scholarly journals Rigorous calculation of nonlinear parameters in graphene-comprising waveguides

2015 ◽  
Vol 118 (2) ◽  
pp. 023105 ◽  
Author(s):  
Dimitrios Chatzidimitriou ◽  
Alexandros Pitilakis ◽  
Emmanouil E. Kriezis
Keyword(s):  
Nonlinear Parameters ◽  
Rigorous Calculation

scholarly journals Complex response analysis of a non-smooth oscillator under harmonic and random excitations

2021 ◽  
Vol 42 (5) ◽  
pp. 641-648
Author(s):  
Shichao Ma ◽  
Xin Ning ◽  
Liang Wang ◽  
Wantao Jia ◽  
Wei Xu
Keyword(s):  
Excitation Frequency ◽  
System Stability ◽  
Response Analysis ◽  
External Excitation ◽  
Stochastic Response ◽  
Impact Oscillator ◽  
Nonlinear Parameters ◽  
Bifurcation Phenomena ◽  
Mc Simulation ◽  
Smooth System

AbstractIt is well-known that practical vibro-impact systems are often influenced by random perturbations and external excitation forces, making it challenging to carry out the research of this category of complex systems with non-smooth characteristics. To address this problem, by adequately utilizing the stochastic response analysis approach and performing the stochastic response for the considered non-smooth system with the external excitation force and white noise excitation, a modified conducting process has proposed. Taking the multiple nonlinear parameters, the non-smooth parameters, and the external excitation frequency into consideration, the steady-state stochastic P-bifurcation phenomena of an elastic impact oscillator are discussed. It can be found that the system parameters can make the system stability topology change. The effectiveness of the proposed method is verified and demonstrated by the Monte Carlo (MC) simulation. Consequently, the conclusions show that the process can be applied to stochastic non-autonomous and non-smooth systems.

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

2021 ◽  
pp. 036119812110237
Author(s):  
Laura Camarena
Keyword(s):  
Machine Learning ◽  
Material Properties ◽  
Resilient Modulus ◽  
Nonlinear Behavior ◽  
Machine Learning Techniques ◽  
Design Parameters ◽  
Nonlinear Parameters ◽  
State Highway ◽  
Learning Techniques ◽  
State Highway Agencies

The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials.

A Frequency Domain Versus a Time Domain Identification Technique for Nonlinear Parameters Applied to Wire Rope Isolators

2000 ◽  
Vol 123 (4) ◽  
pp. 645-650 ◽  
Author(s):  
Gaetan Kerschen ◽  
Vincent Lenaerts ◽  
Stefano Marchesiello ◽  
Alessandro Fasana
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear Dynamical Systems ◽  
Wire Rope ◽  
Restoring Force ◽  
Nonlinear Parameters ◽  
Nonlinear Dynamical ◽  
Domain Identification ◽  
Identification Technique ◽  
Reverse Path Method

The present paper aims to compare two techniques for identification of nonlinear dynamical systems. The Conditioned Reverse Path method, which is a frequency domain technique, is considered together with the Restoring Force Surface method, a time domain technique. Both methods are applied for experimental identification of wire rope isolators and the results are compared. Finally, drawbacks and advantages of each technique are underlined.

On the Nonlinear Viscoelastic Behaviour of the PVC-Coated Fabric

2010 ◽  
Vol 160-162 ◽  
pp. 1476-1481 ◽  
Author(s):  
Wu Lian Zhang ◽  
Xin Ding ◽  
Xu Dong Yang
Keyword(s):  
Viscoelastic Model ◽  
Constitutive Relationship ◽  
Viscoelastic Behaviour ◽  
Viscoelastic Response ◽  
Nonlinear Parameters ◽  
Creep And Recovery ◽  
Nonlinear Viscoelastic ◽  
Coated Fabric ◽  
Nonlinear Viscoelastic Model

The nonlinear viscoelastic response of a PVC-Coated Fabric has been studied. For the needs of the present study, creep and recovery tests in tension of both the warp and the weft directions at the different stress levels were executed while measurements were made of the creep and recovery strain response of the system. For the description of the viscoelastic behaviour of the material, Schapery’s nonlinear viscoelastic model was used. For the description of the nonlinear viscoelastic response and the determination of the nonlinear parameters, a method by using a combination of analytical formulations and numerical procedures based on a modified version of Schapery’s constitutive relationship where an instantaneous plastic and a transient plastic terms were added, has been developed. The method has been successfully applied to the current tests.

Nonlinear Isolator Optimization Using RMS Cost Function

2004 ◽  
Author(s):  
A. Narimani ◽  
M. F. Golnaraghi
Keyword(s):  
Closed Form Solution ◽  
Optimization Method ◽  
Relative Displacement ◽  
Form Solution ◽  
Jump Phenomenon ◽  
Nonlinear Parameters ◽  
Strongly Nonlinear ◽  
Damping Characteristics ◽  
Stiffness And Damping ◽  
Boundary Limits

In this paper using a modified averaging method the frequency response of a general nonlinear isolator is obtained. Stiffness and damping characteristics are considered cubic functions of displacement and velocity through the isolator. Analytical results are compared with those obtained by numerical integration in order to validate the closed form solution for strongly nonlinear isolator. While increasing the nonlinearity in the system improves the response of the isolator, stability and jump avoidance conditions set boundary limits for the parameters. The effects of nonlinear parameters to avoid jump phenomenon are discussed in detail. The set of parameters where the system behaves regularly are found and the nonlinear isolator is optimized based on RMS optimization method. Using this method the RMS function of absolute acceleration of the sprung mass is minimized versus the RMS function of relative displacement.

