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- Oxford Brookes UniversityGB8 papers
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7 papers · 1 filter
Prediction of the energy and exergy performance of F135 PW100 turbofan engine via deep learning
Mohammadreza Sabzehali, Amir Hossein Rabieeb, Mahdi Alibeigia +1
In the present study, the effects of flight-Mach number, flight altitude, fuel types, and intake air temperature on thrust specific fuel consumption, thrust, intake air mass flow r…
Energy-Exergy Analysis and Optimal Design of a Hydrogen Turbofan Engine
Mohammadreza Sabzehali, Somayeh Davoodabadi Farahani, Amir Mosavi
In this study, the effect of inlet air cooling and fuel type on the performance parameters of thrust-specific fuel consumption (TSFC), thermal and exergetic efficiencies, entropy g…
Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit
Weibin Chen, Danial Sharifrazi, Guoxi Liang +3
Streamlined weirs which are a nature-inspired type of weir have gained tremendous attention among hydraulic engineers, mainly owing to their established performance with high disch…
Three isometrically equivalent models of the Finsler-Poincaré disk
Ágnes Mester, Alexandru Kristály
We present the isometry between the 2-dimensional Funk model and the Finsler-Poincaré disk. Then, we introduce the Finslerian Poincaré upper half plane model, which turns out to be…
Systematic review of deep learning and machine learning for building energy
Ardabili Sina, Leila Abdolalizadeh, Csaba Mako +2
The building energy (BE) management has an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown signifi…
Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data
Gergő Pintér, Imre Felde
In this study, Call Detail Records (CDR), covering Budapest, Hungary has been processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is in…