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Full-Text Articles in Aerospace Engineering
Machine Learning In Aerodynamic Shape Optimization, Jichao Li, Xiaosong Du, Joaquim R.R.A. Martins
Machine Learning In Aerodynamic Shape Optimization, Jichao Li, Xiaosong Du, Joaquim R.R.A. Martins
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, …
Studies Of Multiphase Distribution And Flow In Bubble/ Slurry Bubble Columns With /Without Internals And Advanced Sustainable Construction Materials Using Sophisticated Measurement Techniques, Omar Farid
Doctoral Dissertations
"Bubble/slurry bubble columns have been widely studied and employed as multiphase flow reactors with and without vertical heat-exchanging tubes. In order to understand more deeply the effect of the dense internals on the hydrodynamics of bubble/slurry bubble column, systematic experiments were performed to visualize and quantify using the radioactive particle tracking (RPT) technique. A new methodology for implementing the radioactive particle tracking (RPT) technique has been developed to investigate the effect of the dense internals on the hydrodynamics of the bubble/slurry column reactor. Monte Carlo N-Particle (MCNP) simulation has been implemented to simulate the system to generate a large number …