Bio

Hindolo is currently a post doc in the Department of Engineering Science at University of Oxford. His PhD thesis is available at . Hindolo graduated with a first-class bachelor’s degree in electrical/electronic engineering from the University of Sierra Leone in 2010. Two years later, he won a commonwealth shared scholarship to pursue an M.Sc. (Eng.) degree in energy generation at the University of Liverpool, where he graduated with a distinction. His M.Sc. thesis was on the reliability and availability analysis of hydroelectric power plants, using the Bumbuna hydroelectric power plant in Sierra Leone as a case study. Symmetrically sandwiched between his undergraduate, master’s, and PhD studies are his two spells with the Sierra Leone affiliate of the French oil giant, Total. He was the country technical and administrative head of Total’s maintenance team in his second spell, and in consonance with the projects team, championed the successful implementation of several critical engineering projects. Hindolo’s key expertise and interests include, energy systems modelling, mathematical modelling and simulation of complex processes, uncertainty quantification, probabilistic risk assessment and management, reliability and maintenance optimization of complex systems, and algorithm design.

Research Interests: System Reliability Analysis, Maintenance Modelling, Renewable Power Generation and Integration, Resilience Modelling of Complex Systems and Infrastructure

Research project title: Efficient Reliability Modelling & Analysis of Complex Systems with Application to Nuclear Power Plant Safety

Project description: Nuclear power may be our best chance at a permanent solution to the world's energy challenges, owing to its sustainability and environmental friendliness. However, it also poses a great risk to life, property, and the economy, given the possibility of severe accidents, during its generation, transmission, and distribution. These accidents are a result of the susceptibility of the generating plants to component failure, human error, extreme environmental events, targeted attacks, and natural disasters. Given the complexity and high interconnectivity of the systems in question, a small glitch, otherwise known as an initiating event, could cascade to catastrophic consequences. It is, therefore, vital that the vulnerability of a plant to these glitches and their ensuing consequences be ascertained, to ensure that the appropriate mitigating actions are taken. A nuclear power plant by default is equipped with safety systems to deter the propagation of an initiating event. An accident ensues if the safety systems required to mitigate some initiating event are unavailable or incapacitated by the initiating event. The reliability, therefore, as well as the availability of these systems, shape the safety of the plant.

This project aims to develop a series of computationally efficient tools to address the limitations of the existing probabilistic risk assessment tools. To achieve this, the feasibility of well-established concepts like advanced Monte Carlo methods, network theory, probabilistic models, and other novel approaches will be explored. The resulting tools should influence robust decisions in the risk management of nuclear power plants. These tools will be incorporated into the open-source uncertainty quantification tool, OpenCOSSAN (see http://www.cossan.co.uk/software/open-cossan-engine.php), and therefore, readily available to industry, as well as other researchers.

Supervisory team: Edoardo Patelli; Min Lee

ORCID: 0000-0002-9316-3911