Amir Hossein Birjandi

Bubble Effects on the Acoustic Doppler Velocimeter (ADV) Measurements

Acoustic Doppler Velocimeter (ADV) is a useful technique for measuring flow velocities with frequency variations of up to approximately 200 Hz in laboratory settings and in field applications. Although measuring velocity with ADV has advantages over other velocity measurement methods, this technique is sensitive to operating conditions: in addition to noise, the signal can contain spikes with large amplitudes, a disadvantage of ADV. In this study, the effect of bubbles on ADV signals is experimentally assessed in a laboratory setting. Bubbles can intersect the sampling volume and the acoustic beams creating spikes. The impact and amplitude of these spikes is a function of the bubble size and position when it crosses the ADV sampling volume and the acoustic beams. Bubbles that intersect the sampling volume generate spikes in all three velocity directions simultaneously; bubbles that intersect acoustic beams, which span between the sampling volume and the ADV receivers, impact the velocity data in one or two directions, and has a negligible effect in the third direction. Bubbles that intersect the X-direction acoustic beam create spikes in velocity data in both X- and Z-directions, but have no significant impact on the Y-direction; the Y- and X-directions have spikes and the Z-direction is not significantly impacted, when bubbles intersect the Y-direction acoustic beam. In addition, spikes increase the magnitude of the power spectra at high frequencies. Without bubbles, the autocorrelation in the time domain decreases in value as the time-lag increases, approaching zero after 5 seconds. The presences of bubbles cause a large peak in the autocorrelation at a zero time-lag, and no autocorrelation thereafter. Furthermore, the autocorrelation without bubbles permit turbulence length scales to be calculated because of the positive autocorrelation value; unless spikes are removed by using an appropriate filter when bubbles are present, turbulence length scales cannot be calculated because the autocorrelation is zero.Copyright

Reynolds Number Effects on the Freewheeling Behavior of a High Solidity Vertical River Kinetic Turbine

A four-bladed, squirrel-cage, and scaled vertical kinetic turbine was designed, instrumented and tested in the water tunnel facilities at the University of Manitoba. With a solidity of 1.3 and NACA0021 blade profile, the turbine is classified as a high solidity model. Results were obtained for conditions during freewheeling at various Reynolds numbers. In this study, the freewheeling tip speed ratio, which relates the ratio of maximum blade speed to the free stream velocity at no load, was divided into three regions based on the Reynolds number. At low Reynolds numbers, the tip speed ratio was lower than unity and blades were in a stall condition. At the end of the first region, there was a sharp increase of the tip speed ratio so the second region has a tip speed ratio significantly higher than unity. In this region, the tip speed ratio increases almost linearly with Reynolds number. At high Reynolds numbers, the tip speed ratio is almost independent of Reynolds number in the third region. It should be noted that the transition between these three regions is a function of the blade profile and solidity. However, the three-region behavior is applicable to turbines with different profiles and solidities.Copyright

Enhanced Multi-Agent Normal Sampling Technique for Global Optimization

This paper describes an enhanced version of a new global optimization method, Multi-Agent Normal Sampling Technique (MANST) described in reference [1]. Each agent in MANST includes a number of points that sample around the mean point with a certain standard deviation. In each step the point with the minimum value in the agent is chosen as the center point for the next step normal sampling. Then the chosen points of all agents are compared to each other and agents receive a certain share of the resources for the next step according to their lowest mean function value at the current step. The performance of all agents is periodically evaluated and a specific number of agents who show no promising achievements are deleted; new agents are generated in the proximity of those promising agents. This process continues until the agents converge to the global optimum. MANST is a standalone global optimization technique and does not require equations or knowledge about the objective function. The unique feature of this method in comparison with other global optimization methods is its dynamic normal distribution search. This work presents our recent research in enhancing MANST to handle variable boundaries and constraints. Moreover, a lean group sampling approach is implemented to prevent sampling in the same region for different agents. The overall capability and efficiency of the MANST has been improved as a result in the newer version. The enhanced MANST is highly competitive with other stochastic methods such as Genetic Algorithm (GA). In most of the test cases, the performance of the MANST is significantly higher than the Matlab™ GA Toolbox.Copyright