Highlights
- •Acdecom is a Python module that makes the post-processing of acoustic data in flow-ducts easy.
- •It is therefore useful for research and development in academia and industry that relies on ducted sound generation and mitigation, such as HVAC systems, turbo compressors, exhaust piping, jet engines, mufflers, silencers, and liners.
- •Some of the package features: An easy-to-set-up three-step decomposition scheme for experiments and simulations; Predefined wavenumbers, acoustic mode shapes, and attenuation models for the most common geometries and flow conditions; High automatization; High customizability; Well documented package with examples to simplify distribution and usage.
Abstract
acdecom is a Python module for sound-wave decomposition in flow ducts. It embodies an easy-to-use environment to facilitate the post-processing of flow-acoustic fields from experiments and simulations. The published codes are useful for research and development in academia and industry that relies on ducted sound generation and mitigation, such as HVAC systems, turbo compressors, exhaust piping, jet engines, mufflers, silencers, and liners. acdecom’s routines are validated against numerous data sets. Furthermore, they have been used for many publications and conference contributions. To simplify the usage, the code’s documentation is illustrated with examples from experiments and simulations.
Keywords
Code metadata
Tabled
1
| Current code version | v2020.07.27 |
| Permanent link to code/repository used for this code version | https://github.com/SoftwareImpacts/SIMPAC-2020-27 |
| Permanent link to reproducible Capsule | https://codeocean.com/capsule/4407237/tree/v1 |
| Legal Code License | MIT |
| Code versioning system used | git, PyPip |
| Software code languages, tools, and services used | python |
| Compilation requirements, operating environments & tested on Microsoft Windows 10, Ubuntu 19. | |
| If available Link to developer documentation/manual | https://acdecom.readthedocs.io/ |
| contact | [email protected] |
1. Introduction
Fluid-machinery in ducts such as HVAC-fans, compressors, turbines, and jet engines, create noise of a complex nature. Other components, such as mufflers, silencers and liners, are developed to reduce that noise. In order to match the design of sources and absorbers, it is often necessary to quantify their acoustic behavior.
The sound waves inside a duct propagate towards its ends, where they are partially released into the environment and partially reflected back into the duct. The extent of this reflection depends on many factors, but mostly on the duct terminations. Direct measurements of the acoustic pressure along a duct capture a mix of emitted and reflected waves. This is often unwanted as the terminations in applications and laboratory tests differ. A common approach is to split the sound field into its upstream and downstream propagating acoustic modes, which removes the dependency of the test-results on the internal reflection of the test setup. This procedure is called acoustic mode decomposition [
[1]
].Although acoustic mode decomposition is a common approach employed in all disciplines of flow-acoustics, no validated and documented non-commercial software package are presently being used for this purpose. To this end, the codes published in this paper provide an easy-to-use framework for scientists, researchers, and engineers: one that facilitates the acoustic characterization of ducted components through mostly automatized, and customizable acoustic mode decomposition.
Fig. 1Schematic of the three-step procedure to decompose acoustic data with acdecom.
2. Impact
Acoustic mode decomposition is a common procedure in the acoustic community that is not only used for research [
2
, 3
, 4
], but also in engineering disciplines, such as vehicle and aerospace engineering[5
, 6
, 7
, 8
], and for testing and certification [[9]
]. Due to the absence of software packages, most institutes are forced to develop decomposition codes in-house. The validation process, in particular, requires expensive experiments and simulations. Additionally, it is often difficult to exchange data created with in-house codes or to share advanced post-processing code based on non-standardized functionality. In contrast, acdecom delivers an easy-to-setup solution that reduces the need for code-development and the associated risks of malfunction. Due to the module’s customizability, researchers can easily implement their own models while building upon an automatized framework. Furthermore, it simplifies the exchange of post-processed duct-acoustic data within the community and, therefore, improves collaboration between groups from different institutes. Written in the Python programming language and published under the MIT license, it is not subjected to any commercial licensing restrictions and can be used freely by academia and industry. As the Python scripting language has a readable and simplistic nature, the module can be used, understood, and modified by members from both, the scientific and the industrial communities without advanced training or preparation.acdecom was developed in the framework of several academical and industrial projects during the last decade. Therefore, it delivers the functionality that is needed for mode decomposition in a broad range of applications. Additionally, it is validated against many sets of high-fidelity flow-acoustic data. The peer-reviewed scientific journal publications for which acdecom’s code was used can be found in Refs. [
10
, 11
] for experimental procedures, and in Refs. [12
, 13
] for numerical procedures. acdecom’s codes were also used in applied projects to quantify HVAC-noise [- Shur M.
- Strelets M.
- Travin A.
- Christophe J.
- Kucukcoskun K.
- Schram C.
- Sack S.
- Åbom M.
Experimental/numerical study of ducted-fan noise: Effect of duct inlet shape.
AIAA J. 2018; 56https://doi.org/10.2514/1.J055870
[14]
], fan-noise [15
, 16
], and for the design of silencers [[17]
]. One of the contributions to the liner benchmark test initiated by the International Forum of Aviation Research (IFAR) and coordinated by the National Aeronautics and Space Administration (NASA) was supported by acdecom’s routines [- Sack S.
