Abstract
The use of surrogate models in recent times plays a key role in the design process and, over
the past few years, machine learning algorithms are adapting to the use of active metamodelling
techniques. In the present study, Artificial Neural Networks formulation is used as a data–
driven nonlinear model aimed at describing the dynamics of an experimental test campaign
related to the noise emitted by a single–stream jet in under–expanded conditions. The architecture
of the neural network is selected employing a deterministic optimization algorithm,
coupled with data informed tuning of the input parameters. The data set explored here was
acquired in the state–of–the–art aeroacoustic facility at the University of Bristol. Both the nearand
far–field acoustic measurements were carried out for a cold under–expanded jet for Mach
numbers ranging from 1.1 to 1.4. The predictions by the metamodel are in good agreement
with the experimental data, and the results demonstrate the capability of metamodels as a
reliable tool to estimate jet noise in under-expanded flow conditions for a wide range of Mach
numbers and near–field locations.
the past few years, machine learning algorithms are adapting to the use of active metamodelling
techniques. In the present study, Artificial Neural Networks formulation is used as a data–
driven nonlinear model aimed at describing the dynamics of an experimental test campaign
related to the noise emitted by a single–stream jet in under–expanded conditions. The architecture
of the neural network is selected employing a deterministic optimization algorithm,
coupled with data informed tuning of the input parameters. The data set explored here was
acquired in the state–of–the–art aeroacoustic facility at the University of Bristol. Both the nearand
far–field acoustic measurements were carried out for a cold under–expanded jet for Mach
numbers ranging from 1.1 to 1.4. The predictions by the metamodel are in good agreement
with the experimental data, and the results demonstrate the capability of metamodels as a
reliable tool to estimate jet noise in under-expanded flow conditions for a wide range of Mach
numbers and near–field locations.
| Original language | English |
|---|---|
| Title of host publication | 28th AIAA/CEAS Aeroacoustics Conference, 2022 |
| Publisher | American Institute of Aeronautics and Astronautics Inc. (AIAA) |
| ISBN (Print) | 9781624106644 |
| DOIs | |
| Publication status | Published - 17 Jun 2022 |
Publication series
| Name | A collection of technical papers (AIAA/CEAS Aeroacoustics Conference) |
|---|---|
| Publisher | AIAA |
| ISSN (Print) | 1946-7826 |
Bibliographical note
Funding Information:This work has been supported by the European Union’s Horizon 2020 research and innovation program under project ENODISE (Enabling optimized disruptive airframe-propulsion integration concepts) grant agreement No. 860103. Part of this work was supported by Engineering and Physical Sciences Research Council (EPSRC) Grant No. EP/S000917/1.
Publisher Copyright:
© 2022, American Institute of Aeronautics and Astronautics Inc, AIAA., All rights reserved.
Keywords
- jet noise
- supersonic jets
- metamodelling
- artificial neural network (ANN)
- Neural network
- aeroacoustics