Determination Of Model Parameters Research Articles

Analytical Thermal Cable Model for Bundles of Identical Single Wire Cables

Overcurrents may lead to critical temperatures in cable systems. Fuses can protect the cable insulation from damage. As the classically used melting fuses cannot fulfill the growing requirements concerning diagnosis, reset behavior, or fail safety, electronic fuses based on power transistors are more and more introduced in modern systems. As reliable temperature measurements are complex, the electronic fuses are often controlled by the electrical currents flowing through the semiconductor by, e.g., a microcontroller. On the microcontroller the cable temperature is estimated from the current based on an electrothermal cable model. When the temperature exceeds a given threshold the transistor interrupts the current flow. Therefore, accurate and computationally simple thermal cable models are necessary. Complex numerical solutions are inappropriate. For single wire cables several calculation methods are known already, for arrangements of several wires with insulation these methods are not accurate enough. This article presents an analytical thermoelectric calculation method for multiconductor cable arrangements consisting of several identical single wire cables. An approach for the determination of important model parameters is presented and the final model is validated for a power over data line application. The model is exemplarily applied for calculating the temperature developments and ampacities in cable bundles consisting of a different number of cables and for the comparison between a solid and a stranded cable.

Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes–Darcy flow problems

Determination of relevant model parameters is crucial for accurate mathematical modelling and efficient numerical simulation of a wide spectrum of applications in geosciences. The conventional method of choice is the global sensitivity analysis (GSA). Unfortunately, at least the classical Monte-Carlo based GSA requires a high number of model runs. Response surfaces based techniques, e.g. arbitrary Polynomial Chaos (aPC) expansion, can reduce computational effort, however, they suffer from the Gibbs phenomena and high hardware requirements for higher accuracy. We introduce GSA for arbitrary Multi-Resolution Polynomial Chaos (aMR-PC) which is a localized aPC based data-driven polynomial discretization. The aMR-PC allows to reduce the Gibbs phenomena by construction and to achieve higher accuracy by means of localization also for lower polynomial degrees. We apply these techniques to perform the sensitivity analysis for the Stokes–Darcy problem which describes fluid flow in coupled free-flow and porous-medium systems. We consider the Stokes equations in the free-flow region, Darcy’s law in the porous-medium domain and the classical interface conditions across the fluid–porous interface including the conservation of mass, the balance of normal forces and the Beavers–Joseph condition for the tangential velocity. This coupled problem formulation contains four uncertain parameters: the exact location of the interface, the permeability, the Beavers–Joseph slip coefficient and the uncertainty in the boundary conditions. We carry out the sensitivity analysis of the coupled model with respect to these parameters using the Sobol indices on the aMR-PC expansion and conduct the corresponding numerical simulations.

Experimental Calibration of a Homogeneous Substitute Material Model for Reinforced High-Performance Concrete Modeling.

The purpose of this work was to develop a substitute material model for the analysis of reinforced concrete structures. This paper presents proposals to solve the problem of limited calculation time, both to perform simulation models and to perform effective numerical or analytical analyses of structural elements in order to achieve results consistent with experimental results. Achieving this aim is conditional upon the determination of the material model parameters, taking into account the type of structure, the system of reinforcement, and the static strength-deformation parameters of the component materials. A universal procedure is proposed for determining the parameters of the substitute material model on the basis of the homogenization function, in which the homogenization coefficient is assumed as being equal to the effective reinforcement ratio of real reinforced concrete structural elements. In addition, the introduction of a new concrete constraint coefficient to this procedure, which corresponds to the proportionality coefficient of biaxial to uniaxial compressive strength, is proposed. On the basis of the conducted comparative analyses, the possibility of using the hypothetical substitute material model for the design of building elements and structures was confirmed. The average values of the obtained results for individual research series did not differ from the experimental results by more than 8.5%, for both the numerical and analytical models.

Bayesian coarsening: rapid tuning of polymer model parameters

A protocol based on Bayesian optimization is demonstrated for determining model parameters in a coarse-grained polymer simulation. This process takes as input the microscopic distribution functions and temperature-dependent density for a targeted polymer system. The process then iteratively considers coarse-grained simulations to sample the space of model parameters, aiming to minimize the discrepancy between the new simulations and the target. Successive samples are chosen using Bayesian optimization. Such a protocol can be employed to systematically coarse-grained expensive high-resolution simulations to extend accessible length and time scales to make contact with rheological experiments. The Bayesian coarsening protocol is compared to a previous machine-learned parameterization technique which required a high volume of training data. The Bayesian coarsening process is found to precisely and efficiently discover appropriate model parameters, in spite of rough and noisy fitness landscapes, due to the natural balance of exploration and exploitation in Bayesian optimization.

