Assistant Professor, Durham University, United Kingdom
Dr. Hadi Taghavifar is an accomplished Assistant Professor (Research) at Durham University, known for his extensive contributions to advanced energy systems, engine optimization, and emissions reduction. With research experience in both Norway and the UK, he has worked on major projects including the EU SeaTech H2020 and UK REHIP, focusing on hydrogen innovation, LNG fuels, and decarbonization in marine and industrial sectors.
Profile
Google Scholar
Education 🎓
Dr. Taghavifar’s academic path is rooted in mechanical and thermal sciences, with a focus on thermodynamics, fuel technology, and AI-integrated system modeling, forming the backbone of his applied and theoretical research in clean energy systems.
Experience 💼
He has contributed to eight major research projects and two industry-based consultancy collaborations. With 61 peer-reviewed journal publications, three patents (published/in process), and editorial roles with Frontiers in Energy Research and Discover Vehicles (Springer Nature), Dr. Taghavifar has shaped the discourse in fuel optimization and emission control. His citation profile includes 1,774 citations on Google Scholar and 1,482 on Scopus, demonstrating global research impact.
Research Interests 🔬
Dr. Taghavifar focuses on emission reduction technologies, intelligent control systems, AI and optimization for engine performance, nanoparticles in combustion, power-to-X energy integration, and thermodynamic analysis of multigeneration systems.
Publications Top Note 📚
Saraee, H.S., Taghavifar, H., & Jafarmadar, S. (2017). Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network, Applied Thermal Engineering, 113, 663–672. [Cited by 146]
This paper investigated the use of cerium oxide (CeO₂) nanoparticles in diesel engines, revealing enhanced combustion efficiency and reduced emissions. It utilized artificial neural networks to model and predict performance trends, highlighting the synergy of nanotechnology and AI in engine optimization.
Soukht Saraee, H., Jafarmadar, S., Taghavifar, H., & Ashrafi, S.J. (2015). Reduction of emissions and fuel consumption in a compression ignition engine using nanoparticles, International Journal of Environmental Science and Technology, 12, 2245–2252. [Cited by 110]
This study evaluated the impact of nanoparticle additives on emission and fuel economy in diesel engines. The results demonstrated notable reductions in NOx and soot emissions, positioning nanoparticles as viable eco-friendly enhancements for internal combustion systems.
Anvari, S., Taghavifar, H., & Parvishi, A. (2017). Thermo-economical consideration of Regenerative organic Rankine cycle coupling with the absorption chiller systems incorporated in the trigeneration system, Energy Conversion and Management, 148, 317–329. [Cited by 102]
This publication explored the coupling of regenerative organic Rankine cycles with absorption chillers in trigeneration setups. Through detailed thermodynamic and economic analyses, the work proposed an energy-efficient system design for power, heating, and cooling applications.
Taghavifar, H., Khalilarya, S., & Jafarmadar, S. (2014). Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm, Energy, 71, 656–664. [Cited by 92]
Here, a hybrid AI model combining ANN and genetic algorithms was developed to predict diesel spray behavior, offering a precise and adaptive solution to optimize fuel injection strategies in CI engines.
Anvari, S., Mahian, O., Taghavifar, H., Wongwises, S., & Desideri, U. (2020). 4E analysis of a modified multigeneration system designed for power, heating/cooling, and water desalination, Applied Energy, 270, 115107. [Cited by 87]
This article introduced a novel multigeneration system capable of delivering power, heating, cooling, and desalination. The 4E (energy, exergy, economic, and environmental) evaluation illustrated the system’s viability for sustainable development goals in resource-limited environments.
Taghavifar, H., Mardani, A., Mohebbi, A., Khalilarya, S., et al. (2016). Appraisal of artificial neural networks to the emission analysis and prediction of CO₂, soot, and NOx of n-heptane fueled engine, Journal of Cleaner Production, 112, 1729–1739. [Cited by 80]
This research assessed the use of ANNs for predicting harmful emissions in n-heptane-fueled engines. The model showed high prediction accuracy, supporting the role of AI in minimizing experimental costs and enhancing emission regulation planning.
Conclusion 🏆
Through the integration of artificial intelligence, nanoparticle technology, and thermodynamic optimization, Dr. Hadi Taghavifar has made significant strides in the advancement of sustainable and efficient fuel systems. His work stands at the crossroads of academic rigor and industrial relevance, aiming to bridge energy science with practical solutions for global decarbonization. With impactful publications, strong collaborations, and innovative contributions to clean fuel technologies, he is a fitting nominee for the Best Researcher Award.