Determination Of Model Parameters Research Articles

Experiments on the Coal Measures Sandstone’s Dynamic Mechanical Parameter Characteristics under the Combined Action of Temperature and Dynamic Load

This paper conducts impact dynamics experiments on coal measures sandstone in a deep mine via the established dynamic load and temperature split Hopkinson pressure bar (SHPB) experimental system. Firstly, the experimental conditions for the impact dynamics of fine sandstone were determined, with temperatures of 18, 40, 60, 80, and 100 °C, an axial static load range of 1–9 MPa, and a preset bullet incidence velocity of 1.0–5.0 m/s. Secondly, based on the analysis of the basic parameters and physical composition, the dynamic stress and strain responses of fine sandstone under different experimental conditions were obtained, and the change mechanism of its dynamic mechanical process was theoretically analyzed. When the temperature rose from 18 °C to 100 °C, the dynamic peak stress of fine sandstone increased from 36.04 MPa to 73.41 MPa, with an increase of 103.7%. At a temperature of 40 °C, when the axial static load increased from 1 MPa to 9 MPa, the dynamic stress peak of fine sandstone increased from 57.25 MPa to 80.01 MPa, and the corresponding peak strain also showed an increasing trend. The experiment analyzed the variation characteristics of dynamic stress in fine sandstone under the combined action of different strain rates or bullet incidence velocities and different temperatures. In the strain rate range of 47.1 s−1 to 140.9 s−1, there was a significant strain rate effect on the dynamic peak stress and peak strain of fine sandstone, which increased with the increase of strain rate. The study found a polynomial relationship between the dynamic mechanical parameters of fine sandstone and the impact of experimental parameters, with a coefficient of determination greater than 0.9. A dynamic stress constitutive model for fine sandstone under one-dimensional stress state under dynamic load and temperature action was established, and the model validation and parameter determination of dynamic stress changes in fine sandstone under different temperature conditions were carried out. The research results provide a new experimental method for the static and dynamic mechanical analysis of coal and rock masses under complex geological conditions and can provide a basic reference for the prevention and control of dynamic disasters in deep mining processes.

In situ multi-perspective scanning of 3D particle deposition on flat plates with film cooling and determination of practical model parameters

A practical strategy for three-dimensional morphology measurement in the particle deposition process is of great importance to deepen the understanding of deposition physical processes. Due to the methodology limitations, it is difficult to obtain the formation process of deposition morphology. In this work, an in situ multi-perspective scanning (MPS) method using structured light was developed to capture the formation process of deposits. The point cloud registration algorithm was adopted to reconstruct the deposition morphology. Based on the validation results of Vernier caliper, the MPS method provides an uncertainty of ±0.056 mm at 4 mm. To verify the potential application of in situ measurement in gas turbine diagnostics, MPS method was preliminarily applied to the flat plate test with film cooling in particle-laden flow. The application demonstrated that this method successfully reconstructed the deposition morphology on a flat plate in the presence of film cooling. Furthermore, the obtained morphology results revealed that deposition roughness lied in the 0.35–0.56 mm and increased with an increase of blowing ratio. Additional comparisons with film cooling effectiveness showed the deposition distribution was related to the film cooling process. The detailed three-dimensional (3D) deposition morphology determined the mean curvature and mass of deposits, which may improve the prediction accuracy of deposition models in numerical simulations.

A generalized, computationally versatile plasticity model framework - Part I: Theory and verification focusing on tension‒compression asymmetry

