Increasing the Power Production of Vertical-Axis Wind-Turbine Farms Using Synergistic Clustering

Vertical-axis wind turbines (VAWTs) are being reconsidered as a complementary technology to the more widely used horizontal-axis wind turbines (HAWTs) due to their unique suitability for offshore deployments. In addition, field experiments have confirmed that vertical-axis wind turbines can interact synergistically to enhance the total power production when placed in close proximity. Here, we use an actuator line model in a large-eddy simulation to test novel VAWT farm configurations that exploit these synergistic interactions. We first design clusters with three turbines each that preserve the omni-directionality of vertical-axis wind turbines, and optimize the distance between the clustered turbines. We then configure farms based on clusters, rather than individual turbines. The simulations confirm that vertical-axis wind turbines have a positive influence on each other when packed in well-designed clusters: such configurations increase the power generation of a single turbine by about 10 percent. In addition, the cluster designs allow for closer turbine spacing resulting in about three times the number of turbines for a given land area compared to conventional configurations. Therefore, both the turbine and wind-farm efficiencies are improved, leading to a significant increase in the density of power production per unit land area.     

Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio,k-nearest Neighbours,and Random Forest Algorithms

Natural Resources Research - Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is...     

Co‐composting of Municipal Solid Waste with Sawdust: Improving Compost Quality

The aim of present study was to improve compost quality as well as to achieve an optimum C/N ratio in compost, reduction in heavy metal content and increased water‐holding capacity of composting piles in arid areas. Four windrow compost piles were prepared by mixing sawdust at various dosages with municipal solid waste (MSW). The sawdust was mixed with MSW at 0% (MSW0), 16% (MSW16), 32% (MSW32), and 70% (MSW70) on dry weight basis. The compost piles were monitored daily by recording the temperature, while, weekly measurement was done on C/N ratio, moisture, pH, and electrical conductivity (EC). The addition of 16% sawdust increased the initial C/N to the optimum level and decreased N loss during the composting process. The MSW16 and MSW32 exhibited better temperature dynamics and their composting period seemed to be shorter than that of MSW0 and MSW70. Moreover, addition of sawdust caused lowering of moisture loss from the composting piles. Sawdust admixtures also produced lowering of pH and EC values and led to lower heavy metal content in final products. The quality of the final compost makes it possible to propose the use of this experimental procedure for building up a novel mass reduction of the initial composted waste mixtures.     

Groundwater pesticides residue in the southwest of Iran-Shushtar plain

Study area with an area of about 415 km² is located from 31°40′ to 32°05′ northern latitudes and 48°45′ to 49°00′ eastern longitudes 85 km to the north-east of Ahwaz city, in the north of Khuzestan province, and south west of Iran. The purpose of this study is: (1) the determination of the pesticides concentration in the groundwater of the Shushtar plain (Mian-Ab) and (2) the assessment of geology, hydrogeology and anthropogenic activities impacts the groundwater quality. Thirty-seven groundwater samples were taken from product wells based on the standard methods. A simple and efficient automated method for extraction and preconcentration was used. In this method, a pyrrole-based polymer was synthesized and applied as an efficient sorbent for micro-solid-phase extraction. After extraction, analytes were desorbed in ethyl acetate and analyzed using gas chromatography–flame. The study area is surrounded by Aghajari Formation dominated by silt and clay sediments and the Bakhtiari Formation dominated by sand and gravel. Existence of these formations affects the aquifer sediments and the hydrogeological properties. In the study area, the sediments grade from gravel and sand in the north and east into silt and clay to the south and west, respectively. The topsoil in the south of the study area contains more clay sediments. In this study, the concentration of two common herbicides, i.e., 2,4-D and clodinafop propargyl and two pesticides, i.e., permethrin and diazinon, in the groundwater of Mian-Ab aquifer was assessed. Chemical analysis results showed that the 2,4-D residue in the groundwater has the highest concentration (15 ppm). About 50% of the samples have concentration values more than the maximum contamination level based on EPA drinking standard. The pesticides concentrations decrease from the north to the south of the study area. Pesticides influx to the groundwater in the south of the area is prevented or diminished due to the specific geological situation and soil type. Distribution pattern of population centers, which increase to the north of the study area, and the role of groundwater as the main source of drinking water are two important issues that must be considered in management of pesticides use in the area.     

