A numerical study of intake valve jet flapping in a gasoline direct injection engine

Beavis, Nicholas J.; Ibrahim, Salah S.; Malalasekera, Weeratunge

doi:10.1504/ijpt.2018.090360

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…While straightforward, if the average flow is not a prominent feature in the full PIV dataset (i.e. the full PIV dataset is not normally distributed), the ensemble-averaged PIV flow fields can become unrepresentative of the dynamics displayed in the individual snapshots (Chen et al 2013;Beavis et al 2018). This can be the case for datasets characterised by strong CCVs, where opposing vectors can cancel each other out and cause the velocity magnitudes to be artificially diminished in the average flow field (Shen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mode Decomposition for the Comparison of Engine In-Cylinder Flow Fields from Particle Image Velocimetry (PIV) and Reynolds-Averaged Navier–Stokes (RANS) Simulations

Baker

Fang

Shen

et al. 2023

Flow Turbulence Combust

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Validation of Reynolds-averaged Navier–Stokes (RANS) simulation results against experimental data such as flow measurements from particle image velocimetry (PIV) remains a challenge for the development of thermal propulsion systems. This is partly due to cycle-to-cycle variations (CCVs) in the air motion and partly due to uncertainties in the PIV measurement technique, complicating the question of what constitutes a fair validation target for the RANS model. Indeed, an inappropriate validation target can misguide subsequent adjustments of a RANS model. In this work, the ensemble-averaged PIV field is first investigated for its suitability as a validation target for RANS simulations. The relevance index and the velocity histogram distance are used as quantitative metrics to assess the similarity of the ensemble-averaged field to the full dataset of individual PIV cycles. While a high similarity is seen between the average PIV flow field and the individual cycles on the tumble plane, the similarity is lower and more variable on the cross-tumble plane, where there are significant CCVs. Standard (space-only, phase-dependent) proper orthogonal decomposition (POD) is employed as an alternative method of data processing with the aim of providing a fairer comparison to RANS simulations. The cycle-dependence of the standard POD modes is shown to be an aspect that results in many validation targets and an excessively broad validation range, limiting its utility in this context. Dynamic mode decomposition (DMD) and sparsity-promoting dynamic mode decomposition (SPDMD) are then proposed as alternative solutions, capable of extracting flow structures at specific frequencies. The background 0 Hz SPDMD modes exhibit an ability to produce more realistic flow fields with velocity magnitudes that are significantly closer to the individual cycles.

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Section: Introductionmentioning

confidence: 99%

Dynamic Mode Decomposition for the Comparison of Engine In-Cylinder Flow Fields from Particle Image Velocimetry (PIV) and Reynolds-Averaged Navier–Stokes (RANS) Simulations

Baker

Fang

Shen

et al. 2023

Flow Turbulence Combust

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Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction

Baker,

Fang,

Barbato

et al. 2024

Physics of Fluids

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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 characterizations of these airflows are required in order to correctly predict and control the subsequent combustion and emission processes in pursuit of net zero targets. The temporal mean is a common way of representing an ensemble of realizations 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 emission 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 large-eddy 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.

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A numerical study of intake valve jet flapping in a gasoline direct injection engine

Cited by 2 publications

References 12 publications

Dynamic Mode Decomposition for the Comparison of Engine In-Cylinder Flow Fields from Particle Image Velocimetry (PIV) and Reynolds-Averaged Navier–Stokes (RANS) Simulations

Dynamic Mode Decomposition for the Comparison of Engine In-Cylinder Flow Fields from Particle Image Velocimetry (PIV) and Reynolds-Averaged Navier–Stokes (RANS) Simulations

Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction

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