams through a critical literature survey of this topic. Available literature shows a double effect of salts (LiCl, NaCl and KCl) on foam characteristics in the presence of surfactants. To elucidate the underlying mechanisms of the stabilizing effect of salts on foams, the effect of salts on surfactant-free thin liquid films is first discussed, followed by a discussion on the effect of salts on surfactant-stabilized foams with the focus on anionic surfactants. We discuss two distinctive salt concentrations, salt transition concentration in surfactant-free solutions and salt critical concentration in surfactant-laden systems to explain their effects. Using the available data in literature supported by dedicated experiments, we demonstrate the destabilizing effect of salts on foams at and above their critical concentrations in the presence of anionic surfactants. This effect is attributed to retarding the adsorption of the surfactant molecules at the interface due to the formation of nano and micro-scale aggregates.

The enhancement of heat transfer between parallel surfaces, including parallel plates, parallel disks, and two concentric pipes, is vital because of their wide applications ranging from lubrication systems to water purification processes. Various techniques can be utilized to enhance heat transfer in such systems. Adding nanoparticles to the conventional working fluids is an effective solution that could remarkably enhance the heat transfer rate. No published review article focuses on the recent advances in nanofluid flow between parallel surfaces; therefore, the present paper aims to review the latest experimental and numerical studies on the flow and heat transfer of nanofluids (mixtures of nanoparticles and conventional working fluids) in such configurations. For the performance analysis of thermal systems composed of parallel surfaces and operating with nanofluids, it is necessary to know the physical phenomena and parameters that influence the flow and heat transfer characteristics in these systems. Significant results obtained from this review indicate that, in most cases, the heat transfer rate between parallel surfaces is enhanced with an increase in the Rayleigh number, the Reynolds number, the magnetic number, and Brownian motion. On the other hand, an increase in thermophoresis parameter, as well as flow parameters, including the Eckert number, buoyancy ratio, Hartmann number, and Lewis number, leads to heat transfer rate reduction.

The current study deals with modeling of CO2 solubility in various deep eutectic solvents (DESs). Various artificial intelligent methods including PSO-ANN, PSO-ANFIS, LSSVM, and newly proposed correlation were developed, examined and comparatively evaluated. Comprehensive data were gathered from literature and LSSVM and MPR models were found robust and precise models for estimating CO2 solubility in DESs.

Air-breathing is known as a way to reduce the weight, volume, and the cost of PEMFCs. In this study, the thermal management of the high-powered air-breathing PEMFC stacks by applying different cathode flow channel configurations is carried out to improve the stack performance. In order to verify the thermal management results, numerical simulation is also performed. The research results show that a combination of the 50% and 58.3% opening ratios in the air-breathing stack reduces the stack temperature and enhances the temperature distribution uniformity, leading to a better and more stable stack performance. In addition, it is found that the stack performance is significantly improved under the assisted-air-breathing condition. Moreover, the simulation results and the experimental data are basically consistent. It is suggested to adopt the average temperature over the cross-sectional flow region from simulation as fitting the simulation results and the measured data.

Least square support vector regression (LSSVR) is a powerful data-driven method for simulation and forecasting, with two parameters to tune. In this study, these parameters were automatically tuned using the interior search algorithm (ISA) and genetic algorithm (GA). The main purpose is in situ simulation and forecast of monthly groundwater level in Karaj plain, Iran, using historical groundwater level, precipitation, and evaporation data. The results of the interior search algorithm-least support vector regression (ISA-LSSVR) and genetic algorithm-least support vector regression (GA-LSSVR) compared with genetic programming (GP) and adaptive neural fuzzy inference system (ANFIS). Based on average Nash-Sutcliffe criterion, the results revealed that the ISA-LSSVR improves the simulation and forecasting accuracy compared to other methods. Also, the results of the different model structure selection indicate that including precipitation and evaporation does not necessarily improve simulation and forecasting accuracy, but it would increase uncertainty. This increase suggests that groundwater level in the case study is affected by groundwater flow, recharge from leaky urban water infrastructure, and reduced recharge from precipitation due to impervious surfaces in urban areas rather than being solely governed by precipitation and evaporation. Finally, a sensitivity analysis was performed to assess the impacts of optimization algorithm parameters on the simulation and forecasting accuracy. The results indicate high and low sensitivity associated with GA and ISA, respectively. In conclusion, ISA-LSSVR was suggested as the best model due to computational efficiency, low sensitivity to its parameters, and high accuracy compared to other methods.

This article aims to investigate the influence of imposed flow rate oscillation on the bubble behaviors of evaporative flow of R-134a in horizontal narrow annular ducts. Particularly, the effects of amplitudes and periods of the oscillating flow rates along with the vapor quality of the R-134a on the bubble characteristics are examined in details. Moreover, the R-134a flow photos captured at the middle axial location are presented to illustrate the shift between the annular two-phase flow pattern and the bubble nucleation at different operating conditions and time instances. The observations reveal that the imposed mass flow rate oscillation results in the fluctuation of bubble departure diameter and frequency along with the density of active nucleation sites with the same frequencies. The larger amplitudes and longer periods of the mass flow rate oscillation lead to the stronger fluctuations in bubble dynamics. Furthermore, it is found that the bubble nucleation on the heating surface is the dominant mechanism at the low vapor quality of 0.05. However, the annular two-phase flow prevails over a relatively large portion of the periodic cycle at the dryness fraction of 0.5 and over the entire cycle at the dryness fraction of 0.95. Finally, the evaporative heat transfer of R-134a subject to the flow rate oscillation is correlated as a function of the mass flow rate and the vapor quality.