Determination of the strength and elongation distribution of single wool through fiber bundle testing based on acoustic emissions

2020 ◽  
pp. 004051752097509
Author(s):  
Di Lu ◽  
Weidong Yu ◽  
Ning Pan
Keyword(s):  
Fiber Bundle ◽  
Breaking Strength ◽  
Probability Distributions ◽  
Acoustic Emissions ◽  
Single Fiber ◽  
Cumulative Probability ◽  
Tensile Response ◽  
Ae Signal ◽  
Fiber Sample

The method to obtain the breaking strength and elongation distribution of fiber through numerous single fiber tests is tedious and time-consuming. In the present work, a method based on acoustic emission (AE) signals generated by fiber fracture during the fiber bundle test has been developed for estimating the distribution of single wool breaking strength and elongation. AE detection is performed simultaneously during the fiber extension. According to the AE signal, it is proved that every individual fiber break can be detected, and the failure probability distribution of wool elongation can be obtained. Based on this, the single wool breaking strength is estimated from the tensile response of the fiber bundle, and then its distribution can be deduced. Finally, the distributions of breaking strength and elongation determined by the bundle test are compared with that obtained from the single fiber samples. The results show that the method developed in this work can be used to estimate the breaking strength and elongation of the single fiber within the bundle. Their cumulative probability distributions are similar to the results of single fiber sample tests, especially the distribution of the breaking strength.

scholarly journals Objective determination of the sea age of Atlantic salmon from the sizes and dates of capture of individual fish

2010 ◽  
Vol 68 (1) ◽  
pp. 130-143 ◽  
Author(s):  
Philip J. Bacon ◽  
William S. C. Gurney ◽  
Eddie McKenzie ◽  
Bryce Whyte ◽  
Ronald Campbell ◽  
...  
Keyword(s):  
Atlantic Salmon ◽  
Fishery Management ◽  
East Coast ◽  
Probability Distributions ◽  
Age Classes ◽  
Individual Fish ◽  
Bivariate Probability ◽  
Management And Conservation ◽  
Objective Determination

Abstract Bacon, P. J., Gurney, W. S. C., McKenzie, E., Whyte, B., Campbell, R., Laughton, R., Smith, G., and MacLean, J. 2011. Objective determination of the sea age of Atlantic salmon from the sizes and dates of capture of individual fish. – ICES Journal of Marine Science, 68: 130–143. The sea ages of Atlantic salmon indicate crucial differences between oceanic feeding zones that have important implications for conservation and management. Historical fishery-catch records go back more than 100 years, but the reliability with which they discriminate between sea-age classes is uncertain. Research data from some 188 000 scale-aged Scottish salmon that included size (length, weight) and seasonal date of capture on return to the coast were investigated to devise a means of assigning sea age to individual fish objectively. Two simple bivariate probability distributions are described that discriminate between 1SW and 2SW fish with 97% reliability, and between 2SW and 3SW fish with 70% confidence. The same two probability distributions achieve this accuracy across five major east coast Scottish rivers and five decades. They also achieve the same exactitude for a smaller recent dataset from the Scottish west coast, from the River Tweed a century ago (1894/1895), and for salmon caught by rod near the estuary. More surprisingly, they also achieve the same success for rod-caught salmon taken at beats remote from the estuary and including capture dates when some fish could have been in the river for a few months. The implications of these findings for fishery management and conservation are discussed.

Identification of the Minimal Thickness of Cutting Layer Based on the Acoustic Emission Signal

2016 ◽  
Vol 686 ◽  
pp. 39-44 ◽  
Author(s):  
Józef Gawlik ◽  
Joanna Krajewska-Śpiewak ◽  
Wojciech Zębala
Keyword(s):  
Signal Strength ◽  
Machining Process ◽  
Precision Machining ◽  
Emission Signal ◽  
Minimal Thickness ◽  
Machining Allowance ◽  
Forming Precision ◽  
Ae Signal ◽  
Cutting Zone

The chip-forming precision machining process plays a significant role in the mechanical technology. In planning of machining operation, it is crucial to supply the information about the possible minimal value of the machining allowance. For the technologist, when planning the machining operation, it is important to define the minimal thickness of cutting layer correctly. This article presents a new method of describing the start of decohesion process in a workpiece, meaning the determination of the minimal thickness of cutting layer based on the AE signal generated in the cutting zone. The research conducted on the turning of an alloy steel and the analysis of the AE signal strength confirmed that the proposed method opens new possibilities in quickening the identification of the minimal thickness of cutting layer under normal machining conditions.

Opposite Effects for Cations in Enhancing and Suppressing Nucleation of Pathology Crystallization of Gout

CrystEngComm ◽  
2021 ◽  
Author(s):  
anqi da ◽  
aijun ren ◽  
Rong Chen ◽  
Xingde Zhang ◽  
yonglan pan ◽  
...  
Keyword(s):  
Probability Distributions ◽  
Crystal Nucleation ◽  
Cumulative Probability ◽  
Monosodium Urate

In this paper, we focused on the influence of common serum cations on monosodium urate monohydrate (MSUM) crystals nucleation determined from cumulative probability distributions (CPD) of the solute crystal nucleation...

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

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