Influence of asymmetric valve strategy on large-scale and turbulent in-cylinder flows

2019

Fluids

This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the identification and separation of coherent and turbulent velocity fluctuations. Typically, POD filtering requires assumptions to be made on the cumulative energy content of coherent modes and can therefore exclude smaller, but important contributions from lower energy modes. This work introduces a suggested new metric to consider in the selection of POD modes to be included in a reconstruction of coherent and turbulent features. Cross-correlation of POD spatial modes derived from independent samples is used to identify modes descriptive of either coherent (high-correlation) or incoherent (low-correlation) features. The technique is demonstrated through application to a cylinder in cross-flow allowing appropriate analysis to be carried out on the coherent and turbulent velocity fields separately. This approach allows identification of coherent motions associated with cross-flow transport and vortex shedding, such as integral length scales. Turbulent flow characteristics may be analysed independently from the coherent motions, allowing for the extraction of properties such as turbulent length scale.

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

Cross-Correlation of POD Spatial Modes for the Separation of Stochastic Turbulence and Coherent Structures

2019

Fluids

“…For the advancement internal combustion engine control strategies, knowledge of the real-time in-cylinder flow characteristics would be advantageous. Full field direct measurement of such flows often requires bespoke research engines; equipped with enhanced optical access via a transparent cylinder liner and/or a piston window 1,2 . In addition, these measurements, can require extensive processing and produce large amounts of data; both of which make them unsuitable for online active control strategy.…”

Section: Introductionmentioning

confidence: 99%

“…al. 2 . A subset of velocity points are converted to their scalar (u,v) form and used as sensors in the estimation of the remaining velocity field.…”

Section: Introductionmentioning

confidence: 99%

Linear stochastic estimation of the coherent structures in internal combustion engine flow

International Journal of Engine Research

2019

Self Cite

A methodology for estimating the in-cylinder flow of an IC engine from a number of point velocity measurements (sensors) is presented. Particle image velocimetry (PIV) is used to provide reference velocity fields for the linear stochastic estimation (LSE) technique to investigate the number of point measurements required to provide a representative estimation of the flow field. A systematic iterative approach is taken, with sensor locations randomly generated in each iteration to negate sensor location effects. It was found that an overall velocity distribution accuracy of at least 75% may be achieved with 7 sensors and 95% with 35 sensors, with the potential for fewer if sensor locations are optimised. The accuracy of vortex centre location predictions are typically within 2-3 mm -suggesting that the presented technique could characterise individual cycle flow fields by indicating vortex locations, swirl magnitude or tumble for example. With this information on the current cycle a control system may be enabled to activate in-cycle adjustment of injection and/or ignition timing for example to minimise emissions.

“…al. [17] (also known as Karhunen-Loève decomposition or principle component analysis) is shown to effectively separate large-scale coherent flows and small scale turbulence [12,[18][19][20][21]. For this reason it has often been used for the investigation of cyclic variation in the internal combustion engine [6,18,19,22,23].…”

Section: Linear Stochastic Estimationmentioning

confidence: 99%

“…The technique is applied to the flow under a range of engine operating conditions from literature [12], achieved using asymmetric VVT strategies. Thus providing a wide range of structures which could potentially be present in real I.C.…”

Section: Introductionmentioning

confidence: 99%

Towards In-Cylinder Flow Informed Engine Control Strategies Using Linear Stochastic Estimation

SAE Technical Paper Series

2019

Self Cite

Many modern I.C. engines rely on some form of active control of injection, timing and/or ignition timing to help combat tailpipe out emissions, increase the fuel economy and improve engine drivability. However, development of these strategies is often optimised to suit the average cycle at each condition; an assumption that can lead to sub-optimal performance, especially an increase in particulate (PN) emissions as I.C. engine operation, and in-particular its charge motion is subject to cycle-to-cycle variation (CCV). Literature shows that the locations of otherwise repeatable large-scale flow structures may vary by as much 25% of the bore dimension; this could have an impact on fuel break-up and distribution and therefore subsequent combustion performance and emissions. In the presented work, a method is presented that allows full-field flow velocity information to be estimated in real-time from only a limited number of point velocity measurements using linear stochastic estimation (LSE). Three sensor arrangements-single bisecting 'line-of-sight', a central cluster and a circumferential ring-which are deemed applicable to implementation in an I.C. engine are compared over all test flow conditions, with all providing useful estimations of the flow field. It is shown how with even a modest number of point measurements it is possible to achieve at least 85% correlation between estimates and original data allowing cycle characterisation to be achieved. Information gathered from this technique could provide inputs to engine control strategies to account for the CCV of the in-cylinder flow.