Parameter Identification Procedure Research Articles

Development of a simplified heat transfer model of hollow blocks by using finite element method in frequency domain

Hollow blocks or bricks are widely used due to the good thermal insulation, lightweight, acoustic insulation, etc. This paper presents the development of a simplified heat transfer model of hollow block for simple and efficient heat flow calculation. This model is developed based on parameter identification in frequency domain by using finite element method in frequency domain. Firstly, the frequency domain finite element model (FDFEM) of the hollow block is developed by using finite element method in frequency domain to obtain the theoretical frequency characteristics of this structure. Then, the heat transfer function model in the form of s-polynomial (i.e., the s-polynomial heat transfer function model) is identified by using a parameter identification procedure based on the calculated theoretical frequency thermal characteristics. Finally, the simplified heat transfer model, i.e., the Conduction Transfer Function (CTF) coefficients, of this hollow block are derived from the identified s-polynomial heat transfer function model. A case study is presented for the prediction of the dynamic heat transfer of this hollow block by using the simplified heat transfer model. The predicted dynamic heat transfer of this block is validated by experimental data.

Dynamic Parameter Identification of Overactuated Parallel Robots

Offline robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal (the input reference of the motor current loop) with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of the linear least squares technique to calculate the parameters (IDIM-LS technique). However, as actuation-redundant parallel robots are overactuated, their IDIM has an infinity of solution for the force/torque prediction, depending on the value of the desired overconstraint that is a priori unknown in the identification process. As a result, the IDIM cannot be used as it is for such a class of parallel robots. This paper proposes a procedure for the dynamic parameter identification of actuation-redundant parallel robots. The procedure takes advantage of two possible modified formulations for the IDIM of actuation-redundant robots that can be used for identification purpose. The modified IDIM formulations project some or all input torques/forces onto the robot bodies, thus leading to a unique solution of the model that can then be used in the identification process. A systematic and straightforward way to compute these modified IDIM is presented. The identification of the inertial parameters of a planar parallel robot with actuation redundancy, the DualV, is then carried out using these modified IDIM. Experimental results show the validity of the methods.

A six-parameter Iwan model and its application

Iwan model is a practical tool to describe the constitutive behaviors of joints. In this paper, a six-parameter Iwan model based on a truncated power-law distribution with two Dirac delta functions is proposed, which gives a more comprehensive description of joints than the previous Iwan models. Its analytical expressions including backbone curve, unloading curves and energy dissipation are deduced. Parameter identification procedures and the discretization method are also provided. A model application based on Segalman et al.׳s experiment works with bolted joints is carried out. Simulation effects of different numbers of Jenkins elements are discussed. The results indicate that the six-parameter Iwan model can be used to accurately reproduce the experimental phenomena of joints.

Modeling and Parameter Identification of Raw Meal Calcination Process

Raw meal calcination system is a complex mechanical and electrical integration system. This system is different from the traditional machining process without a chemical reaction. Moreover, raw meal calcination in calciner is a complex physical–chemical process, and its dynamic characteristics were rarely researched. In order to verify the rationality of the mechanical structure designed, the dynamic characteristics of the raw meal calcination system need to be analyzed in advance. Therefore, a mathematical model of this process is being called for. The most important variables to be controlled are the calciner temperature and the outlet temperature of preheater C1 (i.e., the no. 1 preheater). Because of the big time constant associated with thermal dynamic characteristics, controlling the two variables is a thorny problem. This paper presents a modeling and parameter identification procedure for a raw meal calcination process. In addition, a parameter identification method based on the T–S fuzzy model and radial basis function was proposed because the specific heat capacity of pulverized coal particles, raw meal, and exhaust cannot be measured. The performance index of parameter identification is to minimize the difference of force response between the simulation and the experiment. Finally, both modeling and parameter identification methods were validated by comparing the results of simulation and experiments.

