scholarly journals Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

2019 ◽  
Vol 20 (13) ◽  
pp. 3216 ◽  
Author(s):  
Yu ◽  
Li ◽  
Li ◽  
Li ◽  
Li ◽  
...  
Keyword(s):  
Phenomenological Models ◽  
Optimization Problem ◽  
Fitness Function ◽  
Model Performance ◽  
Friction Model ◽  
Shaking Table ◽  
Model Parameters ◽  
Magnetorheological Elastomer ◽  
Highly Nonlinear ◽  
Hysteresis Response

Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.

Optimization of A2O BNR processes using ASM and EAWAG Bio-P models: model formulation

2011 ◽  
Vol 46 (1) ◽  
pp. 13-27 ◽  
Author(s):  
Walid El Shorbagy ◽  
Nawras Nabil ◽  
Ronald L. Droste
Keyword(s):  
Activated Sludge ◽  
Operating Cost ◽  
Model Performance ◽  
Problem Definition ◽  
Biological Nutrient Removal ◽  
Total Annual Cost ◽  
Model Parameters ◽  
Model Formulation ◽  
Highly Nonlinear ◽  
Generalized Reduced Gradient

This study is an extended and comprehensive analysis to accomplish optimal sizing for a biological nutrient removal (BNR) system with an A2O BNR activated sludge process using activated sludge models (ASM) kinetic models. A highly nonlinear activated sludge model combined with the EAWAG Bio-P module is formulated and optimized using a generalized reduced gradient solver. Primary and final clarifications are included with the A2O biotreatment scheme along with oxygen-supplying units. This paper includes a detailed description of model formulation, problem definition and discussion of optimal design in terms of capital (CAPEX) and operating (OPEX) cost estimates. The optimization problem is formulated and solved using typical cost factors and operating/design constraints applied to a typical illustrative system treating medium-strength wastewater. Results indicated that maintenance and sludge disposal expenditures represent more than 50% of the total annual cost and 80% of the annual running operating cost. Another major finding was that a primary clarifier is found to be cost ineffective in the A2O BNR process. Sensitivity of the optimal solutions and model performance to varying inflow conditions and to other effluent limits and model parameters will be discussed in another paper.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

scholarly journals Study on Friction in Automotive Shock Absorbers Part 1: Friction Simulation Using a Dynamic Friction Model in the Contact Zone of an FEM Model

Vehicles ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 212-232
Author(s):  
Ludwig Herzog ◽  
Klaus Augsburg
Keyword(s):  
Ride Comfort ◽  
Viscous Friction ◽  
Simulation Method ◽  
Friction Model ◽  
Model Parameters ◽  
Dynamic Friction ◽  
Friction Modeling ◽  
Shock Absorbers ◽  
Operation Conditions ◽  
Wide Range

The important change in the transition from partial to high automation is that a vehicle can drive autonomously, without active human involvement. This fact increases the current requirements regarding ride comfort and dictates new challenges for automotive shock absorbers. There exist two common types of automotive shock absorber with two friction types: The intended viscous friction dissipates the chassis vibrations, while the unwanted solid body friction is generated by the rubbing of the damper’s seals and guides during actuation. The latter so-called static friction impairs ride comfort and demands appropriate friction modeling for the control of adaptive or active suspension systems. In this article, a simulation approach is introduced to model damper friction based on the most friction-relevant parameters. Since damper friction is highly dependent on geometry, which can vary widely, three-dimensional (3D) structural FEM is used to determine the deformations of the damper parts resulting from mounting and varying operation conditions. In the respective contact zones, a dynamic friction model is applied and parameterized based on the single friction point measurements. Subsequent to the parameterization of the overall friction model with geometry data, operation conditions, material properties and friction model parameters, single friction point simulations are performed, analyzed and validated against single friction point measurements. It is shown that this simulation method allows for friction prediction with high accuracy. Consequently, its application enables a wide range of parameters relevant to damper friction to be investigated with significantly increased development efficiency.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Development of an Inverse Plume Model for Mass Eruption Rate in Unsteady Conditions

2021 ◽  
Author(s):  
Stephen A Solovitz
Keyword(s):  
Laboratory Data ◽  
Model Performance ◽  
Volcanic Eruptions ◽  
Reynolds Numbers ◽  
Model Parameters ◽  
Plume Height ◽  
Eruption Rate ◽  
Mass Eruption Rate ◽  
Downstream Effects ◽  
Order Of Magnitude

