Modeling and simulation of filling in dual-scale fibrous reinforcements: state of the art and new methodology to quantify the sink effect

2017 ◽  
Vol 52 (14) ◽  
pp. 1915-1946 ◽  
Author(s):  
Iván D Patiño-Arcila ◽  
Juan D Vanegas-Jaramillo
Keyword(s):  
Modeling And Simulation ◽  
State Of The Art ◽  
Void Formation ◽  
Free Fluid ◽  
Mesoscopic Scale ◽  
Macroscopic Scale ◽  
Sink Effect ◽  
Original Contribution ◽  
Dual Scale

The main advances in the modeling and simulation of the filling phenomenon that takes place in dual-scale fibrous reinforcements used in liquid composites molding processes are grouped and classified in the present work. Special emphasis is done in the classification of the simulation methods according to the dimension of the mesh, the identification of the interface conditions porous medium-free fluid, the comparison between the most used fluid-front tracking techniques and the survey of researches dealing with the non-uniform filling of representative unitary cells, which in turn is responsible for the void formation at mesoscopic scale and the sink effect at macroscopic scale. As an original contribution to this field of study, a new methodology to quantify the sink effect in macroscopic fillings is presented and subsequently assessed by comparing the results of experimental radial injections with numerical results obtained by the dual reciprocity-boundary element method. The proposed methodology is physically consistent and leads to results that are closer to the experimental ones than the results obtained when the sink effect is neglected; however, the accuracy is liable to be improved.

scholarly journals Gestão do Conhecimento em Empresas de Desenvolvimento Ágil de Software: Conceitos e Práticas Essenciais

2015 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Eduardo Sakai ◽  
Ivan L M Ricarte
Keyword(s):  
Software Development ◽  
State Of The Art ◽  
Approach Space ◽  
Research Papers ◽  
Enabling Factors ◽  
Original Contribution ◽  
Development Approach ◽  
Agile Software ◽  
Software Development Techniques

This paper presents the state of the art of knowledge management in software development, organized around key concepts and practices grouped in epistemological and ontological dimensions, development approach, space, and culture. This classification results from the thematic analysis of 35 research papers available on this topic, selected from an initial set of 1620 papers, through bibliographic research that followed the principles of systematic reviews. As an original contribution, this paper presents a classification of agile software development techniques and some potential enabling factors according to the epistemology, ontology, and the SECI (Socialization, Externalization, Combination and Internalization) model.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Review of Control and Energy Management Approaches in Micro-Grid Systems

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 168
Author(s):  
Abdellatif Elmouatamid ◽  
Radouane Ouladsine ◽  
Mohamed Bakhouya ◽  
Najib El Kamoun ◽  
Mohammed Khaidar ◽  
...  
Keyword(s):  
Energy Management ◽  
Predictive Control ◽  
State Of The Art ◽  
Household Demand ◽  
Grid Systems ◽  
Control Approach ◽  
Micro Grid ◽  
Management Approaches ◽  
Air Conditioning Systems

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Multiscale Post-Seismic Deformation Based on cGNSS Time Series Following the 2015 Lefkas (W. Greece) Mw6.5 Earthquake

2021 ◽  
Vol 11 (11) ◽  
pp. 4817
Author(s):  
Filippos Vallianatos ◽  
Vassilis Sakkas
Keyword(s):  
Time Series ◽  
Relaxation Time ◽  
Relaxation Times ◽  
Relaxation Mechanism ◽  
Mesoscopic Scale ◽  
Macroscopic Scale ◽  
Epicentral Area ◽  
Arrhenius Dependence ◽  
Seismic Deformation ◽  
Logarithmic Type

In the present work, a multiscale post-seismic relaxation mechanism, based on the existence of a distribution in relaxation time, is presented. Assuming an Arrhenius dependence of the relaxation time with uniform distributed activation energy in a mesoscopic scale, a generic logarithmic-type relaxation in a macroscopic scale results. The model was applied in the case of the strong 2015 Lefkas Mw6.5 (W. Greece) earthquake, where continuous GNSS (cGNSS) time series were recorded in a station located in the near vicinity of the epicentral area. The application of the present approach to the Lefkas event fits the observed displacements implied by a distribution of relaxation times in the range τmin ≈3.5 days to τmax ≈350 days.

Hydrogel-Based Bioinks for Three-Dimensional Bioprinting: Patent Analysis

2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Ahmed Fatimi
Keyword(s):  
Tissue Engineering ◽  
Detailed Analysis ◽  
State Of The Art ◽  
Three Dimensional ◽  
Patent Analysis ◽  
The State ◽  
Driving Factors ◽  
3D Bioprinting ◽  
Preparation Methods

There are a variety of hydrogel-based bioinks commonly used in three-dimensional bioprinting. In this study, in the form of patent analysis, the state of the art has been reviewed by introducing what has been patented in relation to hydrogel-based bioinks. Furthermore, a detailed analysis of the patentability of the used hydrogels, their preparation methods and their formulations, as well as the 3D bioprinting process using hydrogels, have been provided by determining publication years, jurisdictions, inventors, applicants, owners, and classifications. The classification of patents reveals that most inventions intended for hydrogels used as materials for prostheses or for coating prostheses are characterized by their function or properties Knowledge clusters and expert driving factors show that biomaterials, tissue engineering, and biofabrication research is concentrated in the most patents.

Sign in / Sign up

Export Citation Format

Share Document