Dr. Ahmed Refaat Elmasry
Research fellow, UNN
Shape Memory Alloys (SMA)/Materials development
Background
Dr. Ahmed Elmasry, BSc, MASc, PhD, brings over 12 years of experience in research and academic education, with a strong background in academia and industry. He obtained his PhD in Mechanical Engineering from the University of Northumbria at Newcastle in May 2023, focusing on the use of lightweight materials for high-performance structural applications in the automotive industry. During his MSc research, Dr. Elmasry studied the formability of tailor-welded blanks of advanced high-strength steels (AHSS) for automotive body-in-white (BIW) applications. This research aimed to optimise the use of different thickness sheets/blanks of similar or different steel types for high-stress areas subjected to severe loading conditions, ultimately reducing the weight of the components. In his PhD research, Dr. Elmasry developed and modelled multiphase nanocomposites for automotive lightweighting and crashworthiness analysis, which has influenced his ongoing research and professional endeavours.
Relevant Experience
Dr. Elmasry has participated in various national and international projects, including EU Horizon 2020 and EU Horizon Europe (e.g., SALIENT, GIANCE, ZEVRA), equipping him with a diverse skill set and knowledge base, making him versatile and innovative.His key competencies include a deep understanding of sustainable engineering practices and circular design strategies, proficiency in modelling and simulation, including the development of user-defined subroutines for commercial CAE systems., and skills in programming and algorithm development with extensive experience in C++, Fortran, Python, and finite element analysis (FEA). He has expertise in advanced numerical and multiscale modelling techniques, with several applications published in high-impact journal papers. Overall, Dr. Ahmed Elmasry's contributions to the field are marked by a deep understanding of material properties and a commitment to enhancing the accuracy and reliability of computational models, thus driving forward the capabilities of modern engineering and design tools.