SOFTWARE FOR INTERPRETING THE RESULTS OF VERTICAL ELECTRICAL SOUNDING IN THE FORM OF A THREE-LAYER GEOELECTRIC STRUCTURE

By monitoring the state of the grounding system, electrical engineering personnel must independently solve the problem of interpreting the results of vertical electrical sounding (VES) of the soil using their own knowledge and practical experience. During the interpretation, outdated palettes or expensive software complexes are used, which allow analyzing only two-layer soils. This leads to a large number of errors and inaccuracies in the assessment of the state of the grounding system and worsens their operation. In this regard, a program was developed for the interpretation of the results of the VES in the form of a three-layer geoelectrical structure. The program is implemented in the Delphi programming language and contains two parts: computational and graphic. The computational part of the program uses point source current, Hooke-Jives, equivalence and reliability theory methods to optimize the determination of geoelectric model parameters. The graphical part allows the user to visualize the obtained results and perform their analysis. To confirm the reliability of the developed program, a comparison of the results of the experimental measurement of the resistance of the grounding arrangmants with the results of the calculation using the program was made for 65 substations with a voltage class of 35 kV of one of the regional energy companies of Ukraine. The obtained results showed that the program gives reliable results and can improve the quality of monitoring the condition of grounding systems, and accordingly increase the reliability and safety of operation of electrical stations and substations.

Experimental investigation of the effect of lubricant change on automotive component

Manufacturing problems resulting from lubricant supplier changes are relatively common in automotive press shops. Simulations using computer-aided design (process and tool) detect a significant proportion of manufacturing problems before tools are physically produced. In modern forming codes, an enhanced Coulomb model is used to numerically model the friction phenomena associated with lubrication. In the laboratory condition, determining model parameters through physical measurement is challenging. As a result, it became necessary to design a device that could be utilized in industrial environments. In this study, we present the prediction of a manufacturing problem resulting from a change in the lubricant of an industrial sheet metal part. The measuring device has been developed based on the parameters of the friction model of the lubricant materials. The developed measuring device may be used to determine the pressure and velocity dependent friction model parameters. The measuring device can only measure a limited range of pressures and velocities, so the results are extended to a wider range using a mathematical method. The results are used to demonstrate the effect of lubricant material changes on the forming process using the AutoForm code.

A tool for automatic determination of model parameters using particle swarm optimization

This paper presents a tool developed in EMTP to automatically determine model parameters for matching existing field measurements. The tool uses the particle swarm optimization (PSO) algorithm to calibrate or update existing models. To enhance the performance of the tool, a technique used to improve PSO efficiency is also proposed. Two test cases are presented. The first case aims to determine the parameters of the reactive power control loop in a PV park controller model. The second case finds the unknown parameters in an exciter model of a synchronous machine connected to a grid.

Regression analysis of data based on the method of least absolute deviations in dynamic estimation problems

The use of regression analysis in dynamic problems of system estimation requires a high-speed algorithm of model parameter determination. Moreover, the original data may have stochastic heterogeneity which entails the necessity of the estimates of model parameters be resistant to various data anomalies. However, stable estimation methods, including the least absolute deviations method, are significantly inferior to the parametric ones. The goal of the study is to describe a computationally efficient algorithm for implementing the method of least absolute deviations for dynamic estimation of regression models and to study its capabilities for solving practical problems. This algorithm is based on descending along nodal lines. In this case, instead of the values of the objective function, its derivative in the direction of descent is considered. The computational complexity of the algorithm is also reduced due to the use of the solution of the problem at the previous step as a starting point and efficient updating of observations in the current data sample. The external performance of the proposed dynamic version of the algorithm of gradient descent along nodal lines has been compared with the static version and with the least squares method. It is shown that the dynamic version of the algorithm of gradient descent along the nodal lines make it possible to bring the speed close to that of the least squares method for common practical situations and to use the proposed version in dynamic estimation problems for a wide class of systems.

