Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction
In fluid mechanics research, data gathered from measurements and simulations may be challenging to interpret due to complexities such as transience, non-linearity, and high dimensionality. Velocity data from the airflow through an internal combustion engine often exhibit such properties; nevertheles...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
AIP Publishing
2024
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| _version_ | 1797112455777746944 |
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| author | Baker, SJ Fang, XH Barbato, A Breda, S Magnani, M Fontanasi, S Leach, FCP Davy, MH |
| author_facet | Baker, SJ Fang, XH Barbato, A Breda, S Magnani, M Fontanasi, S Leach, FCP Davy, MH |
| author_sort | Baker, SJ |
| collection | OXFORD |
| description | In fluid mechanics research, data gathered from measurements and simulations may be challenging to interpret due
to complexities such as transience, non-linearity, and high dimensionality. Velocity data from the airflow through an
internal combustion engine often exhibit such properties; nevertheless, accurate characterisations of these airflows are
required in order to correctly predict and control the subsequent combustion and emissions processes in pursuit of net
zero targets. The temporal mean is a common way of representing an ensemble of realisations of velocity fields, but
the averaging process can artificially diminish the magnitudes of the resultant vectors. Accurate representation of these
vector magnitudes is of particular importance, as the velocity magnitudes in the intake airflow are thought to be primary
drivers of the subsequent variation in an engine flow, which influences emissions formation and overall efficiency. As
an alternative to the ensemble mean, this work proposes the application of a dimensionality reduction method known as
the sparsity-promoting dynamic mode decomposition (SPDMD), which can extract core structures from an ensemble
of velocity fields while retaining more realistic vector magnitudes. This is demonstrated for the first time with largeeddy simulation (LES) velocity data and compared to a corresponding set of experimental particle image velocimetry
(PIV) data. The SPDMD 0 Hz modes are shown to be more representative of the velocity magnitudes present in both
datasets. This facilitates more accurate quantification of the differences in vector magnitudes between simulations and
experiments, and more reliable identification of which LES snapshots are closer to the PIV ensemble. |
| first_indexed | 2024-03-07T08:26:03Z |
| format | Journal article |
| id | oxford-uuid:56b6c799-aa3f-4f0f-ae58-1db7ae71225c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:26:03Z |
| publishDate | 2024 |
| publisher | AIP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:56b6c799-aa3f-4f0f-ae58-1db7ae71225c2024-02-15T14:27:12ZExtracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reductionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:56b6c799-aa3f-4f0f-ae58-1db7ae71225cEnglishSymplectic ElementsAIP Publishing2024Baker, SJFang, XHBarbato, ABreda, SMagnani, MFontanasi, SLeach, FCPDavy, MHIn fluid mechanics research, data gathered from measurements and simulations may be challenging to interpret due to complexities such as transience, non-linearity, and high dimensionality. Velocity data from the airflow through an internal combustion engine often exhibit such properties; nevertheless, accurate characterisations of these airflows are required in order to correctly predict and control the subsequent combustion and emissions processes in pursuit of net zero targets. The temporal mean is a common way of representing an ensemble of realisations of velocity fields, but the averaging process can artificially diminish the magnitudes of the resultant vectors. Accurate representation of these vector magnitudes is of particular importance, as the velocity magnitudes in the intake airflow are thought to be primary drivers of the subsequent variation in an engine flow, which influences emissions formation and overall efficiency. As an alternative to the ensemble mean, this work proposes the application of a dimensionality reduction method known as the sparsity-promoting dynamic mode decomposition (SPDMD), which can extract core structures from an ensemble of velocity fields while retaining more realistic vector magnitudes. This is demonstrated for the first time with largeeddy simulation (LES) velocity data and compared to a corresponding set of experimental particle image velocimetry (PIV) data. The SPDMD 0 Hz modes are shown to be more representative of the velocity magnitudes present in both datasets. This facilitates more accurate quantification of the differences in vector magnitudes between simulations and experiments, and more reliable identification of which LES snapshots are closer to the PIV ensemble. |
| spellingShingle | Baker, SJ Fang, XH Barbato, A Breda, S Magnani, M Fontanasi, S Leach, FCP Davy, MH Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
| title | Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
| title_full | Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
| title_fullStr | Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
| title_full_unstemmed | Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
| title_short | Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
| title_sort | extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction |
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