Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Rare opportunity for information on the upper atmosphere of Saturn

We report on taking, successfully, the rare opportunity to monitor photoelectrically the eclipse of Saturn's largest satellite (Titan) and present a light curve. Comparing this light curve with similar ones obtained for Jovian satellites we deduce the Saturnian stratosphere to be relatively clear, at least at the latitude (25° S) probed.     

Facies analysis and sequence stratigraphy of Kometan Formation (Upper Cretaceous) in the imbricated zone,northeastern Iraq

Facies and sequence stratigraphic analysis of the Kometan Formation (Upper Cretaceous) were studied from Kometan village, Kurdistan region of northeastern Iraq. Lithologically, the formation consists of 44 m of white weathering, light grey, thin to medium-bedded highly fractured limestones with chert nodules. Petrographic study of the carbonates shows that both skeletal and non-skeletal grains were present. The skeletal grains include a variety of planktonic foraminifera (including Oligostegina), calcispheres, ostracods, pelecypods, larva ammonite, and echinoderms. Non-skeletal grains include peloids only. Three main microfacies types are distinguished in the studied formation. The results of stable carbon and oxygen isotopes of the studied carbonate samples show negative values of δ¹⁸O. These indicate that the seawater was warm with low salinity during precipitation of the carbonates in the Kometan Formation in northeastern Iraq. The positive δ¹³C values of carbonate samples, in the middle part of the formation, reflect the widespread deposition of organic-rich marine sediments during a transgression and deepening of the basin. Petrographic, facies and stable isotopic analyses revealed that the Kometan Formation was deposited in a warm, basinal, pelagic (open marine) environment with low salinity. The Kometan Formation consists of one complete third-order depositional sequence, separated by a sequence boundary (SB) of type 2. The third-order sequence is subdivided into a transgressive systems tract (TST) and highstand systems tract (HST). This reflects episodes of transgression and still stands of the relative sea level. The TSTs are topped by maximum flooding surface (MFS) characterized by deepening-/fining-upward parasequences implying a retrogradational stacking pattern. The HST is marked by shallowing-/coarsening-upward parasequences implying a progradational stacking pattern.     

Statistically downscaled probabilistic multi‐model ensemble projections of precipitation change in a watershed

This paper presents the development of a probabilistic multi‐model ensemble of statistically downscaled future projections of precipitation of a watershed in New Zealand. Climate change research based on the point estimates of a single model is considered less reliable for decision making, and multiple realizations of a single model or outputs from multiple models are often preferred for such purposes. Similarly, a probabilistic approach is preferable over deterministic point estimates. In the area of statistical downscaling, no single technique is considered a universal solution. This is due to the fact that each of these techniques has some weaknesses, owing to its basic working principles. Moreover, watershed scale precipitation downscaling is quite challenging and is more prone to uncertainty issues than downscaling of other climatological variables. So, multi‐model statistical downscaling studies based on a probabilistic approach are required. In the current paper, results from the three well‐reputed statistical downscaling methods are used to develop a Bayesian weighted multi‐model ensemble. The three members of the downscaling ensemble of this study belong to the following three broad categories of statistical downscaling methods: (1) multiple linear regression, (2) multiple non‐linear regression, and (3) stochastic weather generator. The results obtained in this study show that the new strategy adopted here is promising because of many advantages it offers, e.g. it combines the outputs of multiple statistical downscaling methods, provides probabilistic downscaled climate change projections and enables the quantification of uncertainty in these projections. This will encourage any future attempts for combining the results of multiple statistical downscaling methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Machine Leaning-Based Investigation of the Associations between CMEs and Filaments

In this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The support vector machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the US National Geophysical Data Center (NGDC) filament catalogue and the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph (SOHO/LASCO) CME catalogue are processed to associate filaments with CMEs. In the previous studies, which classified CMEs into gradual and impulsive CMEs, the associations were refined based on the CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data-mining system is created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that can have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type, and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14?000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance.     

Colour contrasts of lunar grounds

Four colour contrasts have been studied for 104 lunar regions between wavelengths 4000 Å and 8000 Å. Distinct colour differences are found. The greatest contrast between topographs occurs at longer wavelengths. The regional colour differences between the two short wavelengths 4035 Å and 5538 Å show a general trend of increasing reddening with age. For the other three colour differences \(\Delta (\tfrac{{{\text{4 7 6 5}}}}{{{\text{7 9 2 2}}}}), \Delta (\tfrac{{{\text{4 7 6 5}}}}{{{\text{6 6 9 2}}}}) and \Delta (\tfrac{{{\text{6 6 9 2}}}}{{{\text{7 9 2 2}}}})\) , a trend of variation with albedo is detected, and the less scatter on points for the colour differences above the wavelength 6000 Å.     

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.     

Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed

Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.     

New contributions to the Upper Senonian stratigraphy in Northern Iraq

The Upper Senonian sediments are well developed in Northern Iraq and represent three types of facies; elastics, reefal, and open-marine. These sediments are classified stratigraphically depending upon similarities in age and lithology. New groups are proposed for the first time; Ruwanduz Reefal Group comprising Aqra Limestone Formation, Bekhme Limestone Formation and Pilsener Limestone Formation, and Zakho Marl Group including Shiranish Formation, Digma Formation and Jib'ab Formation. It is suggested here that Jib'ab Formation is an extension of the Shiranish Formation and should not be treated as a separate lithostratigraphic unit.     

Measurement of stratospheric aerosol on Saturn using an eclipse of Titan

An eclipse of Titan by Saturn was observed on December 20, 1979, to measure the aerosol content in the atmosphere of Saturn. The measurements were made with the 74-in. telescope of the Helwan Observatory, Egypt, in the bandpass 6300–7300 Å and extend to ～5 magnitudes of eclipse darkening. The faint portion of the lightcurve unambiguously requires the presence of aerosol in the lower stratosphere of Saturn. The aerosol extends to at least 20 km above the tropopause and has a one-way stratospheric vertical optical depth of 0.4_?0.02^+0.04 at 6700 Å. The results apply to the sunset terminator at a cronographic latitude of 23°S.