Analysis of In-Cylinder Turbulent Flows in a DISI Gasoline Engine With a Proper Orthogonal Decomposition Quadruple Decomposition

2014 ◽  
Vol 136 (11) ◽  
Author(s):  
Wenjin Qin ◽  
Maozhao Xie ◽  
Ming Jia ◽  
Tianyou Wang ◽  
Daming Liu
Keyword(s):  
Flow Field ◽  
Proper Orthogonal Decomposition ◽  
Turbulent Flows ◽  
Gasoline Engine ◽  
Measurement Data ◽  
Flow Fields ◽  
Orthogonal Decomposition ◽  
Coherent Field ◽  
Piv Measurement ◽  
Proper Orthogonal

The proper orthogonal decomposition (POD) method is applied to analyze the particle image velocimetry (PIV) measurement data and large eddy simulation (LES) result from an in-cylinder turbulence flow field in a four-valve direct injection spark ignition (DISI) engine. The instantaneous flow fields are decomposed into four parts, namely, mean field, coherent field, transition field and turbulent field, respectively, by the POD quadruple decomposition. The filtering method for separating the four flow parts is based on examining the relevance and correlations between different flow fields reconstructed with various POD mode numbers, and the corresponding reconstructed fields have been verified by their statistical properties. Then, the in-cylinder flow evolution and cycle-to-cycle variations (CCV) are studied separately upon the four field parts. Results indicate that each one of the four field parts exhibits its own flow characteristics and has close connection with others. Furthermore, the mean part contains the most kinetic energy of the entire flow field and represents the bulk flow of the original in-cylinder velocity field; the CCV in this part could almost be neglected, while the coherent field part contains larger scale structures and the most fluctuating energy, and possesses the highest CCV level among the four parts.

Effects of Outlier Flow Field on the Characteristics of In-Cylinder Coherent Structures Identified by Proper Orthogonal Decomposition-Based Conditional Averaging and Quadruple Proper Orthogonal Decomposition

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Rui Gao ◽  
Li Shen ◽  
Kwee-Yan Teh ◽  
Penghui Ge ◽  
Fengnian Zhao ◽  
...  
Keyword(s):  
Flow Field ◽  
Proper Orthogonal Decomposition ◽  
Coherent Structures ◽  
Large Scale ◽  
Flow Fields ◽  
Orthogonal Decomposition ◽  
Engine Cylinder ◽  
Conditional Averaging ◽  
Proper Orthogonal ◽  
Engine Cycle

Proper orthogonal decomposition (POD) offers an approach to quantify cycle-to-cycle variation (CCV) of the flow field inside the internal combustion engine cylinder. POD decomposes instantaneous flow fields (also called snapshots) into a series of orthonormal flow patterns (called POD modes) and the corresponding mode coefficients. The POD modes are rank-ordered by decreasing kinetic energy content, and the low-order, high-energy modes are interpreted as constituting the large-scale coherent flow structure that varies from engine cycle to engine cycle. Various POD-based analysis techniques have thus been proposed to characterize engine flow field CCV using these low-order modes. The validity of such POD-based analyses rests, as a matter of course, on the reliability of the underlying POD results (modes and coefficients). Yet a POD mode can be disproportionately skewed by a single outlier snapshot within a large data set, and an algorithm exists to define and identify such outliers. In this paper, the effects of a candidate outlier snapshot on the results of POD-based conditional averaging and quadruple POD analyses are examined for two sets of crank angle-resolved flow fields on the midtumble plane of an optical engine cylinder recorded by high-speed particle image velocimetry (PIV). The results with and without the candidate outlier are compared and contrasted. In the case of POD-based conditional averaging, the presence of the outlier scrambles the composition of snapshot subsets that define large-scale flow pattern variations, and thus substantially alters the coherent flow structures that are identified; for quadruple POD, the shape of coherent structures and the number of modes to define them are not significantly affected by the outlier.

