scholarly journals Diffuse approximation for identification of the mechanical properties of microcapsules

2020 ◽  
pp. 108128652097760
Author(s):  
Carlos Quesada ◽  
Claire Dupont ◽  
Pierre Villon ◽  
Anne-Virginie Salsac
Keyword(s):  
Mechanical Properties ◽  
Active Material ◽  
Liquid Core ◽  
Constitutive Law ◽  
Data Driven ◽  
Membrane Properties ◽  
Mechanical Resistance ◽  
Data Set ◽  
Diffuse Approximation ◽  
Capsule Size

A novel data-driven real-time procedure based on diffuse approximation is proposed to characterize the mechanical behavior of liquid-core microcapsules from their deformed shape and identify the mechanical properties of the submicron-thick membrane that protects the inner core through inverse analysis. The method first involves experimentally acquiring the deformed shape that a given microcapsule takes at steady state when it flows through a microfluidic microchannel of comparable cross-sectional size. From the mid-plane capsule profile, we deduce two characteristic geometric quantities that uniquely characterize the shape taken by the microcapsule under external hydrodynamic stresses. To identify the values of the unknown rigidity of the membrane and of the size of the capsule, we compare the geometric quantities with the values predicted numerically using a fluid-structure-interaction model by solving the three-dimensional capsule-flow interactions. The complete numerical data set is obtained off-line by systematically varying the governing parameters of the problem, i.e. the capsule-to-tube confinement ratio, and the capillary number, which is the ratio of the viscous to elastic forces. We show that diffuse approximation efficiently estimates the unknown mechanical resistance of the capsule membrane. We validate the data-driven procedure by applying it to the geometric and mechanical characterization of ovalbumin microcapsules (diameter of the order of a few tens of microns). As soon as the capsule is sufficiently deformed to exhibit a parachute shape at the rear, the capsule size and surface shear modulus are determined with an accuracy of 0.2% and 2.7%, respectively, as compared with 2–3% and 25% without it, in the best cases (Hu et al. Characterizing the membrane properties of capsules flowing in a square-section microfluidic channel: Effects of the membrane constitutive law. Phys Rev E 2013; 87(6): 063008). Diffuse approximation thus allows the capsule size and membrane elastic resistance to be provided quasi-instantly with very high precision. This opens interesting perspectives for industrial applications that require tight control of the capsule mechanical properties in order to secure their behavior when they transport active material.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals Symmetric Nature of Stress Distribution in the Elastic-Plastic Range of Pinus L. Pine Wood Samples Determined Experimentally and Using the Finite Element Method (FEM)

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 39
Author(s):  
Łukasz Warguła ◽  
Dominik Wojtkowiak ◽  
Mateusz Kukla ◽  
Krzysztof Talaśka
Keyword(s):  
Mechanical Properties ◽  
Finite Element ◽  
Stress Distribution ◽  
Moisture Content ◽  
Tool Geometry ◽  
Wood Fibers ◽  
Data Set ◽  
Plastic Deformations ◽  
Pine Wood ◽  
Elastic Plastic

This article presents the results of experimental research on the mechanical properties of pine wood (Pinus L. Sp. Pl. 1000. 1753). In the course of the research process, stress-strain curves were determined for cases of tensile, compression and shear of standardized shapes samples. The collected data set was used to determine several material constants such as: modulus of elasticity, shear modulus or yield point. The aim of the research was to determine the material properties necessary to develop the model used in the finite element analysis (FEM), which demonstrates the symmetrical nature of the stress distribution in the sample. This model will be used to analyze the process of grinding wood base materials in terms of the peak cutting force estimation and the tool geometry influence determination. The main purpose of the developed model will be to determine the maximum stress value necessary to estimate the destructive force for the tested wood sample. The tests were carried out for timber of around 8.74% and 19.9% moisture content (MC). Significant differences were found between the mechanical properties of wood depending on moisture content and the direction of the applied force depending on the arrangement of wood fibers. Unlike other studies in the literature, this one relates to all three stress states (tensile, compression and shear) in all significant directions (anatomical). To verify the usability of the determined mechanical parameters of wood, all three strength tests (tensile, compression and shear) were mapped in the FEM analysis. The accuracy of the model in determining the maximum destructive force of the material is equal to the average 8% (for tensile testing 14%, compression 2.5%, shear 6.5%), while the average coverage of the FEM characteristic with the results of the strength test in the field of elastic-plastic deformations with the adopted ±15% error overlap on average by about 77%. The analyses were performed in the ABAQUS/Standard 2020 program in the field of elastic-plastic deformations. Research with the use of numerical models after extension with a damage model will enable the design of energy-saving and durable grinding machines.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

