Machine Learning and Its Application to Reacting Flows ML and Combustion /

Chi tiết về thư mục
Tác giả của công ty:	SpringerLink (Online service)
Tác giả khác:	Swaminathan, Nedunchezhian (Biên tập viên), Parente, Alessandro (Biên tập viên)
Tóm tắt:	XI, 346 p. 127 illus., 98 illus. in color. text
Ngôn ngữ:	Tiếng Anh
Được phát hành:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Phiên bản:	1st ed. 2023.
Loạt:	Lecture Notes in Energy, 44
Những chủ đề:	Cogeneration of electric power and heat. Fossil fuels. Thermodynamics. Heat engineering. Heat transfer. Mass transfer. Machine learning. Fossil Fuel. Engineering Thermodynamics, Heat and Mass Transfer. Machine Learning.
Truy cập trực tuyến:	https://doi.org/10.1007/978-3-031-16248-0
Định dạng:	Điện tử Sách

Mục lục:

Introduction
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.

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