Effect of the damping and excitation on the identification of the hydrodynamic parameters for an underwater robotic vehicle

The main objective of this work is to investigate the effects of the damping level as well as different excitation forms on the overall prediction of the hydrodynamic parameters in the equations describing the coupled heave and pitch motions for an Underwater Robotic Vehicle (URV) sailing near the sea surface in random waves. The response of an underwater vehicle heaving and pitching in random waves having wide-band and narrow-band spectra are generated. The RDLRNNT technique is used to identify the hydrodynamic parameters in the equations. The technique is based on a combination of a multiple linear regression algorithm and a neural networks technique. The combination of the classical parametric identification techniques and the neural networks technique provides robust results and does not require a large amount of computer time. The identification technique would be particularly useful in identifying the parameters for both moderately and lightly damped motions under the action of unknown excitations effected by a realistic sea. It is shown that the developed technique produces reliable results for the parameters in the equations describing the coupled heave and pitch motions for a URV.     

Search for HD in Jupiter and Saturn

Upper limits are placed on the D/H ratio in the observed portions of the atmospheres of Jupiter and Saturn from observations at high S/N over the region of the 5-0 R(1) line of HD. The upper limits of 4 × 10^?5 and 6 × 10^?5 D/H on Jupiter and Saturn, respectively, are not inconsistent with present models for abundance ratios in the primitive solar nebula or with other estimates of this quantity from observations.     

Worth of head data in well-capture zone design: deterministic and stochastic analysis

In this study, we examine the effects of conditioning spatially variable transmissivity fields using head and/or transmissivity measurements on well-capture zones. In order to address the challenge posed by conditioning a flow model with spatially varying parameters, an innovative inverse algorithm, the Representers method, is employed. The method explicitly considers this spatial variability.

A number of uniform measurement grids with different densities are used to condition transmissivity fields using the Representers method. Deterministic and stochastic analysis of well-capture zones are then examined. The deterministic study focuses on comparison between reference well-capture zones and their estimated mean conditioned on head data. It shows that model performance due to head conditioning on well-capture zone estimation is related to pumping rate. At moderate pumping rates transmissivity observations are more crucial to identify effects arising from small-scale variations in pore water velocity. However, with more aggressive pumping these effects are reduced, consequently model performance, through incorporating head observations, markedly improves. In the stochastic study, the effect of conditioning using head and/or transmissivity data on well-capture zone uncertainty is examined. The Representers method is coupled with the Monte Carlo method to propagate uncertainty in transmissivity fields to well-capture zones. For the scenario studied, the results showed that a combination of 48 head and transmissivity data could reduce the area of uncertainty (95% confidence interval) in well-capture zone location by over 50%, compared to a 40% reduction using either head or transmissivity data. This performance was comparable to that obtained through calibrating on three and a half times the number of head observations alone.     

Silver and silver-bearing minerals at the Um Samiuki volcanogenic massive sulphide deposit, Eastern Desert, Egypt

The Um Samiuki Zn–Cu–Pb–Ag mineralisation, south Eastern Desert, Egypt is hosted by felsic volcanic rocks which form part of the 712-Ma-old, east-west-trending Shadli Volcanic Belt. Two major occurrences of massive sulphides are present at the top of rhyolitic breccia in the Western and Eastern mine areas. In each occurrence, a bornite-bearing zone is overlain by a pyrite-chalcopyrite-bearing zone and underlain by a disseminated, Cu-depleted zone. In the massive sulphide ore, sphalerite, chalcopyrite, pyrite, galena, bornite and tetrahedrite–tennantite are major minerals, whereas arsenopyrite, pyrrhotite, molybdenite and magnetite are accessory phases. Covellite and digenite are common secondary minerals. Bornite, tetrahedrite–tennantite and covellite contain high amounts of silver (averages of 1.97, 1.39 and 1.82 wt% respectively). Based on mineralogical balance calculations, bornite and covellite accommodate 80% of silver in the Um Samiuki deposit. Ag was incorporated in the crystal structure of the early-crystallised copper sulphides and sulphosalts and silver minerals. The temperature, sequential precipitation of the fluids and the structure of the crystallising phases control the distribution of silver. Post-depositional deformation and metamorphic processes caused liberation, remobilisation and redeposition of silver within the massive sulphides.Editorial handling: D. Lentz     