Study of Time-Resolved Vortex Structure of In-Cylinder Engine Flow Fields Using Proper Orthogonal Decomposition Technique

2015 ◽  
Vol 137 (8) ◽  
Author(s):  
Hanyang Zhuang ◽  
David L.S. Hung ◽  
Hao Chen
Keyword(s):  
Flow Field ◽  
Proper Orthogonal Decomposition ◽  
Vortex Structure ◽  
High Speed ◽  
Internal Combustion Engines ◽  
Flow Fields ◽  
Orthogonal Decomposition ◽  
Vortex Center ◽  
Time Resolved ◽  
Proper Orthogonal

The structure of in-cylinder flow field makes significant impacts on the processes of fuel injection, air–fuel interactions, and flame development in internal combustion engines. In this study, the implementation of time-resolved particle image velocimetry (PIV) in an optical engine is presented. Flow field PIV images at different crank angles have been taken using a high-speed double-pulsed laser and a high-speed camera with seeding particles mixed with the intake air. This study is focused on measuring the flow fields on the swirl plane at 30 mm below the injector tip under various intake air swirl ratios. A simple algorithm is developed to identify the vortex structure and to track the location and motion of vortex center at different crank angles. Proper orthogonal decomposition (POD) has been used to extract the ensemble and variation information of the vortex structure. Experimental results reveal that strong cycle-to-cycle variations exist in almost all test conditions. The vortex center is difficult to identify since multiple, but small scale, vortices exist during the early stage of the intake stroke. However, during the compression stroke when only one vortex center exists in most cycles, the motion of vortex center is found to be quite similar at different intake swirl ratios and engine speeds. This is due to the dominant driving force exerted by the piston’s upward motion on the in-cylinder air.

Study on flow field and aerodynamic noise of electric vehicle rearview mirror

2021 ◽  
pp. 095440702110228
Author(s):  
Xiaowei Hao ◽  
Zhigang Yang ◽  
Qiliang Li
Keyword(s):  
Flow Field ◽  
Proper Orthogonal Decomposition ◽  
Electric Vehicle ◽  
Sound Pressure ◽  
Pressure Level ◽  
Orthogonal Decomposition ◽  
Aerodynamic Noise ◽  
Turbulent Pressure ◽  
Rearview Mirror ◽  
Proper Orthogonal

With the development of new energy and intelligent vehicles, aerodynamic noise problem of pure electric vehicles at high speed has become increasingly prominent. The characteristics of the flow field and aerodynamic noise of the rearview mirror region were investigated by large eddy simulation, acoustic perturbation equations and reduction order analysis. By comparing the pressure coefficients of the coarse, medium and dense grids with wind tunnel test results, the pressure distribution, and numerical accuracy of the medium grid on the body are clarified. It is shown from the flow field proper orthogonal decomposition of the mid-section that the sum of the energy of the first three modes accounts for more than 16%. Based on spectral proper orthogonal decomposition, the peak frequencies of the first-order mode are 19 and 97 Hz. As for the turbulent pressure of side window, the first mode accounts for approximately 11.3% of the total energy, and its peak appears at 39 and 117 Hz. While the first mode of sound pressure accounts for about 41.7%, and the energy peaks occur at 410 and 546 Hz. Compared with traditional vehicle, less total turbulent pressure level and total sound pressure level are found at current electric vehicle because of the limited interaction between the rearview mirror and A-pillar.

Inverse design of aircraft cabin environment using computational fluid dynamics-based proper orthogonal decomposition method

2017 ◽  
Vol 27 (10) ◽  
pp. 1379-1391 ◽  
Author(s):  
Jihong Wang ◽  
Tengfei (Tim) Zhang ◽  
Hongbiao Zhou ◽  
Shugang Wang
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Proper Orthogonal Decomposition ◽  
Flow Fields ◽  
Orthogonal Decomposition ◽  
Inverse Design ◽  
Air Supply ◽  
Spatial Modes ◽  
Cabin Environment ◽  
Proper Orthogonal

To design a comfortable aircraft cabin environment, designers conventionally follow an iterative guess-and-correction procedure to determine the air-supply parameters. The conventional method has an extremely low efficiency but does not guarantee an optimal design. This investigation proposed an inverse design method based on a proper orthogonal decomposition of the thermo-flow data provided by full computational fluid dynamics simulations. The orthogonal spatial modes of the thermo-flow fields and corresponding coefficients were firstly extracted. Then, a thermo-flow field was expressed into a linear combination of the spatial modes with their coefficients. The coefficients for each spatial mode are functions of air-supply parameters, which can be interpolated. With a quick map of the cause–effect relationship between the air-supply parameters and the exhibited thermo-flow fields, the optimal air-supply parameters were determined from specific design targets. By setting the percentage of dissatisfied and the predicted mean vote as design targets, the proposed method was implemented for inverse determination of air-supply parameters in two aircraft cabins. The results show that the inverse design using computational fluid dynamics-based proper orthogonal decomposition method is viable. Most of computing time lies in the construction of data samples of thermo-flow fields, while the proper orthogonal decomposition analysis and data interpolation is efficient.