Abstract Following volcanic eruptions, forecasters need accurate estimates of mass eruption rate (MER) to appropriately predict the downstream effects. Most analyses use simple correlations or models based on large eruptions at steady conditions, even though many volcanoes feature significant unsteadiness. To address this, a superposition model is developed based on a technique used for spray injection applications, which predicts plume height as a function of the time-varying exit velocity. This model can be inverted, providing estimates of MER using field observations of a plume. The model parameters are optimized using laboratory data for plumes with physically-relevant exit profiles and Reynolds numbers, resulting in predictions that agree to within 10% of measured exit velocities. The model performance is examined using a historic eruption from Stromboli with well-documented unsteadiness, again providing MER estimates of the correct order of magnitude. This method can provide a rapid alternative for real-time forecasting of small, unsteady eruptions.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

scholarly journals Incremental model breakdown to assess the multi-hypotheses problem

2018 ◽  
Vol 22 (8) ◽  
pp. 4565-4581 ◽  
Author(s):  
Florian U. Jehn ◽  
Lutz Breuer ◽  
Tobias Houska ◽  
Konrad Bestian ◽  
Philipp Kraft
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Complex Model ◽  
Model Parameters ◽  
Objective Functions ◽  
Final Model ◽  
Incremental Model ◽  
Model Efficiency ◽  
Improved Model ◽  
Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

Analytical and experimental analysis of axial force generated by a drive shaft system

2020 ◽  
Vol 234 (4) ◽  
pp. 691-706 ◽  
Author(s):  
Huayuan Feng ◽  
Subhash Rakheja ◽  
Wen-Bin Shangguan
Keyword(s):  
Dynamic Model ◽  
Axial Force ◽  
Friction Model ◽  
Drive Shaft ◽  
Contact Model ◽  
Model Parameters ◽  
Shaft System ◽  
Multibody Dynamic ◽  
Generated Axial Force ◽  
Operational Factors

The drive shaft system with a tripod joint is known to cause lateral vibration in a vehicle due to the axial force generated by various contact pairs of the tripod joint. The magnitude of the generated axial force, however, is related to various operating factors of the drive shaft system in a complex manner. The generated axial force due to a drive shaft system with a tripod joint and a ball joint was experimentally characterized considering ranges of operational factors, namely, the input toque, the shaft rotational speed, the articulation angle, and the friction. The data were analyzed to establish an understanding of the operational factors on the generated axial force. Owing to the observed significant effects of all the factors, a multibody dynamic model of the drive shaft system was formulated for predicting generated axial force under different operating conditions. The model integrated the roller–track contact model and the velocity-based friction model. Based on a quasi-static finite element model, a new methodology was proposed for identifying the roller–track contact model parameters, namely, the contact stiffness and force index. To further enhance the calculation accuracy of the multibody dynamic model, a new methodology for identifying the friction model parameters and the force index was proposed by using the measured data. The validity of the model was demonstrated by comparing the model-predicted and measured magnitudes of generated axial force for the ranges of operating factors considered. The results showed that the generated axial force of the drive shaft system can be calculated more accurately and effectively by using the identified friction and contact parameters in the paper.

Stick-Slip Compensation of Micro-Positioning Using Elastic-Plastic Static Friction Model

2008 ◽  
Vol 47-50 ◽  
pp. 246-249
Author(s):  
Min Gyu Jang ◽  
Chul Hee Lee ◽  
Seung Bok Choi
Keyword(s):  
Static Friction ◽  
Friction Model ◽  
Smart Structure ◽  
Asperity Contact ◽  
Stick Slip ◽  
Elastic Plastic ◽  
Highly Nonlinear ◽  
Bridge Type ◽  
Structural Parts ◽  
Rough Surface Contact

In this paper, a stick-slip compensation for the micro-positioning is presented using the statistical rough surface contact model. As for the micro-positioning structure, PZT (lead(Pb) zirconia(Zr) Titanate(Ti)) actuator is used to drive the load for precise positioning with its high resolution incorporating with the PID (Proportional Integral Derivative) control algorithm. Since the stick-slip characteristics for the micro structures are highly nonlinear and complicated, it is necessary to incorporate more detailed stick-slip model for the applications involving the high precision motion control. Thus, the elastic-plastic static friction model is used for the stick-slip compensation considering the elastic-plastic asperity contact in the rough surfaces statistically. Mathematical model of the system for the positioning apparatus was derived from the dynamic behaviors of structural parts. Since the conventional piezoelectric actuator generates the short stroke, a bridge-type flexural hinge mechanism is introduced to amplify the linear motion range. Using the proposed smart structure, simulations under the representative positioning motion were conducted to demonstrate the micro-positioning under the stick-slip friction.

scholarly journals Disease control as an optimization problem

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257958
Author(s):  
Miguel Navascués ◽  
Costantino Budroni ◽  
Yelena Guryanova
Keyword(s):  
Disease Control ◽  
Stochastic Models ◽  
Optimization Problem ◽  
Initial Conditions ◽  
Optimization Theory ◽  
Model Parameters ◽  
Grid Search ◽  
Brute Force ◽  
Time Required ◽  
Policy Optimization

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020).

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