Impact sensor network for detection of hypervelocity impacts on spacecraft

Schäfer, F.; Janovsky, R.

doi:10.1016/j.actaastro.2007.02.002

Cited by 38 publications

(22 citation statements)

References 2 publications

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“…For instance, in a target tracking network, the events, i.e., the target location, show a high degree of correlation. On the other hand, impact detection networks, e.g., [20] and [21], typically deal with uncorrelated events.…”

Section: Event Occurrence Modelmentioning

confidence: 99%

Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks

Noori

Ardakani

2011

IEEE Trans. on Mobile Comput.

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Considering event-driven clustered wireless sensor networks, a probabilistic approach for analyzing the network lifetime is presented when events occur randomly over the network field. To this end, we first model the packet transmission rate of the sensors, using the theory of coverage processes and Voronoi tessellation. Then, the probability of achieving a given lifetime by individual sensors is found. This probability is then used to study the cluster lifetime. In fact, we find an accurate approximation for the probability of achieving a desired lifetime by a cluster. Our proposed analysis includes the effect of packet generation model, random deployment of sensors, dynamic cluster head assignment, data compression, and energy consumption model at the sensors. The analysis is presented for event-driven networks, but it comprises time-driven networks as a special case. Computer simulations are used to verify the results of our analysis.

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Section: Event Occurrence Modelmentioning

confidence: 99%

Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks

Noori

Ardakani

2011

IEEE Trans. on Mobile Comput.

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“…Kawai et al [22] presented a single microparticle launching method to simulate the hypervelocity impacts of micrometeoroids and microdebris on space structures. Schäfer and Janovsky [23] used a sensor network to detect hypervelocity impacts on aircraft structures. They performed their experiment using a gas gun for launching the specimens against the target.…”

Section: Introductionmentioning

confidence: 99%

Performance of a Two-stages Gas Gun: Experimental, Analytical and Numerical Analysis

Majzoobi

Rahmati

Kashfi

2019

IJE

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Two stages gas guns are used for various purposes, particularly for mechanical characterization of materials at high rate of deformations. The performance of a two stages gas gun is studied in this work using the theory of the two-stage gas gun proposed by Rajesh, numerical simulation using combined Eulerian/ Lagrangian elements in Autodyna commercial code and experiment using a two stage gas gun developed by the authors of this study. Equations governing the motion of the piston and projectile are solved using Runge-Kutta method. The effects of parameters such as chamber pressure, pump tube pressure and piston mass on the performance of gun are explored. The results of numerical simulation and analytical methods are validated by experiment. Finally, a comparison between the analytical, numerical and experimental results shows that the theory proposed by Rajesh, yields reasonable predictions for the two stage gas gun performance in the first place, and Autodyn software, using combined Eulerian/ Lagrangian elements, gives accurate estimations for gas gun parameters, in the second place. A 3-D working diagram is provided for prediction of projectile velocity for any state of pump and chamber pressures which are the most important variables for a gas gun with a fixed geometry.

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“…This method is unable to give any information about impact load amplitude, and it is difficult to find TOAs from noisy measurements, which may lead to erroneous results . The threshold method, peak, and double peak method are the approaches that could be used to find TOAs. Seydel and Chang reported that the threshold method is more vulnerable to noise than the double peak method.…”

Section: Introductionmentioning

confidence: 99%

Impact event identification in thin plates through real strain measurements

Yang

Ahmari

Selekwa

2016

Struct Control Health Monit

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Impact event identification is a primary concern in many structural health monitoring applications. Model-based inverse analysis is a common approach for system identification as long as the physical model can accurately capture the behavior of structure. A layered analysis including estimation of impact location in the first layer and refinement and reconstruction of impact load time history through inverse analysis in the second layer was proposed and verified well with the numerical results previously. Due to unavoidable noises, implementation of the theory with a modified inverse scheme on a simply supported plate structure is conducted in this study. The real signal data is first prefiltered using a low pass filter. Different alternatives are then proposed for the inverse scheme, and a new parameter, normalized signal energy, is adopted for layer 1 due to its convenience and accuracy compared with the traditional one, that is, the time of flight method. Signal power and filtered signal data are used for layer 2 of the inverse procedure. The results indicate that the modified inverse scheme is capable of detecting impact location and reconstructing impact load time history with a satisfactory precision. KEYWORDSimpact load time history (ILTH), impact location (IL), inverse analysis, normalized signal energy (NSE), signal power

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Impact sensor network for detection of hypervelocity impacts on spacecraft

Cited by 38 publications

References 2 publications

Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks

Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks

Performance of a Two-stages Gas Gun: Experimental, Analytical and Numerical Analysis

Impact event identification in thin plates through real strain measurements

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