Optimized sensor placement for urban flow measurement

Mokhasi, Paritosh; Rempfer, Dietmar

doi:10.1063/1.1689351

Cited by 38 publications

(12 citation statements)

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“…Some research has studied optimal sensor configurations in built environments, either in terms of pollutant dispersion to protect against nuclear, biological, and chemical attacks (NBC) [29] or with the aim to reconstruct a close approximation of the flow field [30]. Recent work by Du et al [31] proposed a methodology to identify optimal sensor locations for wind studies in an urban reservoir.…”

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confidence: 99%

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

Papadopoulou

Raphael

Smith

et al. 2014

Entropy

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Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind OPEN CCESSEntropy 2014, 16 5079 characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.

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confidence: 99%

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

Papadopoulou

Raphael

Smith

et al. 2014

Entropy

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“…Such models for a flow observer also suggest strategies for optimal sensor placement. Examples in this direction are given, for instance, by Mokhasi and Rempfer, 17 Cohen et al, 18 Willcox, 19 and Buffoni et al 20 In the work by Antoniades and Christofides, 21 the sensor placement is optimized together with the controller design for a one-dimensional quasilinear parabolic partial-differential equation modeling a reactiondiffusion phenomenon.…”

Section: Introductionmentioning

confidence: 98%

Feedback control of the vortex-shedding instability based on sensitivity analysis

Camarri

Iollo

2010

Physics of Fluids

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In the present work, a simple proportional feedback control is designed to suppress the vortex-shedding instability in the wake of a prototype bluff-body flow, i.e., the flow around a square cylinder confined in a channel with an incoming Poiseuille flow. Actuation is provided by two jets localized on the cylinder surface and velocity sensors are used for feedback control. This particular configuration is a pretext to propose a more general strategy for designing a controller, which is independent of the type of actuation and sensors. The method is based on the linear stability analysis of the flow, carried out on the unstable steady solution of the equations, which is also the target flow of the control. The idea is to use sensitivity analysis to predict the displacement in the complex plane of some selected eigenvalues, found by the linear stability analysis of the flow, as a function of the control design parameters. In this paper, it is shown that the information provided by only sensitivity analysis carried out on the uncontrolled system is not sufficient to design a controller which stabilizes the flow. Therefore, the control is designed iteratively by successive linearizations. Apart from possible constraints, the position of the sensors, the direction along which velocity is measured, and the feedback coefficients are outputs of the design procedure. The proposed strategy leads to a successful control up to a Reynolds number which is at least twice as large as the critical one for the primary instability, using only one velocity sensor

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“…POD has also been used in identifying spatio-temporal evolving structures in a transitional boundary layer in the work of Rempfer [22]. Extensions of POD have been considered in the work of Willcox [21], Mokhasi & Rempfer [19], Gunes [36] and Everson & Sirovich [17] as a means for approximating the complete velocity fields, using a small number of velocity measurements. Based on the same motivation of predicting velocity fields, linear stochastic estimation (LSE) as first introduced by Adrian [29] has also been used for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

Sequential estimation of velocity fields using episodic proper orthogonal decomposition

Mokhasi

Rempfer

2008

Physica D: Nonlinear Phenomena

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Optimized sensor placement for urban flow measurement

Cited by 38 publications

References 10 publications

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

Feedback control of the vortex-shedding instability based on sensitivity analysis

Sequential estimation of velocity fields using episodic proper orthogonal decomposition

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