Determination Of Model Parameters Research Articles

Numerical investigations on constitutive model parameters of HRB400 and HTRB600 steel bars based on tensile and fatigue tests

Abstract In recent years, HRB400 and HTRB600 steel bars have become the mainstream standard reinforcing steel used in concrete structures in China. However, significant controversy still exists regarding the selection of material constitutive models and the determination of model parameters for buckling, fatigue, hysteresis, and other material characteristics. In this article, an automated process of multi-parameter calculation of the constitutive model for reinforcing steel – simulation accuracy evaluation of the constitutive model – selection of the constitutive model of reinforcing steel is established based on the hybrid programming method using MATLAB and OpenSees software. First, tensile and low-cycle fatigue tests were carried out on HRB400 and HTRB600 steel bars. Second, based on the constitutive model in OpenSees software and the skeleton curve and characteristics such as yielding, fatigue, and hysteresis, the constitutive model parameters of HRB400 and HTRB600 steel bars are determined using indirect and direct fitting methods. Finally, the five similarity parameters of the simulated normalized cumulative hysteretic energy dissipation coefficient are compared with the test results. The results indicate that the simulation accuracy of the Reinforcing Steel model exceeds 72%, which is higher than other four models, making it the best choice for reinforcing steel in numerical simulation.

Determination of progressive damage modelling parameters and calibration studies of 8-harness satin weave S-2 glass fibre reinforced composites

The aim of this article is to present a progressive damage modelling study of 8-harness satin weave S-2 glass fibre-reinforced SC15 epoxy matrix composites. Extensive mechanical properties of the composite were investigated throughout the study. The material was subjected to low-velocity impact (LVI) loading for investigating progressive damage. MAT162 material model in Ls-Dyna commercial finite element software was chosen for modelling damage that occurred in composite structure at the end of LVI test. MAT162 needs different material properties including elastic and strength properties with additional damage parameters. Different standard and non-standard tests were conducted to obtain these properties. Additionally, numerical simulation studies were run to calibrate these material properties. Finally, a set of calibrated material properties of 8HS-S2G/SC15 composite were obtained.

Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

Determination of the two-compartment model parameters of exhaled HCN by fast negative photoionization mass spectrometry

Breath exhaled hydrogen cyanide (HCN) has been identified to be associated with several respiratory diseases. Accurately distinguishing the concentration and release rate of different HCN sources is of great value in clinical research. However, there are still significant challenges due to the high adsorption and low concentration characteristics of exhaled HCN. In this study, a two-compartment kinetic model method based on negative photoionization mass spectrometry was developed to simultaneously determine the kinetic parameters including concentrations and release rates in the airways and alveoli. The influences of the sampling line diameter, length, and temperature on the response time of the sampling system were studied and optimized, achieving a response time of 0.2 s. The negative influence of oral cavity-released HCN was reduced by employing a strategy based on anatomical lung volume calculation. The calibration for HCN in the dynamic range of 0.5–100 ppbv and limit of detection (LOD) at 0.3 ppbv were achieved. Subsequently, the experiments of smoking, short-term passive smoking, and intake of bitter almonds were performed to examine the influences of endogenous and exogenous factors on the dynamic parameters of the model method. The results indicate that compared with steady-state concentration measurements, the kinetic parameters obtained using this model method can accurately and significantly reflect the changes in different HCN sources, highlighting its potential for HCN-related disease research.

Study of hydro-mechanical behaviours of rough rock fracture with shear dilatancy and asperities using shear-flow model

The geometric properties of fracture surfaces significantly influence shear-seepage in rock fractures, introducing complexities to fracture modelling. The present study focuses on the hydro-mechanical behaviours of rough rock fractures during shear-seepage processes to reveal how dilatancy and fracture asperities affect these phenomena. To achieve this, an improved shear-flow model (SFM) is proposed with the incorporation of dilatancy effect and asperities. In particular, shear dilatancy is accounted for in both the elastic and plastic stages, in contrast to some existing models that only consider it in the elastic stage. Depending on the computation approaches for the peak dilatancy angle, three different versions of the SFM are derived based on Mohr-Coulomb, joint roughness coefficient-joint compressive strength (JRC-JCS), and Grasselli's theories. Notably, this is a new attempt that utilizes Grasselli's model in shear-seepage analysis. An advanced parameter optimization method is introduced to accurately determine model parameters, addressing the issue of local optima inherent in some conventional methods. Then, model performance is evaluated against existing experimental results. The findings demonstrate that the SFM effectively reproduces the shear-seepage characteristics of rock fracture across a wide range of stress levels. Further sensitivity analysis reveals how dilatancy and asperity affect hydraulic properties. The relation between hydro-mechanical properties (dilatancy displacement and hydraulic conductivity) and asperity parameters is analysed. Several profound understandings of the shear-seepage process are obtained by exploring the phenomenon under various conditions.

