Development and Application of Bivariate 2D-EMD for the Analysis of Instantaneous Flow Structures and Cycle-to-Cycle Variations of In-cylinder Flow

AbstractEffects of the inlet-stream deflection on the temporal and spatial evolution processes of the in-cylinder flow structures (tumble/swirl) and turbulence intensities in the symmetry and diametral planes of a motored four-valve, four-stroke engine are diagnosed by using a particle image velocimeter. The inception, establishment, and evolution of the tumbling/swirling vortical flow structures during the intake and compression strokes in the engine cylinder with/without inlet-stream deflection are depicted and compared. Quantitative strengths of the rotating vortical flow motions are presented by dimensionless parameters (tumble and swirl ratios) which can represent the mean angular velocity of the vortices in the target plane. The turbulence intensity is calculated by using the measured time-varying velocity data. The results show that by deflecting the inlet air-stream the tumble and swirl ratios of the in-cylinder flow are appreciably increased by about 0.1 and the turbulence intensity is increased by about 5 ∼ 10%.

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PIV Analysis of Instantaneous Flow Structures in an Inkjet Printhead

10.1115/imece1999-0276 ◽

1999 ◽

Author(s):

Hongsheng Zhang ◽

Carl D. Meinhart

Keyword(s):

Particle Image ◽

Fluid Velocity ◽

Ccd Camera ◽

Velocity Fields ◽

Flow Structures ◽

Instantaneous Flow ◽

Piv Technique ◽

Image Velocimetry ◽

Piv Analysis ◽

Inkjet Printhead

Abstract This paper presents experimental measurements and observations of instantaneous flow structures inside an inkjet printhead, using a micron-resolution Particle Image Velocimetry (PIV) system to record visualized flows and calculate velocity fields. The PIV technique uses 700 nm diameter fluorescent flow-tracing particles, a pulsed Nd:YAG laser, an epi-fluorescent microscope and an interline-transfer CCD camera to record images of a flow at two successive instances in time. By measuring how far a set of particles move during a specified duration of time, an estimate of the local fluid velocity can be obtained. An electronic timing strategy has been developed to synchronize the PIV lasers, the CCD camera and the drop ejection system. An overall flow pattern during a 500 μs ejection cycle has been observed by phase-averaging hundreds of instantaneous velocity fields, which were recorded at 2–5 μs intervals throughout the cycle. A velocity field with spatial resolution of approximately 10 μm was obtained near the inkjet nozzle. Meniscus and nodes inside the printhead were also observed and recorded.

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Experimental Interpretation of Compression Ignition In-Cylinder Flow Structures

10.4271/2020-01-0791 ◽

2020 ◽

Author(s):

Tristan Knight ◽

Edward Long ◽

Ruoyang Yuan ◽

Colin Garner ◽

Graham Hargrave

Keyword(s):

Flow Structures ◽

Compression Ignition ◽

Cylinder Flow

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Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine

International Journal of Engine Research ◽

10.1177/1468087420974148 ◽

2020 ◽

pp. 146808742097414

Author(s):

Daniel Dreher ◽

Marius Schmidt ◽

Cooper Welch ◽

Sara Ourza ◽

Samuel Zündorf ◽

...

Keyword(s):

Flow Field ◽

Engine Performance ◽

Feature Learning ◽

Flow Fields ◽

High Energy ◽

Flow Structures ◽

Compression Stroke ◽

Deep Feature ◽

Deep Feature Learning ◽

Cylinder Flow

Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center ([Formula: see text]) with a mean accuracy above chance level. The prediction accuracy from [Formula: see text] to [Formula: see text] is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization.

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Multidimensional In-Cylinder Flow Calculations and Flow Visualization in a Motored Engine

Journal of Fluids Engineering ◽

10.1115/1.2817142 ◽

1995 ◽

Vol 117 (2) ◽

pp. 282-288 ◽

Cited By ~ 12

Author(s):

Bahram Khalighi

Keyword(s):

Flow Visualization ◽

Flow Structures ◽

Intake Valve ◽

Compression Process ◽

Intake Process ◽

Valve Stem ◽

Induction Process ◽

High Turbulence ◽

Visualization Experiments ◽

Cylinder Flow

Multidimensional simulations of coupled intake port/valve and in-cylinder flow structures in a pancake-shape combustion chamber engine are reported. The engine calculations include moving piston, moving intake valve, and valve stem. In order to verify the calculated results, qualitative flow visualization experiments were carried out for the same intake geometry during the induction process using a transient water analog. During the intake process the results of the multidimensional simulation agreed very well with the qualitative flow visualization experiments. An important finding in this study is the generation of a well-defined tumbling flow structure at BDC in the engine. In addition, this tumbling flow is sustained and amplified by the compression process and in turn causes generation of a high turbulence level before TDC. Many interesting features of the in-cylinder flow structures such as tumble, swirl, and global turbulent kinetic energy are discussed.

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