Stochastic uncertainty quantification for multiscale modeling of polymeric nanocomposites

Nanostructured materials are extensively applied in many fields of material science for new industrial applications, particularly in the automotive, aerospace industry due to their exceptional physical and mechanical properties. Experimental testing of nanomaterials is expensive, timeconsuming,challenging and sometimes unfeasible. Therefore,computational simulations have been employed as alternative method to predict macroscopic material properties. The behavior of polymeric nanocomposites (PNCs) are highly complex. The origins of macroscopic material properties reside in the properties and interactions taking place on finer scales. It is therefore essential to use multiscale modeling strategy to properly account for all large length and time scales associated with these material systems, which across many orders of magnitude. Numerous multiscale models of PNCs have been established, however, most of them connect only two scales. There are a few multiscale models for PNCs bridging four length scales (nano-, micro-, meso- and macro-scales). In addition, nanomaterials are stochastic in nature and the prediction of macroscopic mechanical properties are influenced by many factors such as fine-scale features. The predicted mechanical properties obtained by traditional approaches significantly deviate from the measured values in experiments due to neglecting uncertainty of material features. This discrepancy is indicated that the effective macroscopic properties of materials are highly sensitive to various sources of uncertainty, such as loading and boundary conditions and material characteristics, etc., while very few stochastic multiscale models for PNCs have been developed. Therefore, it is essential to construct PNC models within the framework of stochastic modeling and quantify the stochastic effect of the input parameters on the macroscopic mechanical properties of those materials. This study aims to develop computational models at four length scales (nano-, micro-, meso- and macro-scales) and hierarchical upscaling approaches bridging length scales from nano- to macro-scales. A framework for uncertainty quantification (UQ) applied to predict the mechanical properties of the PNCs in dependence of material features at different scales is studied. Sensitivity and uncertainty analysis are of great helps in quantifying the effect of input parameters, considering both main and interaction effects, on the mechanical properties of the PNCs. To achieve this major goal, the following tasks are carried out: At nano-scale, molecular dynamics (MD) were used to investigate deformation mechanism of glassy amorphous polyethylene (PE) in dependence of temperature and strain rate. Steered molecular dynamics (SMD)were also employed to investigate interfacial characteristic of the PNCs. At mico-scale, we developed an atomistic-based continuum model represented by a representative volume element (RVE) in which the SWNT’s properties and the SWNT/polymer interphase are modeled at nano-scale, the surrounding polymer matrix is modeled by solid elements. Then, a two-parameter model was employed at meso-scale. A hierarchical multiscale approach has been developed to obtain the structure-property relations at one length scale and transfer the effect to the higher length scales. In particular, we homogenized the RVE into an equivalent fiber. The equivalent fiber was then employed in a micromechanical analysis (i.e. Mori-Tanaka model) to predict the effective macroscopic properties of the PNC. Furthermore, an averaging homogenization process was also used to obtain the effective stiffness of the PCN at meso-scale. Stochastic modeling and uncertainty quantification consist of the following ingredients: - Simple random sampling, Latin hypercube sampling, Sobol’ quasirandom sequences, Iman and Conover’s method (inducing correlation in Latin hypercube sampling) are employed to generate independent and dependent sample data, respectively. - Surrogate models, such as polynomial regression, moving least squares (MLS), hybrid method combining polynomial regression and MLS, Kriging regression, and penalized spline regression, are employed as an approximation of a mechanical model. The advantage of the surrogate models is the high computational efficiency and robust as they can be constructed from a limited amount of available data. - Global sensitivity analysis (SA) methods, such as variance-based methods for models with independent and dependent input parameters, Fourier-based techniques for performing variance-based methods and partial derivatives, elementary effects in the context of local SA, are used to quantify the effects of input parameters and their interactions on the mechanical properties of the PNCs. A bootstrap technique is used to assess the robustness of the global SA methods with respect to their performance. In addition, the probability distribution of mechanical properties are determined by using the probability plot method. The upper and lower bounds of the predicted Young’s modulus according to 95 % prediction intervals were provided. The above-mentioned methods study on the behaviour of intact materials. Novel numerical methods such as a node-based smoothed extended finite element method (NS-XFEM) and an edge-based smoothed phantom node method (ES-Phantom node) were developed for fracture problems. These methods can be used to account for crack at macro-scale for future works. The predicted mechanical properties were validated and verified. They show good agreement with previous experimental and simulations results

Evaluation of Anterior Chamber Depth and Anterior Chamber Angle Changing After Phacoemulsification in the Primary Angle Close Suspect Eyes

BACKGROUND: Phacoemulsification surgery has the ability to deeply alter the segment anterior morphology, especially in eye with shallow anterior chamber (AC), narrow anterior chamber angle (ACA). However, the changes of anterior chamber depth (ACD) and ACA on the close angle suspect eyes after phacoemulsification have not been mentioned in many studies. So, we conduct this research. AIM: To evaluate the alteration in the ACA and ACD after phacoemulsification in the close angle suspect eyes. METHODS: Interventional study with no control group. Subjects were the primary angle closure suspect (PACS) eyes, that were operated by phacoemulsification with intraocular lens (IOL) at Glaucoma Department of VNIO from December 2017 to October 2018. RESULTS: 29 PACS eyes with cataract were operated by phacoemulsification with intraocular lens. After 3 months of monitoring, the average ACD augmented from 2.082 ± 0.244 to 3.673 ± 0.222 mm. AOD500 increase from 0.183 ± 0.088 to 0.388 ± 0.132 μm, AOD750 increased from 0.278 ± 0.105 to 0.576 ± 0.149 μm. The TISA500 enlarged from 0.068 ± 0.033 to 0.140 ± 0.052 mm2, TISA750 enlarged from 0.125 ± 0.052 to 0.256 ± 0.089 mm2 at the third month (p < 0.01). CONCLUSION: Phacoemulsification surgery increases the ACD and enlarged the angle in the PACS eyes