Numerical simulation of waves in the Caspian Sea: calibration and verification of the observation-based source terms

Ocean Dynamics - Third-generation models employ a host of parameterization schemes to consider the input wind forcing and the wave energy dissipation under different physical settings and...     

An intelligent spatial land use planning support system using socially rational agents

This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained.

This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents.     

Fuzzy logic mineral potential mapping for copper exploration using multi-disciplinary geo-datasets,a case study in seridune deposit,Iran

This paper describes the application of the knowledge-based fuzzy logic method to integrate various exploratory geo-dataset in order to prepare a mineral prospectivity map (MPM) for copper exploration. Different geophysical layers which are derived from the magnetic and the electrical surveys, along with the ones extracted from the background geology (i.e., lithology, fault and alteration) and geochemical data are incorporated in such process. Seridune copper deposit located in the Kerman province of Iran is the case study to delineate its high potential zones of Cu-bearing mineralization for drilling additional boreholes. Four layers from the magnetic data involving upward continuation, analytic signal, reduced to pole and pseudo gravity are assigned in the multi-disciplinary geo-dataset to locate the intrusive complexes responsible for Cu mineralization. The apparent resistivity, chargeability and sulfide factor layers acquired from geo-electrical data are also included in the final preparation of MPM. Then the normalized weights of seven geophysical, three geological and one geochemical evidential layers as main criteria are determined based upon the knowledge of expert decision makers. Fuzzy operators (i.e., Sum and Gamma) are applied to integrate these exploratory features. To evaluate the performance and applicability of the approach, the productivity of the drilled boreholes (Cu concentration multiplied by ore thickness) are used to validate the produced MPMs. It is shown that an optimum correlation coefficient of 0.86 exists between the MPM values and Cu productivity criterion along drilled boreholes.     

Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates

Because of scarcity and high variability of rainfall in arid areas, from one hand, reliable prediction of precipitation in such regions is considerably difficult. Furthermore, in some cases, shortage of observation data and several other limitations may intensify complexity of the forecasting. On the other hand, these regions highly suffer from low availability of water which necessitates development of an appropriate modeling approach to provide as precise as possible predictions of precipitation. Artificial neural networks (ANNs) are expected to be a powerful tool in capturing and analyzing high interannual variability of precipitation in arid climates and, subsequently, in proper prediction of precipitation fluctuations in the future. The end of this paper is to improve ANN predictions of precipitation in arid climates using better training of the network. To this end, two approaches were applied. In the first one, just the rainfall monthly data were considered as input. In the second approach, in addition to precipitation, several exogenous variables of precipitation are considered as input to predict precipitation. The chosen exogenous parameters are either effective on or relevant to the precipitation patterns. Then, several lag times, hidden layer sizes, and training algorithms for different running sums are used in order to produce best forecasts. It was shown that the performance of networks increases significantly by importing more external factors as inputs. The bigger time scales also exhibited better performances. In all the five time scales, smaller lag times (especially one month), bigger hidden layer sizes (especially between 31 and 40), and GDX training algorithm presented the best performance. The highest obtained performance was presented by the network with 10 inputs, 1 month lag, 36 hidden layers, and CGF training method in 18 months running sum with R ² of 0.93.     

Optimal Allocation of Water Resources Using a Two-Stage Stochastic Programming Method with Interval and Fuzzy Parameters

Natural Resources Research - Efficient water allocation is one of the most prominent issues in water resources management. In this research, a two-stage interval-parameter stochastic fuzzy...