Showing papers by "Gilead Tadmor published in 2012"

On least-order flow representations for aerodynamics and aeroacoustics

Abstract: We propose a generalization of proper orthogonal decomposition (POD) for optimal flow resolution of linearly related observables. This Galerkin expansion, termed ‘observable inferred decomposition’ (OID), addresses a need in aerodynamic and aeroacoustic applications by identifying the modes contributing most to these observables. Thus, OID constitutes a building block for physical understanding, leastbiased conditional sampling, state estimation and control design. From a continuum of OID versions, two variants are tailored for purposes of observer and control design, respectively. Firstly, the most probable flow state consistent with the observable is constructed by a ‘least-residual’ variant. This version constitutes a simple, easily generalizable reconstruction of the most probable hydrodynamic state to preprocess efficient observer design. Secondly, the ‘least-energetic’ variant identifies modes with the largest gain for the observable. This version is a building block for Lyapunov control design. The efficient dimension reduction of OID as compared to POD is demonstrated for several shear flows. In particular, three aerodynamic and aeroacoustic goal functionals are studied: (i) lift and drag fluctuation of a two-dimensional cylinder wake flow; (ii) aeroacoustic density fluctuations measured by a sensor array and emitted from a two-dimensional compressible mixing layer; and (iii) aeroacoustic pressure monitored by a sensor array and emitted from a three-dimensional compressible jet. The most ‘drag-related’, ‘lift-related’ and ‘loud’ structures are distilled and interpreted in terms of known physical processes.

Real-time anomaly detection of crowd behavior using multi-sensor information

Abstract: The present disclosure includes systems and methods for detecting an anomaly in crowd behavior. The method includes receiving sensor data representing a crowd, and partitioning the sensor data into local areas forming neighborhoods. The method further includes, for each local area, characterizing motion in the local area to determine real-time estimates of motion of sub-populations based on the sensor data, providing a crowd model for each local area, representing continuous functions describing expected motion near each local area, and determining parametric values of the crowd model based on the real-time estimates of the motion of the sub-populations. The method further includes learning and adapting auxiliary stochastic models characterizing normal evolution of the parametric values of the crowd model over time associated with each local area, and identifying a potential anomaly associated with the local area by comparing predictions from an auxiliary stochastic model with parametric values of the crowd model.

Global stability analysis - a key enabler in reduced order models and flow control

Abstract: In the current study, Reduced Order Models (ROMs) targeting strategies for experimental feedback flow control are discussed. For practical reasons, such models should incorporate a range of flow operating conditions with a small number of degrees of freedom. Standard POD Galerkin models are challenged by overoptimization at one operating condition [1]. The extension of dynamic range with additional global flow stability modes is the first applied technique. Further side constraints for control-oriented ROMs are taken into account by a “least-dimensional” Galerkin approximation based on a novel technique for continuous mode interpolation [2]. This interpolation preserves the model dimension of a single state while covering several states by adjusting (interpolated) modes. The resulting three-dimensional (3D) Galerkin model is presented for the transient flow around NACA-0012 airfoil and shown to be in a good agreement with the corresponding direct numerical simulation (DNS).