Friction drag primarily determines the total drag of transport systems. A promising approach to reduce drag at high Reynolds numbers(> 104) are active transversal surface waves in combination with passive methods l...Friction drag primarily determines the total drag of transport systems. A promising approach to reduce drag at high Reynolds numbers(> 104) are active transversal surface waves in combination with passive methods like a riblet surface. For the application in transportation systems with large surfaces such as airplanes, ships or trains, a large scale distributed real-time actuator and sensor network is required. This network is responsible for providing connections between a global flow control and distributed actuators and sensors. For the development of this network we established at first a small scale network model based on Simulink and True Time. To determine timescales for network events on different package sizes we set up a Raspberry Pi based testbed as a physical representation of our first model. These timescales are reduced to time differences between the deterministic network events to verify the behavior of our model. Experimental results were improved by synchronizing the testbed with sufficient precision. With this approach we assure a link between the large scale model and the later constructed microcontroller based real-time actuator and sensor network for distributed active turbulent flow control.展开更多
基金supported by German Research Foundation(DFG)(No.1779-WA3076/1-1)
文摘Friction drag primarily determines the total drag of transport systems. A promising approach to reduce drag at high Reynolds numbers(> 104) are active transversal surface waves in combination with passive methods like a riblet surface. For the application in transportation systems with large surfaces such as airplanes, ships or trains, a large scale distributed real-time actuator and sensor network is required. This network is responsible for providing connections between a global flow control and distributed actuators and sensors. For the development of this network we established at first a small scale network model based on Simulink and True Time. To determine timescales for network events on different package sizes we set up a Raspberry Pi based testbed as a physical representation of our first model. These timescales are reduced to time differences between the deterministic network events to verify the behavior of our model. Experimental results were improved by synchronizing the testbed with sufficient precision. With this approach we assure a link between the large scale model and the later constructed microcontroller based real-time actuator and sensor network for distributed active turbulent flow control.
