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

2019 ◽  
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.

Investigation of Swirl Ratio Impact on In-Cylinder Flow in a SIDI Optical Engine

2015 ◽  
Author(s):  
Hanyang Zhuang ◽  
David L. S. Hung ◽  
Jie Yang ◽  
Shaoxiong Tian
Keyword(s):  
Flow Field ◽  
Soot Formation ◽  
Flow Characteristics ◽  
Control Valve ◽  
Exhaust Emission ◽  
Swirl Ratio ◽  
Optical Engine ◽  
Crank Angle ◽  
Cylinder Flow ◽  
Intake Swirl

Advanced powertrain technologies have improved engine performance with higher power output, lower exhaust emission, and better controllability. Chief among them is the development of spark-ignition direct-injection (SIDI) engines in which the in-cylinder processes control the air flow motion, fuel-air mixture formation, combustion, and soot formation. Specifically, intake air with strong swirl motion is usually introduced to form a directional in-cylinder flow field. This approach improves the mixing process of air and fuel as well as the propagation of flame. In this study, the effect of intake air swirl on in-cylinder flow characteristics was experimentally investigated. High speed particle image velocimetry (PIV) was conducted in an optical SIDI engine to record the flow field on a swirl plane. The intake air swirl motion was achieved by adjusting the opening of a swirl ratio control valve which was installed in one of the two intake ports in the optical engine. Ten opening angles of the swirl ratio control valve were adjusted to produce an intake swirl ratio from 0.55 to 5.68. The flow structures at the same crank angle degree, but under different swirl ratio, were compared and analyzed using proper orthogonal decomposition (POD). The flow dominant structures and variation structures were interpreted by different POD modes. The first POD mode captured the most dominant flow field structure characteristics; the corresponding mode coefficients showed good linearity with the measured swirl ratio at the compression stroke when the flow was swirling and steady. During the intake stroke, strong intake air motion took place, and the structures and coefficients of the first modes varied along different swirl ratio. These modes captured the flow properties affected by the intake swirl motion. Meanwhile, the second and higher modes captured the variation feature of the flow at various crank angle degrees. In summary, this paper demonstrated a promising approach of using POD to interpret the effectiveness of swirl control valve on in-cylinder swirl flow characteristics, providing better understanding for engine intake system design and optimization.

Fuzzy Logic Approach for Controlling Temperature in Electroosmotic Flow Fields

2009 ◽  
Author(s):  
Saeid Movahed ◽  
Mohammad Eghtesad ◽  
Reza Kamali
Keyword(s):  
Fuzzy Logic ◽  
Flow Field ◽  
Joule Heating ◽  
Electroosmotic Flow ◽  
Flow Fields ◽  
Heating Effect ◽  
Joule Heating Effect ◽  
Model Based ◽  
Fuzzy Logic Approach ◽  
Flow Field Characteristics

By entering technology to the area of micro and nano scales, the design and fabrication of miniaturized instruments such as microelectronic devices, MEMS, NEMS and ..., become very desirable. Many of these devices deal with flow field in micro- and nano-channels. By decreasing the dimensions of channels, the influence of surface effects becomes prominent and cannot be ignored. One of the most charismatic categories of these phenomena is elecrokinetic effect which can result in electroosmotic flow field (EOF) that has many advantages such as being vibration free, being much more compact, having flat-form velocity and etc. These beneficiaries lead to the increasing stimulus of using this type of flow field. Electroosmosis is defined as the motion of ionized liquid relative to the stationary charged surface by an applied electric field. One of the most important disadvantages of EOF is the Joule heating effect, the generation of heat due to the electroosmosis effect. Besides, micro- and nano-channels are usually used as heat sink in miniaturized devices. By considering these facts, it can be concluded that heat characteristics of EOF must be studied carefully in order to manage and control the Joule heating effect for utilizing the cooling characteristics of micro- and nano-channels. Flow field characteristics can be found by solving Navier-Stocks and Energy equations with proper slip boundary conditions. By considering the partial nature of these equations, many conventional model-based control techniques may not be useful. Therefore, one can suggest some non-model based strategies in order to control the properties of flow fields. In the present study, fuzzy logic controllers will be proposed in order to control the temperature and cooling characteristics of micro- and nano-channel heat sinks.

Classifying IC Engine Swirl Fields by Complex Moments

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.

Application of PIV Measurements and Snapshot POD Analysis in a Cylinder Head of Diesel Engine

2017 ◽  
Author(s):  
Zhenyang Zhang ◽  
Hongwei Ma ◽  
Chao Jin ◽  
Cheng Xue ◽  
Yunlong Huang
Keyword(s):  
Diesel Engine ◽  
Flow Field ◽  
Total Energy ◽  
Cylinder Head ◽  
Flow Fields ◽  
Coolant Flow ◽  
Flow Structures ◽  
Fuel Injector ◽  
Piv Measurements ◽  
Water Jacket