Determination of material parameters in constitutive models using adaptive neural network machine learning

A challenge in the constitutive modeling of time dependent, non-linear solids is the identification of potentially large numbers of material parameters. Here we present an efficient method to determine these parameters using a machine learning algorithm based on Singular Value Decomposition (SVD) and an adaptive neural network (NN). SVD compresses training data and provides outputs for the NN. The trained network rapidly computes the material responses for a large set of material parameters. These responses are then compared with experimental data to determine the optimal parameters. We test our algorithm by performing uniaxial cyclic and relaxation tests on three hydrogels with very different time dependent behaviors: a chemically and physically crosslinked polyampholyte (c-PA) gel; a pure physically crosslinked PA (p-PA) gel, and a Poly(vinylalcohol) (PVA) hydrogel with both chemical and physical crosslinks.

Modelling the residual strength degradation in composite materials without using residual strength tests

Residual strength models are widely used to predict the fatigue life of composite laminates under highly variable loads. However, they often require considerable experimental effort to accurately determine model parameters. This paper introduces a new approach for predicting the residual strength of composite materials with less experimental data. The method is based on two common strength-based wearout models, the Sendeckyj model and Schaff and Davidson model. The Sendeckyj model consists of an equation with two model parameters, which describes the shape of the S-N curve by fitting fatigue test data. The Schaff and Davidson model is a single-parameter function which calculates the residual strength based on the number of fatigue cycles. Using a novel mathematical algorithm, the residual strength model parameter in the Schaff and Davidson model is estimated directly from the S–N curve without running any residual strength tests. Five data sets from the literature are used to validate the new methodology. The results show that the residual strength model parameter is dependent on both the stress level and the number of loading cycles experienced at this stress level, both of which are considered in the new strategy. In addition, if the fatigue data are well distributed, the residual strength model parameter estimated by the new strategy is close to the experimental data.

L-SHADE with parameter decomposition for photovoltaic modules parameter identification under different temperature and irradiance

The performance of photovoltaic (PV) system relies on the accurate determination of unknown parameters in PV models, such as photo-generated current, diode current, and resistance. These unknown parameters may vary with different temperature and irradiance conditions, which greatly increases the difficulty of identifying these unknown parameters. Although some parameter identification methods have been proposed to solve such a problem, the accuracy and reliability of solutions obtained by these methods suffers from great challenges when temperature and irradiance are changing. In this paper, a simple and effective approach called success-history adaptation differential evolution with linear population size reduction (L-SHADE) and decomposition referred as (L-SHADED), is presented to accurately and reliably identify the unknown parameters of PV models under different temperature and irradiance. In L-SHADED, firstly, an unknown parameters decomposition technique is used to reduce the complexity of the problem. Then an advanced evolutionary algorithm L-SHADE is employed to identify the optimal unknown parameter values. The performance of proposed L-SHADED has been investigated by testing two single-diode-based PV modules (multi-crystalline KC200GT and mono-crystalline SM55) under different temperature and irradiance. The experimental comparison results demonstrate that proposed L-SHADED is almost 10 times smaller in RMSE than other methods. Furthermore, the coefficient of determination of L-SHADED reaches 1.0 in almost all conditions, which indicates that the parameters identified by L-SHADED are quite accurate.

Digital Twin Based Design and Experimental Validation of a Continuous Peptide Polishing Step

Optimizing or debottlenecking existing production plants is a challenging task. In this case study, an existing reversed phased chromatography polishing step for peptide purification was optimized with the help of a digital twin. The existing batch chromatography was depicted digitally with the general rate model. Model parameter determination and model validation was done with dedicated experiments. The digital twin was then used to identify optimized process variants, especially continuous chromatography steps. MCSGP was found to achieve high purities and yield but at the cost of productivity due to column synchronization. An alternative Continuous Twin Column chromatography process (CTCC) was established that eliminates unnecessary waiting times. Ensuring the same or higher purity compared to the batch process, the continuous process achieved a yield increase of 31% and productivity increase of 27.6%. Experimental long runs confirmed these results.