Biosorption of the Reactive Blue 5G Dye in a Fixed Bed Column Packed with Orange Bagasse: Experimental and Mathematical Modelling

The removal of reactive dye in a fixed bed column packed with orange bagasse was modelled using a model that considers the effects of axial dispersion, external and internal mass transfer limitations, and the equilibrium in the liquid-solid interface. The equilibrium and kinetic parameters were obtained through fit the model to the experimental data. In the parameters identification procedure the PSO optimization method was used. The mathematical model showed good performance when describing the dye adsorption process. Furthermore, orange bagasse is an attractive and cost-effective alternative as an adsorbent to remove reactive blue 5G dye from wastewaters.

Longitudinal Tire Dynamics Model for Transient Braking Analysis: ANCF-LuGre Tire Model

In this investigation, the flexible tire model based on the absolute nodal coordinate formulation (ANCF) is integrated with LuGre tire friction model for evaluation of the longitudinal tire dynamics under severe braking scenarios. The ANCF-LuGre tire model developed allows for considering the nonlinear coupling between the dynamic structural deformation of the tire and its transient tire force distribution in the contact patch using general multibody dynamics computer algorithms. To this end, the contact patch obtained by the ANCF elastic ring tire model is discretized into small strips and the state of friction at each strip is defined by the differential equation associated with the discretized LuGre friction parameters. The normal contact pressure distribution predicted by the ANCF elastic ring elements that are in contact with the road surface are mapped onto the LuGre strips in the contact patch to evaluate the tangential tire force distribution and then the tire forces evaluated at LuGre strips are fed back to the generalized tangential contact forces of the ANCF elastic ring tire model. By doing so, the structural deformation of the ANCF elastic ring tire model is dynamically coupled with the LuGre tire friction in the final form of the governing equations. Furthermore, the systematic and automated parameter identification procedure for the LuGre tire force model is developed. It is shown that use of the proposed procedure with the modified friction curve proposed for wet road conditions leads to accurate prediction of the LuGre model parameters for measured tire force characteristics under various loading and speed conditions. Several numerical examples are presented in order to demonstrate the use of the in-plane ANCF-LuGre tire model for the longitudinal transient dynamics of tires under severe braking scenarios.

Implementation and application of a temperature-dependent Chaboche model

The literature shows that shear fracture of advanced high strength steels (AHSS) is affected by strain hardening at large strain, as well as the temperature dependence of flow stress and strain hardening. The role of non-isotropic hardening, such as would be expected to be important in reverse strain paths as encountered during draw-bend testing or drawing sheet metal into forming dies, has been difficult to assess without a practical constitutive model combining temperature-dependence and non-isotropic hardening capabilities. Such a model has been developed and implemented in Abaqus Standard via the UMAT subroutine. In order to apply and test the constitutive implementation, the material model was fit using alternate parameter-identification procedures starting from compression-tension (CT) data: 1) fit directly from reverse-path, CT data, and 2) fit indirectly, by combining the direct CT data plus extrapolated data at larger strains where the extrapolation uses verified large-strain monotonic hardening character. The resulting material models were used to simulate draw-bend fracture (DBF) tests for six AHSS. The results show that the indirect method improves the predictions of shear fracture significantly, allowing accurate predictions. It was also shown that the influence of non-isotropic hardening aspects are not critical to accurate predictions as long as the high-strain strain hardening is reproduced accurately. These results suggest a practical and effective method for extending measured tensile hardening to otherwise unattainable strains based on the constant ratio (α in the H/V model) of power-law and saturation-stress strain hardening at a given temperature. The success of this approach suggests that α is a material constant (describing the fundamental strain-hardening character) that depends on temperature but is unaffected by the details of transient hardening following abrupt path changes. Furthermore, the essentially transient nature of hardening following path changes is supported.

