Parameter Identification Procedure Research Articles

Surrogate analytical models of damage localization in metal matrix composites

Several parameters entering the constitutive laws of metal matrix composites are not amenable to direct measurement. Their determination is therefore demanded to indirect identification procedures, based on the comparison between the output of some experimental works and the results of the iterative simulation of the performed tests. Largely repetitive numerical analyses can also support the optimized design of composite microstructures, the understanding of the actual stress-transfer mechanisms, and virtual testing. Surrogate analytical models can replace effectively non-linear finite element (FE) computations in parametric studies and identification procedures, reducing execution times and costs without compromising the accuracy of the results. In this context, a frequently used approach consists of the interpolation, by means of radial basis functions (RBFs), of data filtered by proper orthogonal decomposition (POD). This methodology is often used to reproduce the smooth output of different mechanical systems. This work is rather focused on the homogenized response of damaging metal matrix composites subjected to strain localization phenomena, and explores the accuracy achievable in these situations by the POD-RBF approximation of more traditional FE analyses. In all considered cases, the POD-RBF results are accurate, and able to distinguish between apparently similar situations. The approach is flexible, and the performance of the surrogate models can be tailored to the requirements of each application. In particular, various analytical approximations can be introduced to support the design of new microstructures and material couplings, and to understand the role of any material and/or geometry imperfections resulting from the production processes.

Design and testing of a decision tree algorithm for early failure detection in steel truss bridges

This paper develops a methodology for damage identification in steel truss bridges that uses vibration-based monitoring data and a model-based decision tree algorithm. The methodology resorts to a calibrated FE model with an optimization-based parameter identification procedure to simulate and analyze all the potential damages that might affect the structure. The effect of environmental conditions on the modal parameters is also accounted for, which is modeled as structure stiffness variations using the Young’s modulus and forecasted using a surrogate modeling strategy. The feasibility of the methodology is demonstrated on a full-scale bridge in Vilagarcía de Arousa, Spain. The underlying hypotheses used in the algorithm implementation were validated, and the error ponderation and selection bound employed to detect and identify damage were optimized. The results show an average success rate of 95.0% and an average false positive rate of 1.0% in identifying damage indicating its robustness to be extrapolated to other case studies.

Efficient identification of material parameters for an orthotropic hyperelastic nonlinear material model for textile membranes

A recently proposed orthotropic nonlinear material model for general classes of woven fabrics is adapted to glass-PTFE and PES-PVC fabrics. The nonlinear model contains three governing stiffness-related material properties that will be shown to be separately and uniquely identified using a new optimization procedure. The parameter identification procedure is based on the separation into nonlinear model and linear material parameters. In a first step, the model parameters are optimized by minimizing a least-square functional which measures variations of the material parameters identified for each deformation state independently. Once the optimized model parameters are determined, the associated stiffness-related material parameters are uniquely identified. As a result, a highly efficient method is obtained, which allows for the independent identification of model and material parameters. The model parameters are shown to be kept fixed for a specific class of textile membrane materials and only the stiffness-related properties need to be adjusted for a new production batch or a new material from this class. By comparing differently identified parameters, the performance of the proposed procedure is analyzed in a numerical simulation of a roof structure.

Parameter identification and uncertainty quantification of the thermal conductivity tensor for transversely isotropic composite materials

AbstractThe identification of material parameters with a nonlinear least‐squares approach and finite elements is an established task in solid mechanics. There, full‐field experimental data can be employed in the material parameter identification procedure. The focus of this contribution is on the application of full‐field temperature data, measured with infrared thermography, to identify the coefficients of the thermal conductivity tensor of unidirectional fiber‐reinforced composites. Because of the unidirectional fiber reinforcement, transversely isotropic thermal behavior is assumed and the corresponding two thermal conductivities are identified from full‐field data. Beforehand performing the experiments, numerical re‐identifications are used to optimize the experimental procedure in a sensitivity analysis. Further, the uncertainties of the identified material parameters as well as the uncertainties of subsequent simulations are computed with the Gaussian error propagation concept.

