69,399 research outputs found
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
An efficient surrogate model for emulation and physics extraction of large eddy simulations
In the quest for advanced propulsion and power-generation systems,
high-fidelity simulations are too computationally expensive to survey the
desired design space, and a new design methodology is needed that combines
engineering physics, computer simulations and statistical modeling. In this
paper, we propose a new surrogate model that provides efficient prediction and
uncertainty quantification of turbulent flows in swirl injectors with varying
geometries, devices commonly used in many engineering applications. The novelty
of the proposed method lies in the incorporation of known physical properties
of the fluid flow as {simplifying assumptions} for the statistical model. In
view of the massive simulation data at hand, which is on the order of hundreds
of gigabytes, these assumptions allow for accurate flow predictions in around
an hour of computation time. To contrast, existing flow emulators which forgo
such simplications may require more computation time for training and
prediction than is needed for conducting the simulation itself. Moreover, by
accounting for coupling mechanisms between flow variables, the proposed model
can jointly reduce prediction uncertainty and extract useful flow physics,
which can then be used to guide further investigations.Comment: Submitted to JASA A&C
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