To improve the performance of particle image velocimetry in measuring instantaneous velocity fields, direct cross-correlation of image fields can be used in place of auto-correlation methods of interrogation of double- or multiple-exposure recordings. With improved speed of photographic recording and increased resolution of video array detectors, cross-correlation methods of interrogation of successive single-exposure frames can be used to measure the separation of pairs of particle images between successive frames. By knowing the extent of image shifting used in a multiple-exposure and by a priori knowledge of the mean flow-field, the cross-correlation of different sized interrogation spots with known separation can be optimized in terms of spatial resolution, detection rate, accuracy and reliability.

Theory of cross-correlation analysis of PIV images : Image analysis as measuring technique in flows

A review of the applications of nanofluids in solar energy

An overview of modeling and experiments of vortex-induced vibration of circular cylinders

Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.

Solar radiation prediction using Artificial Neural Network techniques: A review

The increasing need for thermal comfort has led to a rapid increase in the use of cooling systems and, consequently, electricity demand for air-conditioning systems in buildings. Heat-driven ejector refrigeration systems appear to be a promising alternative to the traditional compressor-based refrigeration technologies for energy consumption reduction. This paper presents a comprehensive literature review on ejector refrigeration systems and working fluids. It deeply analyzes ejector technology and behavior, refrigerant properties and their influence over ejector performance and all of the ejector refrigeration technologies, with a focus on past, present and future trends. The review is structured in four parts. In the first part, ejector technology is described. In the second part, a detailed description of the refrigerant properties and their influence over ejector performance is presented. In the third part, a review focused on the main jet refrigeration cycles is proposed, and the ejector refrigeration systems are reported and categorized. Finally, an overview over all ejector technologies, the relationship among the working fluids and the ejector performance, with a focus on past, present and future trends, is presented.

https://re.public.polimi.it/bitstream/11311/984261/1/2016_RSER_BesagniMereuInzoli.pdf

Ejector refrigeration: A comprehensive review

Flow structure in the downstream of square and circular cylinders

The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Turkey. For this purpose, the meteorological data of 31 stations spread over Turkey along the years 2000-2006 were used as training (27 stations) and testing (4 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 5.34% and correlation coefficient (R) value was obtained to be about 0.9936 for the testing data set.

Muammer Ozgoren

Papers

Flow structure in the downstream of square and circular cylinders

Estimation of global solar radiation using ANN over Turkey

Performance of a solar ejector cooling-system in the southern region of Turkey

Comparison of flow structures in the downstream region of a cylinder and sphere

Hourly performance prediction of ammonia–water solar absorption refrigeration