Multiscale study on hydrogen mobility in metallic fusion divertor material

For achieving efficient fusion energy production, the plasma-facing wall materials of the fusion reactor should ensure long time operation. In the next step fusion device, ITER, the first wall region facing the highest heat and particle load, i.e. the divertor area, will mainly consist of tiles based on tungsten. During the reactor operation, the tungsten material is slowly but inevitably saturated with tritium. Tritium is the relatively short-lived hydrogen isotope used in the fusion reaction. The amount of tritium retained in the wall materials should be minimized and its recycling back to the plasma must be unrestrained, otherwise it cannot be used for fueling the plasma. A very expensive and thus economically not viable solution is to replace the first walls quite often. A better solution is to heat the walls to temperatures where tritium is released. Unfortunately, the exact mechanisms of hydrogen release in tungsten are not known. In this thesis both experimental and computational methods have been used for studying the release and retention of hydrogen in tungsten. The experimental work consists of hydrogen implantations into pure polycrystalline tungsten, the determination of the hydrogen concentrations using ion beam analyses (IBA) and monitoring the out-diffused hydrogen gas with thermodesorption spectrometry (TDS) as the tungsten samples are heated at elevated temperatures. Combining IBA methods with TDS, the retained amount of hydrogen is obtained as well as the temperatures needed for the hydrogen release. With computational methods the hydrogen-defect interactions and implantation-induced irradiation damage can be examined at the atomic level. The method of multiscale modelling combines the results obtained from computational methodologies applicable at different length and time scales. Electron density functional theory calculations were used for determining the energetics of the elementary processes of hydrogen in tungsten, such as diffusivity and trapping to vacancies and surfaces. Results from the energetics of pure tungsten defects were used in the development of an classical bond-order potential for describing the tungsten defects to be used in molecular dynamics simulations. The developed potential was utilized in determination of the defect clustering and annihilation properties. These results were further employed in binary collision and rate theory calculations to determine the evolution of large defect clusters that trap hydrogen in the course of implantation. The computational results for the defect and trapped hydrogen concentrations were successfully compared with the experimental results. With the aforedescribed multiscale analysis the experimental results within this thesis and found in the literature were explained both quantitatively and qualitatively.

The ratio of diffusion coefficient to mobility for slow electrons in dry air

Using Huxley's solution of the diffusion equation for electron-attaching gases, the ratio of diffusion coefficient D to mobility μ for electrons in dry air was measured over the range 3·06 × 10-17

Analysis of Dynamic Heterogeneity in a Glass Former from the Spatial Correlations of Mobility

The growth of characteristic length scales associated with dynamic heterogeneity in glass-forming liquids is investigated in an extensive computational study of a four-point, time-dependent structure factor defined from spatial correlations of mobility, for a model liquid for system sizes extending up to 351 232 particles, in constant-energy and constant-temperature ensembles. Our estimates for dynamic correlation lengths and susceptibilities are consistent with previous results from finite size scaling. We find scaling exponents that are inconsistent with predictions from inhomogeneous mode coupling theory and a recent simulation confirmation of these predictions.

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Intelligent prediction of the stress-strain response of intact and jointed rocks

An application of Artificial Neural Networks for predicting the stress-strain response of jointed rocks under different confining pressures is presented in this paper. Rocks of different compressive strength with different joint properties (frequency, orientation and strength of joints) are considered in this study. The database for training the neural network is formed from the results of triaxial compression tests on different intact and jointed rocks with different joint properties tested at different confining pressures reported by various researchers in the literature. The network was trained using a three-layered network with the feed-forward back propagation algorithm.About 85% of the data was used for training and the remaining 15% was used for testing the network. Results from the analyses demonstrated that the neural network approach is effective in capturing the stress-strain behaviour of intact rocks and the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different jointed rocks, whose intact strength varies from 11.32 MPa to 123 MPa, spacing of joints varies from 10 cm to 100 cm. and confining pressures range from 0 to 13.8 MPa. (C) 2010 Elsevier Ltd. All rights reserved.

Intelligent Processing Of Zno-Based Ceramics

The Intelligent Decision Support System (IDSS), also called an expert system, is explained. It was then applied to choose the right composition and firing temperature of a ZnO based varistor. 17 refs.

