Understanding the Multi-Scale Effects of Li-Ion Cell Destruction By Locally Induced Heating of Cylindrical Cells

2021 ◽  
Vol MA2021-01 (5) ◽  
pp. 279-279
Author(s):  
Justin Holloway ◽  
Muinuddin Maharun ◽  
Tanveerkhan Pathan ◽  
Melanie J Loveridge
Keyword(s):  
Scale Effects ◽  
Multi Scale ◽  
Cell Destruction ◽  
Li Ion

scholarly journals Understanding Chemomechanical Li-ion Cathode Degradation through Multi-Scale, Multi-Modal X-ray Spectromicroscopy

2018 ◽  
Vol 24 (S2) ◽  
pp. 426-427
Author(s):  
Sam Kalirai ◽  
Kipil Lim ◽  
Bjorn Enders ◽  
Jihyun Hong ◽  
William E. Gent ◽  
...  
Keyword(s):  
X Ray ◽  
Multi Scale ◽  
Cathode Degradation ◽  
Li Ion

Comparing Micro- and Macro-Level Rheological Properties of Polymeric and Reclaimed Asphalt Pavement-Modified Asphalt Binders

2021 ◽  
pp. 036119812110288
Author(s):  
Tandra Bagchi ◽  
Zahid Hossain ◽  
Mohammed Ziaur Rahaman ◽  
Gaylon Baumgardner
Keyword(s):  
Mechanical Properties ◽  
Scale Effects ◽  
Asphalt Pavement ◽  
Linear Trend ◽  
Asphalt Binders ◽  
Reclaimed Asphalt Pavement ◽  
Reclaimed Asphalt ◽  
Multi Scale ◽  
Macro Scale ◽  
Scale Properties

Multi-scale evaluation of the rheological and mechanical properties of asphalt binder has substantial importance in understanding the binder’s micro- and macro-scale properties. This study compares the macro- and micro-scale mechanistic properties of asphalt binders. Test samples used in this study include performance grade binders (PG 64-22) from two different sources along with their modified counterparts. The modifiers include polyphosphoric acid (PPA), styrene-butadiene-styrene (SBS), a combination of SBS and PPA, and reclaimed asphalt pavement. To achieve the goal of this study, atomic force microscope technology was utilized to estimate the asphalt binder’s micro-mechanical properties (e.g., Derjaguin, Muller, Toropov modulus and deformation). On the other hand, data on the macro-scale properties—such as rutting factor (G*/sinδ), consistency and penetration—of the selected binders were analyzed and compared with the aforementioned micro-level properties. The comparative analyses indicated that the micro-mechanical properties of asphalt binders followed a linear trend with the macro-scale properties. The findings of this study are expected to help researchers and pavement professionals in modeling asphalt materials when multi-scale effects are deemed to be necessary.

scholarly journals Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

2018 ◽  
Vol 22 (1) ◽  
pp. 331-350 ◽  
Author(s):  
Abdellah Ichiba ◽  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer ◽  
Philippe Bompard ◽  
...  
Keyword(s):  
Scale Effect ◽  
Hydrological Model ◽  
Scale Effects ◽  
Model Performance ◽  
Scale Dependence ◽  
Urban Hydrology ◽  
Distributed Data ◽  
Hydrological Models ◽  
Multi Scale ◽  
Scale Modelling

Abstract. Hydrological models are extensively used in urban water management, development and evaluation of future scenarios and research activities. There is a growing interest in the development of fully distributed and grid-based models. However, some complex questions related to scale effects are not yet fully understood and still remain open issues in urban hydrology. In this paper we propose a two-step investigation framework to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependence observed within distributed data input into urban hydrological models. Then an intensive multi-scale modelling work is carried out to understand scale effects on hydrological model performance. Investigations are conducted using a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech. The model is implemented at 17 spatial resolutions ranging from 100 to 5 m. Results clearly exhibit scale effect challenges in urban hydrology modelling. The applicability of fractal concepts highlights the scale dependence observed within distributed data. Patterns of geophysical data change when the size of the observation pixel changes. The multi-scale modelling investigation confirms scale effects on hydrological model performance. Results are analysed over three ranges of scales identified in the fractal analysis and confirmed through modelling. This work also discusses some remaining issues in urban hydrology modelling related to the availability of high-quality data at high resolutions, and model numerical instabilities as well as the computation time requirements. The main findings of this paper enable a replacement of traditional methods of “model calibration” by innovative methods of “model resolution alteration” based on the spatial data variability and scaling of flows in urban hydrology.