The characteristic of coolant flow field in the water jacket of a cylinder head plays an important role in heat exchange, which could even influence the diesel engine’s performance and service life. Measurements and analysis methods to coolant flow field are limited by the complex internal geometrical structure of the cylinder head. In this paper, flow fields in a small and complicated spatial structure are measured by particle image velocimetry (PIV) system and the data are analyzed using proper orthogonal decomposition (POD) method. Time varying coolant flow structures located among two valve seats, a fuel injector seat and a side wall in a real cylinder head are measured by a two dimensional PIV system. PIV results of three measuring planes are displayed in different ways to show flow structures in the water jacket. Distinctive areas can be recognized easily in distributions of different flow parameters. A snapshot POD method is employed to analyze PIV data. Flow structures, which contain different amount of energy, are decomposed into different modes by POD method. POD Mode 1 and ensemble mean flow field are compared together and the relevance index shows a relatively high similarity between these two flow fields. The results also indicate a significant convergence of energy distribution. Energy contained in Mode 1 varies from 22% to 61% of the total energy in different measuring planes. 90% of the total energy is captured in top 10% of the total modes which belong to low-order modes. Energy in high-order modes, which occupy more than 60% of the total modes, contains less than 1% of the total energy. In summary, this paper presents the application of PIV measurements to coolant flow field in a real cylinder head and data processing using a snapshot POD method to analyze PIV results. A set of comprehensive properties showing the spatial and temporal characteristics of coolant flow structure is discussed and concluded detailedly. The data obtained can be used to build an experimental database to optimize coolant flow field structures and verify CFD numerical simulations in order to promote coolant flow passage design and simulation credibility of the diesel engine cooling system.

PIV Measurements of Recirculation Patterns in a Stirred Tank

2001 ◽  
Author(s):  
Khaled J. Hammad ◽  
George Papadopoulos
Keyword(s):  
Flow Field ◽  
Flow Behavior ◽  
Three Dimensional ◽  
Vertical Plane ◽  
Vertical Axis ◽  
Transitional Flow ◽  
Piv Measurements ◽  
Vortical Structures ◽  
Blade Passage ◽  
Flow Field Characteristics

Abstract Phase-resolved PIV measurements were performed to reveal the detailed flow features within a triple impeller stirredtank. Two tests were performed: low and high rotational speeds, 175 and 575 RPM, respectively. The tests used an optically transparent mixing vessel to measure the 2D flow field characteristics along a vertical plane passing through the tank center. The measurements disclosed interesting in-plane vortical behavior that when measured at two angular positions with respect to the blade passage further indicated the three-dimensional flow behavior. For the low RPM case, a laminar flow nature was apparent, whereby vortical toroidal structures spanned around the stirrer vertical axis. Six such structures were dominant. For the high RPM case and for θ = 0° six dominant vortical structures were apparent. Their r-z plane location and size were different from that for the low RPM case. With blade passage four of these vortical structures appeared to merge into two, suggesting that constant toroidal vortical structures spanning around the stirrer axis were absent from the high RPM case. A switch between six distinct and four distinct in-plane vortical structures as the blades pass through the measurement plane further suggested a transitional flow field at 575 RPM.

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

2019 ◽  
Vol 22 (1) ◽  
pp. 257-272 ◽  
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.

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.

Influence of Guide Vanes' Angle on Flow Fields of Cylinder Cage Powder Classifier

2012 ◽  
Vol 263-266 ◽  
pp. 843-846 ◽  
Author(s):  
Da Zhi Jiang ◽  
Jing Han
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Flow Field ◽  
Flow Behavior ◽  
Air Flow ◽  
Flow Fields ◽  
Guide Vanes ◽  
Flow Field Characteristics ◽  
Computational Fluid Dynamics Cfd ◽  
The Stability

To research guide vanes' influences on flow fields of cylinder cage powder classifier at different angles, a study of guide vanes under 3 different angles is therefore undertaken examining air flow behavior. The investigation of these flow field characteristics made use of the computational fluid dynamics (CFD) to simulate the air flow in the classifier. The results indicate that smaller angles of guide vanes can increase velocity but damage the stability of flow fields, and that those larger angles will reduce the velocity.

Evaluation of the Proximal Flow Field to Circular and Noncircular Orifices of Different Aspect Ratios

1997 ◽  
Vol 119 (3) ◽  
pp. 349-356 ◽  
Author(s):  
J. G. Myers ◽  
J. F. Fox ◽  
A. M. Elmahdi ◽  
G. J. Perry ◽  
A. S. Anayiotos
Keyword(s):  
Flow Field ◽  
Evaluation Method ◽  
Fluid Dynamic ◽  
Color Doppler ◽  
Flow Fields ◽  
Approximation Technique ◽  
Valvular Regurgitation ◽  
Aspect Ratios ◽  
Flow Field Characteristics

Investigations of valvular regurgitation attempt to specify flow field characteristics and apply them to the proximal isovelocity surface area (PISA) method for quantifying regurgitant flow. Most investigators assume a hemispherical shape to these equivelocity shells proximal to an axisymmetric (circular) orifice. However, in vivo flow fields are viscous and regurgitant openings vary in shape and size. By using centerline profiles and isovelocity surfaces, this investigation describes the flow field proximal to circular and elliptical orifices. Steady, proximal flow fields are obtained with two- and three-dimensional computational fluid dynamic (CFD) simulations. These simulations are verified by in vitro, laser-Doppler velocimetry (LDV) experiments. The data show that a unique, normalized proximal flow field results for each orifice shape independent of orifice flow or size. The distinct differences in flow field characteristics with orifice shape may provide a mechanism for evaluating orifice characteristics and regurgitant flows. Instead of the hemispherical approximation technique, this study attempts to show the potential to define a universal flow evaluation method based on the details of the flowfield according to orifice shape. Preliminary results indicate that Magnetic Resonance (MR) and Color Doppler (CD) may reproduce these flow details and allow such a procedure in vivo.

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