A method for determining the ductile damage parameters of high strength steels and weld metal

The microvoid based Gurson-Tvergaard-Needleman (GTN) model is a powerful tool for predicting ductile fracture behavior, and application of such model to steels and welds needs the identification of microvoid related damage parameters. Currently, there is no standard damage parameter identification method available. In this study the previously proposed complete Gurson model (CGM) where a physical void coalescence mechanism is incorporated into the GTN model is revisited. According to the CGM, the void nucleation process dictates ductile fracture. By adopting the cluster nucleation model with an effective initial void volume fraction as the only controlling parameter, a method is proposed to explicitly determine the effective initial void volume fraction from the strain at maximum load and strain at fracture of a specially designed notched tensile specimen. The proposed equation has been experimentally verified by applying to three high strength materials, including a X80 pipeline steel and associated weld metal, and a 15CrMo steel. A general procedure for damage parameter identification is also suggested. It is argued that the obtained effective initial void volume fraction can be treated as a type of material ductility indicator.

A non-iterative parameter identification procedure for the non-local Gurson–Tvergaard–Needleman model based on standardized experiments

Damage mechanics models exhibit favorable properties such as the intrinsic influence of stress triaxiality on damage evolution and the prediction of crack initiation as well as propagation leading to structural failure. However, their application requires advanced expertise hindering the transfer of these models into industrial practice, especially since the parameter calibration is a key obstacle. In this paper, a simplified procedure is proposed for a non-local extension of the Gurson–Tvergaard–Needleman model (GTN), which is a highly accepted model for ductile failure of metals. The procedure is iteration free and requires experimental input data from only two standardized tests. The parameters are determined using look-up diagrams created on the basis of systematic simulations and made available for different material behavior covering the majority of ductile metals. Benchmark tests for three different steels are conducted to evaluate the robustness of the proposed procedure. The reliability of the GTN model is validated for all investigated materials.

Shelf Life Prediction of Picric Acid via Model-Based Kinetic Analysis of Its Thermal Decomposition.

A priori knowledge of the shelf life of energetic materials (EMs) is relevant due to its direct association with safety and functionality. This paper proposes a quick and reliable approach to predicting the shelf life of EMs whose thermal decomposition is an autocatalytic process once their failure threshold has been defined as a function of the limiting extent of conversion. This approach is based on the assumption of a kinetic law consistent with the autocatalytic behavior and on the subsequent extraction, via a suitable procedure of parameter identification, of the kinetics of thermal decomposition from differential scanning calorimetry (DSC) data gathered under dynamic conditions at three different heating rates. Its reliability is proven for picric acid (PA) through the comparison of kinetic predictions with evaluations of conversion obtained by using high performance liquid chromatography (HPLC) analysis for samples subjected to isothermal and non-isothermal accelerated aging tests, as well as for a sample of naturally aged material, i.e., PA, stored at room temperature for more than 10 years.

Development and Experimental Validation of Novel Thevenin-Based Hysteretic Models for Li-Po Battery Packs Employed in Fixed-Wing UAVs

Lithium batteries employed in lightweight fixed-wing UAVs are required to operate with large temperature variations and, especially for the emerging applications in hybrid propulsion systems, with relevant transient loads. The detailed dynamic modelling of battery packs is thus of paramount importance to verify the feasibility of innovative hybrid systems, as well as to support the design of battery management systems for safety/reliability enhancement. This paper deals with the development of a generalised approach for the dynamic modelling of battery packs via Thevenin circuits with modular hysteretic elements (open circuit voltage, internal resistance, RC grids). The model takes into account the parameters’ dependency on the state of charge, temperature, and both the amplitude and sign of the current load. As a relevant case study, the modelling approach is here applied to the Li-Po battery pack (1850 mAh, 6 cells, 22.2 V) employed in the lightweight fixed-wing UAV Rapier X-25 developed by Sky Eye Systems (Cascina, Italy). The procedure for parameter identification with experimental measurements, obtained at different temperatures and current loads, is firstly presented, and then the battery model is verified by simulating an entire Hybrid Pulse Power Characterisation test campaign. Finally, the model is used to evaluate the battery performance within the altitude (i.e., temperature) envelope of the reference UAV. The experiments demonstrate the relevant hysteretic behaviour of the characteristic relaxation times, and this phenomenon is here modelled by inserting Bouc–Wen hysteresis models on RC grid capacitances. The maximum relative error in the terminal output voltage of the battery is smaller than 1% for any value of state of charge greater than 10%.

