List of forthcoming papers

List of forthcoming papers     

Prediction of Land Subsidence Under Cyclic Pumping Based on Laboratory and Numerical Simulations

In this study subsidence due to groundwater withdrawal was investigated. Kerman Province in Iran is struggling with land subsidence problem due to extensive groundwater withdrawal mainly for farming. The rate and type of groundwater withdrawal has very important impact on settlement rate. In this research, effective parameters on land subsidence caused by groundwater withdrawal were determined by laboratory tests. Sampling had done up to depth of 300 m mainly with remolded specimens from Shams-abad, Nouq plain in Kerman province. Similar to the field preconsolidation pressure was applied on specimens in the laboratory. Rate of applied stress on prepared specimens was similar to effect of oscillation of groundwater level. In order to model the actual soil behavior in the laboratory, one-dimensional consolidation device (odometer) was adopted for testing. In these tests, the effect of loading caused by seasonal oscillation of groundwater table is considered by means of cyclic loading in the testing which has great effect on rate of settlements. The results of tests show that when the water table level periodically increases and decreases the amount of settlement decrease, comparing with the case when the groundwater table drop to a constant level. In order to predict the further effects of groundwater level oscillation and actual field condition on land subsidence, a finite element model based on Biots’ three-dimensional consolidation theory was developed. After calibration of finite element model with laboratory tests, this model was used for prediction the effect of groundwater level oscillation on actual field conditions.     

Chemical identification,antioxidant, cholinesterase inhibitory,and cytotoxic properties of fucoidan extracted from Persian Gulf Sargassum angustifolium

Marine macroalgal sulfated fucose-containing polysaccharides, like fucoidan, have drawn significant attention due to their biotechnological potentials, such as anti-cancer, antioxidant, and anti-cholinesterase activities. The fucoidan derived from brown macroalgae Sargassum angustifolium species (FSA) was investigated for its cytotoxic effects and alterations in cell proliferation, and cell cycle-related gene expression in the present study occurred on NB4 cell line. The results showed that FSA would induce p53, p21, pro-apoptotic genes and increase expression of the p15 gene as a cell arrest marker. Also, FSA inhibited the anti-apoptotic effect of the Bcl-2 gene and decreased dnmt-1 gene expression. FSA significantly exhibited potent 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity (p<0.05) with an IC₅₀ value of 0.157 mg/mL and showed moderate anti-acetylcholinesterase activity with an IC₅₀ value of 1.20 μg/mL. These results indicated the potential of FSA for the development of therapeutic or preventive agents of cancer and Alzheimer’s disease mainly through cytotoxic effect and AChE (acetylcholinesterase) inhibition as well as additional antioxidant capacities.     

Selection of Less Biased Threshold Angles for SAM Classification Using the Real Value–Area Fractal Technique

The accuracy of classification of the Spectral Angle Mapping (SAM) is warranted by choosing the appropriate threshold angles, which are normally defined by the user. Trial‐and‐error and statistical methods are commonly applied to determine threshold angles. In this paper, we discuss a real value–area (RV–A) technique based on the established concentration–area (C–A) fractal model to determine less biased threshold angles for SAM classification of multispectral images. Short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used over and around the Sar Cheshmeh porphyry Cu deposit and Seridune porphyry Cu prospect. Reference spectra from the known hydrothermal alteration zones in each study area were chosen for producing respective rule images. Segmentation of each rule image resulted in a RV–A curve. Hydrothermal alteration mapping based on threshold values of each RV–A curve showed that the first break in each curve is practical for selection of optimum threshold angles. The hydrothermal alteration maps of the study areas were evaluated by field and laboratory studies including X–ray diffraction analysis, spectral analysis, and thin section study of rock samples. The accuracy of the SAM classification was evaluated by using an error matrix. Overall accuracies of 80.62% and 75.45% were acquired in the Sar Cheshmeh and Seridune areas, respectively. We also used different threshold angles obtained by some statistical techniques to evaluate the efficiency of the proposed RV–A technique. Threshold angles provided by statistical techniques could not enhance the hydrothermal alteration zones around the known deposits, as good as threshold angles obtained by the RV–A technique. Since no arbitrary parameter is defined by the user in the application of the RV‐A technique, its application prevents introduction of human bias to the selection of optimum threshold angle for SAM classification.     

Mixed precision parallel preconditioner and linear solver for compositional reservoir simulation

Reservoir simulation role in value creation and strategic management decisions cannot be over emphasized. Simulation of complex challenging reservoirs with millions of grid blocks especially in compositional mode is very time-consuming even with fast modern computers. On the other hand, high price of cluster supercomputers prevents them for being commonly used for fast simulation of such reservoirs. In recent years, the development of many-core processors like cell processors, DSPs, and graphical processing units (GPUs) has provided a very cost-effective hardware platform for fast computational operations. However, programming for such processors is much more difficult than conventional CPUs, and new parallel algorithm design and special parallel implementation methods are needed. Using the computational power of CPUs, GPUs, and/or any other processing unit, Open Computing Language (OpenCL) provides a framework for programming for heterogeneous platforms. In this paper, OpenCL is used to employ the computational power of a GPU to build a preconditioner and solve the linear system arising from compositional formulation of multiphase flow in porous media. The proposed parallel preconditioner is proved to be quite effective, even in heterogeneous porous media. Using data-parallel modules on GPU, the preconditioner/solver runtime reduced at least 1 order of magnitude compared to their serial implementation on CPU.     