- Åbom M.
Modal filters for mitigation of in-duct sound.
in: Proceedings of Meetings on Acoustics, vol. 29. 2016https://doi.org/10.1121/2.0000473
[18]
]. Ongoing work in a new line of research on neural-network-based mode decomposition uses acdecom for the trainings process [- Jones M.G.
- Nark D.M.
- Howerton B.M.
Overview of liner activities in support of the international forum for aviation research.
in: 25th AIAA/CEAS Aeroacoustics Conference. Aeroacoustics Conferences. American Institute of Aeronautics and Astronautics,
2019https://doi.org/10.2514/6.2019-2599
[19]
]. It was also used to postprocess data in the European 7th framework project [- Sack S.
- Åbom M.
Acoustic plane-wave decomposition by means of multilayer perceptron neural networks.
J. Sound Vib. 2020; (URL http://www.sciencedirect.com/science/article/pii/S0022460X20303503)115518https://doi.org/10.1016/j.jsv.2020.115518
[20]
] and in projects related to programs at the Competence Center for Gas Exchange (CCGEx) at the Royal Institute, located Stockholm, Sweden.The documentation of the package includes many relevant examples that can be followed to setup post-processing codes for other studies. This approach was chosen to minimize the effort required to test the module and to make acdecom as accessible as possible for research groups. The example base is extended regularly to include examples for most of acdecom’s possible applications.
3. Functionality
acdecom provides the class WaveGuide, which features automatized functionality to create the mathematical structures for acoustic wave decomposition. A three-step schematic for acoustic mode decomposition with acdecom is provided in Fig. 1. First, the user provides general geometric input regarding the acoustic waveguide, such as dimensions and shape. Many optional parameters can be specified; for example, the properties of the media inside the duct (temperature, density, viscosity, and heat capacity) can be provided here. Second, the sensor locations are initialized. Third, flow-acoustic data can be decomposed. The algorithms use this information to determine the shapes, the phase velocity, and the attenuation of the flow-acoustic modes and map them on the measured or simulated fields. acdecom has predefined functions to compute the modal shapes and velocities for standard geometries (circular and rectangular ducts). This makes the library easy to use for a majority of applications and makes it attractive in industrial setups.
4. Further development
In the current version acdecom implements circular and rectangular ducts, as well as the three most important attenuation models. Future development could focus on implementing more predefined duct shapes and attenuation models. The module is programmed in a way that such implementations are simple and backward compatible.
In the different disciplines, the modes extracted from the sound fields are further post-processed, e.g., to compute the transmission loss of a liner or the sound power of an HVAC-fan. The module can be bundled into a package, that provides area-specific functionality. I favor a community-based development of the module and invite other research groups to contribute to new or improved functionality of the module.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This study was conducted as part of a project within the Competence Center for Gas Exchange (CCGEx) at KTH. The authors would like to acknowledge the Swedish Energy Agency, Volvo Cars, Volvo GTT, Scania, BorgWarner Turbo Systems Engineering, and Wärtsilä for their support and contributions. The authors also wish to acknowledge the financial support of the European Commission
provided in the framework of the FP7 Collaborative Project IDEALVENT (Grant Agreement 314066).
References
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- Experimental/numerical study of ducted-fan noise: Effect of duct inlet shape.AIAA J. 2018; 56https://doi.org/10.2514/1.J055870
C. Schram, J. Christophe, R. Corin, H. Denayer, W. de Roeck, S. Sack, M. Åbom, Innovative noise control in ducts, in: 23rd AIAA/CEAS Aeroacoustics Conference, 2017.
S. Sack, M. Åbom, K. Kucukcoskun, C. Schram, Comparison of the scattering of an operating and a quiescent fan, in: FAN 2015 – International Conference on Fan Noise, Technology and Numerical Methods, Lyon, France, 2015.
M. Shur, M. Strelets, A. Travin, J. Christophe, K. Kucukcoskun, C. Schram, S. Sack, M. Åbom, Effect of inlet distortions on ducted fan noise, in: 22nd AIAA/CEAS Aeroacoustics Conference, 2016.
- Modal filters for mitigation of in-duct sound.in: Proceedings of Meetings on Acoustics, vol. 29. 2016https://doi.org/10.1121/2.0000473
- Overview of liner activities in support of the international forum for aviation research.in: 25th AIAA/CEAS Aeroacoustics Conference. Aeroacoustics Conferences. American Institute of Aeronautics and Astronautics, 2019https://doi.org/10.2514/6.2019-2599
- Acoustic plane-wave decomposition by means of multilayer perceptron neural networks.J. Sound Vib. 2020; (URL http://www.sciencedirect.com/science/article/pii/S0022460X20303503)115518https://doi.org/10.1016/j.jsv.2020.115518
- Idealvent (integrated design of optimal ventilation systems for low cabin and ramp noise).2017 (https://www.idealvent.eu/. (Online; Accessed 10 April 2017))
Article info
Publication history
Accepted:
July 27,
2020
Received:
July 27,
2020
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