Continual model variant of viscous materials Report 2. Experimental proof

Considering the non-uniform character of viscous flow of hard phase, the continual model of hot deformation of porous powder materials has been checked. The procedure of the phenomenological creep parameter identification for the hard phase of powder materials is described. A particular mechanism of viscous flow of hard phase particles is shown to be realized depending on temperature-force strain conditions. Unlike the cast materi- als the powder material creep process, alongside with volume and boundary diffusion, can be controlled by superficial diffusion as well. In case of the same creep mechanism, the offered continual model allows us to use for powder materials directly the phenomenological constants of cast materials.

Dynamic Model Identification for 6-DOF Industrial Robots

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

Experimental Studies on Finite Element Model Updating for a Heated Beam-Like Structure

An experimental study was made for the identification procedure of time-varying modal parameters and the finite element model updating technique of a beam-like thermal structure in both steady and unsteady high temperature environments. An improved time-varying autoregressive method was proposed first to extract the instantaneous natural frequencies of the structure in the unsteady high temperature environment. Based on the identified modal parameters, then, a finite element model for the structure was updated by using Kriging meta-model and optimization-based finite-element model updating method. The temperature-dependent parameters to be updated were expressed as low-order polynomials of temperature increase, and the finite element model updating problem was solved by updating several coefficients of the polynomials. The experimental results demonstrated the effectiveness of the time-varying modal parameter identification method and showed that the instantaneous natural frequencies of the updated model well tracked the trends of the measured values with high accuracy.

A Bayesian Approach for Localization of Acoustic Emission Source in Plate-Like Structures

This paper presents a Bayesian approach for localizing acoustic emission (AE) source in plate-like structures with consideration of uncertainties from modeling error and measurement noise. A PZT sensor network is deployed to monitor and acquire AE wave signals released by possible damage. By using continuous wavelet transform (CWT), the time-of-flight (TOF) information of the AE wave signals is extracted and measured. With a theoretical TOF model, a Bayesian parameter identification procedure is developed to obtain the AE source location and the wave velocity at a specific frequency simultaneously and meanwhile quantify their uncertainties. It is based on Bayes’ theorem that the posterior distributions of the parameters about the AE source location and the wave velocity are obtained by relating their priors and the likelihood of the measured time difference data. A Markov chain Monte Carlo (MCMC) algorithm is employed to draw samples to approximate the posteriors. Also, a data fusion scheme is performed to fuse results identified at multiple frequencies to increase accuracy and reduce uncertainty of the final localization results. Experimental studies on a stiffened aluminum panel with simulated AE events by pensile lead breaks (PLBs) are conducted to validate the proposed Bayesian AE source localization approach.

On the optimal parameter and noise identification on the basis of three parameter probability distributions

The optimality of the procedure of parameter identification is scrutinized in this paper. It was shown, with the relations between the mathematical theory of function approximation, three parameter probability distributions, which can adjust their shape, and the maximum-likelihood method, that the optimal expression of the distance between measured data and model fitting it can be established by using the three parameter probability distributions on the basis of iteration procedure, where the noise contained in the measured signal is extracted as well. The iterative method for optimal system/model parameter identification is presented and tested by the numerical experimentation. Four types of noise added to the simple single-degree-of-freedom system response are considered: Gauss, Cauchy, Laplace and Uniform. The method performs well for the noise types at relatively high noise content in the signal.

Synthesis of optimal electrical stimulation patterns for functional motion restoration: applied to spinal cord-injured patients

We investigated the synthesis of electrical stimulation patterns for functional movement restoration in human paralyzed limbs. We considered the knee joint system, co-activated by the stimulated quadriceps and hamstring muscles. This synthesis is based on optimized functional electrical stimulation (FES) patterns to minimize muscular energy consumption and movement efficiency criteria. This two-part work includes a multi-scale physiological muscle model, based on Huxley's formulation. In the simulation, three synthesis strategies were investigated and compared in terms of muscular energy consumption and co-contraction levels. In the experimental validation, the synthesized FES patterns were carried out on the quadriceps-knee joint system of four complete spinal cord injured subjects. Surface stimulation was applied to all subjects, except for one FES-implanted subject who received neural stimulation. In each experimental validation, the model was adapted to the subject through a parameter identification procedure. Simulation results were successful and showed high co-contraction levels when reference trajectories were tracked. Experimental validation results were encouraging, as the desired and measured trajectories showed good agreement, with an 8.4 % rms error in a subject without substantial time-varying behavior. We updated the maximal isometric force in the model to account for time-varying behavior, which improved the average rms errors from 31.4 to 13.9 % for all subjects.

