Actuator PHM exploiting multi- physics digital twin and machine learning PhD

Program Overview

This self-funded PhD opportunity at Cranfield University focuses on developing advanced control systems for electric actuators using multi-physics digital twins and machine learning. The project aims to enhance actuator reliability, safety, and efficiency, addressing sustainability challenges in engineering. The successful candidate will benefit from travel opportunities, training, and transferable skills development, with potential employment prospects in both industry and academia.

Program Outline

Other:

The provided context focuses on a research opportunity for a PhD in Actuator PHM exploiting multi-physics digital twin and machine learning. It doesn't contain information about a structured degree program. However, here are some key takeaways from the context:

• Project Focus:
• Develop an advanced control system for electric actuators.
• Optimize Battery Management System (BMS) for efficient battery health monitoring.
• Implement real-time fault detection and handling mechanisms.
• Project Benefits:
• Enhance reliability and longevity of electric actuators.
• Improve safety by minimizing potential damage or failure.
• Ensure operational efficiency and minimize downtime.
• Reduce costs through improved maintenance and extended lifespan.
• Key Selling Points:
• Travel opportunities (both within the UK and internationally).
• Applied research addressing sustainability challenges in engineering.
• Funding allocated for internal and external training.
• Develop transferable skills in technical and soft skill areas.
• Employability opportunities in both industry and academia.
• Additional Information:
• Located at Cranfield University, UK.
• Supervised by Dr. Samir Khan, Research Fellow in TLES.
• Open to both UK and international applicants.
• Self-funded PhD opportunity.
• Application deadline: May 22, 2024.
• Start date: September 30, 2024.
• Duration: 3 years.