Tool For Parameter Identification Research Articles

Numerical identification of position-dependent friction coefficients from measured displacement data in a bolt-nut connection

Friction is a complex system affected by microscopical effects and multidisciplinary phenomena. Coulomb's simple friction model with a constant friction coefficient cannot account for all these tribological effects. Nevertheless, this model is still widely utilised for calculations of mechanical applications. In order to reflect the importance of friction as a parameter for functionality, we need more realistic and sophisticated calculations. This is particularly relevant for bolt-nut connections, which serve as motivating example for our study. Our approach is to introduce position-dependent friction coefficients by dividing the contact surface into different friction areas, each characterised by a constant friction coefficient. These coefficients are then adapted to measured displacement data. To this end, we develop a numerical parameter identification tool. The tool combines calculations in Ansys Mechanical, an established Finite Element software, and Microsoft's Visual Basics for Applications for optimisation purposes. We verify the parameter identification tool using the simple model of a block on a planar surface. Within this test scenario, the algorithm converges and provides a good approximation of the friction coefficients. Subsequently, we apply parameter identification to the model of a bolt-nut connection. We perform optical measurements to acquire experimental displacement data. The parameter identification tool demonstrates its functionality. Finally, we discuss future modifications of the procedure, that will enable more realistic and reliable results.

Prediction of CYP-mediated silybin A-losartan pharmacokineticinteractions using physiological based pharmacokinetic modeling.

The concomitant use of herbal products and synthetic drugs necessitates the assessment of their interaction potentials. The herbal hepatoprotective medicine, silybin A inhibits cytochrome P450 (CYP) 2C9 and 3A4 enzymes, thus, may interact with the drugs that are substrates of CYP2C9 and 3A4, such as losartan. The three most prominent genotypes, expressed by CYP2C9 are the CYP2C9*1/*1, CYP2C9*1/*2 and CYP2C9*1/*3. This study aimed to assess silybin A-losartan interaction in different CYP2C9 genotypes using physiological-based pharmacokinetic (PBPK) model approach. The individual PBPK models for silybin A and losartan were developed using PK-Sim®. Losartan pharmacokinetics was predicted with or without co-administration of silybin A in individuals of different CYP2C9 genotypes to find herbal-drug interaction. The predicted drug plasma curves and pharmacokinetic parameters were optimized using parameter identification tool and were compared with reported pharmacokinetic parameters from the published clinical studies for model validation. The silybin-losartan interactions were predicted by change in area under the curve (AUC) and peak systemic concentration (Cmax). The co-treatment of silybin A, 420mg/24h (140mg/8h) with losartan 50mg/24h, exhibited a genotype-dependent change in the losartan's AUC and Cmax. In CYP 2C9*1/*1 genotype, AUC and Cmax of losartan were increased 1.16 and 1.37 folds, respectively falling in a range stipulated for negligible interaction. Increase in AUC and Cmax by 0.873 and 0.294 folds, respectively in CYP2C9*1/*3 after co-administration of silybin A exhibited a minor interaction with losartan. However, in individuals with CYP2C9*1/*2 genotype, the losartan's AUC and Cmax were decreased by 0.01 folds, manifesting a moderate interaction. Hence, in CYP2C9*1/*1 and CYP2C9*1/*3 genotypes, silybin A is a weak CYP inhibitor for losartan while in CYP2C9*1/*2 genotype, the co-administration of silybin consequents into a moderate pharmacokinetic interaction with losartan.

ADAPT — A Diversely Applicable Parameter Identification Tool: Overview and full-field application examples

The free and open source software tool ADAPT (A Diversely Applicable Parameter Identification Tool) for the inverse parameter identification of constitutive material models is presented and applied in combination with Finite Element (FE) simulations. Different types of input data, namely integral data such as forces and field data such as displacement and strain fields, are considered within a multi-objective optimisation scheme. The effect of each of those data sets is investigated in detail for an elasto-plastic and a non-local ductile damage model applied to a dual-phase steel. To demonstrate the wide range of parameter identification applications, several examples are given. The behaviour of a soft polymer using a finite strain non-local visco-elastic damage model is predicted. It is shown that field data such as displacement fields are required to identify the material parameters of a non-local damage model. Differing from existing approaches, high resolution scanning electron microscopy (SEM) data of voids and optical strain fields are employed to identify the material parameters of a Gurson–Tvergaard–Needleman porous plasticity model. This strategy reduces the error in terms of the predicted void area fraction in the particular air bending example considered by approximately 500% compared to a parameter identification approach not using microstructural data. The applied identification scheme contributed to the achievement of a good qualitative agreement for the prediction of the void area fraction in the process chain of hot calibre rolling with subsequent forward rod extrusion of a case-hardened steel by using an uncoupled Lemaitre-type damage model with strain rate and temperature dependency.

