AeroMEMS polyimide based wall double hot-wire sensors for flow separation detection

Buder, U.; Petz, Ralf; Kittel, Moritz; Nitsche, W.; Obermeier, Ε.

doi:10.1016/j.sna.2007.04.058

Cited by 56 publications

(27 citation statements)

References 3 publications

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“…is is useful for example, to simulate networks in which signaling is required for packet ow control, in a way that signals are transferred in a network apart, without interfering with the data. is implementation aims to suite more elaborate NoC architectures, 3 since XDense only uses a single network for its operation.…”

Section: Modeling Stages 31 Design Speci Cationmentioning

confidence: 99%

“…anks to current level of miniaturization on microelectronics and microelectromechanics (MEMS), cyber physical systems can now rely on dense deployments of sensors, allowing the sense of phenomena with granularity as small as few millimeters of sensor inter space and sampling rates up to kilohertz [3]. is is leading to the next generation of aerospace systems, able to sense its structural state and the environment, to e ectively interpret and react to sensed data in real-time [10].…”

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: Modeling Stages 31 Design Speci Cationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…For that, large deployments of sensors may require sensors' interspace to be smaller than that of the spatial granularity of the observed phenomena (for example, of 100 µm or less) and have high sampling rates (in excess of 10 kHz) [13]. Kasagi and others [2] surveyed micro flow sensors that attempt to meet such requirements, for example with sensor arrays.…”

Section: Applicationsmentioning

confidence: 99%

“…The SN deployment inter-space depends on the minimum size of the observed phenomena, and therefore has to be smaller then the minimum turbulent structure size [13]. The number of sensor nodes are chosen based on the area to be covered and we use a 101 × 101 − 1 = 10200 SN with one sink in the center.…”

Section: A Input Phenomena and Performance Metricsmentioning

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 approach is, however, expensive and might impact the flow channel's structural health. For those issues a wide setup of thermal flow sensor approaches on flexible substrates can be found in literature [7][8][9][10][11]. These approaches have in common that the sensors' characteristics are reduced due to their substrate modification which is often coming along with a sensor design differing from the ideal one.…”

Section: Introductionmentioning

confidence: 99%