Phd: Harnessing Innovation for Enhanced Fault Detection in Concentrated Solar Panels (14423)

CEDoc-UM6P-CC-GEP:

GEP Harnessing Innovation for Enhanced Fault Detection in Concentrated Solar Panels

Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

Green Energy Park (GEP), the first platform of its kind in Africa, is an applied research centre that aims to develop green energy solutions and innovative, reliable projects that will enable renewable energies to be better integrated into the industrial sector. The main objective of the GEP, and its various components, is to reinforce the transition towards a more sustainable and resilient environment in Morocco, to provide state-of-the-art facilities to support innovation and research and to ensure contributions to capacity building.

Concentrated Solar Power (CSP) technology stands at the forefront of the solar energy sector, presenting significant potential for sustainable power generation. These thermal systems consist of crucial components including mechanical structures, mirrors, and receiving tubes, all of which are indispensable for their proper functioning. Any error within these components significantly impacts their efficiency and lifespan. As a result, the detection of faults within CSP plants is paramount, both prior to installation and during operation. In this context, the development of innovative solutions for automatically detecting faults inherent to solar CSP systems emerges as an intriguing avenue of research.

The primary goal of this thesis is to advance the understanding and refine the management of fault detection in Concentrated Solar Panels (CSP) by integrating cutting-edge technologies. This involves developing and enhancing systems capable of assessing and qualifying CSP plants. Specifically, the thesis will concentrate on creating software and hardware solutions for automatic inspection by integrating image processing and artificial intelligence algorithms into drones and robots. Additionally, it will focus on improving Deflectometry technology to provide precise evaluations of concentrator facets and optimizing Photogrammetry techniques for on-site control of concentrator structures during both installation and operation phases at the Green Energy Park (GEP). The ideal candidate should propose innovative solutions aimed at effectively detecting faults in CSP systems, thus making significant contributions to the advancement of solar energy technology.

Candidate Profile :

• Master’s degree in materials science, physics, chemistry, or related field.
• Strong background in quantum mechanics, solid-state physics, or statistical mechanics.
• Hands-on experience with DFT/MD simulations using QE, VASP, LAMMPS, and scripting tools.
• Passion for research and development in battery materials.

Competencies & Personal Qualities

The applicants should hold a master’s degree or equivalent in Computer Science, Engineering, Renewable Energy or related field. Proficiency in software development along with image processing and artificial intelligence is highly recommended. Candidates should possess excellent analytical, problem-solving, and communication skills. Additionally, the ability to conduct independent research and effectively collaborate within a team is essential.

       Thesis Director: Pr. Ismail BERRADA (UM6P School of Computing)

       Thesis Co-Director:  Dr. Amine MOULAY TAJ (GEP-Thermal Department)

Candidature Submission:

Applicants should submit:

• A cover letter outlining the candidate's motivation for this phd offer.
• Detailed curriculum vitae.
• Bachelor's transcripts (mandatory)
• Master's transcripts (M1(S1 and S2) an M2 (S3 and S4 if available)  
• One reference letter.

UM6P.