A study on an incompressible polymeric pressurized vessel subjected to bulk degradation

2021 ◽  
pp. 108128652110336
Author(s):  
M Kazemian ◽  
A Moazemi Goudarzi ◽  
A Hassani
Keyword(s):  
Hybrid Method ◽  
Material Properties ◽  
Hoop Stress ◽  
Complex Case ◽  
Processing Unit ◽  
Galerkin Finite Element Method ◽  
Family Time ◽  
Galerkin Finite Element ◽  
Central Processing ◽  
Inner Radius

To study the mechanical behavior of an incompressible polymeric degradable vessel subjected to the neo-Hookean constitutive model, two solution frameworks are introduced. One is combining a recently developed semi-analytical method and the [Formula: see text]-family time approximation (hybrid method). The other is the Standard Galerkin Finite Element Method (SGFEM), which is implemented by providing a script in the FlexPDE commercial software. A deformation-induced evolution law is used to study the dependence of material properties upon time and position in the polymeric vessel during bulk degradation. The convergence of the two proposed methods on degradable vessel responses under the axisymmetric plane-strain conditions is seen. Although the hybrid method, unlike the SGFEM, is implemented as an iteration-based algorithm, it uses highly acceptable central processing unit time because it can directly solve differential equations without converting variables. The FlexPDE method is much easier to extend to more complex case studies because the hybrid method is based on an analytical approach. It is found that less pressure is required to maintain the incompressibility of the material during the degradation. The material response to incompressibility decreases more sharply in the inner radius of the vessel. Initially, the hoop stress decreases in the inner radius but eventually reaches more than its virgin value.

scholarly journals Information management in passenger traffic supporting system design as a multi-criteria discrete optimization task

2017 ◽  
Vol 27 (2) ◽  
pp. 229-238 ◽  
Author(s):  
Adam Galuszka ◽  
Jolanta Krystek ◽  
Andrzej Swierniak ◽  
Carmen Lungoci ◽  
Tomasz Grzejszczak
Keyword(s):  
Information Management ◽  
Hybrid Method ◽  
Integrated System ◽  
Problem Solution ◽  
Processing Unit ◽  
Dynamic Information ◽  
Central Processing ◽  
Passenger Traffic ◽  
Optimization Task ◽  
Passenger Flows

Abstract This paper presents a concept of an Integrated System of Supporting Information Management in Passenger Traffic (ISSIMPT). The novelty of the system is an integration of six modules: video monitoring, counting passenger flows, dynamic information for passengers, the central processing unit, surveillance center and vehicle diagnostics into one coherent solution. Basing on expert evaluations, we propose to present configuration design problem of the system as a multi-objectives discrete static optimization problem. Then, hybrid method joining properties of weighted sum and ε-constraint methods is applied to solve the problem. Solution selections based on hybrid method, using set of exemplary cases, are shown.

Arterial remodeling in response to hypertension using a constituent-based model

2007 ◽  
Vol 293 (5) ◽  
pp. H3130-H3139 ◽  
Author(s):  
Alkiviadis Tsamis ◽  
Nikos Stergiopulos
Keyword(s):  
Material Properties ◽  
Hoop Stress ◽  
Arterial Compliance ◽  
Circumferential Stress ◽  
Strain Energy Function ◽  
Rate Equations ◽  
Wall Thickening ◽  
Arterial Remodeling ◽  
Mechanical Adaptation ◽  
Inner Radius

Hypertension-induced arterial remodeling has been previously modeled using stress-driven remodeling rate equations in terms of global geometrical adaptation (Rachev A, Stergiopulos N, Meister JJ. Theoretical study of dynamics of arterial wall remodeling in response to changes in blood pressure. J Biomech 29: 635–642, 1996) and was extended later to include adaptation of material properties (Rachev A, Stergiopulos N, Meister JJ. A model for geometric and mechanical adaptation of arteries to sustained hypertension. J Biomech Eng 120: 9–17, 1998). These models, however, used a phenomenological strain energy function (SEF), the parameters of which do not bear a clear physiological meaning. Here, we extend the work of Rachev et al. (1998) by applying similar remodeling rate equations to a constituent-based SEF. The new SEF includes a statistical description for collagen engagement, and remodeling now affects material properties only through changes in the collagen engagement probability density function. The model predicts asymptotic wall thickening and unchanged deformed inner radius as to conserve hoop stress and intimal shear stress, respectively, at the final adapted hypertensive state. Mechanical adaptation serves to restore arterial compliance to control levels. Average circumferential stress-strain curves show that the material at the final adapted hypertensive state is softer than its normotensive counterpart. These findings as well as the predicted pressure-diameter curves are in good qualitative agreement with experimental data. The novelty in our findings is that biomechanical adaptation leading to maintenance of compliance at the hypertensive state can be perfectly achieved by appropriate readjustment of the collagen engagement profile alone.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