A phantom-node method with edge-based strain smoothing for linear elastic fracture mechanics

This paper presents a novel numerical procedure based on the combination of an edge-based smoothed finite element (ES-FEM) with a phantom-node method for 2D linear elastic fracture mechanics. In the standard phantom-node method, the cracks are formulated by adding phantom nodes, and the cracked element is replaced by two new superimposed elements. This approach is quite simple to implement into existing explicit finite element programs. The shape functions associated with discontinuous elements are similar to those of the standard finite elements, which leads to certain simplification with implementing in the existing codes. The phantom-node method allows modeling discontinuities at an arbitrary location in the mesh. The ES-FEM model owns a close-to-exact stiffness that is much softer than lower-order finite element methods (FEM). Taking advantage of both the ES-FEM and the phantom-node method, we introduce an edge-based strain smoothing technique for the phantom-node method. Numerical results show that the proposed method achieves high accuracy compared with the extended finite element method (XFEM) and other reference solutions

Interface characterization between polyethylene/silica in engineered cementitious composites by molecular dynamics simulation

Polyethylene is widely adopted in engineered cementitious composites to control the crack width. A clearer knowledge of the PE/concrete interfacial properties is important in developing engineered cementitious composites, which can lead to a limited crack width. Tensile failure and adhesion properties of the amorphous polyethylene/silica (PE/S) interface are investigated by molecular dynamics to interpret the PE/concrete interface. The influence of the PE chain length, the PE chain number and coupling agents applied on silica surface on the interfacial adhesion is studied. An increase of the adhesion strength of the modified silica surface by coupling agents compared with the unmodified silica is found. The failure process, density profile and potential energy evolutions of the PE/S interface are studied. The thermodynamic work of adhesion that quantifies the interfacial adhesion of the PE/S interface is evaluated. The present study helps to understand the interfacial adhesion behavior between ECC and PE, and is expected to contribute to restricting the crack width

Perforator Mapping of the Superficial and Deep Inferior Epigastric Artery in the Abdominal Region of the Vietnamese

BACKGROUND: Previous studies worldwide have investigated the anatomy of the perforators of the deep inferior epigastric arteries to figure out the navigation patterns of the perforators on the abdominal wall. This has been inconsistent amongst the researchers about how to select the perforator to increase the blood supply area for the flap. AIM: To explore the blood supply area of the perforators of the superficial and deep inferior epigastric artery in the abdominal region of the Vietnamese by dissection and 64-slice multislice computed tomography (64-slice MSCT). METHODS: A descriptive cross-sectional study Center from September 2014 to September 2016 on two groups including 30 cadavers fixed by formalin 10% in Anatomy Department of UPNT, and 37 patients getting the 64-slice MSCT abdominal arteries angiogram. RESULTS: The superficial epigastric arteries at the level of the inguinal ligament were located in the middle region, with 96% (right) and 88.5% (left). The anterior superior iliac spine level was in the middle, and lateral regions of 68% and 32% respectively. The level of the umbilical cord was in the lateral region with 66.7% and 85.7%, respectively. There were about 6 perforators of the deep inferior epigastric arteries located in the navel area. These perforators were 70% in the medial region and 30% in the middle region. CONCLUSION: Mapping the blood supply based on the fourth space in the abdominal region in which the superfical inferior epigastric arteries supplied the lateral area. The middle and the internal ones were the perforators of the deep inferior epigastric arteries

Language-Conditioned Affordance-Pose Detection in 3D Point Clouds

Affordance detection and pose estimation are of great importance in many robotic applications. Their combination helps the robot gain an enhanced manipulation capability, in which the generated pose can facilitate the corresponding affordance task. Previous methods for affodance-pose joint learning are limited to a predefined set of affordances, thus limiting the adaptability of robots in real-world environments. In this paper, we propose a new method for language-conditioned affordance-pose joint learning in 3D point clouds. Given a 3D point cloud object, our method detects the affordance region and generates appropriate 6-DoF poses for any unconstrained affordance label. Our method consists of an open-vocabulary affordance detection branch and a language-guided diffusion model that generates 6-DoF poses based on the affordance text. We also introduce a new high-quality dataset for the task of language-driven affordance-pose joint learning. Intensive experimental results demonstrate that our proposed method works effectively on a wide range of open-vocabulary affordances and outperforms other baselines by a large margin. In addition, we illustrate the usefulness of our method in real-world robotic applications. Our code and dataset are publicly available at https://3DAPNet.github.ioComment: Project page: https://3DAPNet.github.i

Anatomical Characteristics of Facial Nerve Trunk in Vietnamese Adult Cadavers

BACKGROUND: In medical literature, there are few studies provided a precise and detailed description of the facial nerve rami and its branches. AIM: Identify several practical anatomic landmarks related to the facial nerve main trunk and its rami. METHODS: A descriptive study, 30 cadavers in the anatomy department of UPNT from October 2012 to April 2015. RESULTS: The average distance from the mandibular angle to the division of the facial nerve is 40.8 mm, and is 86.6% from range 36 – 50 mm. There is 86.7% case in which the facial nerve is in the lateral of the retromandibular vein, and there is a significant difference about both sides. Eighty percent of the case has the superior and inferior ramus in the lateral to the retromandibular vein. There are 2 cases in which the superior ramus makes the circle of the vein. Eighty percent of the facial nerve is in the lateral to the external carotid artery. CONCLUSION: The distance from the mandibular to the division of the facial nerve is longer. The relationship between the superior/inferior ramus and the retromandibular vein maybe not the same in both sides. In some cases, it makes the circle of the vein to cause some complication in the parotid gland surgery