This paper presents a critical review of heat transfer applications of nanofluids. The effects of nanoparticle concentration, size, shape, and nanofluid flow rate on Nusselt number, heat transfer coefficient, thermal conductivity, thermal resistance, friction factor and pressure drop from numerous studies reported recently are presented. Effects of various geometric parameters on heat transfer enhancement of system using nanofluids have also been reviewed. Heat transfer devices covered in this paper include radiators, circular tube heat exchangers, plate heat exchangers, shell and tube heat exchangers and heat sinks. Various correlations used for experimental validation or developed in reviewed studies are also compiled, compared and analyzed. The pros and cons associated to the applications of nanofluids in heat transfer devices are presented in details to determine the future direction of research in this arena.

Recent advances in application of nanofluids in heat transfer devices: A critical review

Improving the cooling performance of automobile radiator with Al2O3/water nanofluid

The objective of this paper is to review the different techniques, which have been used to enhance the heat transfer rate in heat exchanger devices such as solar air heater, cooling blades of turbine and so on using single phase heat transfer fluids. The results of recent published articles with the development of new technologies such as Electrohydrodynamic (EHD) and Magnetohydrodynamics (MHD) are also included. Enhancement of heat transfer in heat exchanger can achieved by means of several techniques. These techniques are grouped into the active and passive method. In the active methods, system need some external power, however, passive method utilize surface modification either on heated surface or insertion of swirl devices in the flow field. Active methods are very complex because of external power supply, although these methods have great potential and can control thermally. Passive methods include artificial roughness, extended surface, winglets, insertion of swirl devices in the flow which alters the flow pattern causes to disturb the thermal boundary layer, and consequently high heat transfer. Passive methods are dominant over active methods because its can easily employed in existing heat exchangers. In this paper, an effort has been made to categorize the active and passive methods and review the various heat transfer techniques applied in heat exchangers. Important results have been listed for ready reference. It has been concluded that either active or passive methods have been employed alone. Based on literature, a combined method have also been recommended which include both active and passive methods.

A comprehensive review on single phase heat transfer enhancement techniques in heat exchanger applications

Numerical study of heat transfer performance of single-phase heat sinks with micro pin-fin structures

Numerical analysis of turbulent convection heat transfer from an array of perforated fins

Performance analysis of a heat exchanger having perforated square fins

Thermal performance analysis and optimum design parameters of heat exchanger having perforated pin fins

SO2 and PM10 are among the air pollutants that play the most effective role in air pollution. In the study, estimates were made in 2020 that these contaminants were affected by the Covid-19 pandemic using recurrent artificial neural networks, one of the deep learning approaches that are very popular today. Data belonging to Sakarya province, where industrialization is concentrated, was obtained from the air monitoring center of the Ministry of Environment and Urbanization, and modeling and optimization was carried out in Python software. The statistical analysis of the obtained SO2 and PM10 estimation results was made and when compared with the actual data, values of 0.67–0.88 in correlation and 2.84–14.09 in RMSE were reached. The results were compared with other studies in the related literature and showed how well deep learning architectures can yield under conditions that negatively affect prediction techniques such as Covid 19. In addition, it has been observed that the PM10 value for Sakarya province exceeds the legal limit three times on some days, especially during peak production periods. 

Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: Case of industrial city Sakarya

The increase in the air transportation density affects global warming negatively by increasing the CO2 emitted to the environment. The issue becomes even more important when the agricultural lands and drinking water resources on the flight routes are considered. This situation leads to the development of certain environmental concerns in the society and makes it necessary for the countries to forecast in the correct direction to develop some preventive strategies. To make a contribution to this issue, emission modeling and forecasts regarding emissions originating from air transportation were made in this study through genetic algorithms, a popular artificial intelligence technique. Using the flight information of 32 European countries, the degree of relationship between the number of flights and passengers and CO2 emission from air transportation was calculated. Based on the highly correlating results obtained, time series models were developed for the UK's domestic and international airline transportation in which the highest number of flights takes place and passengers are carried. Using these models, the forecasts based on the UK's flight numbers until 2029, the number of passengers to be transported, and air transportation-related emissions were made. Results with high correlation values ranging from 0.99 to 0.87 were obtained in the implementations. 

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Modeling and forecasting of CO2 emissions resulting from air transport with genetic algorithms: the United Kingdom case

The correct transmission of the information flow between the patient and the doctor by the assessment scales plays an important role in the correct determination of the treatment approach to be applied. In this study, depression assessment scales were evaluated using a hybrid Multi-Criteria Decision-Making (MCDM) model. In the first part of the study, the criteria that should be in the assessment scale were determined by a questionnaire study applied to doctors. In the second part, criteria weights were calculated with the Analytical Hierarchy Process (AHP), and the assessment scales were evaluated from various aspects with the Weighted Aggregated Sum Product Assessment method (WASPAS). The results show that the most important criteria in a scale are the ability to predict the diagnosis, the follow-up of the recovery process, the understanding of the questions, the ease of evaluation, and the ease of individual use. In addition, it has been observed that psychiatrists attach more importance to the follow-up of the recovery process and the ease of evaluation on a scale. When the findings are examined with sensitivity analysis, it shows that the Beck depression scale is the most appropriate scale in terms of the determined criteria. 

Alparslan Demir

Papers

Performance analysis of a heat exchanger having perforated square fins

Thermal performance analysis and optimum design parameters of heat exchanger having perforated pin fins

Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: Case of industrial city Sakarya

Modeling and forecasting of CO2 emissions resulting from air transport with genetic algorithms: the United Kingdom case

Analysis of the performance of assessment scales with multi-criteria decision-making techniques