A Study of the Nitrogen-Containing Heterocycles on Copolyimide Properties for Proton Exchange Membranes

2013 ◽  
Vol 401-403 ◽  
pp. 563-566 ◽  
Author(s):  
Yu Han Li ◽  
Wei Jian Wang ◽  
Yu Fei Chen ◽  
Lei Wang
Keyword(s):  
Mechanical Properties ◽  
Thermal Stability ◽  
Proton Conductivity ◽  
Water Uptake ◽  
Main Chain ◽  
Proton Exchange Membranes ◽  
Proton Exchange ◽  
Membrane Properties ◽  
Dimensional Changes ◽  
Sulfonated Polyimide

Containing pyrimidine and pyridine monomers were incorporated respectively into the main chain of a sulfonated polyimide in order to investigate the effect of nitrogen-containing heterocycles on membrane properties such as water uptake and proton conductivity. With increasing content of the nitrogen-containing heterocycles, water uptake of membranes and dimensional changes remarkable decrease. The copolymer showed higher thermal stability (desulfonation temperature up to 330 °C) and reasonable good mechanical properties. These membranes also showed higher proton conductivity, which was comparable or even higher than Nafion 117.

A Zebrafish Embryo Behaves both as a “Cortical Shell – Liquid Core” Structure and a Homogeneous Solid when Experiencing Mechanical Forces

2014 ◽  
Vol 20 (6) ◽  
pp. 1841-1847 ◽  
Author(s):  
Fei Liu ◽  
Dan Wu ◽  
Ken Chen
Keyword(s):  
Mechanical Properties ◽  
Mechanical Behavior ◽  
Zebrafish Embryo ◽  
Large Strain ◽  
Liquid Core ◽  
Small Strain ◽  
Core Structure ◽  
Mechanical Forces ◽  
Cortical Shell ◽  
A Cell

AbstractMechanical properties are vital for living cells, and various models have been developed to study the mechanical behavior of cells. However, there is debate regarding whether a cell behaves more similarly to a “cortical shell – liquid core” structure (membrane-like) or a homogeneous solid (cytoskeleton-like) when experiencing stress by mechanical forces. Unlike most experimental methods, which concern the small-strain deformation of a cell, we focused on the mechanical behavior of a cell undergoing small to large strain by conducting microinjection experiments on zebrafish embryo cells. The power law with order of 1.5 between the injection force and the injection distance indicates that the cell behaves as a homogenous solid at small-strain deformation. The linear relation between the rupture force and the microinjector radius suggests that the embryo behaves as membrane-like when subjected to large-strain deformation. We also discuss the possible reasons causing the debate by analyzing the mechanical properties of F-actin filaments.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Yugang Yu ◽  
Gad Allon ◽  
Meiyan Wang ◽  
Zhentai Zhang
Keyword(s):  
Operations Management ◽  
Service Operations ◽  
Optimization Techniques ◽  
Data Driven ◽  
Analytical Models ◽  
Data Set ◽  
Service Operations Management ◽  
Level Data ◽  
Home Appliance ◽  
Research Challenge

To support the 2021 Manufacturing & Service Operations Management (MSOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers.

