Secondary flow near an undulatory surface induced by wall blocking effect

Prandtl's secondary mean motions of the second kind near an undulating surface were explained in terms of turbulent blocking effect and kinematic boundary conditions at the surface, and its order of magnitude was estimated. Isotropic turbulence is distorted by the undulating surface of wavelength λ and amplitude h with a low slope, so that h « λ. The prime mechanism for generating the mean flow is that the far-field Isotropic turbulence is distorted by the non-local blocking effect of the surface to become anisotropic axisymmetric turbulence near the surface with principal axis that is not aligned with the local curvature of the undulation. Then the local analysis can be applied and the mechanism is similar to the mean flow generation mechanism for homogeneous axisymmetric turbulence over a planer surface, i.e. gradients of the Reynolds stress caused by the turbulent blocking effect generate the mean motions. The results from this simple analysis are consistent with previous exact analysis in which the effects of curvature are strictly taken into account. The results also qualitatively agree with flow visualization over an undulating surface in a mixing-box.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.

Synchronization with partial state feedback on SO(n)

In this paper we consider the problem of constructing a distributed feedback law to achieve synchronization for a group of k agents whose states evolve on SO(n) and which exchange only partial state information along communication links. The partial state information is given by the action of the state on reference vectors in ℝn. We propose a gradient based control law which achieves exponential local convergence to a synchronization configuration under a rank condition on a generalized Laplacian matrix. Furthermore, we discuss the case of time-varying reference vectors and provide a convergence result for this case. The latter helps reach synchronization, requiring less communication links and weaker conditions on the instantaneous reference vectors. Our methods are illustrated on an attitude synchronization problem where agents exchange only their relative positions observed in the respective body frames. ©2009 IEEE.

Manopt, a matlab toolbox for optimization on manifolds

Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design effcient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on. The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. By dealing internally with most of the differential geometry, the package aims particularly at lowering the entrance barrier. © 2014 Nicolas Boumal.