AeroMEMS Sensor Arrays for Time Resolved Wall Pressure and Wall Shear Stress Measurements

Berns, Andreas; Obermeier, Ε.

doi:10.1007/978-3-642-01106-1_23

Cited by 10 publications

(10 citation statements)

References 14 publications

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“…euing and communication delays are accounted at the packet-level, therefore we do not model the physical layers aspects such as electrical interference and packet transmission errors. 2 Communication baudrate and packet size are de ned, so that the temporal behavior approximates the one observed on hardware implementation. Computation delays are absent at this point.…”

Section: Methodsmentioning

confidence: 99%

“…2 e 2D mesh networks we are interested on, utilize physically very short range wired links, which are minimally subject to transmission errors, and for this reason we give low priority to transmission error modeling. …”

Section: Methodsmentioning

confidence: 99%

“…AFC systems have very high spatial and temporal constraints [2], and such dense sensing poses huge challenges in terms sensing, specially regarding interconnectivity and timely data acquisition and processing. Current sensor networks fail to address this requisites due to key scalability issues of cost, communication time, interconnectivity, processing time, power and reliability [7].…”

Section: Introductionmentioning

confidence: 99%

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Simulation Module and Tools for XDense Sensor Network

Loureiro

Santos

Rangarajan

et al. 2017

Proceedings of the Workshop on Ns-3 - 2017 WNS3

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We present a NS-3 module developed for wired 2D mesh grid sensor network systems, that resemble Network-onChip architectures. It has been designed to enable complex feature extraction from sensed data in realtime with distributed processing. We provide the design specifications, communicationand processing delay models and a high level system model for XDense using NS-3. We validate our module by comparing its performance with a hardware implementation. Simulation Module and Tools for XDense Sensor Network ABSTRACTWe present a NS-3 module developed for wired 2D mesh grid sensor network systems, that resemble Network-on-Chip architectures. It has been designed to enable complex feature extraction from sensed data in realtime with distributed processing. We provide the design speci cations, communication and processing delay models and a high level system model for XDense using NS-3. We validate our module by comparing its performance with a hardware implementation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulation Module and Tools for XDense Sensor Network

Loureiro

Santos

Rangarajan

et al. 2017

Proceedings of the Workshop on Ns-3 - 2017 WNS3

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“…Kasagi and others [2] surveyed micro flow sensors that attempt to meet such requirements, for example with sensor arrays. Validations are usually done offline by analyzing logged data of the sensors individually connected to channels of an multiplexed analogue-to-digital converter (ADC) [14]. Moreover, there are applications that demand real-time data to perform closed loop actuation for active flow control (AFC), which leads to even tighter requirements.…”

Section: Applicationsmentioning

confidence: 99%

Distributed Sensing of Fluid Dynamic Phenomena with the XDense Sensor Grid Network

Loureiro

Rangarajan

Tovar

2015

2015 IEEE 3rd International Conference on Cyber-Physical Systems, Networks, and Applications

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XDense is a proposed wired mesh grid sensor network system tailored for scenarios that benefit from thousands of sensors per square meter. XDense has scalable network topology and protocols, customizable to application specifics, that enables complex feature extraction in realtime from observed phenomena by exploiting communication and distributed processing capabilities of such network topologies.XDense has been designed with closed-loop CPS applications like active flow control of aircraft wing surfaces in mind. It uses a plug-n-play architecture that allows dimensioning of application specific networks. In this paper, we evaluate the performance of XDense in a fluid dynamic application scenario. With experiments on feature detection and realtime scenarios, we demonstrate the potential of the architecture and discuss practical implementation issues. Abstract-XDense is a proposed wired mesh grid sensor network system tailored for scenarios that benefit from thousands of sensors per square meter. XDense has scalable network topology and protocols, customizable to application specifics, that enables complex feature extraction in realtime from observed phenomena by exploiting communication and distributed processing capabilities of such network topologies. XDense has been designed with closed-loop CPS applications like active flow control of aircraft wing surfaces in mind. It uses a plug-nplay architecture that allows dimensioning of application specific networks. In this paper, we evaluate the performance of XDense in a fluid dynamic application scenario. With experiments on feature detection and realtime scenarios, we demonstrate the potential of the architecture and discuss practical implementation issues.

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“…This allows for the resolution of all the relevant scales, making it feasible to gather complete information on the small scale structure of wall bounded flows [16]. Berns and Obermeier [17] employed an array of very small MEMS pressure sensors in order to measure the transient wall pressure and wall shear stress, but these sensors were also flush mounted into the model. While much work has been done to measure wall static pressure and its relationship with turbulence, it is a quantity that BLDS simply can't measure.…”

Section: Introductionmentioning

confidence: 99%

Microphone-Based Pressure Diagnostics for Boundary Layer Transition

Lillywhite¹

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Microphone-based Pressure Diagnostics for Boundary Layer Transition Spencer LillywhiteAn experimental investigation of the use low-cost microphones for unsteady total pressure measurement to detect transition from laminar to turbulent boundary layer flow has been conducted. Two small electret condenser microphones, the Knowles FG-23629 and the FG-23742, were used to measure the pressure fluctuations and considered for possible integration with an autonomous boundary layer measurement system. Procedures to determine the microphones' maximum sound pressure levels and frequency response using an acoustic source provided by a speaker and a reference microphone. These studies showed that both microphones possess a very flat frequency response and that the max SPL of the FG-23629 is 10 Pa and the max SPL of the FG-23742 is greater than 23 Pa. Several sensor-probe configurations were developed, and the three best were evaluated in wind tunnel tests. Measurements of the total pressure spectrum, time signal, and the root-mean-square were taken in the boundary layer on a sharp-nose flat plate in the Cal Poly 2 foot by 2 foot wind tunnel at dynamic pressures ranging between 135 Pa and 1350 Pa, corresponding to freestream velocities of 15 m/s to 47 m/s. The pressure spectra were collected to assess the impact of the probe on the microphone frequency response. The two configurations with long probes showed peaks in the pressure spectra corresponding to the resonant frequencies of the probe. The root-mean-square of the pressure fluctuations did not vary much between the different probes. The root-meansquare of the pressure fluctuations collected in turbulent boundary layers were found to be 10% of the local freestream dynamic pressure and decreased to 3.5% as the freestream dynamic pressure was increased. The RMS of the pressure fluctuations taken in both laminar boundary layers and in the freestream varied between 0.5% and 2.5% of the local freestream dynamic pressure. The large difference between the RMS of the pressure fluctuations in laminar and turbulent boundary layers taken at low dynamic pressures suggests that this system is indeed capable of distinguishing between laminar and turbulent flow. The drop in the RMS of the pressure fluctuations as dynamic pressure increased is indicative of insufficient maximum sound pressure level of the microphone resulting in clipping of the pressure fluctuation; this is confirmed through inspection of the pressure time signal and spectrum. Thus, a microphone with higher maximum sound pressure level is needed for turbulence detection at higher dynamic pressures. Alternatively, it may be possible to attenuate the total pressure fluctuation signal.

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AeroMEMS Sensor Arrays for Time Resolved Wall Pressure and Wall Shear Stress Measurements

Cited by 10 publications

References 14 publications

Simulation Module and Tools for XDense Sensor Network

Simulation Module and Tools for XDense Sensor Network

Distributed Sensing of Fluid Dynamic Phenomena with the XDense Sensor Grid Network

Microphone-Based Pressure Diagnostics for Boundary Layer Transition

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