Kniha Emulation of Complex Fluid Flows Xingjian Wang

Emulation of Complex Fluid Flows

Projection-Based Reduced-Order Modeling and Machine Learning

Jazyk: Angličtina
Vazba: Pevná
Vydavatel: De Gruyter
Dostupnost: Skladem u dodavatele
Odesíláme za 14-21 dnů
4 322
While artificial intelligence has made significant strides in imaging and natural language processin...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Pevná
Vydáno
2025
Stránek
135
EAN
9783111631356
Enbook ID
49153686
Vydavatel
Hmotnost
403
Rozměry
170 x 240

Kompletní popis

While artificial intelligence has made significant strides in imaging and natural language processing, its utilization in engineering science remains relatively new. This book aims to introduce machine learning techniques to facilitate the emulation of complex fluid flows. The work focuses on projection-based reduced-order models (ROMs) that condense high-dimensional data into a low-dimensional subspace by leveraging principal components. Techniques like proper orthogonal decomposition (POD) and convolutional autoencoder (CAE) are utilized to configure this subspace, establishing a functional mapping between input parameters and solution fields. The applicability of POD-based ROMs for spatial and spatiotemporal problems are explored across various engineering scenarios, including flow past a cylinder, supercritical turbulent flows, and hydrogen-blended combustion. To capture intricate dynamics, common POD, kernel-smoothed POD, and common kernel-smoothed POD methods are developed in sequence. Additionally, the effectiveness of POD and CAE in capturing nonlinear features are compared. This book is designed to benefit graduate students and researchers interested in the intersection of data and engineering sciences.

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