Experimental Parameter Identification Research Articles

Dynamic evolution analysis and parameter optimization design of data-driven network infectious disease model

Background and Objective:globalization and population mobility have increased the spread of infectious diseases and challenged public health security. This paper proposes a complex network epidemic model with nonlinear incidence rate and quadratic transmission. The Turing pattern, sensitivity analysis and parameter identification of the epidemic model under different network structures are studied; Methods:this paper discusses the Turing pattern of the model under different network structures, and identifies the key parameters of the model through sensitivity analysis. The influence of network dimension on the spread of infectious diseases on random networks is also explored, and the problems of minimum path and minimum cover set of random networks are further discussed. We also carry out parameter identification experiments, adopt gradient descent algorithm to realize heterogeneous spatial fitting pattern of red blood cell plasma and simulate the transmission path of COVID-19 through Markov chain Monte Carlo fitting experiment, verifying the effectiveness of the model; Results:the necessary conditions for Turing instability on homogeneous and heterogeneous networks are found. On the heterogeneous lattice network, we observe the special patterns of equal density population. Sensitivity analysis shows that the higher the infection rate, the more infected people. On random networks, the higher the dimension, the better the effect of suppressing the spread of infectious diseases. Through comparison experiment, it is found that gradient descent algorithm has the best performance in parameter identification experiments. Red blood cell plasma fitting experiment reveals the spatial density distribution of infection rate; Conclusions:this study provides theoretical support for the prevention and control of infectious diseases, and the complex network model can simulate the transmission process of infectious diseases more accurately. Sensitivity analysis and parameter identification experiments reveal the key influencing factors of propagation and the role of network structure. The effectiveness of the model is supported by actual data, which is helpful for the government health departments to formulate scientific prevention and control strategies.

Experimental Evaluation of Under Slab Mats (USMs) Made from End-of-Life Tires for Ballastless Tram Track Applications.

The growing population of urban areas results in the need to deal with the noise pollution from the transportation system. This study presents experimental test results of static and dynamic elastic characteristics of under slab mats (USMs) according to the procedure of DIN 45673-7. Prototype USMs based on recycled elastomeric materials, i.e., SBR granules and fibres produced from waste car tires, are analysed. Vibration isolation mats with different thicknesses (10, 15, 20, 25, 30, and 40 mm), densities (500 and 600 kg/m3), and different degrees of space filling (no holes, medium holes, large holes) are considered. Moreover, a practical application of the laboratory test results of USMs in the design of ballastless track structures of two different types (with a concrete slab and longitudinal beams) is presented. Deflections of the rail and the floating slab system, as well as stresses acting on the mat, are determined according to EN 16432-2. The use of shredded rubber from recycled car tires as a material component of sustainable and environmentally friendly tram track structures may be one of the most effective ways to manage rubber waste within the current trend toward a circular economy, and this study intends to introduce methods for experimental identification and analytical selection of basic static and dynamic parameters of prototype USMs.

A method for predicting needle insertion deflection in soft tissue based on cutting force identification

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young’s modulus ( E ) and Poisson’s ratio ( ν ) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Experimental parameter identification of scalable white and grey box jounce bumper models for ride comfort simulation

The design of a passenger car’s ride and handling goals requires an enormous amount of hardware and prototypes. To avoid time-consuming testing activities, one crucial trend is the virtualisation of suspension development. Another goal of the virtualisation is the determination of vehicle properties at an early stage of the development process. Highly accurate suspension models are required to transfer the full vehicle’s characteristics into simulation tools. Since the jounce bumper is an important component of the suspension system, modelling its functional behaviour is essential for virtualised chassis development. Therefore, two dynamic modelling approaches are derived and compared in this contribution. One approach is based on local linear model networks and thus can be seen as a grey box model. The other approach consists of white box rheological elements, which allow the physical interpretability of the model parameters. The comparison of the approaches is based on experimental parameter identification, which includes synthetic and real excitations recorded on representative roads. The advantages of the proposed approaches will be discussed in terms of their suitability for the virtual design of the driving behaviour.