An automated system for determining soil parameters: Case study

The success of numerical analysis depends on several factors. One of the keys factors is the accurate determination of constitutive model parameters. Determining these parameters from in-situ tests has several advantages compared to laboratory tests, from lower costs to minimal disturbance of the soil. However, it is not possible to derive soil parameters directly from in-situ tests results, since correlations are required. The literature offers a wide range of correlations which increases the uncertainty during interpretation. The ongoing research project APD (Automated Parameter Determination) investigates the possibilities of automated parameter identification from in-situ tests using a graph-based approach. In the present paper, existing correlations – developed for cone penetration tests - are validated by comparing their output to laboratory results. The Norwegian GeoTest Sites (NGTS) infrastructure project consists of five tests sites in different soils in Norway. The data from the soft clay site located in Onsoy, south-eastern Norway was used in the validation process. A web-based application “Datamap” that has been developed to capture, organize, and classify geotechnical research data has been used to obtain in-situ and laboratory tests data. The further validation of existing correlations to derive accurate constitutive parameters from in-situ tests is part of ongoing research.

Modeling initiation and propagation of thermal runaway in pouch Li-ion battery cells: Effects of heating rate and state-of-charge

The implementation of lithium-ion batteries (LIB) in battery energy storage systems (BESS) for utility-scale renewable energy generation has led to increased fire and explosion hazards. These hazards are attributable to the heat and flammable gases released during LIB thermal runaway (TR). TR can be triggered within an individual LIB cell and cause intra-cell TR propagation (TRP), the energy release from which causes TR in adjacent cells, leading to inter-cell TRP. Safety assessments are critical to preventing such incidents, and numerical modeling is a key tool in developing such assessments. This work presents an inverse modeling approach utilizing measurements of cell-level experiments of large-format 63Ah pouch cells used in BESS. Such an approach is a practical alternative to existing approaches that rely on a detailed thermochemical characterization of LIB internal components which are seldom available when dealing with commercial batteries encountered in BESS. Owing to the critical role played by gas and solid venting for external flaming and inter-cell TRP, we develop an approach to capture these quantities in the model. Two key parameters affecting TRP are the state-of-charge (SOC) and heating rate. The former provides some control of the system hazard, the latter affects TR behavior of LIB cells subject to different levels of preheating often encountered in BESS fires. We develop the capability to capture the effects of these parameters in the model. To determine model parameters, we rely on existing single-cell experimental data at different heating rates, and augment this with new experiments for the same type of cells at different SOCs. The modeling results recover the experimental TR and intra-cell TRP behavior quantitatively. Analysis reveals that, at lower heating rates, the temperature is greater at the time of the onset of TR, but the location is farther away from the heated cell surface; its location and the greater internal temperature then facilitate faster intra-cell TRP. When SOC is lower, the reduced internal heat release delays the onset of TR, and we find that its location moves away from the heated surface and intra-cell TRP is slower. Our findings provide physical insights to TR initiation and intra-cell propagation in support of large-scale TRP model development.

A data-driven approach for linking models of large-scale bridges and monitoring data

Predictive maintenance of large-scale structures such as bridges requires precise numerical models to describe their current condition. Typically, solving an inverse problem is necessary to determine model parameters from Structural Health Monitoring (SHM) data. However, conventional methods such as Finite Element Updating through optimization algorithms demand substantial computational resources, as numerous parameter combinations need to be assessed to identify the optimal model state in each calibration step. Consequently, these methods are only partially suitable for creating digital twins of bridges.This paper introduces an alternative approach by treating the inverse problem as a model parameter classification problem. This involves establishing a model database that covers a wide range of damage states. Notably, this method eliminates the need for multiple simulations during the application phase, as simulations are performed only once in an offline context. Subsequently, a classification algorithm is trained based on this database, enabling real-time selection of the best-fit model for practical applications using SHM data, without the necessity for additional simulations.Transparency in algorithm decisions is crucial for infrastructure maintenance, therefore, optimal classification trees from the field of interpretable machine learning are employed. Decision trees offer a balance between high accuracy and interpretability while providing additional advantages, such as sensor placement evaluation. In summary, this approach demonstrates the potential for linking numerical models with SHM data through the application of interpretable machine learning techniques, facilitating real-time decision-making for the preservation and management of critical infrastructure.

An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online parameter identification. An adaptive radial basis correction-differential support vector machine model is constructed to correct the state of charge value by considering the dynamic characteristic parameters. It greatly reduces estimation error and noise, while monitoring the critical conditions for safe and reliable online battery operation. The estimation effects of the proposed model are verified under hybrid pulse power characterization and dynamic stress test working conditions. The maximum error values obtained are 0.037 % and 0.336 %, respectively, thus achieving high-precision estimation. The proposed method is adaptive to real-time battery management applications, laying a foundation for robust state estimation of lithium-ion batteries used in urban transportation electric vehicles.