Data-based mechanistic modeling approach for predicting thermal response of conductive food during heating processes

The application of a data based mechanistic modeling approach to predict thermal histories of conductive foodstuffs during heating is reported. In the experiment, minced fish was completely filled in 307x113 steel cans as the conductive food. Step increase in heating medium was applied while the product temperature was recorded. The simplified refined instrument variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. A first order transfer function model proved to be sufficiently good in describing the heat transfer from heating medium to product with a high statistical significance (R2 > 0.99). In this model, a parameter related to the heat transfer coefficient was found and could be used to predicting of product temperature during heating processes.

Data-based mechanistic modeling approach for predicting thermal response of conductive food during heating processes

The application of a data based mechanistic modeling approach to predict thermal histories of conductive foodstuffs during heating is reported. In the experiment, minced fish was completely filled in 307x113 steel cans as the conductive food. Step increase in heating medium was applied while the product temperature was recorded. The simplified refined instrument variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. A first order transfer function model proved to be sufficiently good in describing the heat transfer from heating medium to product with a high statistical significance (R2 > 0.99). In this model, a parameter related to the heat transfer coefficient was found and could be used to predicting of product temperature during heating processes.

Identification of modal parameters of non-stationary systems with the use of wavelet based adaptive filtering

The Operational Modal Analysis (OMA) is a common tool for identification of parameters of mechanical structures during operation. Modal analysis can be applied for linear, stationary and undamped systems or systems with small and proportional damping. To apply this technique to other systems, mainly to non-stationary systems, new procedures are required.The paper focuses on the application of time–frequency signal filtration to the recursive method of the modal parameters' identification based on operational measurements, dedicated for non-stationary systems. The presented technique uses an adaptive wavelet signal filtering method to separate signal components and reduce the model order. This approach considerably facilitates selection of the wavelet function parameters and significantly improves the quality of the separated modal components. Thanks to the reduction of model order, estimation of modal parameters can be performed using a relatively simple mathematical formula. This approach significantly reduces the demand for computing power which has a direct impact on system's costs and modal parameter's estimation time. This is particularly an important problem when the system parameters are changing rapidly and the information about this changes is required in real-time. The algorithm allows assessing the quality of the estimated parameters by simultaneous estimation of confidence bounds. The method has been tested on numerical models, experimental laboratory test rig and applied to real data.

An Efficient Evolutionary Approach to Parameter Identification in a Building Thermal Model

Thermal models of buildings are often used to identify energy savings within a building. Given that a significant proportion of that energy is typically used to maintain building temperature, establishing the optimal control of the buildings thermal system is important. This requires an understanding of the thermal dynamics of the building, which is often obtained from physical thermal models. However, these models require detailed building parameters to be specified and these can often be difficult to determine. In this paper, we propose an evolutionary approach to parameter identification for thermal models that are formulated as an optimization task. A state-of-the-art evolutionary algorithm, i.e., SaNSDE+, has been developed. A fitness function is defined, which quantifies the difference between the energy-consumption time-series data that are derived from the identified parameters and that given by simulation with a set of predetermined target model parameters. In comparison with a conventional genetic algorithm, fast evolutionary programming, and two state-of-the-art evolutionary algorithms, our experimental results show that the proposed SaNSDE+ has significantly improved both the solution quality and the convergence speed, suggesting this is an effective tool for parameter identification for simulated building thermal models.

The Study of Loss-of-Coolant Accident Parameter Identification for a BWR-4 Plant

In this study the parameter identification tool (PIT) for the loss-of-coolant accident (LOCA) of a BWR-4 plant with Mark-I containment, Chinshan nuclear power plant (NPP) in north Taiwan, is developed. The PIT is composed of the Simplex search algorithm and the MAAP5 code with the integration approach presented by Tsai et al. The postulated LOCA is assumed to occur after station blackout sequence with the break elevation of 17 m and the break area of 2.1×10-2 m2 . Through the developed PIT, the break elevation of 16.93 m and the break area of 2.1×10-2 m2 are identified with the imitated plant data, the RPV pressure and the shroud water level, generated by MAAP5 code. The timing of events and source terms in the LOCA of Chinshan NPP can be predicted with by the MAAP5 code with the identified parameters. It demonstrates that the PIT is helpful and important in severe accident management.

