Introduction to Linear Dynamic Viscoelasticity of Elastomers

2021 ◽  
Vol 94 (4) ◽  
pp. 123-129
Author(s):  
Kenji URAYAMA
Keyword(s):  
Dynamic Viscoelasticity ◽  
Linear Dynamic

scholarly journals Linear Dynamic Viscoelasticity of Dual Cross-Link Poly(Vinyl Alcohol) Hydrogel with Determined Borate Ion Concentration

Gels ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 71
Author(s):  
Takuro Taniguchi ◽  
Kenji Urayama
Keyword(s):  
Vinyl Alcohol ◽  
Ion Concentration ◽  
Poly Vinyl Alcohol ◽  
Relaxation Dynamics ◽  
Cross Link ◽  
Dynamic Viscoelasticity ◽  
Linear Dynamic ◽  
Cross Links ◽  
Pva Gel ◽  
Pva Hydrogel

We investigated the linear dynamic viscoelasticity of dual cross-link (DC) poly(vinyl alcohol) (PVA) (DC-PVA) hydrogels with permanent and transient cross-links. The concentrations of incorporated borate ions to form transient cross-links in the DC-PVA hydrogels (CBIN) were determined by the azomethine-H method. The dynamic viscoelasticity of the DC-PVA hydrogel cannot be described by a simple sum of the dynamic viscoelasticity of the PVA gel with the same permanent cross-link concentration and the PVA aqueous solution with the same borate ion concentration (CB = CBIN) as in the DC-PVA gel. The DC-PVA hydrogel exhibited a considerably higher relaxation strength, indicating that the introduction of permanent cross-links into temporary networks increases the number of viscoelastic chains with finite relaxation times. In contrast, the relaxation frequency (ωc) (given by the frequency at the maximum of loss modulus) for the DC-PVA hydrogel was slightly lower but comparable to that for a dilute PVA solution with the same CB. This signifies that the relaxation dynamics of the DC-PVA hydrogels is essentially governed by the lifetime of an interchain transient cross-link (di-diol complex of boron). The effect of permanent cross-linking on the relaxation dynamics was observed in the finite broadening of the relaxation-time distribution in the long time region.

Non-Linear Dynamic Viscoelasticity of Xanthan Gum Solutions

2014 ◽  
pp. 176-183
Author(s):  
J. A. Carmona ◽  
P. Ramírez ◽  
N. Calero ◽  
M. C. García ◽  
J. Muñoz
Keyword(s):  
Xanthan Gum ◽  
Dynamic Viscoelasticity ◽  
Linear Dynamic ◽  
Non Linear

Modelling and control of an alkaline water electrolysis process

2018 ◽  
Vol 1 (2) ◽  
pp. 9-14
Author(s):  
Marisol Cervantes-Bobadilla ◽  
Ricardo Fabricio Escobar Jiménez ◽  
José Francisco Gómez Aguilar ◽  
Tomas Emmanuel Higareda Pliego ◽  
Alberto Armando Alvares Gallegos
Keyword(s):  
Process Model ◽  
Water Electrolysis ◽  
Alkaline Water Electrolysis ◽  
Alkaline Water ◽  
Electrolysis Process ◽  
Linear Dynamic ◽  
Energy Current ◽  
Identification Technique ◽  
Nonlinear Static ◽  
And Control

In this research, an alkaline water electrolysis process is modelled. The electrochemical electrolysis is carried out in an electrolyzer composed of 12 series-connected steel cells with a solution 30% wt of potassium hydroxide. The electrolysis process model was developed using a nonlinear identification technique based on the Hammerstein structure. This structure consists of a nonlinear static block and a linear dynamic block. In this work, the nonlinear static function is modelled by a polynomial approximation equation, and the linear dynamic is modelled using the ARX structure. To control the current feed to the electrolyzer an unconstraint predictive controller was implemented, once the unconstrained MPC was simulated, some restrictions are proposed to design a constrained MPC (CMPC). The CMPC aim is to reduce the electrolyzer's energy consumption (power supply current). Simulation results showed the advantages of using the CMPC since the energy (current) overshoots are avoided.

Simultaneous Determination of Epinephrine and Tyrosine Using a Glassy Carbon Electrode Amplified with ZnO-Pt/CNTs Nanocomposite

2019 ◽  
Vol 15 (2) ◽  
pp. 166-171 ◽  
Author(s):  
Ali Samadzadeh ◽  
Iran Sheikhshoaie ◽  
Hassan Karimi-Maleh
Keyword(s):  
Glassy Carbon Electrode ◽  
Glassy Carbon ◽  
Simultaneous Analysis ◽  
Urine Samples ◽  
Carbon Electrode ◽  
Linear Dynamic ◽  
Biological Compounds ◽  
Blood And Urine Samples ◽  
Human Blood And Urine

Background: Simultaneous analysis of epinephrine and tyrosine as two effective and important biological compounds in human blood and urine samples are very important for the investigation of human health. Objective: In this research, a highly effective voltammetric sensor fabricated for simultaneous analysis of epinephrine and tyrosine. The sensor was fabricated by the modification of glassy carbon electrode with ZnO-Pt/CNTs nanocomposite (ZnO-Pt/CNTs/GCE). The synthesized nanocomposite was characterized by SEM method. The ZnO-Pt/CNTs/GCE showed two separated oxidation signals at potential ~220 mV and 700 mV for epinephrine and tyrosine, respectively. Also, we detected linear dynamic ranges 0.5-250.0 µM and 1.0-220 µM with a limit of detections 0.1 µM and 0.5 µM for the determination of epinephrine and tyrosine, respectively. The ZnO-Pt/CNTs/GCE was used for the determination of epinephrine and tyrosine in blood serum and human urine samples.

scholarly journals On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3837
Author(s):  
Rafael Orellana ◽  
Rodrigo Carvajal ◽  
Pedro Escárate ◽  
Juan C. Agüero
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Dynamic Systems ◽  
Gaussian Mixture ◽  
Error Model ◽  
Fault Detection And Diagnosis ◽  
Deterministic Models ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

scholarly journals Non-linear dynamic joint selection strategy for Hinge controlled masonry arches

2020 ◽  
Author(s):  
Gabriel Stockdale ◽  
Vasilis Sarhosis ◽  
Gabriele Milani
Keyword(s):  
Selection Strategy ◽  
Masonry Arches ◽  
Linear Dynamic ◽  
Non Linear
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