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Field Variations (field + variation)

Distribution by Scientific Domains

Earth and Environmental Science	60%

Selected Abstracts

Fluid injection and surface deformation at the KTB location: modelling of expected tilt effects

GEOFLUIDS (ELECTRONIC), Issue 1 2005
T. JAHR
Abstract This investigation is indented to explore the relationship between changes in pore fluid pressure and deformation of the land surface induced by a large-scale injection experiment at the KTB site. Deformation will be monitored by ASKANIA borehole tiltmeters at five locations. During the year 2003, a network of borehole tiltmeters was installed, data transmission links established and tested, and recording of tilt data started. Our first main interest was to receive data sets of all stations well before the injection experiment to start in May 2004, to be able to evaluate local site effects. Thus, the separation of injection-induced effects will be more reliable. Principal 3D numerical modelling (poro-elastic modelling and investigations, using the finite element method, FEM) of poro-elastic behaviour showed that significant tilt amplitudes can be expected during controlled fluid injection. Observed deformation will be investigated within the framework of the fluid flow behaviour and resulting deformation. Two models have been used: a coupled hydro geomechanical finite element model (abaqus) and, as a first step, also a multi-layered poro-elastic crust (poel). With the numerical model two effects can be quantified: (i) the deformation of the upper crust (tilt measurements) and (ii) the spatial distribution and the changes of material properties in the KTB area. The main aim of the project is to improve the knowledge of coupled geomechanic,hydraulic processes and to quantify important parameters. Thus, the understanding of fracture-dominated changes of the hydrogeological parameters will be enhanced, geomechanical parameter changes and the heterogeneity of the parameter field quantified. In addition, the induced stress field variation can be explained, which is believed to be mainly responsible for the increase of local seismic activity. Here, we introduce the tiltmeter array at the KTB site, the modelling for a poro-elastic crust and the preliminary FEM modelling. [source]

Investigation of field variation in multi-pole magnetic components

PHYSICA STATUS SOLIDI (A) APPLICATIONS AND MATERIALS SCIENCE, Issue 12 2007
Kuo-Chi Chiu
Abstract Using traditional methods, a fine magnetic pole pitch of less than 1 mm was very difficult to achieve and a complicated magnetization system was required. A linear wire circuit pattern was designed and formed on the printed circuit board (PCB) with a periodic structure, which provides a loop allowing the current to flow in opposite directions to induce different magnetic fields among the wire circuit. Correspondingly, a multi-pole magnetic component with a fine magnetic pole pitch of less than 1 mm was accomplished. Various multi-pole magnetic components with different pitch sizes of 400 ,m and 500 ,m were fabricated to investigate the field variation. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

New procedures to decompose geomagnetic field variations and application to volcanic activitiy

GEOPHYSICAL JOURNAL INTERNATIONAL, Issue 1 2008
Ikuko Fujii
SUMMARY We report the development of numerical procedures for extracting long-term geomagnetic field variations caused by volcanic activity from an observed geomagnetic field by using statistical methods. The newly developed procedures are to estimate the trend from the observed data, as well as variations of non-volcanic origin such as periodic components, components related to external geomagnetic variations and observational noise. We also aim at referring to data obtained at a remote standard geomagnetic observatory rather than using a temporarily installed reference site for reasons of data quality. Two different approaches,a Bayesian statistical method and a Kalman filter method,are applied to decompose the geomagnetic field data into four components for comparison. The number of filter coefficients and the degree of condition realizations are optimized on the basis of minimization of the information criteria. The two procedures were evaluated by using a synthetic data set. Generally, the results of both methods are equally sufficient. Subtle differences are seen at the first, several data points due to arbitrarily selected initial values in the case of the Kalman filter method and at the smaller residual for the Bayesian statistical method. The largest differences are in computation time and memory size. The Kalman filter method runs a thousand times faster on a testing workstation and requires less memory than the Bayesian method. The Kalman filter method was applied to the total intensity data at Kuchi-erabu-jima volcano. The result suggests that the procedure works reasonably well. [source]

Development of a Segmented Model for a Continuous Electrophoretic Moving Bed Enantiomer Separation

BIOTECHNOLOGY PROGRESS, Issue 6 2003
Brian M. Thome
With the recent demonstration of a continuous electrophoretic "moving bed" enantiomer separation at mg/h throughputs, interest has now turned to scaling up the process for use as a benchtop pharmaceutical production tool. To scale the method, a steady-state mathematical model was developed that predicts the process response to changes in input feed rate and counterflow or "moving bed" velocities. The vortex-stabilized apparatus used for the separation was modeled using four regions based on the different hydrodynamic flows in each section. Concentration profiles were then derived on the basis of the properties of the Piperoxan-sulfated ,-cyclodextrin system being studied. The effects of different regional flow rates on the concentration profiles were evaluated and used to predict the maximum processing rate and the hydrodynamic profiles required for a separation. Although the model was able to qualitatively predict the shapes of the concentration profiles and show where the theoretical limits of operation existed, it was not able to quantitatively match the data from actual enantiomer separations to better than 50% accuracy. This is believed to be due to the simplifying assumptions involved, namely, the neglect of electric field variations and the lack of a competitive binding isotherm in the analysis. Although the model cannot accurately predict concentrations from a separation, it provides a good theoretical framework for analyzing how the process responds to changes in counterflow rate, feed rate, and the properties of the molecules being separated. [source]