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001452647 001__ 1452647
001452647 003__ OCoLC
001452647 005__ 20230314003311.0
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001452647 007__ cr\un\nnnunnun
001452647 008__ 230112s2023\\\\sz\a\\\\o\\\\\001\0\eng\d
001452647 019__ $$a1356891635
001452647 020__ $$a9783031162480$$q(electronic bk.)
001452647 020__ $$a303116248X$$q(electronic bk.)
001452647 020__ $$z9783031162473
001452647 020__ $$z3031162471
001452647 0247_ $$a10.1007/978-3-031-16248-0$$2doi
001452647 035__ $$aSP(OCoLC)1358406676
001452647 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001452647 049__ $$aISEA
001452647 050_4 $$aTJ254.5
001452647 08204 $$a541/.3610285631$$223/eng/20230112
001452647 24500 $$aMachine learning and its application to reacting flows :$$bML and combustion /$$cNedunchezhian Swaminathan, Alessandro Parente, editors.
001452647 264_1 $$aCham :$$bSpringer,$$c2023.
001452647 300__ $$a1 online resource (xi, 346 pages) :$$billustrations (some color).
001452647 336__ $$atext$$btxt$$2rdacontent
001452647 337__ $$acomputer$$bc$$2rdamedia
001452647 338__ $$aonline resource$$bcr$$2rdacarrier
001452647 4901_ $$aLecture notes in energy,$$x2195-1292 ;$$vvolume 44
001452647 500__ $$aIncludes index.
001452647 5050_ $$aIntroduction -- ML Algorithms, Techniques and their Application to Reactive Molecular Dynamics Simulations -- Big Data Analysis, Analytics & ML role -- ML for SGS Turbulence (including scalar flux) Closures -- ML for Combustion Chemistry -- Applying CNNs to model SGS flame wrinkling in thickened flame LES (TFLES) -- Machine Learning Strategy for Subgrid Modelling of Turbulent Combustion using Linear Eddy Mixing based Tabulation -- MILD Combustion-Joint SGS FDF -- Machine Learning for Principal Component Analysis & Transport -- Super Resolution Neural Network for Turbulent non-premixed Combustion -- ML in Thermoacoustics -- Concluding Remarks & Outlook.
001452647 5060_ $$aOpen access.$$5GW5XE
001452647 520__ $$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. .
001452647 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 12, 2023).
001452647 650_0 $$aCombustion$$xData processing.
001452647 650_0 $$aMachine learning.
001452647 650_0 $$aTurbulence$$xData processing.
001452647 655_0 $$aElectronic books.
001452647 7001_ $$aSwaminathan, Nedunchezhian,$$eeditor.
001452647 7001_ $$aParente, Alessandro,$$eeditor.
001452647 77608 $$iPrint version: $$z3031162471$$z9783031162473$$w(OCoLC)1338198249
001452647 830_0 $$aLecture notes in energy ;$$v44.$$x2195-1292
001452647 852__ $$bebk
001452647 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-031-16248-0$$zOnline Access$$91397441.2
001452647 909CO $$ooai:library.usi.edu:1452647$$pGLOBAL_SET
001452647 980__ $$aBIB
001452647 980__ $$aEBOOK
001452647 982__ $$aEbook
001452647 983__ $$aOnline
001452647 994__ $$a92$$bISE