Data analysis for direct numerical simulations of turbulent combustion: from equation-based analysis to machine learning
This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2020]
|
| Schlagworte: | |
| Zusammenfassung: | This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data.The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences |
| Beschreibung: | 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 |
| Beschreibung: | ix, 292 Seiten Illustrationen, Diagramme 468 grams |
| ISBN: | 9783030447205 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047583098 | ||
| 003 | DE-604 | ||
| 005 | 20220107 | ||
| 007 | t | ||
| 008 | 211110s2020 a||| |||| 00||| eng d | ||
| 020 | |a 9783030447205 |c pbk |9 978-3-030-44720-5 | ||
| 024 | 3 | |a 9783030447205 | |
| 035 | |a (OCoLC)1181988516 | ||
| 035 | |a (DE-599)BVBBV047583098 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T | ||
| 245 | 1 | 0 | |a Data analysis for direct numerical simulations of turbulent combustion |b from equation-based analysis to machine learning |c Heinz Pitsch, Antonia Attili, editors |
| 264 | 1 | |a Cham, Switzerland |b Springer |c [2020] | |
| 300 | |a ix, 292 Seiten |b Illustrationen, Diagramme |c 468 grams | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a 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 | ||
| 520 | |a This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data.The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences | ||
| 650 | 4 | |a Computer mathematics | |
| 650 | 4 | |a Fluid mechanics | |
| 650 | 4 | |a Mathematical physics | |
| 650 | 4 | |a Thermodynamics | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Environment | |
| 650 | 4 | |a Mathematics | |
| 653 | |a Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik | ||
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 700 | 1 | |a Pitsch, Heinz Günter |d 1966- |0 (DE-588)120034638 |4 edt | |
| 700 | 1 | |a Attili, Antonio |0 (DE-588)1216567824 |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-030-44718-2 |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032968445 | ||
Datensatz im Suchindex
| _version_ | 1804182937933971456 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author2 | Pitsch, Heinz Günter 1966- Attili, Antonio |
| author2_role | edt edt |
| author2_variant | h g p hg hgp a a aa |
| author_GND | (DE-588)120034638 (DE-588)1216567824 |
| author_facet | Pitsch, Heinz Günter 1966- Attili, Antonio |
| building | Verbundindex |
| bvnumber | BV047583098 |
| ctrlnum | (OCoLC)1181988516 (DE-599)BVBBV047583098 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03844nam a2200421 c 4500</leader><controlfield tag="001">BV047583098</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220107 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">211110s2020 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783030447205</subfield><subfield code="c">pbk</subfield><subfield code="9">978-3-030-44720-5</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9783030447205</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1181988516</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047583098</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data analysis for direct numerical simulations of turbulent combustion</subfield><subfield code="b">from equation-based analysis to machine learning</subfield><subfield code="c">Heinz Pitsch, Antonia Attili, editors</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham, Switzerland</subfield><subfield code="b">Springer</subfield><subfield code="c">[2020]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">ix, 292 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">468 grams</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data.The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fluid mechanics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical physics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Thermodynamics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Environment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pitsch, Heinz Günter</subfield><subfield code="d">1966-</subfield><subfield code="0">(DE-588)120034638</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Attili, Antonio</subfield><subfield code="0">(DE-588)1216567824</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-030-44718-2</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032968445</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047583098 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:33:46Z |
| indexdate | 2024-07-10T09:15:29Z |
| institution | BVB |
| isbn | 9783030447205 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032968445 |
| oclc_num | 1181988516 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | ix, 292 Seiten Illustrationen, Diagramme 468 grams |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| record_format | marc |
| spelling | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning Heinz Pitsch, Antonia Attili, editors Cham, Switzerland Springer [2020] ix, 292 Seiten Illustrationen, Diagramme 468 grams txt rdacontent n rdamedia nc rdacarrier 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 This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data.The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences Computer mathematics Fluid mechanics Mathematical physics Thermodynamics Computer science Environment Mathematics Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik (DE-588)4143413-4 Aufsatzsammlung gnd-content Pitsch, Heinz Günter 1966- (DE-588)120034638 edt Attili, Antonio (DE-588)1216567824 edt Erscheint auch als Online-Ausgabe 978-3-030-44718-2 |
| spellingShingle | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning Computer mathematics Fluid mechanics Mathematical physics Thermodynamics Computer science Environment Mathematics |
| subject_GND | (DE-588)4143413-4 |
| title | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning |
| title_auth | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning |
| title_exact_search | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning |
| title_exact_search_txtP | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning |
| title_full | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning Heinz Pitsch, Antonia Attili, editors |
| title_fullStr | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning Heinz Pitsch, Antonia Attili, editors |
| title_full_unstemmed | Data analysis for direct numerical simulations of turbulent combustion from equation-based analysis to machine learning Heinz Pitsch, Antonia Attili, editors |
| title_short | Data analysis for direct numerical simulations of turbulent combustion |
| title_sort | data analysis for direct numerical simulations of turbulent combustion from equation based analysis to machine learning |
| title_sub | from equation-based analysis to machine learning |
| topic | Computer mathematics Fluid mechanics Mathematical physics Thermodynamics Computer science Environment Mathematics |
| topic_facet | Computer mathematics Fluid mechanics Mathematical physics Thermodynamics Computer science Environment Mathematics Aufsatzsammlung |
| work_keys_str_mv | AT pitschheinzgunter dataanalysisfordirectnumericalsimulationsofturbulentcombustionfromequationbasedanalysistomachinelearning AT attiliantonio dataanalysisfordirectnumericalsimulationsofturbulentcombustionfromequationbasedanalysistomachinelearning |