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Published by Springer Nature Switzerland AG, Cham, 2021
ISBN 10: 3030447200 ISBN 13: 9783030447205
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Paperback. Condition: new. Paperback. 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. This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030447170 ISBN 13: 9783030447175
Language: English
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Hardcover. Condition: new. Hardcover. 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. This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 3030447170 ISBN 13: 9783030447175
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Published by Springer International Publishing, Springer Nature Switzerland Mai 2021, 2021
ISBN 10: 3030447200 ISBN 13: 9783030447205
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Add to basketTaschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 304 pp. Englisch.
Published by Springer International Publishing, Springer International Publishing Mai 2020, 2020
ISBN 10: 3030447170 ISBN 13: 9783030447175
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Add to basketBuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 304 pp. Englisch.
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Language: English
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Published by Springer Nature Switzerland AG, Cham, 2021
ISBN 10: 3030447200 ISBN 13: 9783030447205
Language: English
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Add to basketPaperback. Condition: new. Paperback. 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. This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030447170 ISBN 13: 9783030447175
Language: English
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Add to basketHardcover. Condition: new. Hardcover. 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. This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by Springer International Publishing Mai 2021, 2021
ISBN 10: 3030447200 ISBN 13: 9783030447205
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 304 pp. Englisch.
Published by Springer International Publishing Mai 2020, 2020
ISBN 10: 3030447170 ISBN 13: 9783030447175
Language: English
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 304 pp. Englisch.
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