An Out-of-Box Approach to Integrate an Optimization Algorithm and MAAP5 for Parameter Identification

The modular accident analysis program (MAAP) is a fast-running severe accident analysis tool with which the timing of key events and source terms in a severe accident are assessed. The idea of combining MAAP and an optimization algorithm to identify the realistic accident parameters in terms of minimizing the discrepancies between the plant data and the simulation results is straightforward. In 2008 Chien and Wang first compiled the combination of the MAAP4 source codes and a Simplex code as a computer-aided tool for the loss-of-coolant accident (LOCA) of the Kuosheng nuclear power plant (NPP). The break area and break elevation were successfully identified. However, in that approach to putting the idea into practice was that hard data dependence exists between MAAP and the optimization algorithm. Tedious tracing and modification work is required to ensure all plant variables in MAAP source codes with the exception of the adjusted accident parameters are identical at the beginning of every simulation. The plant- and accident-specific development features also easily limit the applications of this idea to the nuclear industry, like being boxed in.In this study a so-called “out-of-box” approach is proposed that can omit the limits of the idea applications on severe accident management. A parameter identification tool developed in this approach for the same postulated LOCA of the Kuosheng NPP is carried out for verification and validation. It demonstrates the advantages of successful parameter identification, less programming efforts, and no plant- and accident-specific features.

Parameter optimization and uncertainty analysis in a model of oceanic CO 2 uptake using a hybrid algorithm and algorithmic differentiation

Methods and results for parameter optimization and uncertainty analysis for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Schartau and Oschlies, simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. Our aim is to identify parameters and fit the model output to given observational data. For this model, it has been shown that a satisfactory fit could not be obtained, and that parameters with comparable fits can vary significantly. Since these results were obtained by evolutionary algorithms (EA), we used a wider range of optimization methods: A special type of EA (called quantum-EA) with coordinate line search and a quasi-Newton SQP method, where exact gradients were generated by Automatic/Algorithmic Differentiation. Both methods are parallelized and can be viewed as instances of a hybrid, mixed evolutionary and deterministic optimization algorithm that we present in detail. This algorithm provides a flexible and robust tool for parameter identification and model validation. We show how the obtained parameters depend on data sparsity and given data error. We present an uncertainty analysis of the optimized parameters w.r.t. Gaussian perturbed data. We show that the model is well suited for parameter identification if the data are attainable. On the other hand, the result that it cannot be fitted to the real observational data without extension or modification, is confirmed.

Modelling of viscoelastic material behaviour close to the glass transition temperature

AbstractIn this contribution we investigate the mechanical behaviour of polyurethane over a range of different but constant temperatures from the glass to the viscoelastic state. Therefore uniaxial tension tests are performed on dogbone specimens under different isothermal conditions. In this manner an experimental data set is provided. As a theoretical basis we present the well known thermomechanically coupled one dimensional linear viscoelastic material model which is able to display the experimentally observed material behaviour. For this we adopt temperature dependent relaxation times. The introduced model parameters are identified via a standard parameter identification tool. Finally, the experimental results are compared with the ones of simulations of the identified model parameters. (© 2009 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Data-based mechanistic modelling of three-dimensional temperature distribution in ventilated rooms filled with biological material

In this paper, a data-based mechanistic modelling approach was developed to real time monitoring and online adaptive control of three-dimensional temperature distribution in both an individual biological product and in a given movement. Boxes of potatoes were used as biological material. During the experiments, step inputs on an air inlet temperature were applied while airspace and potatoes temperature were recorded. The simplified refined instrument variable algorithm was used as a model parameter identification tool to obtain the best model order and parameters. By model compacting, several physically meaningful parameters were found to present the temperature distribution between the products and the airspace. A third order transfer function from the dynamic response of airspace from inlet air temperature with a high coefficient of determination R T 2 > 0.9 and a low standard error (SE < 0.01) explained the heat exchange in a system. Two physically meaningful parameters were found from the model parameters. A local volumetric fresh air concentration β 1 was defined to the temperature distribution in the airspace, and a local ‘cooling rate’ α 2 was presented for the product temperature distribution. The values of β 1 and α 2 existing in the model could be used to design a control system for real time monitoring and online adaptive control of three-dimensional temperature distribution in the process room.

Data-based mechanistic modelling for controlling in three dimensions the temperature distribution in a room filled with obstacles

In storage rooms for agricultural products, there are losses due to poor temperature distribution. To control temperature distributions in process rooms filled with obstacles, a three dimensional dynamic model is required to calculate the three dimensional dynamic responses product temperature distribution in three dimensions as a response to changes in process inputs such as ventilation rate and inlet temperature. In this study, a data-based mechanistic model for temperature responses at different positions in both airspace and obstacles was developed. In the first step, plastic balls with known characteristics were used as obstacles. During the experiments, step increases in air inlet temperature were applied while the airspace temperature and the air inside the balls were recorded. The simplified refined instrument variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. Using model compacting, several physically meaningful parameters were found to represent the temperature distribution between the products and the airspace. A third-order transfer function developed from the air inlet and airspace temperature data enabled the dynamic response of airspace temperature to be explained. A local volumetric fresh air concentration β 1 was determined from the temperature distribution in the airspace with a high coefficient of determination R 2 >0.99 and a low error 0.1 °C. A first-order transfer function model proved to be sufficiently good in describing the heat transfer from airspace to obstacles with a high statistical significance ( R 2 >0.99). In this model, a parameter related to the heat transfer coefficient α 2 was found to represent the temperature distribution between the obstacles. The values of β 1 and α 2 in the model could be used to design a control system for real time monitoring and the online adaptive control of the temperature distribution in the process room in three dimensions.