A New Uncertainty Analysis-Based Framework for Data-Driven Computational Mechanics

2021 ◽  
pp. 1-22
Author(s):  
Xu Guo ◽  
Zongliang Du ◽  
Chang Liu ◽  
Shan Tang
Keyword(s):  
Phase Space ◽  
Uncertainty Analysis ◽  
Distinctive Feature ◽  
Computational Mechanics ◽  
Problem Formulation ◽  
Solution Set ◽  
Data Driven ◽  
Data Set ◽  
Classical Counterpart ◽  
Single Solution

Abstract In the present paper, a new uncertainty analysis-based framework for data-driven computational mechanics (DDCM) is established. Compared with its practical classical counterpart, the distinctive feature of this framework is that uncertainty analysis is introduced into the corresponding problem formulation explicitly. Instated of only focusing on a single solution in phase space, a solution set is sought for in order to account for the influence of the multi-source uncertainties associated with the data set on the data-driven solutions. An illustrative example provided shows that the proposed framework is not only conceptually new, but also has the potential of circumventing the intrinsic numerical difficulties pertaining to the classical DDCM framework.

scholarly journals Engineering the Surface and Mechanical Properties of Water Desalination Membranes Using Ultralong Carbon Nanotubes

Membranes ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 106 ◽  
Author(s):  
Yehia M. Manawi ◽  
Kui Wang ◽  
Viktor Kochkodan ◽  
Daniel J. Johnson ◽  
Muataz A. Atieh ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Carbon Nanotubes ◽  
Aspect Ratio ◽  
Water Flux ◽  
Contact Angles ◽  
Water Desalination ◽  
Membrane Properties ◽  
Porous Membranes ◽  
Water Contact ◽  
And Performance

In this work, novel polysulphone (PS) porous membranes for water desalination, incorporated with commercial and produced carbon nanotubes (CNT), were fabricated and analyzed. It was demonstrated that changing the main characteristics of CNT (e.g., loading in the dope solutions, aspect ratio, and functionality) significantly affected the membrane properties and performance including porosity, water flux, and mechanical and surface properties. The water flux of the fabricated membranes increased considerably (up to 20 times) along with the increase in CNT loading. Conversely, yield stress and Young’s modulus of the membranes dropped with the increase in the CNT loading mainly due to porosity increase. It was shown that the elongation at fracture for PS/0.25 wt. % CNT membrane was much higher than for pristine PS membrane due to enhanced compatibility of commercial CNTs with PS matrix. More pronounced effect on membrane’s mechanical properties was observed due to compatibility of CNTs with PS matrix when compared to other factors (i.e., changes in the CNT aspect ratio). The water contact angle for PS membranes incorporated with commercial CNT sharply decreased from 73° to 53° (membrane hydrophilization) for membranes with 0.1 and 1.0 wt. % of CNTs, while for the same loading of produced CNTs the water contact angles for the membrane samples increased from 66° to 72°. The obtained results show that complex interplay of various factors such as: loading of CNT in the dope solutions, aspect ratio, and functionality of CNT. These features can be used to engineer membranes with desired properties and performance.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Automatic Evaluation of the Mechanical Properties of Steels Maraging using Digital Image Processing Techniques

2020 ◽  
Author(s):  
Angélica Alves Viana ◽  
Savio Lopes Rabelo ◽  
José Daniel de Alencar Santos ◽  
Venceslau Xavier de Lima Filho ◽  
Douglas De Araújo Rodrigues ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Fracture Toughness ◽  
Maraging Steel ◽  
Raw Material ◽  
High Fracture Toughness ◽  
Mechanical Resistance ◽  
High Fracture ◽  
Traditional System ◽  
Transformation Methods ◽  
Processing Techniques

Some strategic sectors of the economy require that the raw material of their machines and equipment have mechanical properties that satisfy their use. Maraging steel is a material of great concern since it is necessary to have a high mechanical resistance associated with high fracture toughness. The traditional tests to determine the fracture toughness of this material before use in applications are the Charpy and KIC tests. However, this process is characterized by being exhaustive and requiring specialized and trained professionals. Thus, to reverse this situation, this work proposes a new approach to determine the mechanical properties of maraging steel. For this, initially, the method removes any artifacts present in the image resulting from the mode of acquisition. In sequence, this works tested the method Extended Minimum Transformation (EMT) and mathematical morphology to find these markers of the regions of the dimples. Then, the Adaptive Thresholding, Optimal Global Thresholdusing the Otsu Method and Watershed transformation methods were used to segment the dimples. In the end, the diameter of the dimples and the toughness of the material were calculated. Tests are carried out and compared with the result obtained by specialists using the traditional system to evaluate the proposed approach. The results obtained were satisfactory for the application because the proposed approach presented speed and precision to the conventional methods.

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