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    A conceptual framework for combining artificial neural networks with computational aeroacoustics for design development.

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    Name:
    Whitbrook_2018_A_conceptual_fr ...
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    Description:
    Final conference paper
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    Authors
    McKee, Claire
    Harmanto, Dani cc
    Whitbrook, Amanda cc
    Affiliation
    University of Derby
    Issue Date
    2018-03
    
    Metadata
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    Abstract
    This paper presents a preliminary method for improving the design and development process in a way that combines engineering design approaches based on learning algorithms and computational aeroacoustics. It is proposed that machine learning can effectively predict the noise generated by a coaxial jet exhaust by utilizing a database of computational experiments that cover a variety of flow and geometric configurations. A conceptual framework has been outlined for the development of a practical design tool to predict the changes in jet acoustics imparted by varying the fan nozzle geometry and engine cycle of a coaxial jet. It is proposed that computational aeroacoustic analysis is used to generate a training and validation database for an artificial neural network. The trained network can then predict noise data for any operational configuration. This method allows for the exploration of noise emissions from a variety of fan nozzle areas, engine cycles and flight conditions. It is intended that this be used as a design tool in order to reduce the design cycle time of new engine configurations and provide engineers with insight into the relationship between jet noise and the input variables.
    Citation
    McKee, C., Harmanto, D., Whitbrook, A. (2018). A Conceptual Framework for Combining Artificial Neural Networks with Computational Aeroacoustics for Design Development. In: Proceedings of the International Conference on Industrial Engineering and Operations Management, Bandung, Indonesia, March 6 - 8, 2018, pp: 741-747
    Publisher
    Industrial Engineering and Operations Management Society (IEOM)
    Journal
    Proceedings of the International Conference on Industrial Engineering and Operations Management
    URI
    http://hdl.handle.net/10545/622161
    Additional Links
    http://ieomsociety.org/ieom2018/papers/165.pdf
    http://ieomsociety.org/ieom2018/
    http://ieomsociety.org/ieom2018/proceedings/
    Type
    Meetings and Proceedings
    Language
    en
    ISSN
    21698767
    ISBN
    9781532359446
    Collections
    Department of Mechanical Engineering & the Built Environment

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