LEADER 04975nam 22007335 450 001 9910639881803321 005 20230101120809.0 010 $a3-031-16248-X 024 7 $a10.1007/978-3-031-16248-0 035 $a(CKB)5700000000342387 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/96208 035 $a(MiAaPQ)EBC7167859 035 $a(Au-PeEL)EBL7167859 035 $a(OCoLC)1358406676 035 $a(DE-He213)978-3-031-16248-0 035 $a(PPN)267808801 035 $a(EXLCZ)995700000000342387 100 $a20230101d2023 u| 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Its Application to Reacting Flows $eML and Combustion /$fedited by Nedunchezhian Swaminathan, Alessandro Parente 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 electronic resource (346 p.) 225 1 $aLecture Notes in Energy,$x2195-1292 ;$v44 311 $a3-031-16247-1 327 $aIntroduction -- ML Algorithms, Techniques and their Application to Reactive Molecular Dynamics Simulations -- Big Data Analysis, Analytics & ML role -- ML for SGS Turbulence (including scalar flux) Closures -- ML for Combustion Chemistry -- Applying CNNs to model SGS flame wrinkling in thickened flame LES (TFLES) -- Machine Learning Strategy for Subgrid Modelling of Turbulent Combustion using Linear Eddy Mixing based Tabulation -- MILD Combustion?Joint SGS FDF -- Machine Learning for Principal Component Analysis & Transport -- Super Resolution Neural Network for Turbulent non-premixed Combustion -- ML in Thermoacoustics -- Concluding Remarks & Outlook. 330 $aThis open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world?s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and ?greener? combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation. . 410 0$aLecture Notes in Energy,$x2195-1292 ;$v44 606 $aCogeneration of electric power and heat 606 $aFossil fuels 606 $aThermodynamics 606 $aHeat engineering 606 $aHeat transfer 606 $aMass transfer 606 $aMachine learning 606 $aFossil Fuel 606 $aEngineering Thermodynamics, Heat and Mass Transfer 606 $aMachine Learning 606 $aThermodynamics 615 0$aCogeneration of electric power and heat. 615 0$aFossil fuels. 615 0$aThermodynamics. 615 0$aHeat engineering. 615 0$aHeat transfer. 615 0$aMass transfer. 615 0$aMachine learning. 615 14$aFossil Fuel. 615 24$aEngineering Thermodynamics, Heat and Mass Transfer. 615 24$aMachine Learning. 615 24$aThermodynamics. 676 $a621.312132 700 $aSwaminathan$b Nedunchezhian$0522419 701 $aParente$b Alessandro$01337937 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910639881803321 996 $aMachine Learning and Its Application to Reacting Flows$93057667 997 $aUNINA