جستجوی مقالات مرتبط با کلیدواژه « solar water heater system » در نشریات گروه « مکانیک »

The main part of the solar heating system is the solar collectors, the most common of which are the flat plate collector and the evacuated tube collector. In domestic use, solar hot water systems based on flat plate collectors are used more because of their low cost and minimal maintenance. Also, the outlet temperature of the collector corresponds to most domestic hot water needs. Solar hot water systems' performance varies with environmental conditions changes (atmospheric temperature, solar radiation, wind speed). This article modeled an indoor solar hot water system using TRANSYS software. This modeling is the development of a solar hot water system model based on an active flat plate collector. The developed model can predict the performance of the solar hot water system for different climatic and seasonal changes in the year. The system's long-term performance for Iranian weather conditions is evaluated and presented in the paper. Performance parameters such as the functional energy efficiency of the collector and hourly and monthly thermal efficiency are presented and discussed. Among the simulation results, it can be mentioned that the solar hot water system with a collector area of 5 square meters can meet more than 70% of the 400 liter needs of a household in Ahvaz, Iran.

In this study, after fabricating a solar parabolic water heater, an efficient model is suggested to predict the efficiency of the solar water heater system (SWHS). Artificial neural networks (ANN) can create logical relations among the input parameters and target(s). As efficiency is trained a function of the input parameters, when conditions are desirable to measure the data, a network-trained function can be used to predict the efficiency of the solar system. The used data for the neural network analysis were measured by using experiments on a parabolic trough collector, during four days in June. Variables such as solar radiation, ambient temperature and the output fluid temperature of the collector were considered as input parameters and the efficiency of the solar parabolic water heater was used as the output neural network. Different ANN models are presented based on the various input parameters and neurons. The ANN6 model with a 4-10-1 structure, with a root mean square error (RMSE) of 0.0061 and regression coefficient for train data (Rtrain) of 0.99995, is the most accurate among the presented models. By increasing the input parameters, the RMSE decreases and accuracy of the models increases. When experimental tests are not impossible in similar conditions, the presented model can help researchers predict the efficiency of studied SWHS by saving time and costs.