Optical Ortho-Rectification for Image-Based Stream Surface Flow Observations Using a Ground Camera

Image-based stream flow observation consists of three components: (i) image acquisition, (ii) ortho-rectification, and (iii) an image-based velocity estimation. Ortho-rectification is a type of coordinate transformation. When ortho-rectifying a raster image, pixel interpolation is needed and causes the degradation of image resolution, especially in areas located far from the camera and in the direction parallel to the viewing angle. When measuring the water surface flow of rivers with a wide channel width, reduced and distorted image resolution limits the applicability of image-based flow observations using terrestrial image acquisition. Here, we propose a new approach for ortho-rectification using an optical system. We employed an optical system embedded in an ultra-short throw projector. In the proposed approach, ortho-rectified images were obtained during the image acquisition phase, and the image resolution of recorded images was almost uniform in terms of physical coordinates. By conducting field measurements, characteristics of the proposed approach were validated and compared to a conventional approach.

Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow

River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI=ν0.1/ρρcν1, where ν, ρ, and ρcν1 are the spatial-clustering level, the seeding density, and the reference seeding density at ν=1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 % and 16.1 % were calculated – using PTV and PIV, respectively – by employing the optimal frame window.

Physical Quantity Synergy in the Internal Flow Field and Application in Radial-Inflow Turbines

The internal flow field and loss distributions are quite complicated in the radial-inflow turbine. It is necessary to reinforce physical understandings of the relationship between the flow and loss. Inspired by the synergy principle in the convective heat transfer, the synergy applicable for the radial turbine is innovatively derived from the Navier-Stokes equations. According to the mathematical expression, the smaller the synergy angle is, the higher flow resistance and loss should be. The paper attempts to assess the validation of the synergy principle in the radial turbine based on numerical simulations firstly, then the relationship between the synergy angle and loss is analyzed in detail. It is found that the regions where high total pressure loss coefficient and high dimensionless entropy generation locate correspond to the relatively small synergy angle, which agrees well with the mathematical analysis. The relatively low streamwise synergy angle corresponds to the high-loss regions near the suction side and wake on the blade-to-blade stream surface. The relatively low spanwise and circumferential synergy angle correspond to the high-loss regions near the tip clearance and wake on the span-theta stream surface. Under off-designed conditions, the synergy principle also shows great performance as an apparent negative correlation of total pressure loss coefficient versus circumferential synergy angle could be perceived.