Analysis of the filtering effect of the stochastic estimation and accuracy improvement by sensor location optimization

Arnault, Anthony; Dandois, Julien; Monnier, Jean-Claude; Delva, Jérôme; Foucaut, Jean-Marc

doi:10.2514/6.2016-3257

Cited by 2 publications

(3 citation statements)

References 9 publications

(8 reference statements)

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“…Sensor locations are chosen at random as literature discussed suggests that sensor location can impact the accuracy of resulting estimations. The purpose of this work is not to optimise sensor location selection (for that, see Arnault et al 22 and Cohen et al 24 ) but instead to provide a guide for the number of sensors required for an acceptable accuracy of estimation. Therefore, locations are randomised and the investigation is repeated a total of 10 times, and the mean is taken to exclude the effects of sensor location.…”

Section: Estimation Methodologymentioning

confidence: 99%

“…In a large number of studies, sensors used in the LSE technique have been in the form of wall pressure sensors 3,4,10,21 or microphone-based sensors 9 as these quantities are often less intrusive to measure. Arnault et al 22 describe the limitations in estimating smaller scales of turbulence using wall-based pressure measurements. However, the study goes on to demonstrate that improvements may be made by considering sensor location optimisation according to an algorithm developed by Muradore et al 23 Arnault et al 22 found improvement when the sensor locations were close to the extrema of POD modes which agrees with work by Cohen et al 24…”

Section: Introductionmentioning

confidence: 99%

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

Butcher

Spencer

2019

International Journal of Engine Research

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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.

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Section: Estimation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Butcher

Spencer

2019

International Journal of Engine Research

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“…al. [29] who show the limitations in the estimation of smaller turbulent structures which may not correlate well with these measurements. The study does however progress to show that it is possible to optimize the sensor locations to improve correlation using an algorithm developed by Muradore et.…”

Section: Linear Stochastic Estimationmentioning

confidence: 99%

Time-Volume Estimation of Velocity Fields From Non-Synchronous Planar Measurements Using Linear Stochastic Estimation

Butcher

Spencer

2019

Volume 4A: Combustion, Fuels, and Emissions

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With increasing complexity of aerodynamic devices such as gas turbine fuel swirl nozzles (FSN) and combustors, the need for time-resolved full volume flow characterisation is becoming greater. Even with modern advancements in both numerical and experimental methods, this remains a challenging area. The work presented in this paper combines multiple non-synchronous planar measurements to reconstruct an estimate of a synchronous, instantaneous flow field of the whole measurement set. Temporal information is retained through the linear stochastic estimation (LSE) technique. The technique is described, applied and validated with a simplified combustor and FSN geometry flow for which 3-component, 3-dimensional (3C3D) flow information is known from published tomographic PIV[1]. Using the tomographic PIV data set, multiple virtual ‘planes’ may be extracted to emulate single planar PIV measurements and produce the correlations required for LSE. In this example, multiple parallel planes are synchronised with a single perpendicular plane that intersects each of them. As the underlying data set is volumetric, the measured velocity is known a priori and therefore can be directly compared to the estimated velocity field for validation purposes. The work shows that when the input time-resolved planar velocity measurements are first POD (proper orthogonal decomposition) filtered, high correlation between the estimations and the validation velocity volumes are possible. This results in estimated full volume velocity distributions which are available at the same time instance as the input field — i.e. a time resolved velocity estimation at the frequency of the single input plane. While 3C3D information is used in the presented work, this is necessary only for validation; in true application planar technique would be used. The study concludes that provided the number of sensors used for input LSE exceeds the number of POD modes used for pre-filtering, it is possible to achieve correlation greater than 99%.

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Analysis of the filtering effect of the stochastic estimation and accuracy improvement by sensor location optimization

Cited by 2 publications

References 9 publications

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

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

Time-Volume Estimation of Velocity Fields From Non-Synchronous Planar Measurements Using Linear Stochastic Estimation

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