Dr. Peng Gong

Assistant Professor

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Biography

Dr. Peng Gong completed his PhD at the University of Sheffield in 2016, focusing on novel low-energy consumption manufacturing design for high strength low alloy steels, for which he was awarded the Brunton Medal, the top prize for metallurgical PhD research. He has years of experience working in thermodynamic analysis, microstructure characterization, and measurement of mechanical properties, including studies on hydrogen embrittlement. He secured a competitive Early Career Researcher grant from the EPSRC for the SUSTAIN Future Manufacturing Hub, where he served as a Principal Investigator for an EPSRC funded program development of new martensitic steels. In 2023, he was awarded an EPSRC fellowship focusing on the design of reduced activation ferritic martensitic (RAFM) wall blankets for fusion reactors. Dr. Gong joined the Department of Materials at the University of Manchester in April 2024, concentrating on sustainable materials design for manufacturing applications in aerospace, nuclear reactors, and wind turbines. His primary research interests include hydrogen transmission and storage, recycling obsolete steels, and advanced electrical steel design and manufacturing for next-generation electrical machines, as well as wall blanket materials design for fusion reactors.

Research Interests

Requirements for University of Manchester

Master Program
Requirements
GPA Requirement
Required:3.3
IELTS
Listening
Required:6
Reading
Required:6
Writing
Required:6
Speaking
Required:6
Overall
Required:7
TOEFL
Listening
Required:20
Reading
Required:20
Writing
Required:20
Speaking
Required:20
Total
Required:100
Prerequisites
Bachelor's degree in a relevant engineering or science subject Strong background in mathematics and physics
Application Checklist
  • Academic transcripts
  • Two academic references
  • Personal statement
  • CV/Resume
  • English language proficiency certificate
Specialization Notes

Includes MSc in Advanced Electrical Power Systems and MSc in Communications and Signal Processing.