Analysis of Unsteady Flow Structures in a Radial Turbomachine by Using Proper Orthogonal Decomposition

2018 ◽  
Author(s):  
Matthias Witte ◽  
Benjamin Torner ◽  
Frank-Hendrik Wurm
Keyword(s):  
Flow Field ◽  
Unsteady Flow ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Mode Shapes ◽  
Flow Structures ◽  
Periodic Flow ◽  
Noise Emission ◽  
Turbulent Flow Field ◽  
Proper Orthogonal

Tonalities in hydro and airborne noise emission are a known problem of turbomachines, wherein the tonalities in the noise spectrum are associated with the different orders of the blade passing frequency (BPF). The proper orthogonal decomposition (POD) method was utilized to find the relationship between the fluctuations in the pressure field at the BPF orders which are the origin of the noise emission and the correlated fluctuations in the turbulent velocity field in terms of coherent, periodic flow structures. In order the provide the input data for the POD analysis, a URANS k-ω-SST scale adaptive simulation (SAS) of the turbulent flow field in a single stage radial pump under part load conditions was performed. Compared to traditional two equation turbulence models this approach is less dissipative and allows the development of small scale turbulence structures and is therefore an appropriate method for this study. In order to compute the POD correlation matrix Sirovich’s “Methods of Snapshots” was applied to the unsteady pressure and velocity fields from the CFD simulation. The discrimination of coherent, periodic flow structures and the incoherent, chaotic turbulence was carried out by analyzing the POD eigenvalue distributions, the POD mode shapes and the spectral properties of the POD time coefficients. Five coupled POD mode pairs were identified in total, which were strictly correlated with the 1st, 2nd, 3rd, 4th and 5th order of the BPF and therefore responsible for the noise emission at these discrete frequencies. The coherent structures were explored on the basis of the spatial POD velocity und pressure mode shapes and in terms of vortical structures after an additional phase averaging. The scope of this study is to introduce an enhanced collection of post processing techniques which are capable of analyzing highly unsteady flow fields from numerical simulations in a better way than is possible by just using traditional techniques like the evaluation of integral or time averaged quantities. The identified coherent flow structures and their associated pressure fluctuations are key elements for a proper comprehension of the internal dynamics of the turbulent flow field in a turbomachine and therefore essential for the understanding of the noise generation processes and the optimization of such machines.

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

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.

scholarly journals Proper Orthogonal Decomposition Analysis and Dispersion Characteristics of Resonant Acoustic Flow

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaopeng Wang ◽  
Shifu Zhu ◽  
Song Chen ◽  
Ning Ma ◽  
Zhe Zhang
Keyword(s):  
Flow Field ◽  
Energy Distribution ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Small Scale ◽  
Energy Structure ◽  
Dispersion Characteristics ◽  
Distribution Law ◽  
Uniform Dispersion ◽  
Proper Orthogonal

The investigation on the flow field and mixing characteristics of resonant sound mixing is of great significance for the dispersion mixing of superfine materials. In order to simulate the flow field and dispersion characteristics of resonant acoustic mixing, a gas-liquid-solid three-phase flow model based on the coupled level-set and volume-of-fluid (CLSVOF) and discrete particle model (DPM) was established. The CLSVOF model solves the gas-liquid interface, and the DPM model tracks the particle position. Then, the particle image velocimetry (PIV) experiment was performed using a self-made resonance acoustic hybrid prototype under different oscillation accelerations, and the radial velocity distribution between the experiment and simulation was compared. Finally, the proper orthogonal decomposition (POD) is used to decompose the flow field under different oscillation accelerations and fill levels, and the energy distribution law and the energy structure of different scales are extracted. The results show that the energy of the instantaneous flow field of the resonant sound is mainly concentrated in the low-order mode, and a close relationship was revealed between the energy distribution law and dispersion behavior of particles. The larger the small-scale coherent structures distribute, the more energy it has and the more favorable it is for fast and uniform dispersion.

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