Mathematical Modelling of Energy Systems and Fluid Machinery

The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning t...

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Bibliographic Details
Other Authors: Morini, Mirko (Editor), Pinelli, Michele (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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024 7 |a 10.3390/books978-3-0365-0551-0  |c doi 
041 0 |a English 
042 |a dc 
072 7 |a TB  |2 bicssc 
100 1 |a Morini, Mirko  |4 edt 
700 1 |a Pinelli, Michele  |4 edt 
700 1 |a Morini, Mirko  |4 oth 
700 1 |a Pinelli, Michele  |4 oth 
245 1 0 |a Mathematical Modelling of Energy Systems and Fluid Machinery 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2021 
300 |a 1 electronic resource (256 p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
653 |a centrifugal pump 
653 |a double hidden layer 
653 |a Levenberg-Marquardt algorithm 
653 |a performance prediction 
653 |a thermal energy storage 
653 |a stratification 
653 |a dynamic simulation 
653 |a heating 
653 |a double-channel sewage pump 
653 |a critical wall roughness 
653 |a numerical calculation 
653 |a external characteristics 
653 |a axial-flow pump 
653 |a impeller 
653 |a approximation model 
653 |a optimization design 
653 |a multi-disciplinary 
653 |a blade slot 
653 |a orthogonal test 
653 |a numerical simulation 
653 |a Francis turbine 
653 |a anti-cavity fins 
653 |a draft tube 
653 |a vortex rope 
653 |a low flow rates 
653 |a internal flow characteristics 
653 |a unsteady pressure 
653 |a energy recovery 
653 |a turboexpander 
653 |a throttling valves 
653 |a CFD 
653 |a modelling techniques 
653 |a Kaplan turbine 
653 |a draft tube optimization 
653 |a CFD analysis 
653 |a DOE 
653 |a response surface 
653 |a single-channel pump 
653 |a CFD-DEM coupling method 
653 |a particle features and behaviors 
653 |a solid-liquid two-phase flows 
653 |a computational fluid dynamics (CFD) 
653 |a artificial neural network (ANN) 
653 |a subcooled boiling flows 
653 |a uncertainty quantification (UQ) 
653 |a Monte Carlo dropout 
653 |a deep ensemble 
653 |a deep neural network (DNN) 
653 |a intake structures 
653 |a physical hydraulic model 
653 |a free surface flow 
653 |a free surface vortices 
653 |a vertical pump 
653 |a design considerations 
653 |a magnetocaloric effect 
653 |a coefficient of performance 
653 |a refrigeration 
653 |a capacity 
653 |a mathematical modelling 
653 |a energy systems 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/3803  |7 0  |z Get Fullteks 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/76378  |7 0  |z DOAB: description of the publication