Parameter Identification and Modeling of a Pneumatic Proportional Valve with Applicability in Control Design of Servo-Pneumatic Systems

The key element in a servo-pneumatic actuation system is the control valve. This paper presents a method for parameter identification and modeling the flow characteristic of a FESTO MPYE-5-1/8-HF-101B proportional valve. An experimental setup and a procedure for parameter identification is presented in the paper. Simulation and experimental results are in agreement and the model of the valve could be used in high-accuracy positioning control design of pneumatic servo-systems.

Tilting-Pad Journal Bearings With Active Lubrication Applied as Calibrated Shakers: Theory and Experiment

In recent years, a continuous research effort has transformed the conventional tilting-pad journal bearing (TPJB) into a mechatronic machine element. The addition of electromechanical elements provides the possibility of generating controllable forces over the rotor as a function of a suitable control signal. Such forces can be applied in order to perform parameter identification procedures “in situ,” which enables evaluation of the mechanical condition of the machine in a noninvasive way. The usage of a controllable bearing as a calibrated shaker requires obtaining the bearing specific frequency dependent calibration function, i.e., the transfer function between control signal and force over the rotor. This work presents a theoretical model of the calibration function for a TPJB with active lubrication. The bearing generates controllable forces by injecting pressurized oil directly into the bearing clearance. The injected flow is controlled by means of a servovalve. The theoretical model includes the dynamics of the hydraulic system using a lumped parameter approach, which is coupled with the bearing oil film using a modified form of the Reynolds equation. The oil film model is formulated considering an elastothermohydrodynamic lubrication regime. New contributions to the mathematical modeling are presented, such as the inclusion of the dynamics of the hydraulic pipelines and the obtention of the bearing calibration function by means of harmonic analysis of a linearized form of the controllable bearing constitutive equations. The mathematical model is used to study the relevance and effects of different parameters on the calibration function, aiming at providing general guidelines for the active bearing design. Finally, experimental results regarding the calibration function and the usage of the studied bearing as a calibrated shaker provide insight into the possibilities of application of this technology.

Новый метод использования нейроэмуляторов в психофизиологии

The status of the processing of visual or auditory analyzer sensory reactions was examined at 146 children when moving from Orgy in Tuapse. For processing of the results was used neuromotor. It is established that the aggregate doesn’t clearly differentiate the differences between psychophysical functions before and after the latitudinal movements of the person. The substantial changes in the significance of the auditory analyzer were determined at 146 people in a pair-wise comparison by means of neuromotor. Once the authentication procedure by means of neuromotor distorts the results and its use is impractical. The increase in the number of iterations provides a predictable accuracy of the parameter identification procedure. The possibility of separating in different comparison groups, and one group (before and after exposure to these or other factors) was determined.

Functional state modelling approach validation for yeast and bacteria cultivations

In this paper, the functional state modelling approach is validated for modelling of the cultivation of two different microorganisms: yeast (Saccharomyces cerevisiae) and bacteria (Escherichia coli). Based on the available experimental data for these fed-batch cultivation processes, three different functional states are distinguished, namely primary product synthesis state, mixed oxidative state and secondary product synthesis state. Parameter identification procedures for different local models are performed using genetic algorithms. The simulation results show high degree of adequacy of the models describing these functional states for both S. cerevisiae and E. coli cultivations. Thus, the local models are validated for the cultivation of both microorganisms. This fact is a strong structure model verification of the functional state modelling theory not only for a set of yeast cultivations, but also for bacteria cultivation. As such, the obtained results demonstrate the efficiency and efficacy of the functional state modelling approach.

