Mechanistic diffusion model for slow dynamic behavior in materials

2021 ◽  
Vol 150 ◽  
pp. 104355
Author(s):  
J.A. Bittner ◽  
J.S. Popovics
2019 ◽  
Vol 25 (23-24) ◽  
pp. 2863-2874 ◽  
Author(s):  
Jingjing Zhang ◽  
Diyi Chen ◽  
Hao Zhang ◽  
Beibei Xu ◽  
Huanhuan Li ◽  
...  

Hydraulic generating systems are widely modeled in the literature for investigating their stability properties by means of transfer functions representing the dynamic behavior of the reservoir, penstock, surge tank, hydro-turbine, and the generator. Traditionally, in these models the electrical load is assumed constant to simplify the modeling process. This assumption can hide interesting dynamic behaviors caused by fluctuation of the load as actually occurred. Hence, in this study, the electrical load characterized with periodic excitation is introduced into a hydraulic generating system and the responses of the system show a novel dynamic behavior called the fast–slow dynamic phenomenon. To reveal the nature of this phenomenon, the effects of the three parameters (i.e., differential adjustment coefficient, amplitude, and frequency) on the dynamic behaviors of the hydraulic generating system are investigated, and the corresponding change rules are presented. The results show that the intensity of the fast–slow dynamic behaviors varies with the change of each parameter, which provides reference for the quantification of the hydraulic generating system parameters. More importantly, these results not only present rich nonlinear phenomena induced by multi-timescales, but also provide some theoretical bases for maintaining the safe and stable operation of a hydropower station.


2015 ◽  
Vol 21 (51) ◽  
pp. 18563-18565 ◽  
Author(s):  
Alexey N. Bilyachenko ◽  
Alexey I. Yalymov ◽  
Alexander A. Korlyukov ◽  
Jérôme Long ◽  
Joulia Larionova ◽  
...  

2017 ◽  
Vol 32 (4) ◽  
pp. 3321-3322 ◽  
Author(s):  
Zaiyu Chen ◽  
Minghui Yin ◽  
Yun Zou ◽  
Ke Meng ◽  
ZhaoYang Dong

2020 ◽  
Vol 13 (4) ◽  
pp. 1269-1278 ◽  
Author(s):  
Kyojin Ku ◽  
Byunghoon Kim ◽  
Sung-Kyun Jung ◽  
Yue Gong ◽  
Donggun Eum ◽  
...  

We propose a new lithium diffusion model involving coupled lithium and transition metal migration, peculiarly occurring in a lithium-rich layered oxide.


Author(s):  
Don van Ravenzwaaij ◽  
Han L. J. van der Maas ◽  
Eric-Jan Wagenmakers

Research using the Implicit Association Test (IAT) has shown that names labeled as Caucasian elicit more positive associations than names labeled as non-Caucasian. One interpretation of this result is that the IAT measures latent racial prejudice. An alternative explanation is that the result is due to differences in in-group/out-group membership. In this study, we conducted three different IATs: one with same-race Dutch names versus racially charged Moroccan names; one with same-race Dutch names versus racially neutral Finnish names; and one with Moroccan names versus Finnish names. Results showed equivalent effects for the Dutch-Moroccan and Dutch-Finnish IATs, but no effect for the Finnish-Moroccan IAT. This suggests that the name-race IAT-effect is not due to racial prejudice. A diffusion model decomposition indicated that the IAT-effects were caused by changes in speed of information accumulation, response conservativeness, and non-decision time.


Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.


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