Hydraulic-based testing and material modelling to investigate uniaxial compression of thermoset and thermoplastic polymers in quasistatic-to-dynamic regime

The strain rate dependence of thermoplastics and thermosets in the range 10-3–102 s−1 is still unclear due to experimental difficulties. This interval cannot be fully treated using a quasistatic-based electromechanical machine nor a high-speed test relying on the Kolsky bar method. This paper presents a uniaxial compression test set-up to cover the aforementioned range providing robust stress–strain measurements up to 100 s−1. This set-up is assessed at room temperature with a semi-crystalline polymer (SCP) polyether-ether-ketone (PEEK) KT880-NT and a toughened thermoset epoxy PR520. High-speed 3D stereo digital image correlation (DIC) and an infra-red camera are used for data acquisition. The macroscopical true stress–strain curves demonstrate the importance of the thermal softening effect taking place in this range, accompanied with a temperature rise. In terms of characterization, an advanced unified SCP (USCP) model is employed to quantify the rate- and temperature-dependence for both polymers. A straightforward FEM-based parameter identification procedure is proposed requiring only two compressive stress–strain curves. The USCP model is further applied to PEEK 450G from literature to highlight the features of SCPs. The comparison of PEEK and PR520 is discussed pointing out the characterization challenges due to the transitional quasistatic-to-dynamic testing regime.

Two-stage parametric identification procedure to predict satellite orbital motion

<span>The paper presents a new step-by-step procedure for constructing a navigation satellite motion model. At the first stage of the procedure, the parameters of the radiation pressure model are estimated using the maximum likelihood method. The statistic estimator based on the continuous-discrete adaptive unscented Kalman filter is proposed for the solar radiation model parameters estimation. Step-by-step scheme of filtering algorithm used for the software development are given. At the second stage, the parameters of the unaccounted perturbations model are estimated based on the results of residual differences measurements. The obtained results lead to significant improvement of prediction quality of the satellite trajectory.</span>

A simplified intragranular description of dislocation density heterogeneities to improve dynamically recrystallized grain size predictions

In current mesoscopic mean-field and full-field models, nucleation of recrystallized grains remains poorly described compared to the physical complexity of the phenomenon, leading to noticeable discrepancy with regards to the size of recrystallized grains. In the present work, a simplified intragranular description of dislocation density heterogeneities is introduced in a recrystallization model in order to improve recrystallized grain size predictions. A detailed material-dependent model parameters identification procedure is provided and discussed. Experimental microstructure evolutions of an Inconel 718 Ni-base superalloy are compared to the model predictions as a validation case.

Simulations of new COVID-19 pandemic waves in Ukraine and in the world by generalized SIR model

New waves of the COVID-19 pandemic in Ukraine, which began in the summer of 2021, and after holidays in the middle of October 2021, were characterized by the almost exponential growth of smoothed daily numbers of new cases. This is a matter of great concern and the need to immediately predict the epidemic dynamics in order to assess the maximum possible values of new cases, the risk of infection, and the number of deaths. The generalized SIR-model and corresponding parameter identification procedure were used to simulate and predict the dynamics of two new epidemic waves in Ukraine and one worldwide. Results of calculations show that new cases in Ukraine will not stop appearing before November 2022. The pandemic can continue for another ten years if the global situation with vaccination, testing, and treatment does not change.

Identification of Dynamic Parameters and Frequency Response Properties of Active Hydraulic Mount with Oscillating Coil Actuator: Theory and Experiment

Active hydraulic mounts (AHMs) provide an effective solution for refining ride comfort noise and vibration in passenger cars. AHMs with inertia tracks, decoupler membranes, and oscillating coil actuators (AHM-IT-DM-OCAs) have been extensively studied owing to their compact structures and strong damping characteristics in the low-frequency band. This study focuses on the full parameter identification based on the distinct features of external dynamics, which can be used to obtain an accurate and reliable estimate of the transfer functions required for active control algorithms. A lumped parameter model was established for the AHM-IT-DM-OCAs, and the analytical frequency bands were defined by the two resonance frequencies of the fluid channel and actuator mover. Methods for nonlinear model simplification were proposed in different bands and verified theoretically. Based on the simplified models, the distinct features of the active and passive dynamics are successively revealed, which include three resonances and seven horizontal segments. Subsequently, a series of experimental studies on the distinct features were carried out, which agreed well with the theoretical results. However, owing to the limitations of the test equipment and fixture modalities, only the distinct features of one fixed point, two resonance peaks, and three horizontal segments can be used for parameter identification. Based on the validated distinct features, a procedure for full parameter identification is proposed, and all six key parameters are identified. The obtained results showed good consistency and rationality, indicating that this approach can be used for the transfer function estimation of the primary and secondary paths of the AHM-IT-DM-OCAs.