Magnetic hysteresis model using chained plays

A magnetic vector hysteresis model is presented using play hysterons as building blocks. The main difference compared to earlier play based magnetic hysteresis models is that instead of forming a superposition of plays with different coercivities, this work uses a set of identical plays which are connected in a chain so that the output of one play acts as input to the next. Like models using a superposition, this yields far more complex minor loop behavior than a single play is capable of. Comparisons are made with measurements on a nonoriented electric steel for BH curves and alternating and rotational losses, showing satisfactory agreement. The approach is found to have both advantages and disadvantages compared to models using superpositions of plays. The problem of determining model parameters becomes simpler and the model can be reversed so either magnetic field or magnetization may be used as known variable. On the other hand, there is no apparent connection to underlying physical processes.

Reconstructing Primary Voltages Across Inductive VTs — Part I: Methodology

Inductive voltage transformers (VTs) are the most widely used instrument transformers in power grids operating at 35 kV and below. Frequently, however, during disturbances, the secondary signals are not replicas of the primary voltages because of eddy current effects, capacitive effects, and nonlinearities. In this two-part paper, a new inverse method considering the frequency-dependent characteristics and nonlinearities is proposed to reconstruct the primary voltage from a distorted secondary signal. In Part I, the framework of the method is presented and then realized with two inverse models. An inverse black-box model is used to compensate the eddy currents and capacitive effects, and an inverse duality-derived model is used to consider the nonlinearities. Methods for model parameter determination are described. Experimental validation and analysis of the proposed method are afforded in Part II. The inverse method effectively improves the primary voltage measurement of VTs without any auxiliary high-voltage equipment. The proposed method provides accurate primary voltage waveforms of VTs during disturbances.

Performance simulation method and state of health estimation for lithium-ion batteries based on aging-effect coupling model

Accurate simulation of characteristics performance and state of health (SOH) estimation for lithium-ion batteries are critical for battery management systems (BMS) in electric vehicles. Battery simplified electrochemical model (SEM) can achieve accurate estimation of battery terminal voltage with less computing resources. To ensure the applicability of life-cycle usage, degradation physics need to be involved in SEM models. This work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle (ISP) model. Firstly, three mechanisms of solid electrolyte interface (SEI) film growth throughout life cycle are analyzed, and an SEI film growth model of lithium-ion battery is built coupled with the ISP model. Then, a series of identification conditions for individual cells are designed to non-destructively determine model parameters. Finally, battery aging experiment is designed to validate the battery performance simulation method and SOH estimation method. The validation results under different aging rates indicate that this method can accurately estimate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.

Determination of partial denitrification kinetic model parameters based on batch tests and metagenomic sequencing

In this study, a model was developed to investigate the partial denitrification(PD) process. The heterotrophic biomass (XH) proportion in the sludge was determined to be 66.4% based on metagenomic sequencing. The kinetic parameters were first calibrated, then validated using the batch tests results. The results showed rapid decreases in the chemical oxygen demand (COD) and nitrate concentrations and gradual increases in the nitrite concentrations in the first four hours, then remained constant from 4 to 8 h. Anoxic reduction factor (ηNO3 and ηNO2) and half saturation constant (KS1 and KS2) were calibrated at 0.097, 0.13, 89.28 mg COD/L, and 102.29 mg COD/L, respectively. Whereas the simulation results demonstrated that the increase in carbon-to-nitrogen (C/N) ratios and the reduction in XH contributed to the increase in the nitrite transformation rate. This model provides potential strategies for optimizing the PD/A process.

Evaluation of Electric Power Quality in the Ship-Integrated Electrical Power System with a Main DC Bus and Power Semiconductor Electric Drives as Part of the Electric Propulsion Complex

The relevance of the work is connected to the energy efficiency of specialized vessels of the technical fleet. The purpose of the study was to determine and evaluate the power quality indicators associated with the non-sinusoidal shape of the voltage and current curves in the electrical power system of the marine platform support vessel, which contains powerful semiconductor propulsion electric drives, taking into account the inherent and parasitic parameters of the power three-phase cable lines. A simplified one-line diagram of an electric power system with a DC main bus was the object of the study, which was compiled as a result of the analysis of analog systems typical for the indicated type of vessels. The phenomenon of voltage and current distortion caused by the presence of higher harmonics generated by power semiconductor converters in a three-phase ship network was the subject of the research. For the experimental study of the quality of electric power according to the simplified one-line scheme of the electric power system in MATLAB Simulink, its model was created. Based on the proven methods of calculating the ship’s electrical equipment, a methodology was developed for the reliable determination of model parameters. According to the results of the experiment in MATLAB Simulink, qualitative and quantitative indicators were obtained regarding the non-sinusoidality of the linear voltage and current of the three-phase network (curve shapes, amplitude spectra, distortion coefficients), and their comparative analysis with the current norms and standards was performed. In contrast to the previous ones, the methodology for assessing the quality of electricity in the studied electric power system takes into account its circuit, mode features, and the presence of a parasitic capacitance “phase to ground” of a three-phase network, and it can be used in solving similar non-trivial tasks for various similar structurally modified systems.

