Discrepancies in Fracture Related Anysotropy Direction Obtained from Multi Scale Data

2018 ◽  
Author(s):  
I. Bogatyrev ◽  
E. Kharyba ◽  
K. Ezhov ◽  
T. Olneva ◽  
D. Semin ◽  
...  
Keyword(s):  
Multi Scale ◽  
Scale Data

scholarly journals MorphoNet: an interactive online morphological browser to explore complex multi-scale data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Bruno Leggio ◽  
Julien Laussu ◽  
Axel Carlier ◽  
Christophe Godin ◽  
Patrick Lemaire ◽  
...  
Keyword(s):  
Multi Scale ◽  
Scale Data

scholarly journals Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153971 ◽  
Author(s):  
Tianxiang Cui ◽  
Yujie Wang ◽  
Rui Sun ◽  
Chen Qiao ◽  
Wenjie Fan ◽  
...  
Keyword(s):  
Primary Production ◽  
River Basin ◽  
Heihe River Basin ◽  
Heihe River ◽  
Multi Scale ◽  
Scale Data ◽  
The Heihe River Basin

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

Influence of Experimental Uncertainty on Prediction of Holistic Multi-Scale Data Center Energy Efficiency

2011 ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Niru Kumari ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Experimental Analysis ◽  
Data Center ◽  
Data Centers ◽  
Operating Conditions ◽  
Coefficient Of Performance ◽  
System Level ◽  
Scale Model ◽  
Multi Scale ◽  
Scale Data

As the energy footprint of data centers continues to increase, models that allow for “what-if” simulations of different data center design and management paradigms will be important. Prior work by the authors has described a multi-scale energy efficiency model that allows for evaluating the coefficient of performance of the data center ensemble (COPGrand), and demonstrated the utility of such a model for purposes of choosing operational set-points and evaluating design trade-offs. However, experimental validation of these models poses a challenge because of the complexity involved with tailoring such a model for implementation to legacy data centers, with shared infrastructure and limited control over IT workload. Further, test facilities with dummy heat loads or artificial racks in lieu of IT equipment generally have limited utility in validating end-to-end models owing to the inability of such loads to mimic phenomena such as fan scalability, etc. In this work, we describe the experimental analysis conducted in a special test chamber and data center facility. The chamber, focusing on system level effects, is loaded with an actual IT rack, and a compressor delivers chilled air to the chamber at a preset temperature. By varying the load in the IT rack as well as the air delivery parameters — such as flow rate, supply temperature, etc. — a setup which simulates the system level of a data center is created. Experimental tests within a live data center facility are also conducted where the operating conditions of the cooling infrastructure are monitored — such as fluid temperatures, flow rates, etc. — and can be analyzed to determine effects such as air flow recirculation, heat exchanger performance, etc. Using the experimental data a multi-scale model configuration emulating the data center can be defined. We compare the results from such experimental analysis to a multi-scale energy efficiency model of the data center, and discuss the accuracies as well as inaccuracies within such a model. Difficulties encountered in the experimental work are discussed. The paper concludes by discussing areas for improvement in such modeling and experimental evaluation. Further validation of the complete multi-scale data center energy model is planned.

scholarly journals Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

2020 ◽  
Vol 101 (3) ◽  
pp. 1583-1619
Author(s):  
Giuseppe Quaranta ◽  
Giovanni Formica ◽  
J. Tenreiro Machado ◽  
Walter Lacarbonara ◽  
Sami F. Masri
Keyword(s):  
Differential Evolution Algorithm ◽  
Northern Region ◽  
Multidimensional Analysis ◽  
Data Driven ◽  
Type Model ◽  
Epidemic Spreading ◽  
Epidemic Dynamics ◽  
Multi Scale ◽  
Evolution Algorithm ◽  
Scale Data

Abstract The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

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