Microstructural Modeling of Ferroelectric Materials: State of the Art, Challenges and Opportunities

2008 ◽  
Vol 606 ◽  
pp. 119-134 ◽  
Author(s):  
Sarah Leach ◽  
R. Edwin Garcia
Keyword(s):  
State Of The Art ◽  
Ferroelectric Materials ◽  
Ferroelectric Ceramics ◽  
Ongoing Research ◽  
Microstructural Modeling ◽  
Phase Field Models ◽  
Challenges And Opportunities ◽  
Properties Of Materials ◽  
Independent Behavior ◽  
Film Form

In the last ten years of ongoing research in the modeling of polycrystalline ferroelectric ceramics a myriad of analytical and numerical implementations have emerged to predict and support the engineering of ferroelectrics in both its single-crystal and polycrystalline forms. Traditional atomistic approaches capture the intrinsic behaviors, and have led to great improvements in the chemistries of these systems. Similarly, macroscopic engineering approaches have focused on the development of phenomenological descriptions that capture the empirical static and time-independent behavior. At the interface of these two apparently divorced approaches, thermodynamic-based microstructural evolution descriptions inspired in phase field models have risen as the necessary link between the atomic and macroscopic levels. This new and emerging methodology starts from the predicted behaviors given by their atomic counter-parts, and resolves the effects of grain boundaries, and de-convolves the grain-grain mesoscopic interactions. Much of the future of ferroelectrics lies in the delivery of improved chemistries and microstructures, and on bridging the understanding currently existing atomistic and continuum descriptions. Overall, it is expected that current and emerging technological challenges will be the driving force to minimize ferroelectric fatigue and realize lead-free materials with performances close to currently existing (lead containing) ones. Moreover, it is expected that while an accurate understanding of the intrinsic properties of materials are key to define improved ferroelectric solids, it will be the detailed understanding of the extrinsic response of ferroelectric materials, in both bulk and thin film form, that will take these materials to reach the highest performances possible.

scholarly journals Disaster Resilience of Optical Networks: State of the Art, Challenges, and Opportunities

2021 ◽  
pp. 100619
Author(s):  
Jacek Rak ◽  
Rita Girão-Silva ◽  
Teresa Gomes ◽  
Georgios Ellinas ◽  
Burak Kantarci ◽  
...  
Keyword(s):  
Optical Networks ◽  
State Of The Art ◽  
Disaster Resilience ◽  
Challenges And Opportunities

scholarly journals Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
State Of The Art ◽  
Critical Role ◽  
Semantic Segmentation ◽  
Energy System ◽  
Aerial Images ◽  
Potential Analysis ◽  
3D Data ◽  
Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

scholarly journals Integrated multiscale biomaterials experiment and modelling: a perspective

2016 ◽  
Vol 6 (1) ◽  
pp. 20150098 ◽  
Author(s):  
Markus J. Buehler ◽  
Guy M. Genin
Keyword(s):  
State Of The Art ◽  
Biological Systems ◽  
The State ◽  
Computational Power ◽  
Multiscale Models ◽  
Predictive Capacity ◽  
Hierarchical Levels ◽  
Challenges And Opportunities

Advances in multiscale models and computational power have enabled a broad toolset to predict how molecules, cells, tissues and organs behave and develop. A key theme in biological systems is the emergence of macroscale behaviour from collective behaviours across a range of length and timescales, and a key element of these models is therefore hierarchical simulation. However, this predictive capacity has far outstripped our ability to validate predictions experimentally, particularly when multiple hierarchical levels are involved. The state of the art represents careful integration of multiscale experiment and modelling, and yields not only validation, but also insights into deformation and relaxation mechanisms across scales. We present here a sampling of key results that highlight both challenges and opportunities for integrated multiscale experiment and modelling in biological systems.

scholarly journals Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

2020 ◽  
Author(s):  
Thijs Dhollander ◽  
Adam Clemente ◽  
Mervyn Singh ◽  
Frederique Boonstra ◽  
Oren Civier ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
State Of The Art ◽  
Specific Information ◽  
Analysis Framework ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
P Diffusion ◽  
Modelling Techniques ◽  
The Rich

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organisation. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "fixel-based analysis" (FBA) framework that implements bespoke solutions to this end, and has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to fixel-based analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of current fixel-based analysis studies (until August 2020), categorised across a broad range of neuroscientific domains, listing key design choices and summarising their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the fixel-based analysis framework, and outline some directions and future opportunities.

Development of high-insulating materials with aerogel for protective clothing applications – an overview

2021 ◽  
Vol 112 (2) ◽  
pp. 164-172
Author(s):  
Agnieszka Greszta ◽  
Sylwia Krzemińska ◽  
Grażyna Bartkowiak ◽  
Anna Dąbrowska
Keyword(s):  
Thermal Conductivity ◽  
Thermal Insulation ◽  
Protective Clothing ◽  
State Of The Art ◽  
Knitted Fabrics ◽  
Insulating Materials ◽  
Textile Materials ◽  
Wide Range ◽  
Properties Of Materials ◽  
Hot Environments

Abstract Aerogels are ultra-light solids with extremely low thermal conductivity (even lower than air), thanks to which they have a huge potential in a wide range of applications. The purpose of this publication is to present the state-of-the art knowledge of the possibility of using aerogels to increase the thermal insulation properties of clothing materials intended for use in both cold and hot environments. Various methods of aerogels application to textile materials (non-woven, woven and knitted fabrics) are discussed, indicating their advantages and limitations. Numerous research studies confirm that aerogels significantly improve the thermal insulation properties of materials, but due to their delicate and brittle structure and their tendency to dusting, their application still poses considerable problems.

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