Identification of the Asymmetric Transmission Error and Gear Mesh Dynamic Parameters using Full-Spectrum Responses in a Geared-Rotor System

A dominant source of vibration in geared-rotor systems are the gear mesh fault parameters. They include the asymmetric transmission error, phases of transmission error, the gear mesh stiffness, the gear mesh damping and the gear runouts. The present work deals with the experimental identification of aforementioned parameters. A mathematical model of geared-rotor system has been developed using Lagrangian dynamics. Equations of motion are transformed into frequency domain using the full-spectrum response analysis. These transformed equations are used to develop an identification algorithm based on least-squares fit to estimate the transmission error and gear mesh dynamic parameters. The system identification algorithm is initially verified using numerical simulations. The robustness of the algorithm is checked by introducing white Gaussian noise in the simulated responses. A geared-rotor experimental rig was developed and used to measure responses at gear locations in two orthogonal directions. Measured responses are transformed in frequency domain using the full-spectrum analysis and used in the present novel identification algorithm to identify the gear parameters. The identified parameters are validated by comparing the numerically generated full-spectrum response using experimentally estimated parameters and that from the experimental rig. Conflict of Interest Statement The authors declare no conflicts of interest.

Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification

To support more sustainable construction, this paper experimentally investigates the cyclic behavior of laminated timber (Laminated Veneer Lumber (LVL)) and glubam (Glue Laminated Bamboo) connections with slotted-in steel plates in terms of experimental test, numerical simulations and parameter identification. Experimental tests included eight different configurations: two materials (LVL and glubam), two bolt diameters (8 and 10 mm), and one or two bolts. Two different cyclic-loading protocols were applied for each type of connection: only tension and tension/compression. The observed behavior is then compared to a finite element model developed in OpenSeesPy, which takes into account factors such as sliding, contact, pinching, cyclic stiffness, and strength degradation. To identify the best set of parameters for the model, three different approaches are considered: genetic algorithm, fast deterministic neural network, and probabilistic Bayesian method. First, the model identification is carried out by means of a genetic algorithm-based optimization. The parameter-identification results are evaluated in terms of elastic stiffness, yielding point, and ductility. Next, a sensitivity analysis is performed to determine the significance of the parameters, and an innovative approach combining neural network and sensitivity analysis is proposed for fast and preliminary parameter identification. Then, probabilistic Bayesian identification is employed to calculate the posterior distribution of the model parameters identified and the confidence bounds of the estimated response. Finally, different model identification parameters are compared and suggestions for algorithm selection are provided.

Review of battery models and experimental parameter identification for lithium-ion battery equivalent circuit models

The growing use of electric vehicles has led to an ever-increasing demand for efficient and reliable management systems to control the behavior of lithium-ion batteries, especially with respect to heat generation and state-of-charge. Understanding these patterns constitutes a major new challenge for these batteries, as remaining ignorant of their behavior can result in decreased performance, shorter service life and even safety dangers. This review provides an overview of the different modeling techniques applied to simulate battery behavior. Different methods using equivalent electrical circuit models are discussed, covering both simple battery models and more complex equivalent electrical circuit models, with a focus on the 2RC-Thévenin circuit model. In this context, parameter approach methods for these systems are reviewed. In addition, laboratory tests are run to identify the various model parameters for a lithium-ion battery. This comprehensive study is designed to guide scientists and engineers in the selection and use of suitable tools for state-of-charge and battery health studies.

Modelling and experimental parameter identification for fasteners in composite–aluminium bolted structures

Deformation of composite–aluminium bolted joints subjected to uniaxial quasistatic tension load is modelled using an Iwan model for each fastener. Experimental data obtained with the digital image correlation technique are used to identify a suitable Iwan model distribution function and parameters to represent the bolts in single-shear and double-shear bolted lap joints. It is seen that the model, with parameters identified from the experiments, represents the displacements of the joint correctly, and an implementation in a commercial finite element software is demonstrated. The proposed model is intended as a computationally efficient structural element for modelling structures containing multiple fasteners.

A New Pelican Optimization Algorithm for the Parameter Identification of Memristive Chaotic System

A memristor is a kind of nonlinear electronic component. Parameter identification for memristive chaotic systems is a multi-dimensional variable optimization problem. It is one of the key issues in chaotic control and synchronization. To identify the unknown parameters accurately and quickly, we introduce, in this paper, a modified Pelican Optimization Algorithm (POA) called the fractional-order chaotic Pareto Pelican Optimization Algorithm (FPPOA). First, the pelican population’s diversity is augmented with the integration of a fractional chaotic sequence. Next, the utilization of the Pareto distribution is incorporated to alter the hunting strategy of pelicans in the POA. These measures are effective in hastening the speed of finding an optimal solution and circumventing local optimization issues. Thirdly, the FPPOA is used to determine the values of the parameters of the simplest memristive chaotic system, which has a property of conditional symmetry. The proposed algorithm was evaluated during simulations, where it was utilized to solve six objective functions of varying unimodal and multimodal types. The performance of the FPPOA exceeds three traditional swarm intelligence optimization algorithms. In the parameter identification experiment, the results for the parameters with the FPPOA had error rates all within a 1% range. Extensive testing shows that our new strategy has a faster rate of convergence and better optimization performance than some other traditional swarm algorithms.

