Sediment samples were collected from ten selected sites of the lower Meghna River estuary,and six heavy metals were analyzed with Atomic Absorption Spectrophotometry(AAS)to assess the contamination level and the metals’association with sediment grain size.The current results revealed that the mean concentrations of the studied metals were ranked in descending order of iron(Fe)(1.29×103 mg/kg)>zinc(Zn)(42.41 mg/kg)>lead(Pb)(12.48 mg/kg)>chromium(Cr)(10.59 mg/kg)>copper(Cu)(6.22 mg/kg)>cadmium(Cd)(0.28 mg/kg).The geo-accumulation,contamination,and pollution load indexes suggested that the lower Meghna river estuary was not contaminated by Fe,Zn,Pb,Cr,and Cu.The mean size of the sediment ranged from 28.92 to 126.2 mm,and the Pearson correlation coefficient showed a significant association between Fe and Pb(coefficient of determination,r2=0.836;p<0.05),and no significant correlation was found between individual metals and grain size,indicating no or low influence on the metals distribution. 相似文献
The prediction of the total resistance of planing crafts at high speeds is very important. In this paper, a combined method is investigated for determining the hydrodynamic characteristics of planing crafts in the calm water. The study consists of a potential-based boundary element method (BEM) for the induced pressure resistance, the boundary layer theory for the frictional resistance and practical method for the spray resistance. The planing surface is represented by a number of elements with constant velocity potential at each element. The unknown-induced pressure is obtained by using the free surface elevation condition and the Kutta condition at the transom stern. Hydrodynamic-induced resistance and lift are determined by the calculated dynamic pressure distributions. The boundary layer analysis method is based on calculations of the momentum integral equation applied to obtain the frictional resistance. A particular practical approach is introduced to present the region of the upwash geometry for the spray. A numerical program has been developed for the present research and applied to the hull form of the craft. Four different hull forms of Series 62 model 4666 planing craft are presented. It is shown that the present combined method is efficient and the results are in good agreement with the experimental measurements over a wide range of volumetric Froude numbers. 相似文献
The endurance time(ET) method is a dynamic analysis in which structures are subjected to intensifying excitations, also known as ET excitation functions(ETEF). The ET method is a tool for structural response prediction. The main advantage of the ET method over conventional approaches is its much lower demand for computational efforts. The concept of acceleration spectra is used in generating existing ETEFs. It is expected that ETEF acceleration spectra increase consistently with time and remain proportional to a target spectrum. Nonlinear unconstrained optimization is commonly used to generate ETEFs. Generating new ETEFs is a complicated time-consuming mathematical problem. If the target acceleration spectrum changes, new ETEFs must be generated. This study intends to modify existing ETEFs to be compatible with a desired acceleration spectrum. This process, called spectral matching, obviates the need for using the complicated generating procedure in simulating new ETEFs. ETEFs spectral matching is introduced in this paper for the first time. A Fourier-based method for ETEFs spectral matching is proposed. This algorithm is then applied in a case study. Results are presented to prove the efficiency of the algorithm. 相似文献
Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.
Natural Resources Research - Natural resources are a nation’s wealth, and the use of this wealth depends on the nation’s developmental objective. The goal of this work is to determine... 相似文献