Collins Ogbodo

About

I am a doctoral researcher passionate about decision support systems at the intersection of machine learning, structural engineering and digital twins. My research within the Dynamic Research Group focuses on the development of digital twin technologies to support decision making and improve structural dynamics testing in the energy, aircraft, infrastructure and space industry.

Particularly, I develop frameworks that combine structural simulation, virtual testing and reinforcement learning to provide agent-based test support during systems testing, improving test outcome.

I am currently a PhD candidate under the University of Sheffield EPSRC DTP Case Conversion with Siemens Industry Software NV at the School of Mechanical, Aerospace and Civil Engineering

Feel free to get in touch! LinkedIn

Research

Development of Simulation-based Digital Twin for Smart Space HVAC System : Case Study of a Phytotron System.

MSc. (Hons) thesis project

Advised by Prof. Henni Ouerdane; Co-advised by Prof. Laurent Gentzbittel

Controlled Environment Agriculture (CEA) offers a solution to modern agricultural challenges such as urbanization, resource limitations, and energy efficiency. By automating systems and monitoring key factors like temperature, humidity, and carbon dioxide, CEA creates optimal conditions for year-round crop production. However, energy consumption remains a hurdle for sustainability.

This research focuses on developing a simulation-based digital twin (DT) model for a phytotron system to enhance energy efficiency. The DT prototype predicts indoor temperature and energy needs in real-time while factoring in plant interactions with the environment. It utilizes advanced simulations and virtual sensors to provide cost-effective monitoring and insights for sustainable energy management.

Thermo-economic Analysis of Stirling Enginer Integrated Power Plant

BEng. (Hons) thesis project

Advised by Dr. Ofodu, J.C

This project investigates the integration of Stirling engines with gas turbines to optimize power generation by utilizing waste heat from the gas turbine to power the Stirling engine, significantly boosting efficiency and reducing environmental impact. Through simulations, the study demonstrated thermodynamic improvements, including enhanced energy efficiency and exergy efficiency. Environmental benefits were also evident, with a reduction in exhaust gas temperature and enthalpy, showcasing effective waste heat recovery. Economically, while the integration resulted in higher life-cycle costs, the cost per unit of energy decreased slightly, presenting potential long-term savings. This innovative approach highlights a sustainable and efficient path forward, offering valuable insights for engineers and policymakers addressing energy challenges.

CV

Below is an up-to-date version of my CV. for more information, kindly check out my Google Scholar profile and Github.

More details are available on LinkedIn and a fully length academic styled CV is available on request

Contact

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Elements

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