Energy demands throughout the globe has been increasing and the detrimental effects of carbon emissions on the environment by use of non-renewable resources has impacted life on the planet. The changing climate has caused an increase in natural calamities all over the globe. Many countries in the world have started to produce power using renewable resources like solar, biomass, wind energy, nuclear energy and green fuels.
Though there are several technologies for power generation using the above sources, efficient design of these systems still needs lot of research. Mathematical modeling would play a vital role in design of state of the art technologies. Advanced nuclear power plants need special mention since they involve naturally driven safety systems where the complex phenomena of boiling, condensation and thermal stratification take place. These are difficult to model as there is more than one phase coupled with turbulence models, near wall phenomena, coalescence and break up, etc. Scaling up of such systems and their innovative design to reduce stratification requires the help of mathematical modeling. Other opportunities include Computational Fluid Dynamics (CFD) modeling for design of wind turbines for power generation using wind energy. Power generation from biomass involves use of gasifiers which has complex set of reactions and mostly two or three phases which are difficult to model using CFD at industrial scales.
CFD can play a vital role in understanding the hydrodynamics of present designs of various technologies and design of various technologies. Further, with the increase in computational power CFD can play vital role in the successful scaleup of these technologies. Another major and important aspect is the Artificial Intelligence/Machine Learning that can play a vital role in better design of power generation systems. The data generated from CFD simulations would be used for developing data driven models and training those models for good predictions.
The Research Topic will focus on the role of analytical modeling and CFD modeling in various systems in power generation through renewable sources. Deep learning coupled with CFD Modeling in these fields is currently less explored and can be a good opportunity for researchers around the globe. The following themes are of particular interest, although the collection is not limited to:
• Scaleup of power generation techniques like hybrid solar chimneys, solar tower technologies using CFD
• Opportunities and challenges in two phase CFD modeling of boiling, condensation in nuclear safety systems
• CFD modeling of thermal stratification as energy storage
• Ways and means to mitigate thermal stratification in nuclear power generation safety systems
• Deep learning coupled with CFD modeling of renewable energy systems.
Energy demands throughout the globe has been increasing and the detrimental effects of carbon emissions on the environment by use of non-renewable resources has impacted life on the planet. The changing climate has caused an increase in natural calamities all over the globe. Many countries in the world have started to produce power using renewable resources like solar, biomass, wind energy, nuclear energy and green fuels.
Though there are several technologies for power generation using the above sources, efficient design of these systems still needs lot of research. Mathematical modeling would play a vital role in design of state of the art technologies. Advanced nuclear power plants need special mention since they involve naturally driven safety systems where the complex phenomena of boiling, condensation and thermal stratification take place. These are difficult to model as there is more than one phase coupled with turbulence models, near wall phenomena, coalescence and break up, etc. Scaling up of such systems and their innovative design to reduce stratification requires the help of mathematical modeling. Other opportunities include Computational Fluid Dynamics (CFD) modeling for design of wind turbines for power generation using wind energy. Power generation from biomass involves use of gasifiers which has complex set of reactions and mostly two or three phases which are difficult to model using CFD at industrial scales.
CFD can play a vital role in understanding the hydrodynamics of present designs of various technologies and design of various technologies. Further, with the increase in computational power CFD can play vital role in the successful scaleup of these technologies. Another major and important aspect is the Artificial Intelligence/Machine Learning that can play a vital role in better design of power generation systems. The data generated from CFD simulations would be used for developing data driven models and training those models for good predictions.
The Research Topic will focus on the role of analytical modeling and CFD modeling in various systems in power generation through renewable sources. Deep learning coupled with CFD Modeling in these fields is currently less explored and can be a good opportunity for researchers around the globe. The following themes are of particular interest, although the collection is not limited to:
• Scaleup of power generation techniques like hybrid solar chimneys, solar tower technologies using CFD
• Opportunities and challenges in two phase CFD modeling of boiling, condensation in nuclear safety systems
• CFD modeling of thermal stratification as energy storage
• Ways and means to mitigate thermal stratification in nuclear power generation safety systems
• Deep learning coupled with CFD modeling of renewable energy systems.
