Rojin Asadi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Rojin Asadi has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Erfan Amini
School of Civil Engineering, College of Engineering, University of Tehran, Tehran 13145, Iran
3 shared publications - Mehdi Neshat
University of Technology Sydney, Australia
3 shared publications - Danial Golbaz
School of Civil Engineering, College of Engineering, University of Tehran, Tehran 13145, Iran
2 shared publications - Mahdieh Nasiri
School of Mechanical Engineering, Iran University of Science and Technology, Tehran 13114, Iran
2 shared publications - Meysam Majidi Nezhad
Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Roma, Italy
2 shared publications - Seyed Taghi Omid Naeeni
Faculty of Civil Engineering, College of Engineering, University of Tehran, Tehran 1638855713, Iran
2 shared publications
Latest Publications
- Open Access
- 23 November 2022
- Published by Elsevier in Energy Reports
- Vol. 8, 15446-15479
Layout and design optimization of ocean wave energy converters: A scoping review of state-of-the-art canonical, hybrid, cooperative, and combinatorial optimization methods
- 23 November 2022
- Published by Elsevier in Energy Reports
- Vol. 8, 15446-15479
Open AccessLayout and design optimization of ocean wave energy converters: A scoping review of state-of-the-art canonical, hybrid, cooperative, and combinatorial optimization methods
- Open Access
- 1 October 2021
- Published by MDPI AG in Sustainability
- Vol. 13 (19) , 10932
Comparative Study of Oscillating Surge Wave Energy Converter Performance: A Case Study for Southern Coasts of the Caspian Sea
The search for renewable energy supplies for today’s global energy demand, particularly ocean wave energy for coastal areas, has become undeniably widespread in the last two decades. The Caspian Sea represents an immense opportunity for using ocean renewable energy, especially considering its long shoreline. In this study, the locations with maximum potential wave energy were chosen in the central, eastern, and western zones of the Caspian Sea’s southern coasts. Accordingly, the wave and bathymetric data were used as the input to calculate the oscillating surge wave energy converter’s flap geometric dimensions based on previous studies. Then, the geometric model was designed, and then the wave energy converters were modeled in the Wave Energy Converter Simulator (WEC-Sim) module in the MATLAB software. Furthermore, eight models in each sea state were simulated to find the best value of the PTO damping coefficient, which led to the highest capture factor. Finally, all the external forces on the WEC’s flap and the converter’s power output results were compared, taking into account the effects of the flap height on the total power output. It was found that Nowshahr port has more potential than the Anzali and Amirabad ports, as the converter’s absorbed power proved to be 16.7 kW/m (Capture factor = 63%) at this site. Consequently, by conducting a comparative analysis between the selected sites, the excitation, radiation damping, and power take-off forces were scrutinized. The results show that the highest applied forces to the converter’s flap occurred at Nowshahr port, followed by the Anzali and Amirabad ports, due to the directional characteristics of the waves at the central coasts of the Caspian Sea.
- 1 October 2021
- Published by MDPI AG in Sustainability
- Vol. 13 (19) , 10932
Open AccessComparative Study of Oscillating Surge Wave Energy Converter Performance: A Case Study for Southern Coasts of the Caspian Sea
The search for renewable energy supplies for today’s global energy demand, particularly ocean wave energy for coastal areas, has become undeniably widespread in the last two decades. The Caspian Sea represents an immense opportunity for using ocean renewable energy, especially considering its long shoreline. In this study, the locations with maximum potential wave energy were chosen in the central, eastern, and western zones of the Caspian Sea’s southern coasts. Accordingly, the wave and bathymetric data were used as the input to calculate the oscillating surge wave energy converter’s flap geometric dimensions based on previous studies. Then, the geometric model was designed, and then the wave energy converters were modeled in the Wave Energy Converter Simulator (WEC-Sim) module in the MATLAB software. Furthermore, eight models in each sea state were simulated to find the best value of the PTO damping coefficient, which led to the highest capture factor. Finally, all the external forces on the WEC’s flap and the converter’s power output results were compared, taking into account the effects of the flap height on the total power output. It was found that Nowshahr port has more potential than the Anzali and Amirabad ports, as the converter’s absorbed power proved to be 16.7 kW/m (Capture factor = 63%) at this site. Consequently, by conducting a comparative analysis between the selected sites, the excitation, radiation damping, and power take-off forces were scrutinized. The results show that the highest applied forces to the converter’s flap occurred at Nowshahr port, followed by the Anzali and Amirabad ports, due to the directional characteristics of the waves at the central coasts of the Caspian Sea.
- Open Access
- 1 May 2021
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 9 (5) , 490
A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia
One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC’s PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.
- 1 May 2021
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 9 (5) , 490
Open AccessA Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia
One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC’s PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.