Large-scale simulations of phase separation of elastically coherent binary alloy systems

1999 ◽  
Vol 59 (13) ◽  
pp. 8646-8659 ◽  
Author(s):  
Daniel Orlikowski ◽  
Celeste Sagui ◽  
Andrés Somoza ◽  
Christopher Roland
Keyword(s):  
Phase Separation ◽  
Binary Alloy ◽  
Large Scale ◽  
Large Scale Simulations ◽  
Alloy Systems

Three-Dimensional Simulations of Phase Separation in Model Binary Alloy Systems with Elasticity

1997 ◽  
Vol 481 ◽  
Author(s):  
D. Orlikowski ◽  
C. Sagui ◽  
A. S. Somoza ◽  
C. Roland
Keyword(s):  
Phase Separation ◽  
Binary Alloy ◽  
Large Scale ◽  
Three Dimensional ◽  
Elastic Field ◽  
Dimensional System ◽  
Elastic Fields ◽  
Slowing Down ◽  
Three Dimensional Simulations ◽  
Alloy Systems

ABSTRACTWe report on large-scale three-dimensional simulations of phase separation in model binary alloy systems in the presence of elastic fields. The elastic field has several important effects on the morphology of the system: the ordered domains are subject to shape transformations, and spatial ordering. In contrast to two-dimensional system, no significant slowing down in the growth is observed. There is also no evidence of any “reverse coarsening” of the domains.

An HPC Framework for Large Scale Simulations and Visualizations of Oil Spill Trajectories

2013 ◽  
Author(s):  
Jian Tao ◽  
Werner Benger ◽  
Kelin Hu ◽  
Edwin Mathews ◽  
Marcel Ritter ◽  
...  
Keyword(s):  
Oil Spill ◽  
Large Scale ◽  
Large Scale Simulations

scholarly journals Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

SLEEP ◽  
2021 ◽  
Author(s):  
Dorothee Fischer ◽  
Elizabeth B Klerman ◽  
Andrew J K Phillips
Keyword(s):  
Large Scale ◽  
Research Question ◽  
Group Differences ◽  
Regularity Index ◽  
Wake Patterns ◽  
Health Related ◽  
Phase Deviation ◽  
Unbiased Estimates ◽  
Large Scale Simulations ◽  
Larger Sample

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Universality class of the motility-induced critical point in large scale off-lattice simulations of active particles.

Soft Matter ◽  
2021 ◽  
Author(s):  
Claudio Maggi ◽  
Matteo Paoluzzi ◽  
Andrea Crisanti ◽  
Emanuela Zaccarelli ◽  
Nicoletta Gnan
Keyword(s):  
Phase Separation ◽  
Critical Point ◽  
Computer Simulations ◽  
Large Scale ◽  
Universality Class ◽  
Critical Behaviour ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Active Particles ◽  
Two Dimensional Model

We perform large-scale computer simulations of an off-lattice two-dimensional model of active particles undergoing a motility-induced phase separation (MIPS) to investigate the systems critical behaviour close to the critical point...

Brain stem – from general view to computational model based on switchboard rules of operation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Włodzisław Duch ◽  
Dariusz Mikołajewski
Keyword(s):  
Brain Stem ◽  
Large Scale ◽  
Ionic Channels ◽  
General View ◽  
The Central Nervous System ◽  
Network States ◽  
Large Scale Simulations ◽  
Influence Of Noise ◽  
The Brain ◽  
Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

scholarly journals Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations

2015 ◽  
Vol 9 ◽  
Author(s):  
Eric Y. Hu ◽  
Jean-Marie C. Bouteiller ◽  
Dong Song ◽  
Michel Baudry ◽  
Theodore W. Berger
Keyword(s):  
Nonlinear Dynamics ◽  
Large Scale ◽  
Large Scale Simulations
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