Chosen methods determining flow parameters based on non-invasive techniques

Spis treści ebooka

• Cover
• Title page
• Copyright page
• Table of contents
• Preface
• Abbreviations
• Nomenclature
• Summary
• Introduction
• CHAPTER 1. Multiphase Flow Measurements
  • 1.1. Multiphase flow metering
  • 1.2. Multiphase flow classification
  • Summary
• CHAPTER 2. Tomographic Imaging Systems
  • 2.1. Electrical resistance tomography
  • 2.2. Electrical capacitance tomography
  • 2.3. Reconstruction algorithms
  • Summary
• CHAPTER 3. Algorithms for the Real-Time Measurement of Multiphase Flow Parameters Based on Tomographic Images
  • 3.1. Methods for multiphase flow velocity determination
    • 3.1.1. Pixel to pixel based cross-correlation method for flow velocity calculation
    • 3.1.2. Best-correlated pixel method (Mosorov et al., 2002)
    • 3.1.3. Virtual channel concept method (Dyakowski, Mosorov et al. 2003)
    • 3.1.4. Weighted-mean phases method (Mosorov, 2006A)
    • 3.1.5. Phase spectrum method for time delay estimation using twin plane electrical capacitance tomography (Mosorov, 2006B)
  • 3.2. Algorithms for swirl flow velocity measurements
    • 3.2.1. The centre of gravity method (Saoud, Mosorov, Grudzień, 2016)
    • 3.2.2. Spatio-temporal cross-correlation method for swirl flow velocity measurement (Saoud, Mosorov, 2017)
    • 3.2.3. Estimation of the rotation angle of gas/solid swirl flow by subpixel image resizing (Mosorov, Sankowski, 2018)
  • 3.3. Flow pattern tracing method (Mosorov, 2008)
  • 3.4. Estimation of tomography in mixing processes based on tomographic images (Mosorov, 2015B)
  • Summary
• CHAPTER 4. Algorithms for the Radioactive Particle Tracking Technique
  • 4.1. MCNP5 code in radioactive particle tracking (Mosorov, Abdullah, 2011)
    • 4.1.1. The MCNP5 model
    • 4.1.2. Algorithm validation
  • 4.2. Iterative position reconstruction algorithm for radioactive particle techniques (Mosorov, 2013)
    • 4.2.1. Algorithm description
    • 4.2.2. Algorithm validation
  • 4.3. Strategy for fitting source strength and reconstruction procedure in radioactive particle tracking (Mosorov, 2015A)
  • 4.4. Modelling of dynamic experiments in the MCNP5 environment (Mosorov et al. 2016)
    • 4.4.1. Theory
    • 4.4.2. Dynamic model benchmarking
  • 4.5. The Lambert-Beer Law in time domain form and its application (Mosorov, 2017)
    • 4.5.1. The Lambert-Beer Law in time form
    • 4.5.2. Simulation results
  • Summary
• Conclusion
• Reference
• About Author

pokaż cały spis treści

ukryj recenzje