scholarly journals Travel-Time Difference Extracting in Experimental Study of Rayleigh Wave Acoustoelastic Effect

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Hu Eryi ◽  
Ying Shao

In order to identify the travel-time difference accurately in the experimental study of Rayleigh wave acoustoelastic effect, an experimental system is constructed by the ultrasonic pulser-receiver, digital oscilloscope, Rayleigh wave transmitter and receiver, and a personal computer. And then, the digital correlation method and the Fourier transform frequency analysis method are used to obtain the travel-time difference of the Rayleigh wave corresponding to different subsurface stress conditions. Furthermore, the simulated ultrasonic signals are used to verify the reliability of the two kinds of ultrasonic signal information extracting algorithms, respectively. Finally, the proposed signal processing methods are applied to extract the time delay between different Rayleigh wave signals corresponding to different subsurface stress level.

2022 ◽  
Vol 167 ◽  
pp. 108594
Author(s):  
Guang-Heng Luo ◽  
Jian-Wen Pan ◽  
Jin-Ting Wang ◽  
Feng Jin

Author(s):  
А.М. Устинов ◽  
А.А. Клопотов ◽  
А.И. Потекаев ◽  
Ю.А. Абзаев ◽  
В.С. Плевков

Author(s):  
Johannes Gruber ◽  
Santhanakrishnan Narayanan

Cargo cycles are gaining more interest among commercial users from different business sectors, and they compete with cars in urban commercial transport. Though many studies show the potential of cargo cycles, there is still a reluctance to deploy them. One possible reason for this is the lack of knowledge regarding their suitability in relation to travel time. Therefore, this study aims to explore cargo cycles’ travel time performance by quantifying the travel time differences between them and conventional vehicles for commercial trips. The authors compare real-life trip data from cargo cycles with Google’s routed data for cars. By doing this, the authors explore the factors affecting the travel time difference and propose a model to estimate this difference. The attributes for the model were selected keeping in mind the ease of obtaining values for the variables. Results indicate cycling trip distance to be the most significant variable. The study shows that expected travel time difference for trips with distances between 0 and 20 km (12.4 mi) ranges from -5 min (cargo cycle 5 min faster) to 40 min with a median of 6 min. This value can decrease if users take the optimal cycling route and the traffic conditions are worse for cars. Although what is an acceptable amount of travel time difference depends on the user, practitioners can be certain of the travel time difference they can expect, which enables them to assess the suitability of cargo cycles for their commercial operations.


2019 ◽  
Vol 93 (S1) ◽  
pp. 176-177
Author(s):  
Yulan Li ◽  
Rizheng He ◽  
Baoshan Wang ◽  
Jiangyong Yan ◽  
Yao Li

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