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Sofiane KHELLADI

Full Professor at Arts et Métiers Institute of Technology
Head of Fluid Engineering and Energy Systems Laboratory -  LIFSE
(Laboratoire d'Ingénierie des Fluides et des Systèmes Énergétiques - LIFSE)

151 boulevard de l'Hôpital, 75013 Paris
sofiane.khelladi@ensam.eu

Research Activities

Professor Sofiane KHELLADI’s research focuses on several key domains: advanced numerical methods for fluid dynamics and energy systems, multi-phase flows, multi-physics coupling, as well as the aero-hydrodynamics and acoustics of turbomachinery. His scientific approach primarily emphasizes theoretical and numerical methods, with a strong foundation in computational fluid dynamics (CFD) and computational aeroacoustics (CAA). This is complemented by experimental work, which serves primarily for validation and calibration of numerical models, ensuring their accuracy and practical applicability in solving complex fluid and energy system challenges. He also engages in multi-disciplinary research, integrating fluid mechanics, thermodynamics, and acoustics to optimize the performance of turbomachines and related energy systems.

Publications

Books

Buy on: 

Industrial Strategies and Solutions for 3D Printing: Applications and Optimization 1st Edition

by Hamid Reza Vanaei, Sofiane Khelladi, Abbas Tcharkhtchi


Abstract
Although the published books or even review papers have covered different features in the field of 3D printing, almost all of them approach the topic from a specific focus. However 3D printing should be considered as a multidisciplinary field; aside from its rapid growth in novel approaches such as data science or artificial neural networks the topic applies comprehensively and rigorously to big data for optimization purposes.

This book provides an overall review of the 3D printing process, the applicable materials, controlling factors, state-of-the-art concepts and applications in different industries, and process optimization considerations. The first part of the book focuses on the applications of 3D printing process while the second part highlights the role of different 3D printing features toward optimization purposes with particular attention on the influence these features play on optimization purposes as well as the role of machine learning in optimization purposes.

Publisher ‏ : ‎ Wiley; 1st edition (May 29, 2024)

Language ‏ : ‎ English

Hardcover ‏ : ‎ 320 pages

ISBN-10 ‏ : ‎ 139415030X

ISBN-13 ‏ : ‎ 978-1394150304

Book chapters

Patents

Editorials