Controllable superposed flow fields based on eccentric cylinder flow

Stereo Particle Image velocimetry data was collected over high aspect ratio flexible cylinders (L/a = 1.5 to 3 × 105) to evaluate the axial development of the turbulent boundary layer where the boundary layer thickness becomes significantly larger than the cylinder diameter (δ/a>>1). The flexible cylinders are approximately neutrally buoyant and have an initial length of 152 m and radii of 0.45 mm and 1.25 mm. The cylinders were towed at speeds ranging from 3.8 to 15.4 m/sec in the David Taylor Model Basin. The analysis of the SPIV data required a several step procedure to evaluate the cylinder boundary flow. First, the characterization of the flow field from the towing strut is required. This evaluation provides the residual mean velocities and turbulence levels caused by the towing hardware at each speed and axial location. These values, called tare values, are necessary for comparing to the cylinder flow results. Second, the cylinder flow fields are averaged together and the averaged tare fields are subtracted out to remove strut-induced ambient flow effects. Prior to averaging, the cylinder flow fields are shifted to collocate the cylinder within the field. Since the boundary layer develops slowly, all planes of data occurring within each 10 meter increment of the cylinder length are averaged together to produce the mean boundary layer flow. Corresponding fields from multiple runs executed using the same experimental parameters are also averaged. This flow is analyzed to evaluate the level of axisymmetry in the data and determine if small changes in cylinder angle affect the mean flow development. With axisymmetry verified, the boundary flow is further averaged azimuthally around the cylinder to produce mean boundary layer profiles. Finally, the fluctuating velocity levels are evaluated for the flow with the cylinder and compared to the fluctuating velocity levels in the tare data. This paper will first discuss the data analysis techniques for the tare data and the averaging methods implemented. Second, the data analysis considerations will be presented for the cylinder data and the averaging and cylinder tracking techniques. These results are used to extract relevant boundary layer parameters including δ, δ* and θ. Combining these results with wall shear and momentum thickness values extracted from averaged cylinder drag data, the boundary layer can be well characterized.

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Temporal evolution analysis of in-cylinder flow by means of proper orthogonal decomposition

International Journal of Engine Research ◽

10.1177/1468087420917246 ◽

2020 ◽

pp. 146808742091724

Author(s):

Li Shen ◽

Kwee-Yan Teh ◽

Penghui Ge ◽

Fengnian Zhao ◽

David LS Hung

Keyword(s):

Proper Orthogonal Decomposition ◽

Internal Combustion Engines ◽

Temporal Evolution ◽

Flow Fields ◽

Orthogonal Decomposition ◽

Temporal Behavior ◽

Decomposition Approach ◽

Cylinder Flow ◽

Proper Orthogonal ◽

Low Order

In-cylinder flow fields and their temporal evolution have strong effect on the combustion dynamics of internal combustion engines. Proper orthogonal decomposition is a statistical tool to analyze these flow fields by decomposing them into flow patterns (known as proper orthogonal decomposition modes) and corresponding coefficients with their contribution to the ensemble flow kinetic energy successively maximized. However, neither of the two prevailing proper orthogonal decomposition approaches satisfactorily describes the temporal behavior of the flow fields. The phase-dependent proper orthogonal decomposition approach is limited to analyzing spatial flow structures at a certain engine phase. The phase-invariant proper orthogonal decomposition approach attempts to account for both spatial and temporal variations, but at the expense of diminished statistical and physical significance. In this article, we seek to understand the temporal behavior of tumble flow fields by analyzing the evolution of low-order phase-dependent proper orthogonal decomposition modes over multiple crank angles. The concept of relevance index is first generalized to enable comparison between two vectorial fields of different sizes. This metric is then used to quantify the directional similarities between the two lowest proper orthogonal decomposition modes obtained at sequential crank angles. The mode shapes are observed to evolve gradually and naturally over most crank angles, but change significantly at certain crank angles during intake. The results indicate that each of the low-order modes features strong velocity fluctuations in different regions of the tumble plane, and different numbers of modes are needed to represent the dominant features of tumble flow at different engine phases. Based on this understanding, we propose to use the partial sum of those proper orthogonal decomposition modes and their coefficients to form a low-order approximation model of the in-cylinder tumble flow, in order to reduce flow field complexity and noise while retaining its major spatial and temporal features.

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Velocity Distribution of Extensional Flow Fields in an Eccentric Cylinder of an Extensional Extruder

Journal of Macromolecular Science Part B ◽

10.1080/00222348.2018.1527517 ◽

2018 ◽

Vol 57 (11-12) ◽

pp. 732-745 ◽

Cited By ~ 1

Author(s):

Zan Huang ◽

Peifang Luo ◽

Junjie Tong ◽

Fa Su

Keyword(s):

Velocity Distribution ◽

Extensional Flow ◽

Flow Fields ◽

Eccentric Cylinder

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The Effects of In-Cylinder Flow Fields and Injection Timing on Time-Resolved Hydrocarbon Emissions in a 4-Valve, DISI Engine

10.4271/2000-01-1905 ◽

2000 ◽

Cited By ~ 7

Author(s):

Terrence Alger ◽

Matthew Hall ◽

Ronald D. Matthews

Keyword(s):

Flow Fields ◽

Hydrocarbon Emissions ◽

Injection Timing ◽

Time Resolved ◽

Cylinder Flow

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Systematic Multi-Index Validations of SIDI Engine Flow Field LES Computations Using Crank Angle-Resolved PIV Measurements

ASME 2019 Internal Combustion Engine Division Fall Technical Conference ◽

10.1115/icef2019-7145 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mengqi Liu ◽

Fengnian Zhao ◽

Xuesong Li ◽

Min Xu ◽

Zongyu Yue ◽

...