Modelling of Three-dimensional Air Temperature Distributions in Porous Media

In this paper, three-dimensional (3D) temperature distributions in a ventilated empty room and room filled with obstacles were compared experimentally to evaluate the presence of biological material, such as stored products, on the temperature uniformity. During experiments, step inputs in inlet air temperature were applied and temperature responses at 36 sensor locations were recorded to develop a data-based mechanistic (DBM) model of the temperature response at different positions in the room. The simplified refined instrumental variable (SRIV) algorithm was used as model parameter identification tool to obtain the best model order and parameters. The developed model demonstrated to be an accurate representation of the system in both empty and porous media. The predicted temperature in the room had a good correlation, coefficient of determination R2>0·99, with the measured data and the model provided several physically meaningful parameters to present the 3D temperature distribution in the porous media. The average accuracy was 0·1 °C.

Combining CFD and data-based mechanistic (DBM) modelling approaches

This paper outlines a methodology for the reduction of a complex computational fluid dynamics (CFD) model using data-based mechanistic (DBM) modelling technique. For this study a full scale, adiabatic walled ventilated installation was used. Primarily, a CFD model that is three dimensional, transient and turbulent was proposed and solved by a commercial package CFX 4.3 in order to obtain informative data that will be used to build a DBM model. At the inlet a step rise in temperature was applied and the ventilation rate is kept steady. Turbulence is modelled by the widely used standard k– ϵ model. Temperature responses at 36 monitoring positions inside the installation were extracted out of the simulation. In the second stage of the work, for the same ventilation system, a DBM model was formulated from an energy balance equation. The simplified refined instrumental variable (SRIV) algorithm is employed as model parameter identification tool. The obtained highly accurate model parameters for all monitoring positions demonstrate the possibility of reducing complex CFD models using identification technique.

Simulation des Mikrorisswachstums bei zyklischer Beanspruchung

In order to take into account the influence of the microstructure on the damage process a parameter identification tool designed to create the simulation parameters for the simulation of microcrack growth subjected to repetitive loading is presented. A comparison between the simulation results and the experimental results shows in the case of uniaxial loading a good agreement. The essential reason to integrate a parameter identification tool in the simulation software is to describe the effects of various loading sequences, multiaxial stress states as well as overloads on the fatigue lifetime. Considering fatigue microcrack growth microstructural barriers as well as the state of stress play an essential role.

Parameter Identification for FOC Induction Motors Using Genetic Algorithms with Improved Mathematical Model

This paper applies genetic algorithms (GAs) to the problem of parameter identification for field orientation control (FOC) of induction motors. The motor's general model, which is generally used for speed control applications, is improved for the purpose of improving the parameter identification accuracy by the assumption that the stator self inductance Ls is identical to the rotor self inductance Lr. The motor's dynamic response to a direct on-line start is used to estimate the parameters. Results are presented for both the general and improved mathematical models of the induction motor, using different levels of measurement noise. For comparison, the results of a simple random search (SRS) method under the same condition are also given. The results show that the improved model increases significantly the parameter identification accuracy and that the performance of the GA method is much better than that of a simple random search technique. It is concluded that the GA is a powerful tool for parameter identification.

Joint inversion of tracer tests using reversed flow fields

Two successive dipole tracer experiments were performed in a fractured rock at the Grimsel Test Site in February/March 1993. In both experiments NaCl was used as a tracer and the extraction rate was twice the injection rate. In the second experiment injection and extraction were interchanged (‘reverse experiment’). The withdrawal rate remained twice the injection rate. Long tailing was characteristic for the breakthrough curves in both experiments. The tests were interpreted using a single fracture flow model. Tracer transport is described by advection/dispersion along the fracture allowing for diffusion into an immobile matrix. We were able to interpret the breakthrough curves for both experiments by one unique set of parameters, describing transport and baseflow. Uniqueness could only be achieved when using the information from both experiments. We conclude that performing a reverse experiment is an indispensable tool for parameter identification in dipole tracer tests. A sensitivity analysis proved that not only the exchange of tracer between the mobile zone and the immobile matrix but also transversal dispersivity is responsible for the tailing in the breakthrough curves. Further, the typical exchange time between mobile and immobile media was too small to be attributed to mere molecular diffusion into the matrix. The interpretation of the estimated parameters allows us to conclude that we did not observe matrix diffusion but a fast exchange of tracer between the mobile zone and both the adjacent rock and less pervious zones within the fracture. Analysis of the joint covariance matrices showed that the estimated transport parameters have small errors.