PERANGKAT KERAS ( HARDWARE ) PADA KOMPUTER

2020 ◽  
Author(s):  
Siti Kumala Dewi
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device ◽  
System P ◽  
System 2

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Berdasarkan fungsinya, perangkat keras terbagi menjadi :1.Sistem Perangkat Keras Masukan (Input Device System )2.Sistem Pemrosesan ( Central Processing System/ Central Processing Unit(CPU)3.Sistem Perangkat Keras Keluaran ( Output Device System )4.Sistem Perangkat Keras Tambahan (Peripheral/Accessories Device System)

Effect of log loading on the performance of wood room

2014 ◽  
Vol 29 (2) ◽  
pp. 201-210
Author(s):  
Ari Isokangas ◽  
Kari Ala-Kaila ◽  
Markku Ohenoja ◽  
Aki Sorsa ◽  
Kauko Leiviskä
Keyword(s):  
Product Quality ◽  
Material Properties ◽  
Raw Material ◽  
Performance Criteria ◽  
Processing Unit ◽  
Level Control ◽  
Paper Mills ◽  
Remarkable Effect ◽  
Loading Process ◽  
Small Wood

Abstract The purpose of this paper is to analyse the log loading process of wood room, which is typically the first processing unit in pulp and paper mills. The aim is to improve the log loading process to obtain production with a constant log flow of well de-iced logs to the debarking drum. This way it is possible to reduce costs and enhance product quality. The research was carried out utilising a log loading simulator. The parameters of the simulation model were selected on the basis of process observations on a mill. The results indicate that it is essential to adjust the process and equipment parameters, raw material properties and truck loader operation together in order to reach the target capacity with minimum costs. Especially the speed of the infeed conveyor affects all performance criteria and should be selected carefully. In addition, wood yard logistics and raw material properties have a remarkable effect on the wood room performance. The results can be utilised in mills to allow the upper level control perform in a planned way so that small wood loss and good product quality can be obtained.

scholarly journals Performance of Adaptive Unstructured Mesh Modelling in Idealized Advection Cases over Steep Terrains

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 444 ◽  
Author(s):  
Jinxi Li ◽  
Jie Zheng ◽  
Jiang Zhu ◽  
Fangxin Fang ◽  
Christopher. Pain ◽  
...  
Keyword(s):  
Unstructured Mesh ◽  
Computational Cost ◽  
Adaptive Mesh ◽  
Galerkin Finite Element Method ◽  
Adaptive Meshes ◽  
Galerkin Finite Element ◽  
Computational Costs ◽  
Cut Cell ◽  
Discontinuous Galerkin Finite Element ◽  
Terrain Following

Advection errors are common in basic terrain-following (TF) coordinates. Numerous methods, including the hybrid TF coordinate and smoothing vertical layers, have been proposed to reduce the advection errors. Advection errors are affected by the directions of velocity fields and the complexity of the terrain. In this study, an unstructured adaptive mesh together with the discontinuous Galerkin finite element method is employed to reduce advection errors over steep terrains. To test the capability of adaptive meshes, five two-dimensional (2D) idealized tests are conducted. Then, the results of adaptive meshes are compared with those of cut-cell and TF meshes. The results show that using adaptive meshes reduces the advection errors by one to two orders of magnitude compared to the cut-cell and TF meshes regardless of variations in velocity directions or terrain complexity. Furthermore, adaptive meshes can reduce the advection errors when the tracer moves tangentially along the terrain surface and allows the terrain to be represented without incurring in severe dispersion. Finally, the computational cost is analyzed. To achieve a given tagging criterion level, the adaptive mesh requires fewer nodes, smaller minimum mesh sizes, less runtime and lower proportion between the node numbers used for resolving the tracer and each wavelength than cut-cell and TF meshes, thus reducing the computational costs.

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