Chemical process modelling using the extracted informative data sets based on attenuating excitation inputs

BackgroundOperation designs on rapid developed advanced chemical processes require proper models and parameter identification of these models needs input-output data sets with high information content. The optimal parameter identification experiment requires additional persistently exciting input to excite the chemical process for informative data sets, which ensure the process dynamic information. However, the optimal identification experiment with persistently exciting input is a time-consuming, high-cost task which may interfere with the process operation. As the requirement of low-cost identification, these experiments cannot be applied, some limited external excitations or extracted informative segments from historical data can support chemical process modeling. MethodsAttenuating excitation is a typical limited excitation can be used as identification experiment input. This paper proposed the identification experiments using informative data sets composed of attenuating excitation inputs, designed by users or extracted from historical process data. A goal-orientated hybrid algorithm based on Expectation - Maximization method and Exhaustive method is proposed to achieve rapid and effective informative segments selection and obtain required parameter estimates. This method also can be used to verify the theorical attenuating excitation inputs design. Significant findingsA numerical case and an ethylene distillation column process demonstrate the feasibility of identification with attenuating excitation inputs and the robustness of the proposed algorithm. The result is capable for attenuating excitation input design or historical informative data mining for chemical process modeling, which has less or no impact and cost on chemical normal operations, decreases the process information waste and improves the data utilization.

Contribution to the research of the possibility of application of two-parameter frequency analysis in the experimental identification of torsional vibration parameters of elastic shafts in vehicles power transmissions

During exploitation, motor vehicles are exposed to vibrational loads that lead to fatigue of users and materials of their aggregates. Therefore, vibrations must be studied even in the earliest stages of design, using mathematical models, experiments, or their combinations. In theoretical considerations, vibrations of concentrated masses are usually observed , although, with the recent development of numerical methods (especially finite element methods), attention is also being paid to vibrations of vehicle elastic systems. This usually involves idealizations, especially regarding exploitation conditions and interconnections of motor vehicle aggregates. In this paper, an attempt has been made to develop a method for identifying real vibrational loads of elastic power transmission shafts in vehicles (engine, rail, etc.) under exploitation conditions. Namely, 2D Fourier transformation was used for two-parameter frequency analysis. The possibilities of applying the procedure were illustrated on an idealized elastic torsionally loaded shaft. The conducted research showed that two-parameter frequency analysis can be used in generating torsional vibrations of elastic shafts of vehicles in laboratory conditions.

Exposing Heterogeneous Degradation in Prismatic Lithium-Ion Cells

The energy density and lifetime of lithium-ion cells for automotive applications have both substantially improved in recent years. This enables electric vehicles in both the consumer and commercial sectors that have greater range, better reliability, and longer service lives [1]. These factors all contribute to the widespread adoption of electric vehicles. However, inhomogeneities exist within cells that contribute to uneven internal degradation and, when severe, can trigger rapid failure. These inhomogeneities can be related to manufacturing tolerances or distributions of component parameters [2], as well as gradients in internal or external conditions. As such, the reality of heterogeneous aging is a complex, multivariate problem to solve.In this latest work, we cycle automotive-grade prismatic cells for several thousand cycles (until 10-30 % capacity fade) and harvest components for post mortem aging characterization and experimental parameter identification. The constituent electrodes are a Ni-rich layered oxide (approximately LiNi0.76Mn0.15Co0.09O2) and graphite. In addition to microscopy and electrochemical testing on harvested electrodes, we quantify local lithium plating on graphite with our recently developed method utilizing ex-situ, 7Li nuclear magnetic resonance spectroscopy [3]. We believe this to be the first report of spatially-resolved lithium plating quantification in commercial lithium-ion cells. Clear patterns emerge (see Figure 1), reflecting the geometry of the cell and showing reproducible heterogeneity that cannot be attributed solely to defects in manufacture. Several tests even confirm local lithium plating and unusually rapid degradation at comparatively mild cycle conditions (slow charge/discharge, low state-of-charge, and room temperature).The wound jellyroll design in contemporary prismatic cells enables very high packing efficiency and energy density, but includes inherently weak points from where lithium plating and internal stresses [4] can propagate. In some cases, cells achieve satisfactory lifetimes without accelerated aging. In others, heterogeneities cause extreme local degradation that triggers end-of-life even though other regions may remain relatively intact. We explore the causes of these patterns and question how such damage can occur, even under cycling conditions usually considered safe.

