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Data Analysis for Direct Numerical Simulations of Turbulent Combustion [[electronic resource] ] : From Equation-Based Analysis to Machine Learning / / edited by Heinz Pitsch, Antonio Attili
| Data Analysis for Direct Numerical Simulations of Turbulent Combustion [[electronic resource] ] : From Equation-Based Analysis to Machine Learning / / edited by Heinz Pitsch, Antonio Attili |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 532.0527015118 |
| Soggetto topico |
Computer mathematics
Fluid mechanics Mathematical physics Thermodynamics Computer science Environment Computational Mathematics and Numerical Analysis Engineering Fluid Dynamics Theoretical, Mathematical and Computational Physics Computer Science, general Environment, general |
| ISBN | 3-030-44718-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNISA-996418278703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Data Analysis for Direct Numerical Simulations of Turbulent Combustion [[electronic resource] ] : From Equation-Based Analysis to Machine Learning / / edited by Heinz Pitsch, Antonio Attili
| Data Analysis for Direct Numerical Simulations of Turbulent Combustion [[electronic resource] ] : From Equation-Based Analysis to Machine Learning / / edited by Heinz Pitsch, Antonio Attili |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 532.0527015118 |
| Soggetto topico |
Computer mathematics
Fluid mechanics Mathematical physics Thermodynamics Computer science Environment Computational Mathematics and Numerical Analysis Engineering Fluid Dynamics Theoretical, Mathematical and Computational Physics Computer Science, general Environment, general |
| ISBN | 3-030-44718-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNINA-9910484834403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||