Data analysis for direct numerical simulations of turbulent combustion : from equation-based analysis to machine learning /: from equation-based analysis to machine learning. ([2020])
- Record Type:
- Book
- Title:
- Data analysis for direct numerical simulations of turbulent combustion : from equation-based analysis to machine learning /: from equation-based analysis to machine learning. ([2020])
- Main Title:
- Data analysis for direct numerical simulations of turbulent combustion : from equation-based analysis to machine learning
- Further Information:
- Note: Heinz Pitsch, Antonio Attili, editors.
- Other Names:
- Pitsch, Heinz
Attili, Antonio - Contents:
- Partial A-Posteriori LES of DNS Data of Turbulent Combustion.- Application of the Optimal Estimator Analysis to Turbulent Combustion Modeling.- Reduced Order Modeling of Rocket Combustion Flows.- Dynamic Mode Decompositions: A Tool to Extract Structure Hidden in Massive Dataset.- Analysis of Combustion-Modes Through Structural and Dynamic Technique.- Analysis of the Impact of Combustion On Turbulence: Triadic Analysis, Wavelets, Structure Functions, Spectra.- Analysis of Flame Topology and Burning Rates.- Dissipation Element Analysis of Turbulent Combustion.- Higher Order Tensors for DNS Data Analysis and Compression.- Covariant Lyapunov Vector Analysis of Turbulent Reacting Flows.- CEMA Analysis Applied to DNS Data.- Combined Computational Singular Perturbation-Tangential Stretching Rate Diagnostics of Large.- Scale Simulations of Reactive Turbulent Flows: Feature Tracking, Time Scale Characterization, and Cause/Effect Identification.- Genetic Algorithms Applied to LES Model Development.- Sub-grid Scale Signal Reconstruction: From Discrete and Iterative Deconvolution Operators to Convolutional Neural Networks.- Machine Learning for Combustion Rate Shaping.- Machine Learning of Combustion LES Models from DNS.- Developing Artificial Neural Networks Based Models for Complex Turbulent Flow by Utilizing DNS Database.
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2020
- Extent:
- 1 online resource
- Subjects:
- 532/.0527
Turbulence -- Mathematical models
Big data
Machine learning
Electronic books
Electronic books - Languages:
- English
- ISBNs:
- 9783030447182
3030447189 - Related ISBNs:
- 9783030447175
3030447170 - Notes:
- Note: Description based on online resource; title from digital title page (viewed on June 26, 2020).
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.508460
- Ingest File:
- 03_085.xml