Experimental identification of wheel-surface model parameters: various terrain conditions.

Since the wheel interaction with a certain terrain cases (asphalt, concrete) are known and well described in case of straightforward motion and non-slip and slip cornering conditions, the skid-steered wheeled vehicles case needs to be analyzed. Side-slip for various attack angle has to be investigated. The main area of interest of research that is shown in the project is energy demand calculation of skid-steered wheeled vehicles in various terrain conditions. Certain cases of all-electric vehicles with individual electric motors per wheel demand a precise assessment of longitudinal and lateral forces in order to perform the fully controlled turn. Experimental stand designed and developed by authors allows to test the wheel-surface interaction for various terrain conditions and different driving directions. Test data were acquired for dry and wet sand and granite pavement. Traction and side forces were acquired and used to identify the wheel-soil interaction model parameters for unpropelled wheel. Results in a form of time series including longitudinal and lateral forces show the relation between attack angle, load and surface conditions in terms of stick and slip phenomenon that is essential for skid-steering dynamics calculations. Measurement results are then used for calculation of longitudinal and lateral forces coefficients as a function of attack angle and vertical load. Test were performed in natural environment, thus they are affected by changeable conditions. Multiple runs are used for elimination of that influence. Described experiments are a part of the project that includes results generalization using test validated FEM model. Described work is not intended to develop new ground-tire interaction models, it is focused on numerically efficient traction effort calculation method for various conditions including passive mode—unpropelled wheel.

Nonlinear damping quantification from phase-resonant tests under base excitation

The present work addresses the experimental identification of amplitude-dependent modal parameters (modal frequency, damping ratio, Fourier coefficients of periodic modal oscillation). Phase-resonant testing has emerged as an important method for this task, as it substantially reduces the amount of data required for the identification compared to conventional frequency-response testing at different excitation/response levels. In the case of shaker–stinger excitation, the applied excitation force is commonly measured in order to quantify the amplitude-dependent modal damping ratio from the phase-resonant test data. In the case of base excitation, however, the applied excitation force is challenging or impossible to measure. In this work we develop an original method for damping quantification from phase-resonant tests. It relies solely on response measurement; it avoids the need to resort to force measurement. The key idea is to estimate the power provided by the distributed inertia force imposed by the base motion. We develop both a model-free and a model-based variant of the method. We validate the developed method first in virtual experiments of a friction-damped and a geometrically nonlinear system, and then in a physical experiment involving a thin beam clamped at both ends via bolted joints. We conclude that the method is highly robust and provides high accuracy already for a reasonable number of sensors.

ANSYS Simulation of the Thermomechanical Behavior of a Large-Sized Composite Mandrel with Consideration of Viscoelasticity

The article addresses the modeling of the process of manufacturing a large-sized shell, given the thermomechanical behavior and viscoelasticity of the composite mandrel. The results of the experimental identification of viscoelasticity parameters of the examined material are presented. A numerical algorithm for adapting the experimental data for the ANSYS Mechanical APDL finite element analysis package is proposed. A Prony series expansion of the relaxation kernel is used as a model for describing the material behavior. The effect of temperature on the rate of relaxation processes is taken into account through the application of a temperature-time analogy according to the Williams–Landel–Ferry formula. The selected model with the calculated parameters was implanted into the commercial package of ANSYS Mechanical APDL. Simulation of two process steps of manufacturing a large-sized product was performed: winding and heat treatment of the shell. For this purpose, the quasistatic problem of mechanics and unsteady thermal conduction under conditions of convective heat transfer were solved by the finite element method. The influence of thermomechanical behavior of the mandrel material on the normal pressure acting on the mandrel surface as a function of temperature and force factors was estimated quantitatively and qualitatively. It was found that with respect to the nonlinear behavior of the composite material, the pressure level decreases by 50% compared to the case of using models of elastic behavior. This result justifies the importance of using complex models of material behavior in studying long-term technological processes, especially those associated with high-temperature effects.

Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter

In order to solve the problem of parameter identification of nonlinear ship motion model in ship autonomous navigation control, a real-time parameter identification method based on nonlinear Gaussian filtering algorithm and nonlinear ship response model is proposed. It is proved theoretically that the influence of parameter drift on parameter identification can be reduced by increasing the number of observers and filters, and the system identification accuracy can be improved. The validity of the proposed method is verified by parameter identification experiments based on Zig-zag motion simulation data of Mariner standard ship model. Simulation results show that compared with EKF, the nonlinear Gaussian filter algorithm can effectively improve the parameter identification accuracy and reduce the computational complexity. The application of parallel structure is helpful to improve the identification accuracy and convergence rate of nonlinear Gaussian filter algorithm.

Calibration Method for Industrial Robots Based on the Principle of Perigon Error Close

For the problem that the self-error of industrial robot calibration device has influence on calibration accuracy, a calibration method of industrial robots based on the principle of Perigon Error Close is proposed. In the method, the theory that the sum of circular indexing interval errors around a circle is zero is applied to robot calibration to improve robot calibration accuracy. The calibration principle of the proposed method is provided in detail and the calibration equation is derived in this paper. The calibration system based on the proposed method was constructed with one semiconductor laser and two position sensing detectors (PSDs) fixed on a rotary table. Based on the position error data obtained from laser spot position on the PSDs, the robot kinematics parameter errors were identified by using Levenberg Marquardt (LM) algorithm. The robot calibration experimental setup was constructed and the related verification experiments were carried out. The model parameter identification experiment validates the feasibility of the proposed method for industrial robot calibration. The calibration compensation experiment results of industrial robots show that the maximum position error of the robot is reduced by 71.9% and the average position error is reduced by 77.8%, which validates the effectiveness of the proposed method for industrial robot calibration.

Two different approaches for parameter identification in a spatial–temporal rumor propagation model based on Turing patterns

The reaction–diffusion model can well describe the dynamics of groups in space and time to help us to study their behavioral mechanism. Based on the rumor propagation process, an example of reaction–diffusion model defined on continuous space is proposed in this paper to study some common problems under Turing instability. Firstly, the necessary conditions for Turing instability are studied. Furthermore, the amplitude equation of the system is derived through multi-scale analysis and weak linear analysis, and the theoretical conditions for the appearance of hexagon pattern, stripe pattern and mixed structure pattern near Turing bifurcation are given. In the numerical simulation, we verify the correctness of the theoretical analysis and find that the cross-diffusion coefficient can change the pattern type. Finally, based on a statistical method and a convolutional neural network, we have carried out a parameter identification experiment of the system respectively. The results show that the second method performs well and the first method has relatively large error in some cases.

Modeling the Mullins effect of rubbers used in constrained‐layer damping applications

AbstractThe benefits of incorporating rubber interlayers in lightweight laminates, such as fiber‐metal laminates, in order to compensate for their usually undesirable dynamic behavior have been studied in previous works [1,2]. In such constrained‐layer damping laminates, the rubber layers undergo large deformations due to their comparably low stiffness. This motivates the consideration of large strain phenomena commonly found in rubbers even when global laminate deformations are small such as in linear dynamic analysis. This work specifically addresses the cyclic softening of filled rubbers commonly known as the Mullins effect. As this effect significantly influences the elastic properties of the material, a change in the dynamic behavior of the laminate is to be expected. A constitutive model based on the work of Dorfmann and Ogden [3] for the prediction of the cyclic softening as well as residual strains upon unloading is presented in this study. Special consideration is given to the implementation of the model for use in a commercial implicit finite element solver by building on the work of Connolly et al. [4]. The model is validated against experimental data and compared to a current state‐of‐the‐art model with regard to its predictive quality and computational efficiency. Furthermore, the experimental identification of material parameters for said model is addressed.

Challenges in developing of 3D nonlinear viscoelastic models

Due to development of different material systems and structural elements, there is growing need for more complex material models, since linear elasticity or even more complicated linear viscoelasticity cannot capture the nuances of behaviour of these materials. This study describes the process and difficulties of development of 3D nonlinear viscoelastic model. Study proposes two models with different level of complexity and methodology for experimental parameter identification. The limitations of both of these models have been discussed.