In-Cylinder Flow Evolution Using Tomographic Particle Imaging Velocimetry in an Internal Combustion Engine

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Avinash Kumar Agarwal ◽  
Suresh Gadekar ◽  
Akhilendra Pratap Singh
Keyword(s):  
Internal Combustion ◽  
High Energy ◽  
Particle Imaging Velocimetry ◽  
Compression Stroke ◽  
Engine Cylinder ◽  
Intake Stroke ◽  
Flow Evolution ◽  
Cylinder Flows ◽  
Particle Imaging ◽  
Cylinder Flow

In-cylinder flows in internal combustion (IC) engines have always been a focus of study in order to gain better understanding of fuel–air mixing process and combustion optimization. Different conventional experimental techniques such as hot wire anemometry (HWA), laser Doppler anemometry (LDA), and numerical simulations have been grossly inadequate for complete understanding of the complex 3D flows inside the engine cylinder. In this experimental study, tomographic particle imaging velocimetry (PIV) was applied in a four-valve, single-cylinder optical research engine, with an objective of investigating the in-cylinder flow evolution during intake and compression strokes in an engine cycle. In-cylinder flow seeded with ultra-fine graphite particles was illuminated by a high energy, high frequency Nd:YLF laser. The motion of these tracer particles was captured using two cameras from different viewing angles. These two-directional projections of flowfield were used to reconstruct the 3D flowfield of the measurement volume (36 × 25 × 8 mm3), using multiplicative algebraic reconstruction technique (MART) algorithm. Captured images of 50 consecutive engine cycles were ensemble averaged to analyze the in-cylinder flow evolution. Results indicated that the in-cylinder flows are dependent on the piston position and spatial location inside the engine cylinder. The randomness of air-flow fields during the intake stroke was very high, which became more homogeneous during the compression stroke. The flows were found to be highly dependent on Z plane location inside the engine. During the intake stroke, flows were highly turbulent throughout the engine cylinder, and velocities vectors were observed in all directions. However, during the compression stroke, flow velocities were higher near the injector, and they reduced closer to the valves. Absolute velocity during compression stroke was mainly contributed by the out of plane velocity (Vz) component.

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

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.

STREAKED PARTICLE IMAGING VELOCIMETRY AND SIZING IN A SPRAY

1995 ◽  
Vol 5 (4-5) ◽  
pp. 403-416 ◽  
Author(s):  
D. C. Herpfer ◽  
S. Jeng ◽  
S. M. Jeng
Keyword(s):  
Particle Imaging Velocimetry ◽  
Particle Imaging

Numerical study of fluid dynamic flow within an internal combustion engine cylinder

2017 ◽  
Author(s):  
Ana Marta Souza ◽  
Antônio César Valadares de Oliveira ◽  
Enrico Temporim Ribeiro ◽  
Francisco Souza ◽  
Marcelo Colombo Chiari
Keyword(s):  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Numerical Study ◽  
Fluid Dynamic ◽  
Internal Combustion ◽  
Dynamic Flow ◽  
Engine Cylinder

scholarly journals Particle imaging velocimetry experiments and lattice-Boltzmann simulations on a single sphere settling under gravity

2002 ◽  
Vol 14 (11) ◽  
pp. 4012-4025 ◽  
Author(s):  
A. ten Cate ◽  
C. H. Nieuwstad ◽  
J. J. Derksen ◽  
H. E. A. Van den Akker
Keyword(s):  
Lattice Boltzmann ◽  
Particle Imaging Velocimetry ◽  
Lattice Boltzmann Simulations ◽  
Single Sphere ◽  
Particle Imaging

Recommendations for selecting the characteristics of diamond bars when honing cylinder liners of internal combustion engines

2020 ◽  
pp. 492-498
Author(s):  
A.M. Kolokatov
Keyword(s):  
Internal Combustion Engines ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Engine Cylinder ◽  
Processing Modes ◽  
Cylinder Liners

The characteristics of diamond bars when honing engine cylinder liners are analyzed and general recommendations are given for their selection and processing modes.

Turbulence Anisotropy Investigations in an Internal Combustion Engine

2021 ◽  
Author(s):  
James R MacDonald ◽  
Claudia Fajardo
Keyword(s):  
Internal Combustion Engine ◽  
Isotropic Turbulence ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Two Dimensional ◽  
Velocity Data ◽  
Compression Stroke ◽  
Turbulence Anisotropy ◽  
Anisotropy Tensor ◽  
Component Velocity

Abstract The assumption of isotropic turbulence is commonly incorporated into models of internal combustion engine (ICE) in-cylinder flows. While preliminary analysis with two-dimensional velocity data indicates that the turbulence may tend to isotropy as the piston approaches TDC, the validity of this assumption has not been fully investigated, partially due to lack of three-component velocity data in ICEs. In this work, the velocity was measured using two-dimensional, three-component (2D-3C) particle image velocimetry in a single-cylinder, motored, research engine to investigate the evolution of turbulence anisotropy throughout the compression stroke. Invariants of the Reynolds stress anisotropy tensor were calculated and visualized, through the Lumley triangle, to investigate turbulence states. Results showed the turbulence to be mostly anisotropic, with preferential tendency toward 2D axisymmetry at the beginning of the compression stroke and approaching isotropy near top-dead-center. Findings provide new insights into turbulence in dynamic, bounded flows to assist with the development of physics-based, quantitative models.

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