The Mechanics of Adhesion: Current and Future Research Trends

2008 ◽  
Author(s):  
George G. Adams
Keyword(s):  
Molecular Dynamics ◽  
Scale Effects ◽  
Contact Region ◽  
Panel Discussion ◽  
Research Trends ◽  
Future Research ◽  
Research Needs ◽  
Multi Scale ◽  
Dynamics Simulations ◽  
Two Bodies

As the size of the contact region between two bodies decreases to the micro- and nano-scale, the effect of adhesion becomes increasingly important. As introductory remarks to a panel discussion on this topic, we briefly review recent research in the mechanics of adhesion and discuss future research needs. Attention is focused on adhesion with plastic deformation, molecular dynamics simulations, and multi-scale effects.

A Multi-Scale Model for Contact Between Rough Surfaces

2005 ◽  
Author(s):  
Robert L. Jackson ◽  
Jeffrey L. Streator
Keyword(s):  
Scale Effects ◽  
Rough Surfaces ◽  
Measurement Techniques ◽  
Surface Profile ◽  
Scale Model ◽  
Real Surface ◽  
Normal Contact ◽  
Multi Scale ◽  
Fractal Analyses ◽  
Deformation Mechanics

This work describes a non-statistical multi-scale model of the normal contact between rough surfaces. The model produces predictions for contact area as a function of contact load, and is compared to the traditional Greenwood and Williamson (GW) and Majumdar and Bhushan (MB) rough surface contact models, which represent single-scale and fractal analyses, respectively. The current model incorporates the effect of asperity deformations at multiple scales into a simple framework for modeling the contact between nominally flat rough surfaces. Similar to the “protuberance upon protuberance” theory proposed by Archard, the model considers the effect of having smaller asperities located on top of larger asperities in repeated fashion with increasing detail down to the limits of current measurement techniques. The parameters describing the surface topography (areal asperity density and asperity radius) are calculated from an FFT performed of the surface profile. Thus, the model considers multi-scale effects, which fractal methods have addressed, while attempting to more accurately incorporate the deformation mechanics into the solution. After the FFT of a real surface is calculated, the computational resources needed for the method are very small. Perhaps surprisingly, the trends produced by this non-statistical multi-scale model are quite similar to those arising from the GW and MB models.

scholarly journals Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs

2020 ◽  
Author(s):  
Ryan Bartelme ◽  
Michael Behrisch ◽  
Emily Jean Cain ◽  
Remco Chang ◽  
Ishita Debnath ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Scale Effects ◽  
Environmental Data ◽  
Phenotypic Data ◽  
Multi Scale ◽  
Data Schema ◽  
The Face ◽  
Disparate Data ◽  
Knowledge Graphs

The interplay between an organism's genes, its environment, and the expressed phenotype is dynamic. These interactions within ecosystems are shaped by non-linear multi-scale effects that are difficult to disentangle into discrete components. In the face of anthropogenic climate chance, it is critical to understand environmental and genotypic influences on plant phenotypes and phenophase transitions. However, it is difficult to integrate and interoperate between these datasets. Advances in the fields of ontologies, unsupervised learning, and genomics may overcome the disparate data schema. Here we present a framework to better link phenotypes, environments, and genotypes of plant species across ecosystem scales. This approach utilizing phenotypic data, knowledge graphing, and deep learning, serves as the groundwork for a new scientific sub-discipline: “Computational Ecogenomics”

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