The injection of impurity granules into fusion research discharges can serve
as a catalyst for ELM events. For sufficiently low ELM frequencies, and granule
sizes above a threshold, this can result in full control of the ELM cycle,
referred to as ELM pacing. For this research, we extend the investigation
to conditions where the natural ELM frequency is too high for ELM pacing to
be realized. Utilizing multiple sizes of lithium granules and classifying their
effects by granule size, we demonstrate that ELM mitigation through frequency
multiplication can be used at ELM triggering rates that nominally make ELM pacing
unrealizable. We find that above a size threshold, injected granules promptly
trigger ELMs and commensurately enhance the ELM frequency . Below this threshold
size, injection of an individual granule does not always lead to the prompt
triggering of an ELM; however, collective ablation in the edge pedestal region
does enhance the ELM frequency. Specifically, Li granules too small to individually
trigger ELMs were injected into EAST H-mode discharges at frequencies up to 2.3 kHz;
collectively the granules were observed to enhance the natural ELM frequency up to
620 Hz, resulting in a ~2.4x multiplication of the natural ELM frequency and a 50%
decrease of the ELM size.
Piaggi, Pablo M; Gartner, Thomas E; Car, Roberto; Debenedetti, Pablo G
The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate in part due to the challenges associated with providing definitive experimental evidence. Pioneering work by Mishima and Stanley [Nature 392, 164 (1998) and Phys.~Rev.~Lett. 85, 334 (2000)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected location of the liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in slope of the melting curves, Mishima suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. Here, we explore this conjecture using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII using this model and find that the melting lines of all the studied ice polymorphs are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in slope of the melting lines upon crossing of the locus of maximum compressibility of the liquid. Finally, we analyze critically the literature in light of our findings, and conclude that the scenario in which melting curves are supercritical is favored by the most recent computational and experimental evidence. Thus, although the preponderance of experimental and computational evidence is consistent with the existence of a second critical point in water, the behavior of the melting lines of ice polymorphs does not provide strong evidence in support of this viewpoint, according to our calculations.
Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
Cara L. Buck; Jonathan D. Cohen; Field, Brent; Daniel Kahneman; Samuel M. McClure; Leigh E. Nystrom
Studies of subjective well-being have conventionally relied upon self-report, which directs subjects’ attention to their emotional experiences. This method presumes that attention itself does not influence emotional processes, which could bias sampling. We tested whether attention influences experienced utility (the moment-by-moment experience of pleasure) by using functional magnetic resonance imaging (fMRI) to measure the activity of brain systems thought to represent hedonic value while manipulating attentional load. Subjects received appetitive or aversive solutions orally while alternatively executing a low or high attentional load task. Brain regions associated with hedonic processing, including the ventral striatum, showed a response to both juice and quinine. This response decreased during the high-load task relative to the low-load task. Thus, attentional allocation may influence experienced utility by modulating (either directly or indirectly) the activity of brain mechanisms thought to represent hedonic value.