Keyword(s):

Flow Field ◽

Flow Fields ◽

Model Verification ◽

Piv Measurements ◽

Multi Index ◽

Global Indices ◽

Comparison Results ◽

Crank Angle ◽

Flow Field Characteristics ◽

Cylinder Flow

Abstract In-cylinder flow fields make significant impacts on the fuel atomization, fuel mixture formation, and combustion process in spark ignition direct injection (SIDI) engines. In recent years, model-based simulation approaches are preferred in regard to investigating the transient in-cylinder flow field characteristics. Most commonly, the simulation models are validated using single representative flow field at a typical crank angle measured by particle image velocimetry (PIV). However, it provides only limited knowledge about the flow field which is highly three-dimensional and of transient nature. In this study, crank angle-resolved PIV measurements are conducted on three distinct planes inside the cylinder to capture the transient process of flow field characteristics which vary with the crank angle. These three planes consist of one tumble plane through the spark plug tip, one tumble plane along two intake ports, and one swirl plane at 30 mm below the cylinder head. Large eddy simulation (LES) is employed for the numerical computations using the CONVERGE codes. On the basis of large datasets for both temporal and spatial domains, a multi-index systematic validation approach is conducted. Crank angle-resolved calculations of global indices and local indices are implemented using the flow fields velocity data obtained from both PIV and LES on select planes. Global indices reveal the trends in similarities of different crank angle degrees and locations, while local indices give the detail comparison results. In summary, with the systematic multi-index validation approach, the crucial crank angle degrees and locations for model verification will be detected. Furthermore, the corresponding critical flow features are analyzed. Practical guideline of flow field validation is proposed.

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Classifying IC Engine Swirl Fields by Complex Moments

ASME 2019 Internal Combustion Engine Division Fall Technical Conference ◽

10.1115/icef2019-7132 ◽

2019 ◽

Author(s):

Kwee-Yan Teh ◽

Penghui Ge ◽

Fengnian Zhao ◽

David L. S. Hung

Keyword(s):

Velocity Field ◽

Flow Field ◽

High Speed ◽

Flow Fields ◽

Flow Patterns ◽

Analysis Approach ◽

Flow Mode ◽

Mean Velocity ◽

Ic Engine ◽

Cylinder Flow

Abstract Engine in-cylinder flow varies from cycle to cycle, which contributes to variation of the mixing and combustion processes between fuel and air. Such flow field cyclic variability at the macroscopic scale is distinct from random fluctuations at the microscopic scale about the ensemble mean velocity field due to turbulence. At the extreme, the mean velocity field may bear no resemblance to any instantaneous flow field within the ensemble. Rather, these instantaneous fields may appear multimodal. Yet previous attempts to define and identify the flow modes were either qualitative (by visual inspection), or based on strict point-by-point velocity difference between two flow fields. The former approach is clearly subjective; the latter does not accommodate translational and rotational variations of in-cylinder flow patterns relative to a flow mode. Such spatial variations, in location and orientation, of the flow patterns can be quantified by the technique of complex moment normalization. The algebraic properties of complex moments are also intimately related to the geometric and physical properties of two-dimensional/two-component flow fields. In this paper, we take the normalized moments as flow field attributes for further cluster analysis. This analysis approach is demonstrated using a set of in-cylinder flow fields obtained by high-speed particle image velocimetry on a swirl plane of a research optical engine operating under low intake swirl setting. The resulting classification of the flow fields into several clusters (flow modes) are discussed, and the potential and limitations of the analysis approach are appraised.

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Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance

International Journal of Engine Research ◽

10.1177/1468087419833269 ◽

2019 ◽

Vol 22 (1) ◽

pp. 257-272 ◽

Cited By ~ 3

Author(s):

Alexander Hanuschkin ◽

Steffen Schober ◽

Johannes Bode ◽

Jürgen Schorr ◽

Benjamin Böhm ◽

...

Keyword(s):

Machine Learning ◽

Particle Image Velocimetry ◽

Particle Image ◽

Flow Fields ◽

Effective Pressure ◽

Indicated Mean Effective Pressure ◽

Image Velocimetry ◽

Crank Angle ◽

Flow Features ◽

Cylinder Flow

Cycle-to-cycle variations in an optically accessible four-stroke direct injection spark-ignition gasoline engine are investigated using high-speed scanning particle image velocimetry and in-cylinder pressure measurements. Particle image velocimetry allows to measure in-cylinder flow fields at high spatial and temporal resolution. Binary classifiers are used to predict combustion cycles of high indicated mean effective pressure based on in-cylinder flow features and engineered tumble features obtained during the intake and the compression stroke. Basic in-cylinder flow features of the mid-cylinder plane are sufficient to predict combustion cycles of high indicated mean effective pressure as early as 180 degree crank angle before the top dead center at 0 degree crank angle. Engineered characteristic tumble features derived from the flow field are not superior to the basic flow features. The results are independent of the tested machine learning method (multilayer perceptron and boosted decision trees) and robust to hyper-parameter selection.

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Measurement of 3-D In-Cylinder Flow Fields Using Doppler Global Velocimetry

10.4271/2004-01-1409 ◽

2004 ◽

Cited By ~ 4

Author(s):

Oliver Dingel ◽

Joern Kahrstedt ◽

Kai Behnk ◽

Stefan Zuelch ◽

Thomas Seidel

Keyword(s):

Flow Fields ◽

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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