Energy and Sustainable Futures: Proceedings of the 3rd ICESF, 2022 / edited by Jonathan D. Nixon, Amin Al-Habaibeh, Vladimir Vukovic, Abhishek Asthana.

• Superior document: Springer Proceedings in Energy,
• Author: Nixon, Jonathan D.
• TeilnehmendeR: Al-Habaibeh, Amin.; Vukovic, Vladimir.; Asthana, Abhishek.
• Place / Publishing House: Cham : : Springer Nature Switzerland :, Imprint: Springer,, 2023.
• Year of Publication: 2023
• Edition: 1st ed. 2023.
• Language: English
• Series: Springer Proceedings in Energy,
• Physical Description: 1 online resource (353 pages)

This is an open access book. This book contains research papers presented at the 3rd International Conference on Energy and Sustainable Futures (ICESF), which took place at Coventry University, UK, in 2022. The ICESF is an annual conference organised by the UK-based Doctorial Training Alliance (DTA) programme. It is a multidisciplinary conference focused on addressing the future challenges and opportunities for meeting global energy targets and sustainable development goals. The conference brought together academic researchers, industry experts and research students to showcase the latest innovations and research on a wide range of topics in the areas of energy and sustainability, including • renewable energy; • ICT and control; • computational fluid dynamics; • optimization; • conventional energy sources; • energy governance; • materials in energy research; • energy storage and • energy access.

Table of Contents

Investigating Energy Cost Impact on Private Residential Buildings in the West Midlands Region of the UK -- Towards an Effective Artificial Intelligence Systems for Condition Monitoring of Off-Shore Wind Turbines: The Application of Sensor Fusion -- CFD Based Aerodynamics conjugate Heat Transfer and Airgap Fluid Flow Thermal Analysis to a Wheel Hub Motor for Electric Scooters -- Learning from the past fora sustainable future: Environmental monitoring and 3D modeling to assess the thermal performance of heritage buildings -- Oil and gas supply chain: Analysing stakeholder sustainability risk perception -- using machine learning to predict synthetic fuel spray penetration from limited experimental data without computational flluid dynamics.