Experimental and numerical study on charging processes of an ice‐on‐coil thermal energy storage system

In this study, an external melt ice‐on‐coil thermal storage was studied and tested over various inlet conditions of secondary fluid—glycol solution—flow rate and temperature in charging process. Experiments were conducted to investigate the effect of inlet conditions of secondary fluid and validate the numerical model predictions on ice‐on‐coil thermal energy storage system. The total thermal storage energy and the heat transfer rate in the system were investigated in the range of 10 l min ^?1?V??60 l min ^?1. A new numerical model based on temperature transforming method for phase change material (PCM) described by Faghri was developed to solve the problem of the system consisting of governing equations for the heat transfer fluid, pipe wall and PCM. Numerical simulations were performed to investigate the effect of working conditions of secondary fluid and these were compared with the experimental results. The numerical results verified with experimental investigation show that the stored energy rises with increasing flow rate a decreasing tendency. It is also observed that the inlet temperature of the fluid has more influence on energy storage quantity than flow rate. Copyright © 2006 John Wiley & Sons, Ltd.     

Solution-processed LiF for work function tuning in electrode bilayers

Although ambient processing is the key to low-cost organic solar cell production, high-vacuum thermal evaporation of LiF is often a limiting step, motivating the exploration of solution processing of LiF as an alternative electrode interlayer. Submonolayer films are realized with the assistance of polymeric micelle reactors that enable LiF particle deposition with controlled nanoscale surface coverage. Scanning Kelvin probe reveals a work function tunable with nanoparticle coverage with higher values than that of bare indium tin oxide (ITO).     

Heuristic methods for wind energy conversion system positioning

This paper considers the problem of determining the locations of wind generators in a wind farm consisting of many generators. The objective is to find a generator placement that maximizes profit, which is the product of the cost efficiency of the generators and the total power output from the wind farm. Generator placement is significant because if generator A is located close to generator B and is located downwind of generator B then the power output of generator A is reduced by an amount that varies with the distance between the generators. The problem can be formulated using mathematical programming but to solve the problem one cannot employ traditional optimization methods. Therefore, a greedy improvement heuristic methodology is developed and described in detail. The effectiveness of the proposed heuristic is demonstrated on a suite of test problems. These results indicate that the proposed method represents an effective solution strategy for this problem.     

An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database

This study focuses on development of an energy benchmarking model utilizing U.S. Commercial Buildings Energy Consumption Survey (CBECS) Database. An artificial neural networks (ANN) method based approach was used in the study. Office type buildings in the CBECS database were used in the benchmarking model development and weighted energy use intensity (EUI) was selected as the benchmarking index. The benchmarking model included input variables describing building's physical properties, occupancy and climate. Yearly electricity consumption per square meter, or EUI, was estimated by the ANN model. The correlation coefficient for each census division benchmarking model varied between 0.45 and 0.73, and mean squared error (MSE) varied between 9.60 and 15.25. It was observed that when the data set for a census division was grouped by different climate zones, ANN benchmarking model provided more accurate predictions. It was also observed that ANN model provides more accurate estimations when compared with predictions obtained with multi-linear regression models. For comparison, the MSE values varied between 10.24 and 40.43. Overall, the ANN model proved itself a better prediction model for energy benchmarking. Copyright © 2006 John Wiley & Sons, Ltd.