IntroductionMore than 30% of the heat energy generated by the engine is transferred by the cooling system. If this heat transfer is not accomplished properly, then the engine heat will increase and it will wear the parts by removing oil film between the pieces. A cooling system is used to remove this heat. The radiator is an important component of this system. Increasing heat transfer in the car engine by the cooling system is possible by using two methods of changing the radiator geometry and optimizing it and using fluids with high thermal properties. In this research, we investigated the improvement of radiator thermal performance using nanofluids using a laboratory model. The effect of nanoparticle volume fraction and cooling flow rate on heat transfer rate, and heat transfer coefficient was investigated.Materials and MethodsIn this research, a laboratory model was designed and manufactured to evaluate the thermal performance of the MF 285 tractor radiator using nanofluid. In this laboratory model, water was combined and used as a base fluid with nanoparticles AL2O3. 20 nm nanoparticles with volume percentages of 1 to 4% were used. An electric stirrer and magnetic stirrer were used to prepare the nanofluid. For the produced fluid to be usable, add SDBS surfactant to it. The temperature of the inlet fluid to the radiator was 85 °C and the cooling fluid flow rate was 3.18 to 15.08 (lit. min-1 )) and the airflow rate was 3.2 to 6.4 (m s-1). Two T-type thermocouples are installed to measure the inlet and outlet temperature of the radiator and two other front and rear fans to measure the inlet and outlet air temperature and four more are installed on the radiator to measure the radiator body temperature.Results and DiscussionThe results show that in nanofluid with a 4% volume fraction compared to a 1% volume fraction, it can be seen an increase of 8.7% in density, 7.7% in viscosity, and 9.1% in thermal conductivity, and also a decrease of 8.8% in specific heat. The maximum temperature difference between the inlet and outlet sensors of the radiator when the thermostat is open and the cooling fluid flows through the radiator is 12 to 15 °C. By increasing the speed of the electromotor from 40 Hz to 50 Hz, the temperature of the water cooling fluid at the outlet part becomes 4.7 °C cooler and the air temperature at the outlet part becomes 7.3 °C warmer. As the speed of the electromotor increases, the rate of heat transfer increases. At the maximum value of airflow and cooling fluid, by adding 4% by volume of nanoparticles to the base fluid, the rate of heat transfer can be increased about 37% compared to the base fluid. Compared to water, nanofluid containing 4% by volume of AL2O3 at maximum speed has a 28% increase in heat transfer coefficient. Also, by increasing the electric motor speed from 20 Hz to 40 Hz, the heat transfer coefficient of pure water shows about 26% increase and the nanofluid shows an average of 29% increase.ConclusionIncreasing the volume fraction of nanoparticles suspended AL2O3 in the base fluid increases the density, viscosity, and thermal conductivity, which increases the heat transfer rate and reduces the outlet temperature of the radiator. The presence of nanofluid in the engine cooling system increases the heat transfer from the radiator, and despite this feature, the size and weight of the radiator can be reduced without affecting its heat transfer performance. It can also improve heat transfer performance by increasing the cooling flow rate and the airflow rate.

In recent decades, drastic land use changes in Golestan province caused to reduce a substantial amount of Hyrcanian forest. To investigate the changes, land cover maps produced using Landsat satellite imagery classification of sensors TM from 1984, 2012 and 2016 respectively used as input data in Land Change Modeler (LCM) to predict land cover changes in 2030. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used. In continue to investigate the role of land use changes in water yield as one of ecosystem services was discussed. The results show the accuracy of artificial neural network with the ROC equal to 0. 949, the ratio Hits/False Alarms equal to 57 percent and the figure of merit is equal to 11 percent. Land use change modeling results showed that from 1984 to 2012, The most prominent changes were related to reduction of forest cover. This process modeling using artificial neural network showed, from 2016 to 2030 forest cover will be reduced about 30361 hectares. The results of water yield study showed that runoff in the area, particularly in the East and North East area has increased. This increase in the amount of runoff occurred as a result of land use change on forest ecosystems to agriculture. Results of this study improve our understanding of hydrological consequences of land-use changes, and provide needed knowledge for effectively developing and managing land-use for sustainability and productivity in the Gorgan-rood watershed.

Water accounting frameworks apply as a tool for organizing water data and water resources assessment. Adopting SEEA – Water framework, the paper will go through integrated water resource assessment in the Ajabshir study area in 2006 and 2016. The assessment was carried out based on the indicators associated to the different dimensions of water security in the area such as water resources, economic and social. According to the indicators of water resources dimension, agriculture, with more than 96% of water consumption, has the most effect on water stress. According to the intensity of water consumption in 2006 and 2016, the local water resource is highly unsustainable. Per capita renewable water increased from 835 m3 per person in 2006 to 1179 m3 per person in 1395. That is mainly due to the decrease of 17% of the population in 2016 compared to that in 2006, decrease in outflow to Lake Urmia because of the GHALEH-CHAY dam, as well as the further transbasin water import. The marginal value changes in the economic productivity of water in the agricultural sector indicate that the economic drivers of agricultural sector were highly dominating the growth mechanism in the area, which can result in ignoring strategic water resources restrictions in favor of short-term individual economic gains. In spite of decline in water consumption in the service and constant water consumption in the industrial sectors, increase in services and industrial revenues has led to an increase in the economic productivity of water in those sectors.