Microstructural characteristics and complex loading conditions in the deformation of metallic materials lead to complexity in mechanical responses. In this study, we propose a generalized constitutive framework that reproduces the plastic anisotropy and asymmetry under various loading conditions. Particularly, the developed model can accurately capture distinctive flow stress, plastic flow and strain hardening between tensile and compressive dominant loadings under a wide range of stress states. The model is based on the stress triaxiality dependence of state variable (or weighting factor) newly incorporated in the existing plasticity theory to keep the computational efficiency and versatility. For example, the new generalized framework can be applied to widely employed Hill48, Yld2k-2d, and Poly6 as a class of associated flow rule-based yield functions, as well as Stoughton-Yoon2009 and Min2016 yield functions for the non-associated flow rule. Also, the model is adaptable when selecting the yield function under tension or compression, which efficiently controls the degree of accuracy in anisotropic modeling under tension and compression. The generalized plasticity framework is validated comprehensively by demonstrating the predictive capability for anisotropy in yield stress and plastic flow of metallic materials with different crystal structures. Moreover, the model can efficiently capture the continuous evolution of asymmetric yield surfaces as functions of strain, temperature, and strain rate. Finally, the identification procedure of the model is discussed by demonstrating the analytical determination of model parameters utilizing the experimental or generic material data obtained from various loading conditions such as tension, compression, plane strain loading, and pure shear.

A comprehensive parametrization approach for the Hubble parameter in scalar field dark energy models

This study proposes a novel parametrization approach for the dimensionless Hubble parameter i.e. E2(z)=A(z)+β(1+γB(z)) in the context of scalar field dark energy models. The parametrization is characterized by two functions, A(z) and B(z), carefully chosen to capture the behavior of the Hubble parameter at different redshifts. We explore the evolution of cosmological parameters, including the deceleration parameter, density parameter, and equation of state parameter. Observational data from Cosmic Chronometers (CC), Baryonic Acoustic Oscillations (BAO), and the Pantheon＋ datasets are analyzed using MCMC methodology to determine model parameters. The results are compared with the standard ΛCDM model using the Planck observations. Our approach provides a model-independent exploration of dark energy, contributing to a comprehensive understanding of late-time cosmic acceleration.

Experimental Study of Hardening Small Strain Model Parameters for Strata Typical of Zhengzhou and Their Application in Foundation Pit Engineering

The hardening small strain (HSS) model, which considers the non-linear characteristics of the shear modulus of soil in the small strain range, is commonly utilized in the numerical analysis of excavations in sensitive environments. In order to ascertain the parameters of an HSS model for strata typical of Zhengzhou, an extensive series of laboratory tests was executed. Subsequently, a statistical analysis was utilized to establish the relationships between the principal parameters. A three-dimensional finite element model was established based on the excavation of representative strata in Zhengzhou. The accuracy and applicability of the model parameters were verified using the field measurement data. In addition, an analysis of the sensitivity of the main HSS model parameters to the foundation pit deformation was also carried out. The results show that the HSS model can analyze excavation deformation with greater precision than the hardening soil (HS) model or the Mohr–Coulomb (MC) model. The small strain parameters G0ref and γ0.7 have a great influence on the horizontal displacement of the support and the vertical displacement of soil behind the wall, respectively. The research results serve as a reliable foundation for both the analysis of excavation deformation and the determination of HSS model parameters for strata typical of Zhengzhou.

An inverse method for automatic determination of material models for metal cutting based on multi-objective optimization

Cutting simulation is a crucial tool that enables engineers and operators to optimize machining processes virtually, before producing physical parts. The accuracy of these simulations relies heavily on validated models, encompassing both friction and material parameters. The prevalent technique for calibrating material models in cutting simulations is the inverse method. This state-of-the-art approach indirectly determines model parameters by comparing simulated outcomes with experimental data. However, the manual calibration process can be complex and time-consuming due to the intricacies of numerical simulation setups and the abundance of material model parameters. To address these challenges, this paper presents a novel fully-automated calibration approach utilizing multi-objective optimization algorithms. This approach integrates a modular design, simplifying the calibration process and enabling automatic calibration of any model parameters within cutting simulations. The approach has been successfully applied to calibrate the model parameters of AISI 1045 and X30CrMoN15-1 materials. Moreover, through a comparison of various optimization algorithms, this paper underscores the efficiency of the swarm optimizer in calibrating model parameters, particularly in scenarios with restricted computational resources.

Rooftop unit comparison calculator: a framework for comparing performance of rooftop units with building energy simulation

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. It also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles.

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.