The mobility of oxygen ions in CaF2

The a.c. conductivity of CaF2 samples containing a fine dispersion of CaO particles has been measured in the temperature range 630 to 1100 K. The conductivity of the dispersed solid electrolyte is two orders of magnitude higher than that for pure polycrystalline CaF2 in the middle of the temperature range. Transport measurements on pure single crystals of CaF2 and polycrystalline samples, with and without CaO dispersion, using Fe+FeO and pure Fe as electrodes, clearly indicate that fluorine ions are the only migrating ionic species with a transport number of almost unity, contrary to the suggestion of Chou and Rapp [1, 2]. The enhanced conductivity of the dispersed solid electrolyte probably arises from two effects. A small solubility of oxygen in CaF2 results in an increase in the fluorine vacancy concentration and conductivity. Adsorption of fluorine ions on the surface of the dispersed particles of CaO results in a space charge region around each particle with enhanced conductivity. Measurements on a galvanic cell incorporating CaF2 as the solid electrolyte and oxide electrodes show that the e.m.f. is a function of the activity of CaO at the electrode/electrolyte interface. The response to an oxygen potential gradient is, therefore, through an exchange reaction, which establishes an equivalent fluorine potential at the electrode/electrolyte interface.

Development of atmospheric pressure ionization ion mobility spectrometry and ion mobility spectrometry mass spectrometry

This study is focused on the development and evaluation of ion mobility instrumentation with various atmospheric pressure ionization techniques and includes the following work. First, a high-resolution drift tube ion mobility spectrometer (IMS), coupled with a commercial triple quadrupole mass spectrometer (MS), was developed. This drift tube IMS is compatible with the front-end of commercial Sciex mass spectrometers (e.g., Sciex API-300, 365, and 3000) and also allows easy (only minor modifications are needed) installation between the original atmospheric pressure ion source and the triple quadrupole mass spectrometer. Performance haracteristics (e.g.,resolving power, detection limit, transmission efficiency of ions) of this IMS-MS instrument were evaluated. Development of the IMS-MS instrument also led to a study where a proposal was made that tetraalkylammonium ions can be used as chemical standards for ESI-IMS. Second, the same drift tube design was also used to build a standalone ion mobility spectrometer equipped with a Faraday plate detector. For this highresolution (resolving power about 100 shown) IMS device, a multi-ion source platform was built, which allows the use of a range of atmospheric pressure ionization methods, such as: corona discharge chemical ionization (CD-APCI), atmospheric pressure photoionization (APPI), and radioactive atmospheric pressure chemical ionization (R-APCI). The multi-ion source platform provides easy switching between ionization methods and both positive and negative ionization modes can be used. Third, a simple desorpion/ionization on silicon (DIOS) ion source set-up for use with the developed IMS and IMS-MS instruments was built and its operation demonstrated. Fourth, a prototype of a commercial aspiration-type ion mobility spectrometer was mounted in front of a commercial triple quadrupole mass spectrometer. The set-up, which is simple, easy to install, and requires no major modifications to the MS, provides the possibility of gathering fundamental information about aspiration mobility spectrometry.

Kinetic approach to dislocation bending in low-mobility films

A kinetic model has been developed for dislocation bending at the growth surface in compressively stressed low-mobility films such as III-V nitrides. It is based on a reduction in the number of atoms at the growth surface. Stress and nonstress sources of driving force for such a reduction are discussed. A comparison between the derived equations and experimentally measured stress evolution data yields good agreement between the predicted and observed angles through which dislocations bend.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

ICADA: Intelligent computer aided defect analysis for castings

An intelligent computer aided defect analysis (ICADA) system, based on artificial intelligence techniques, has been developed to identify design, process or material parameters which could be responsible for the occurrence of defective castings in a manufacturing campaign. The data on defective castings for a particular time frame, which is an input to the ICADA system, has been analysed. It was observed that a large proportion, i.e. 50-80% of all the defective castings produced in a foundry, have two, three or four types of defects occurring above a threshold proportion, say 10%. Also, a large number of defect types are either not found at all or found in a very small proportion, with a threshold value below 2%. An important feature of the ICADA system is the recognition of this pattern in the analysis. Thirty casting defect types and a large number of causes numbering between 50 and 70 for each, as identified in the AFS analysis of casting defects-the standard reference source for a casting process-constituted the foundation for building the knowledge base. Scientific rationale underlying the formation of a defect during the casting process was identified and 38 metacauses were coded. Process, material and design parameters which contribute to the metacauses were systematically examined and 112 were identified as rootcauses. The interconnections between defects, metacauses and rootcauses were represented as a three tier structured graph and the handling of uncertainty in the occurrence of events such as defects, metacauses and rootcauses was achieved by Bayesian analysis. The hill climbing search technique, associated with forward reasoning, was employed to recognize one or several root causes.