Thermodynamic optimization of Stirling heat engine with methane gas using finite speed thermodynamic model

Heat Transfer ◽  
2021 ◽  
Author(s):  
Kiran Mansuriya ◽  
Bansi D. Raja ◽  
Ali R. Yıldız ◽  
Anurag Mudgal ◽  
Vivek K. Patel
Keyword(s):  
Thermodynamic Model ◽  
Heat Engine ◽  
Thermodynamic Optimization ◽  
Finite Speed ◽  
Methane Gas

scholarly journals Thermodynamic Optimization for an Endoreversible Dual-Miller Cycle (DMC) with Finite Speed of Piston

Entropy ◽  
2018 ◽  
Vol 20 (3) ◽  
pp. 165 ◽  
Author(s):  
Zhixiang Wu ◽  
Lingen Chen ◽  
Huijun Feng
Keyword(s):  
Thermodynamic Optimization ◽  
Miller Cycle ◽  
Finite Speed

Optimization of powered Stirling heat engine with finite speed thermodynamics

2016 ◽  
Vol 108 ◽  
pp. 96-105 ◽  
Author(s):  
Mohammad H. Ahmadi ◽  
Mohammad Ali Ahmadi ◽  
Fathollah Pourfayaz ◽  
Mokhtar Bidi ◽  
Hadi Hosseinzade ◽  
...  
Keyword(s):  
Heat Engine ◽  
Finite Speed

Many-objective thermodynamic optimization of Stirling heat engine

Energy ◽  
2017 ◽  
Vol 125 ◽  
pp. 629-642 ◽  
Author(s):  
Vivek Patel ◽  
Vimal Savsani ◽  
Anurag Mudgal
Keyword(s):  
Heat Engine ◽  
Thermodynamic Optimization

scholarly journals The challenge of RNA branching prediction: a parametric analysis of multiloop initiation under thermodynamic optimization

2020 ◽  
Author(s):  
Svetlana Poznanović ◽  
Fidel Barrera-Cruz ◽  
Anna Kirkpatrick ◽  
Matthew Ielusic ◽  
Christine Heitsch
Keyword(s):  
Molecular Structure ◽  
Thermodynamic Model ◽  
Parametric Analysis ◽  
Prediction Accuracy ◽  
5S Rrna ◽  
Optimal Structure ◽  
Complete Analysis ◽  
Thermodynamic Optimization ◽  
The Cost ◽  
Insight Into

AbstractPrediction of RNA base pairings yields insight into molecular structure, and therefore function. The most common methods predict an optimal structure under the standard thermodynamic model. One component of this model is the equation which governs the cost of branching, where three or more helical “arms” radiate out from a multiloop (also known as a junction). The multiloop initiation equation has three parameters; changing those values can significantly alter the predicted structure. We give a complete analysis of the prediction accuracy, stability, and robustness for all possible parameter combinations for a diverse set of tRNA sequences, and also for 5S rRNA. We find that the accuracy can often be substantially improved on a per sequence basis. However, simultaneous improvement within families, and most especially between families, remains a challenge.

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