At the large scale turbine rig (LSTR) at Technische Universität Darmstadt, Darmstadt, Germany, the aerothermal interaction of combustor exit flow conditions on the subsequent turbine stage is examined. The rig resembles a high pressure turbine and is scaled to low Mach numbers. A baseline configuration with an axial inflow and a swirling inflow representative for a lean combustor is modeled by swirl generators, whose clocking position toward the nozzle guide vane (NGV) leading edge can be varied. A staggered double-row of cylindrical film cooling holes on the endwall is examined. The effect of swirling inflow on heat transfer and film cooling effectiveness is studied, while the coolant mass flux rate is varied. Nusselt numbers are calculated using infrared thermography and the auxiliary wall method. Boundary layer, turbulence, and five-hole probe measurements as well as numerical simulations complement the examination. The results for swirling inflow show a decrease of film cooling effectiveness of up to 35% and an increase of Nusselt numbers of 10–20% in comparison to the baseline case for low coolant mass flux rates. For higher coolant injection, the heat transfer is on a similar level as the baseline. The differences vary depending on the clocking position. The turbulence intensity is increased to 30% for swirling inflow.
Purpose
The purpose of this study is to provide a standard method for flow velocity measurements with phase‐contrast (PC) MRI. This method can be used for in vitro studies that place high demands on measurement accuracy. Clinically relevant PC MRI techniques can be validated using this method before being applied in vivo.
Methods
Many motion‐related errors in PC MRI, particularly flow misregistration, depend on the timing of the encoding gradients in the pulse sequence. By synchronizing all encoding gradients and shortening the overall encoding interval, these errors can be significantly reduced. Based on this concept, a single‐point PC MRI method is proposed.
Results
Flow experiments were conducted in vitro. No considerable errors were found in the velocity data of the proposed method. For comparison, a conventional PC MRI technique showed up to 100% local velocity deviation and up to 35% flow rate deviation in the same experiments.
Conclusions
With the proposed method, the overall measurement accuracy is significantly increased compared to conventional PC MRI techniques. Due to long acquisition times and high specific absorption rates, this method can only be applied in vitro.
This study focuses on the measurement accuracy of Magnetic Resonance Velocimetry (MRV) in high-speed turbulent flows. One of the most prominent errors in MRV is the displacement error, which describes the misregistration of spatial coordinates and velocity components in moving fluids. Displacement errors are particularly critical for experiments with high flow velocity and high spatial resolution. The degree of displacement error also depends on the sequence structure of the MRV technique. In this study, two MRV sequence types are examined regarding their measurement capabilities in highspeed turbulent flows: a conventional MRV sequence based on the popular "4D FLOW" technique, and a newly developed sequence, named "SYNC SPI". Compared to conventional MRV, SYNC SPI is designed for high measurement accuracy, and not for imaging speed, which limits its application to statistically stationary flows. Both sequence types are evaluated in a flow experiment with a converging-diverging nozzle. Time-averaged results are presented for velocities up to 12 m/s at the throat. Supported by Particle Imaging Velocimetry, it is shown that SYNC SPI is capable of acquiring accurate velocity data in these highly turbulent flows. In contrast, the data from the conventional MRV sequence exhibits substantial displacement errors with a maximum displacement of 21 mm. The long acquisition time is the main disadvantage of the SYNC SPI sequence. Therefore, it is examined if undersampling and non-linear reconstruction, known as Compressed Sensing, can be utilized to make data acquisition more efficient. In the presented measurements, Compressed Sensing is successfully applied to shorten the acquisition time by up to 70% with almost no reduction in measurement accuracy.
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