In subsea processing systems, oil industry increasingly demands accurate and stable continuous measurement of the percent water in crude oil production streams (watercut) over the entire 0 to 100% range. Differential Dielectric Sensors (DDS) have been developed as independent tools connected with multiphase meters for process management and composition measurement. DDS is unique in its use of very low noise and high sensitivity differential measurements between two identical sensors and the use of physics based models for multiphase flow characterization. Existing watercut tools predominantly depend on empirical data and correlations that are sensitive to fluid properties and therefore are limited in their general applicability. The main objective of this work is to develop appropriate mathematical models for the sensor. Finite Element Analysis (FEA) and experimental investigations have been conducted to validate the sensor analytical models. This study provides the scientists and engineers a powerful tool for process management and control in the oil industry.
Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I analyze the data? To answer these questions the book draws on ideas from algorithm design and analysis, computer systems, and statistics and data analysis. The wide-ranging discussion includes a tutorial on system clocks and CPU timers, a survey of strategies for tuning algorithms and data structures, a cookbook of methods for generating random combinatorial inputs, and a demonstration of variance reduction techniques. The book can be used by anyone who has taken a course or two in data structures and algorithms. A companion website, AlgLab (www.cs.amherst.edu/alglab) contains downloadable files, programs and tools for use in experimental projects.
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