Particle damping: Noise characteristics and large-scale simulation

2017 ◽  
Vol 24 (17) ◽  
pp. 3920-3930 ◽  
Author(s):  
Masato Saeki ◽  
Takahiro Mizoguchi ◽  
Mika Bitoh
Keyword(s):  
Computational Methods ◽  
Large Scale ◽  
Memory Usage ◽  
Mass Ratio ◽  
Large Scale Simulation ◽  
Noise Characteristics ◽  
Particle Damping ◽  
Particle Dampers ◽  
Scale Particle ◽  
Large Scale Simulations

The performance of a large-scale particle damper in a vertical vibrating system was investigated experimentally and theoretically. To use particle dampers on an industrial scale, their noise characteristics must be clarified and a large-scale simulation is essential. This paper presents the results of an experimental investigation of the effects of the particle material, mass ratio and diameter on the amount of noise generated by a particle damper. In the theoretical analysis, two computational methods for conducting large-scale simulations of particle damping are proposed. The validity of the numerical methods is examined by comparison with experimental results. It is found that the calculation time and memory usage are decreased considerably by using the computational methods.

Large-Scale Simulation for Particle Damping

2018 ◽  
Author(s):  
Masato Saeki ◽  
Mika Bitoh
Keyword(s):  
Computational Methods ◽  
Large Scale ◽  
Natural Frequencies ◽  
Large Particles ◽  
Particle Damping ◽  
The Difference ◽  
Particle Dampers ◽  
Original Size ◽  
Scale Particle ◽  
Large Scale Simulations

Particle damping is an effective method of passive vibration control. Although it has been widely used in various structural damping applications, there are some points to be examined. Few studies have focused on the difference in the effectiveness of particle dampers for systems with different natural frequencies. Also, a computational scheme for conducting large-scale simulations has not been established. The authors previously presented some computational methods for predicting large-scale particle damping. The calculations are performed using equivalent large particles instead of the original-size particles. However, the range of the radius of the equivalent large particles for which these methods are effective is still incompletely understood. The objective of this study is to experimentally examine the difference in the effectiveness of particle dampers for systems with different natural frequencies and to investigate the relationship between the radius of the equivalent large particles and the validity of the computational methods.

New algorithms for large-scale simulation of particle damping

2016 ◽  
Vol 2016 (0) ◽  
pp. 244
Author(s):  
Mika BITOH ◽  
Takahiro MIZOGUCHI ◽  
Masato SAEKI
Keyword(s):  
Large Scale ◽  
Large Scale Simulation ◽  
Particle Damping ◽  
New Algorithms

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

LARGE-SCALE SIMULATION OF OIL RECOVERY BY SURFACTANT-POLYMER FLOODING

2016 ◽  
Vol 4 (1) ◽  
pp. 12-31 ◽  
Author(s):  
D.Zh. Akhmed-Zaki ◽  
T.S. Imankulov ◽  
B. Matkerim ◽  
B.S. Daribayev ◽  
K.A. Aidarov ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Large Scale ◽  
Polymer Flooding ◽  
Large Scale Simulation

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 Synthesis of Metal Organic Frameworks by Ball-Milling

Crystals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Cheng-An Tao ◽  
Jian-Fang Wang
Keyword(s):  
Ball Milling ◽  
Large Scale ◽  
Raw Materials ◽  
Metal Organic Frameworks ◽  
Research Topic ◽  
Mass Ratio ◽  
Time Consumption ◽  
Metal Organic ◽  
Short Time ◽  
Made In

Metal-organic frameworks (MOFs) have been used in adsorption, separation, catalysis, sensing, photo/electro/magnetics, and biomedical fields because of their unique periodic pore structure and excellent properties and have become a hot research topic in recent years. Ball milling is a method of small pollution, short time-consumption, and large-scale synthesis of MOFs. In recent years, many important advances have been made. In this paper, the influencing factors of MOFs synthesized by grinding were reviewed systematically from four aspects: auxiliary additives, metal sources, organic linkers, and reaction specific conditions (such as frequency, reaction time, and mass ratio of ball and raw materials). The prospect for the future development of the synthesis of MOFs by grinding was proposed.

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.

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