Identification of the mechanical properties of the carbon fiber and the interphase region based on computational micromechanics and Kriging metamodel

Computational micromechanics has been widely used to predict the mechanical properties of fiber reinforced composite. The accuracy of the prediction depends largely on the precise definition of the mechanical parameters of the constituents, the fiber, the matrix and the interphase, which has been increasingly recognized as a transition region between the fiber and the matrix. The mechanical properties of the matrix and the axial modulus of the carbon fiber can be measured directly. However, the interphase effect and other properties of the carbon fiber are difficult to be obtained through experimental methods. This paper deals with the parameter identification of the mechanical properties of the T300 carbon fiber and the interphase region by means of numerical methods. Firstly, a microstructure based three-phase finite element model in which interphase region has been simulated by cohesive elements was developed to predict the elastic properties of the unidirectional carbon fiber reinforced composite. By combining the finite element model with the Latin Hypercube Sampling method, metamodels based on Kriging method were then developed to explicitly parameterize the relationship between the elastic properties of the composite and the input parameters. The mechanical properties of the T300 carbon fiber and the interphase region of the studied composite were finally obtained by an inverse parameter identification procedure based on the constructed Kriging metamodel and the tested mechanical properties of the composite. Benefiting from the proposed procedure, more reliable input parameters were extracted for the T300 carbon fiber and the interphase modeling, resulting in more precise prediction of the elastic properties of the carbon fiber reinforced composites through finite element simulation.

Robust Nonlinear Adaptive Control of a “Boost” Converter via Algebraic Parameter Identification

This paper presents the design of a robust nonlinear adaptive controller for trajectory tracking maneuvers of the nonminimum phase output voltage on a dc-to-dc “boost” power converter with uncertain time-varying parameters. The unknown parameter variations concern both the resistive load and the input voltage supply value. A generalized proportional-integral (GPI) indirect control, exploiting the flatness property of the system, performs fast adaptations on the feedback controller and on the desired output reference trajectory owing to a fast online algebraic parameter identification procedure. The required updating of the algebraic parameter identification process is periodically triggered to cope with the time variations of the unknown plant parameters. The proposed control scheme is shown to be quite effective for handling the significant plant uncertainties when tested through experiments on a laboratory prototype. An adaptive linear quadratic regulator optimal control based on feedback linearization was designed in order to compare its performance against that of the proposed GPI adaptive scheme. An integral square error index was used for the evaluation.

Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels

In this paper, a model-based approach is presented to quantify the effective synchrony between hippocampal areas from depth-EEG signals. This approach is based on the parameter identification procedure of a realistic Multi-Source/Multi-Channel (MSMC) hippocampal model that simulates the function of different areas of hippocampus. In the model it is supposed that the observed signals recorded using intracranial electrodes are generated by some hidden neuronal sources, according to some parameters. An algorithm is proposed to extract the intrinsic (solely relative to one hippocampal area) and extrinsic (coupling coefficients between two areas) model parameters, simultaneously, by a Maximum Likelihood (ML) method. Coupling coefficients are considered as the measure of effective synchronization. This work can be considered as an application of Dynamic Causal Modeling (DCM) that enables us to understand effective synchronization changes during transition from inter-ictal to pre -ictal state.The algorithm is first validated by using some synthetic datasets. Then by extracting the coupling coefficients of real depth-EEG signals by the proposed approach, it is observed that the coupling values show no significant difference between ictal, pre-ictal and inter-ictal states, i.e. either the increase or decrease of coupling coefficients has been observed in all states. However, taking the value of intrinsic parameters into account, pre-seizure state can be distinguished from inter-ictal state. It is claimed that seizures start to appear when there are seizure-related physiological parameters on the onset channel, and its coupling coefficient toward other channels increases simultaneously.As a result of considering both intrinsic and extrinsic parameters as the feature vector, inter-ictal, pre-ictal and ictal activities are discriminated from each other with an accuracy of 91.33% accuracy.