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.     

Monitoring above-zone temperature variations associated with CO2 and brine leakage from a storage aquifer

CO₂ injection in saline aquifers induces temperature changes owing to processes such as Joule–Thomson cooling, endothermic water vaporization, exothermic CO₂ dissolution besides the temperature discrepancy between injected and native fluids. CO₂ leaking from the injection zone, in addition to initial temperature contrast due to the geothermal gradient, undergoes similar processes, causing temperature changes in the above zone. Numerical simulation tools were used to evaluate temperature changes associated with CO₂ leakage from the storage aquifer to an above-zone monitoring interval and to assess the monitorability of CO₂ leakage on the basis of temperature data. The impact of both CO₂ and brine leakage on temperature response is considered for three cases (1) a leaky well co-located with the injection well, (2) a leaky well distant from the injector, and (3) a leaky fault. A sensitivity analysis was performed to determine key operational and reservoir parameters that control the temperature signal in the above zone. Throughout the analysis injection-zone parameters remain unchanged. Significant pressure drop upon leakage causes expansion of CO₂ associated with Joule–Thomson cooling. However, brine may begin leaking before CO₂ breakthrough at the leakage pathway, causing heating in the above zone. Thus, unlike the pressure which increases in response to both CO₂ and brine leakage, the temperature signal may differentiate between the leaking fluids. In addition, the strength of the temperature signal correlates with leakage velocity unlike pressure signal whose strength depends on leakage rate. Increasing leakage conduit cross-sectional area increases leakage rate and thus increases pressure change in the above zone. However, it decreases leakage velocity, and therefore, reduces temperature cooling and signal. It is also shown that the leakage-induced temperature change covers a small area around the leakage pathway. Thus, temperature data will be most useful if collected along potential leaky wells and/or wells intersecting potential leaky faults.     

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.     

The Arabian Plate: unique fit of the earth's surface jig saw puzzle

The Arabian Plate is important and unique in many ways. The worker wants to highlight the important features characterizing the Arabian Plate. It is a unique fit of the earth's surface jig saw puzzle, different than all other lithospheric plates. It has the three known main tectonic plate boundaries, divergent, convergent and conservative ones. These boundaries are the Red Sea and Gulf of Aden, Zagros-Taurus and Dead Sea, respectively. It has three main well-defined and sharp plate boundaries, and it is surrounded by three major plates, African, Eurasian and Indian plates. The Red Sea and Gulf of Aden form the divergent boundary and spreading center. The Dead Sea Transform Fault (the Gulf of Aqaba Transform Fault) represents the conservative boundary and transform fault system. The Zagros-Taurus Thrust (Zagros-Taurus-Bitlis Thrust and Fold Belt) represents the convergent boundary and collision zone. The Arabian Plate incorporates a wide range and variety and subvariety of all three rock types, igneous, metamorphic and sedimentary rocks, this in addition to all kinds of structures. Among these are folding with major fold belts, faulting, foliation, lineation and diapirism. Transform, transcurrent, normal, graben, reverse, thrust faults are all represented one way or another. The tectonics of the Arabian shield, which forms a major part of the Arabian Plate, has long tectonic history prior to the formation of the Red Sea. After the opening and formation of the latter, the tectonics of the Arabian shield became affected and controlled by its tectonics. The Arabian Plate includes the Arabian Platform which has a relatively different setting of tectonics represented by the Central Arabian Graben. The Arabian Plate contains one of the best representative outcropped ophiolite sequences in the world. The Arabian Plate most importantly incorporates most of world oil reserve. Seismic and volcanic activities are also manifested and affected many areas in the Arabian Plate.     

Estimation and validation of atmospheric water vapor content using a MODIS NIR band ratio technique based on AIRS water vapor products

Atmospheric water vapor validation needs simultaneous, well-defined, and independent information which are not easily available causing limitations in the development of remote sensing water vapor retrieval algorithms. This study is concerned with the retrieval of total atmospheric water vapor content and its validation. A band ratio method has been used to estimate the water vapor content based on Moderate Resolution Imaging Spectroradiometer (MODIS) Near InfraRed (NIR) data. The method uses MODIS bands 17, 18, and 19 as NIR bands and band 2 to remove the land cover reflectance. Furthermore, the Atmospheric Infrared Sounder (AIRS) has been used for both algorithm development and analysis of the results. The method has been modified to take into account the dry condition of the central parts of Iran. Using some various datasets, the method is implemented and evaluated quantitatively. The validation of the water vapor estimates has been undertaken by an analysis of AIRS data. The validation results shows error as low as 9 % for the estimated water vapor using the MODIS NIR band ratio method.