Shear strength characteristics of sand-mixed with granular rubber

Waste materials such as waste tires, rubbers, and plastic materials are normally produced in every society, entering the environment and causing serious problems. These problems may be somehow reduced by finding applications for them in engineering, for example, as lightweight materials for backfill in geotechnical projects. To this aim, this paper demonstrates how shear strength characteristics of sand mixed with various percentages of waste garden hose grains are altered. A relatively, uniform sand has been mixed with waste hose grains in loose and slightly compacted states. Waste hose grains were prepared with special popular machinery in a local area. Various sand-rubber mixtures having 0%, 10%, 15%, 20%, 50%, 70%, and 100% waste hose particles by weight were chosen. The rubber grains were distributed in the sand such that uniform mixtures are obtained. In order to compare the shear strength of different sand-rubber samples, two compaction states were considered. The results show that the influencing parameters on shear strength characteristics of sand-rubber mixtures are normal stress, mixture unit weight, and rubber content. With the selected waste hose particles, compaction states, and rubber contents, the initial friction angle φ₁ does not change significantly. However, an apparent cohesion appears in the mixtures. From environmental point of view and due to lightweight material for the sand-waste hose particles, it may be useful to use such materials in corresponding geotechnical projects. This revised version was published online in July 2006 with corrections to the Cover Date.     

Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis

Geotechnical and Geological Engineering - Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion...     

Liquefaction resistance from cyclic simple and triaxial shearing: a comparative study

Acta Geotechnica - While cyclic triaxial (CTX) tests are widely used in liquefaction studies due to their simplicity, direct simple shear (DSS) tests and their ilk (e.g., the combined triaxial...     

A GIS-based multi-criteria analysis model for earthquake vulnerability assessment using Choquet integral and game theory

Developing an accurate model for discharge estimation techniques of the ungauged river basin is a crucial challenge in water resource management especially in under-development regions. This article is a thorough review of the historical improvement stages of this topic to understand previous challenges that faced researchers, the shortfalls of methods and techniques, how researchers prevailed and what deficiencies still require solutions. This revision focuses on data-driven approaches and GIS-based methods that have improved the accuracy of estimation of hydrological variables, considering their advantages and disadvantages. Past studies used artificial intelligence and geo-statistical methods to forecast the runoff at ungauged river basins, and mapping the spatial distribution has been considered in this study. A recommendation for future research on the potential of a hybrid model utilizing both approaches is proposed and described.     

A Comparative Study Between Least Square and Total Least Square Methods for Time–Series Analysis and Quality Control of Sea Level Observations

In this study, the quality control of tide gauge observations is investigated by two methods of least square (LS-HE) and total least square harmonic estimation (TLS-HE). Particularly, it is shown how to deal with unexpected anomalies, including outliers, offset and gap in the time series of sea level height. To do so, at first the time series is constructed and then a method based on variance threshold is used to eliminate the possible outliers in the observations. Subsequently, a noise assessment algorithm is implemented and the signal is processed to find the possible times of offsets and to eliminate their corresponding observations from the time series. Finally, the signal is checked to find the periods of gap within the time series and then the gap area is predicted with correct observations. Gap filling analysis is performed in two contexts. In the first, only the significant frequencies of tide are considered in the modelling procedure, while in the second, all possible frequencies according to the period of observations are included. Our results show that for modelling and gap filling, the TLS-HE method has a better performance in a comparison with LS-HE method. Although, for offset and outlier detections, the LS-HE is recommended. It also indicates that the TLS-HE method provides a regular solution for gap filling analysis while LS-HE method needs a regularization scheme for which LSQR regularization is used.     

Optimal design of soil dynamic compaction using genetic algorithm and fuzzy system

Computational intelligent techniques, such as fuzzy and genetic algorithm, have proven to be useful in modeling of complex nonlinear phenomena such as dynamic compaction. Dynamic compaction method is used to improve the mechanical behavior of underlying soil layers especially loose granular materials. The method involves the repeated impart of high-energy impacts to the soil surface using steel or concrete tampers with weights ranging 10–40 ton and with drop heights ranging 10–40 m. A relatively exact estimation of dynamic compaction level is of major concern to geotechnical engineers. This paper develops a fuzzy set base method for the analysis of dynamic compaction phenomenon. In this model, the input variables are tamper weight, height of tamper drop, print spacing, tamper radius, number of impact and soil layer geotechnical properties. The main shortcoming of this technique is uncertainty to locate the best sketch of dynamic compaction to gain maximum effect of this method of soil improvement. Therefore, this paper describes the incorporation of genetic algorithm methodology using fuzzy system for determining the optimum design of dynamic compaction. Subsequently, it will be shown that the genetic algorithm has some abilities in the optimization of dynamic compaction design. Also different manners of this algorithm are compared and then the optimized structure of genetic algorithm will be suggested for dynamic compaction.