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Data-driven Investigations on Physics and Characteristics of Flow-blurring Spray Using Machine Learning

Vaezi, Erfan | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55706 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Morad, Mohammad Reza
  7. Abstract:
  8. This research investigates the break-up physics and spray characteristics of flow-blurring spray by implementing machine learning on numerical and experimental datasets. To do so, five crucial parameters of atomization, including SMD, axial and radial velocity components, penetration length, break-up length, and spray angle, are selected to be studied. Firstly, size and velocity distribution datasets are gathered using available experimental papers. Prior to modeling by Multi-Layer Perceptron neural networks, the datasets were pre-processed in terms of the existence of multi-value and outlier instances. Secondly, the physics of mixing flow inside the injection system was numerically simulated utilizing ANSYS Fluent package. The proposed numerical solver consisted of RANS formulation, k-e Realizable turbulence model, and Volume of Fluid method. The solver could accurately not only screen effective thermofluidic and kinematic parameters but also prepare regression models based upon the corresponding dimensionless numbers. Furthermore, the neural networks are applied in order to model the penetration length parameter in terms of scaler and vector approaches. Lastly, the shadowgraph technique was implemented to analyze both break-up length and spray angle. In this regard, a conventional matrix of experiments was utilized to prepare datasets based upon shadowgraph technique. All in all, this research depicts the capabilities of machine learning in terms of screening, modeling, and classifying datasets of spray characteristics. The results suggest utilizing data-driven techniques in order to not only reduce the cost and time of investigation but also provide accurate physical models
  9. Keywords:
  10. Spray Characteristics ; Data Driven Modeling ; Shadowgraphy Method ; Numerical Simulation ; Flowblurring Injector ; Multi-Layer Perceptron (MLP)

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