A micromechanical approach to TPU mechanical properties: Framework and experimental validation

Mechanistic understanding of the mechanical properties of elastomeric thermoplastic polyurethane (TPU) rely on the hard segment content (HSC) as the sole predictor of mechanical properties for a given chemistry, regarding the TPU morphology as a nanocomposite of hard domains in a softer matrix. In order to go beyond this binary view of TPU structure-property relationships, the behavior of the soft segment-rich domain and the interphase between the hard and soft domains should be given more consideration. This work explores the application of several common micromechanical models to a series of TPUs with increasing HSC. A framework to determine model parameters (phase volume and modulus) for TPUs in general was developed. The ability of the model to capture the reinforcement behavior was evaluated in the context of the experimentally determined mechanical properties and morphology. An Eshelby double inclusion (EDI) model was used to compare two and three phase models. The soft matrix modulus, Em, was used as the fitting parameter to match the experimental Young's modulus for a series of TPUs with increasing HSC. The procedure reveals that Em must also increase as a function of HSC to realize the degree of reinforcement found experimentally. The design of TPU for specific applications would greatly benefit from the ability to predict stress-strain behavior from models which represent the complex TPU morphology.

SENSITIVITY ANALYSIS AND IMPACT OF AN IMPERFECT VACCINE OF TWO STRAINS OF HEPATITIS B VIRUS INFECTION

A mathematical model considering two strains of hepatitis B virus (HBV) chronic carriers, to assess the impact of dose-structured imperfect vaccine, in a population, is designed and analyzed. The model is shown to have a locally and globally asymptotically stable disease-free equilibrium (DFE) whenever its associated reproduction number is numerically less than unity. Numerical analysis of the model shows that with the expected 50% minimum efficacy of the first vaccine dose, vaccinating 55% of the susceptible population with the first vaccine dose will be sufficient to effectively control the spread of hepatitis B infection. Such effective control can also be achieved if 50% of the first vaccine dose recipients take the second dose. Threshold analysis reveals that an imperfect HBV vaccine should have positive or negative population-level effect. Latin hypercube sampling–PRCC analysis illustrates that disease transmission rate, birth rate, natural death rate and proportion of children born with maternal immunity are most influential parameters in the disease dynamics. In this paper, the sensitivity analysis based on mathematical and in addition statistical techniques have been performed to determine the significance of the model parameters. It is observed that a number of the parameters play an important role to determine the magnitude of the basic reproduction number. Sensitivity analysis is achieved to determine model parameters’ importance in disease dynamics. It is observed that the reproduction number is the most responsive quantity to the potent transmission rate of HBV and in addition also vital to control the spread of the disease.

The impact of polarity score on real option valuation for multistage projects

In most cases, the valuation of the investments characterized by various stages with a high level of uncertainty is done through the compound real option valuation (ROV). This decision making support can consider various types of uncertainty that can affects these investment phases, such as that linked to technology. Specifically, within the category of uncertain investments there are the broadband opportunities that can be valued as real options in order to quantify the risks associated with the investment. However, since ROV theory has no definitive way to determine model parameters based on market information, we propose one that can adjust them dynamically. In this paper, to include this aspect in the project valuation, we have unified the ROV with the sentiment analysis, a natural language processing technique that allows us to quantify the polarity of expressions in natural language numerically. In particular, the inherent risks related to the different phases of the project can be extracted from the information present in the surrounding environment and published in newspapers. From there, we obtain a sentiment score which, through appropriate manipulations, manages to modify the evaluation of the success probabilities of each stage. Then, we embed these success probabilities in the ROV in order to provide a valuation methodology that includes the impact of information on the investment decision.