Shock and Sine Response of Rigid Structures on Nonlinear Mounts

1991 ◽  
Author(s):  
R. Dufour ◽  
J. Der Hagopian ◽  
M. Pompei ◽  
C. Garnier
Keyword(s):  
Numerical Simulation ◽  
Power Spectrum ◽  
Dynamic Behavior ◽  
Dynamic Environment ◽  
Rigid Bodies ◽  
Nonlinear Parameters ◽  
Large Power ◽  
Passive Damper ◽  
Highly Nonlinear

Abstract The dynamic environment of embarqued structures such as radars or more generally electronic equipments consists of impacts, sine and large power spectrum excitations. Under these real conditions and amongst different kinds of isolation, the passive damper with nonlinear parameters can provide good performances. This paper is concerned with the dynamic behavior of rigid bodies on highly nonlinear mounts. The numerical simulation and the experiment carried out, show that the load-deflection behavior of the dampers have to be slightly ajusted with respect to impact vibrations to obtain a well designed behavior.

Abstract 302: Sex Differences In Nonlinear Dynamics And Their Circadian Variation In Control Dogs And In A New Arrhythmogenic Canine Model Of Heart Failure

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
Yujie Zhu ◽  
Steven M Pogwizd
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Failure ◽  
Sex Differences ◽  
Sex Difference ◽  
Circadian Variation ◽  
Holter Monitoring ◽  
Canine Model ◽  
Nonlinear Parameters ◽  
Arrhythmogenic Substrate ◽  
Males And Females

Introduction: Females can be more arrhythmogenic than males, and this sex difference can persist with development of chronic heart failure (CHF). The aim of this study was to investigate sex differences in the arrhythmogenic substrate in control dogs and in a new arrhythmogenic canine model of CHF. Methods: CHF was induced in 30 dogs by aortic insufficiency and aortic constriction. Holter monitoring assessed VT and PVCs from 30 dogs, as well as traditional HRV measures and nonlinear dynamics (including correlation dimension (CD), detrended fluctuations analysis α1 (DFAα1), and Shannon entropy (SE)) at baseline, 240 days (240d) and 720 days (720d) after CHF induction. Results: At baseline, females had lower LF/HF (0.27±0.03 vs 0.33±0.02, p=0.04), CD (1.60±0.17 vs 2.21±0.15, p=0.01), DFAα1 (0.62±0.03 vs 0.72±0.03, p=0.03), and SE (2.99±0.02 vs 3.10±0.03, p=0.03 vs males). Females lacked circadian variation in LF/HF, DFAα1, and SE while males had circadian variation in all of these. Of 11 dogs with frequent runs of VT and PVCs, 95% and 91% of total VT runs and total PVCs, respectively, were in females. With CHF, all these linear and nonlinear parameters progressively declined in males and females. CHF females had less decline in LF/HF than males so that by 720 days there was no more sex difference (0.24±0.06, 0.17±0.03 in females vs 0.22±0.05, 0.18±0.01 in males at 240d, 720d). However, for nonlinear parameters of CD, DFAα1, and SE, CHF females had lower values than males (CD: 1.56±0.21, 0.99±0.32 vs 1.87±0.24, 1.50±0.34; DFAα1: 0.51±0.05, 0.43±0.04 vs 0.54±0.07, 0.48±0.04; and SE 2.93±0.08, 2.76±0.08 vs 3.01±0.11, 2.91±0.04 in females vs males at 240d, 720d). With CHF, circadian variation in CD, DFAα1, and SE were lost in both males and females. Conclusions: There are sex differences in the arrhythmogenic substrate in control dogs and in this new arrhythmogenic canine model of moderate CHF. At baseline, females have lower sympathetic stimulation, reduced cardiac chaos, and loss of circadian variation in nonlinear dynamics. With CHF, sex differences in nonlinear dynamics persist; this reflects a loss of complexity and fractal properties that could contribute to increased arrhythmias in female CHF dogs.

scholarly journals FETAL TYPE CLASSIFICATION BASED ON FETAL ELECTROCARDIOGRAPH IN NON-STRESS TESTS

2021 ◽  
pp. 2140052
Author(s):  
JIANLI LIU ◽  
YIMIN YANG ◽  
SONG ZHANG ◽  
XUWEN LI ◽  
LIN YANG ◽  
...  
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Classification Model ◽  
Support Vector ◽  
Stress Tests ◽  
Nonlinear Parameters ◽  
Design Algorithm ◽  
Long Time

Electronic fetal heart rate (FHR) monitoring is a technical means to evaluate the state of the fetus in the uterus by monitoring FHR. The main purpose is to detect intrauterine hypoxia and take corresponding medical measures timely. Because the fetus sleeps quietly for up to 1 hour sometimes, ultrasound Doppler is not easy to continuously detect for a long time. The electronic fetal monitor obtains the fetal heart rate, which not only improves the accuracy and comfort, but also the convenient implementation of long-term monitoring. It is beneficial to reduce perinatal fetal morbidity and mortality. This study used maternal–fetal Holter monitor which is based on the technology of fetal electrocardiograph (FECG) to collect the FHR, and then design algorithm to extract the baseline FHR, acceleration, variation, sleep-wake cycle and nonlinear parameters. There were significant differences in the 22 parameters between the normal and the suspicious group. Using the 22 characteristic parameters, the support vector machine was used to classify the normal and the suspected group of fetuses. 80% of the data was used to train a classification model. The remaining 20% of the data was used as a test set and its accuracy reached 93.75%.

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