Analysis of Transmission Line Models for PEMFC Impedance Modelling

The behaviour of Polymer Electrolyte Membrane Fuel Cells (PEMFC) shows a complex, non-linear dependence on material properties and operation conditions. Therefore, modelling and model parameterization by electrochemical characterization of PEMFCs is challenging. To deconvolute the different loss mechanisms in PEMFCs, impedance spectra are recorded and modelled by equivalent circuit models. Transmission Line Models (TLMs) are a well-established, physicochemical approach for modelling the impedance of PEMFC electrodes. TLMs consider the processes occurring in an electrode and the model parameters are directly related to material and microstructural characteristics. PEM electrodes have inhomogeneous distributions of the material, the current density, stored water, etc. Assuming a homogeneous electrode with homogeneous current distribution, the basic TLM shown in [1] turned during impedance fitting out to be inaccurate in operation conditions where these assumptions do not hold.To increase the fit accuracy, literature proposes a number of adapted TLMs. These TLMs have additional components and therefore degrees of freedom. This enables these models to take additional processes and the microstructure into consideration.In this contribution an incremental 1 cm² PEMFC ensuring zero gradient operation is characterized by means of impedance spectroscopy. Different operating conditions are varied within wide ranges and additional characterization methods, like H2/N2 operation were applied.Different TLMs proposed in literature are evaluated by fitting them to the measured spectra with and without considering the information gained from additional measurements. The DRT was applied as additional criteria in the fitting.In general, the accuracy of a fit increased with the number of free fitting parameters. Thus more complex TLMs accounting for additional physicochemical processes provide a lower error in the fit. Nevertheless the physicochemical meaningfulness of the determined model parameters is sometimes uncertain. It will be shown that for a meaningful parameterization by CNLS-fitting such TLMs require additional tests and characterization methods.[1] Heinzmann Marcel; Weber André; Ivers-Tiffée Ellen. „Impedance modelling of porous electorde structures in polymer electrolyte membrane fuel cells“. In: Journal of Power Sources (2019).

Entropy scaling framework for transport properties using molecular-based equations of state

Modeling transport properties is important for many applications. In this work, an entropy scaling framework for modeling transport properties using molecular-based equations of state (EOS) is presented. It can be applied for modeling the viscosity, thermal conductivity, and self-diffusion coefficients. The framework is formulated in a general way such that it can be coupled with various EOS. It is demonstrated that the model, when coupled to molecular-based EOS, provides not only good descriptions of existing data but also reasonable predictions in a wide range of states covering liquid, gaseous, supercritical, and meta-stable regions. Moreover, the model can be used for reliably predicting transport properties of mixtures. The universal parameters of the model were fitted to computer experiment data of the Lennard-Jones fluid. This procedure provides inherently a robust form of the basic scaling function. Thereby, only few data points are required for the determination of the component-specific model parameters. The applicability of the developed framework is demonstrated for model fluids as well as for a wide variety of real substances including non-polar, polar, and associating pure fluids and mixtures.

Exploiting fucoxanthin mono-carrier nanoparticles to modulate digestion and metabolic regulation in an obesity model

Fucoxanthin (FX), a natural compound derived from marine algae, and FX-based nanoparticles (FZ) were investigated for their potential in regulating glucose and lipid metabolism. Gavage interventions with FX, FZ, and zein hydrolysate (ZH) were administered to obese mice, resulting in significant outcomes. These interventions led to variations in growth rates, reduced blood glucose levels, improved lipid profiles, and ameliorated liver pathology. Our mRNA expression analysis revealed modulation of key genes involved in glucose metabolism. Gut analysis, including short-chain fatty acid detection and 16S rRNA gene sequencing, provided insights into gut microbiota modulation. Notably, differences observed in vivo between FX and FZ were attributed to their distinct release rates during in vitro digestion in the stomach and intestine. This comprehensive study validates our methods, modeling, and metabolic parameter determination, reinforcing the significance of our results. In conclusion, our findings highlight the promising potential of FX-encapsulated interventions in modulating metabolic pathways. Furthermore, they emphasize the crucial role of gut microbiota dynamics in the strategic mitigation of disorders associated with obesity.

Characterization of Viscoelastic Properties Considering the Nonrelaxation for Filled Rubber

The influence of nonrelaxation on the Payne effect of carbon black-filled rubber is studied. The prestrain is introduced in the Kraus model in the form of exponential growth. Combined with studies of temperature correlations, an explicit model for predicting the Payne effect at different prestrains and temperatures is developed. Dynamic mechanical analyses are performed to determine model parameters and validate the proposed model. To further verify the proposed model, the heat buildup of rubber columns under dynamic tensile load is tested and simulated. The comparison between simulated and measured data shows that the simulation considering nonrelaxation is more accurate than without considering. With the increase of prestrain, the accuracy of considering nonrelaxation becomes more obvious.

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.