LaSDI: Parametric Latent Space Dynamics Identification

Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantification, and other various decision-making applications. This paper presents a data-driven framework for parametric latent space dynamics identification procedure that enables fast and accurate simulations. The parametric model is achieved by building a set of local latent space model and designing an interaction among them. An individual local latent space dynamics model achieves accurate solution in a trust region. By letting the set of trust region to cover the whole parameter space, our model shows an increase in accuracy with an increase in training data. We introduce two different types of interaction mechanisms, i.e., point-wise and region-based approach. Both linear and nonlinear data compression techniques are used. We illustrate the framework of Latent Space Dynamics Identification (LaSDI) enable a fast and accurate solution process on various partial differential equations, i.e., Burgers’ equations, radial advection problem, and nonlinear heat conduction problem, achieving O(100)x speed-up and O(1)% relative error with respect to the corresponding full order models.

Open-source tool for battery module simulation and design

Electric and hybrid vehicles are two of the most promising solutions to meet the new emission requirements of the transportation sector. The energy storage system is their most critical component in terms of performance. Therefore, battery modeling plays an important role for designing and controlling battery modules. Up to now, the design of battery modules is conditioned by the use of expensive tools that involve long simulation times; in response to this, the present work introduces an open-source tool developed in Python to study the performance of battery modules and contribute to their design. To show the scope and use of the tool, a theoretical case study is presented. In particular, the effect of slight differences in cells behavior, due to manufacturing process or aging, is shown at the module level. A SOC difference of 2.5%, due to current imbalances, was obtained among the cells after discharge for a small battery module with new cells. This result points out the importance of accurate and fast module models to correctly predict the remaining travel range and the need for an online parameters identification procedure. In addition, the temperature distribution in the module along with the heat generated by the cells, also estimated by the tool, can be used for proper design and control of the battery cooling system.

Parameter Identification and Modeling of Hardening to Preserve Identical Predictions under Monotonic Loading

Advanced hardening models accurately describe the transient plastic behavior (reyielding, stagnation, resumption...) after various strain-path changes (reverse, orthogonal...). However, a common drawback of these models is that they usually predict monotonic loading with lower accuracy than the regular isotropic hardening models. Consequently, the finite element predictions using these models may sometimes lose in accuracy, in spite of their tremendous theoretical superiority. This drawback has been eliminated in the literature for the Chaboche isotropic-kinematic hardening model. In this work, a generic approach is proposed for advanced hardening models. Arbitrary models could be successfully compensated to preserve rigorously identical predictions under monotonic loading. A physically-based model involving a 4th order tensor and two 2nd order tensors was used for the demonstration. The parameter identification procedure was greatly simplified by rigorously decoupling the identification of isotropic hardening parameters from the other parameters.

Using Reduced Basis Approximation for Efficient Surrogate-Based Inverse Identification of Thermo-Hydro-Mechanical Parameters from an In Situ Heating Test

In this paper, a reduced-order model built from the reduced basis (RB) method is used as a surrogate for an inverse identification problem related to coupled thermo-hydro-mechanical (THM) processes. The RB space—spanned by solutions of the governing THM equations—is constructed using a greedy adaptive procedure guided by an a posteriori error estimator that selects the optimal snapshot points in a given parametric space. The RB model is assessed in terms of accuracy and computational cost reduction for the three-dimensional transient coupled problem described by the ATLAS III small-scale in situ heating test. The substantial system size reduction and the associated significant computational gain result in a surrogate model suitable for parameter identification procedures in the Boom Clay material. The effectiveness of the proposed strategy is demonstrated by performing inverse analysis based on direct-search and genetic algorithm (GA) optimization supported by real sensor measurement data where 800 times faster computational speed-up was achieved.

IDENTIFICATION OF PARAMETERS FOR CHARACTERIZATION AT HIGH-TEMPERATURE OF 38MnVS6 STEEL USED IN HOT FORGING PROCESSES

Finite element reliability is highly sensitive to the input data quality and constitutive equations used. In this article, two constitutive equations for modeling the plastic flow of metals are used. Two equations commonly employed in metal forming finite element analysis were selected, e.g., the Hensel-Spittel and Johnson-Cook. Compression tests were conducted at various temperatures and strain rates to obtain the plastic flow stress curves. These experimental results were used as a target to fit the constitutive equation parameters. The parameter identification procedure was conducted by solving the so-called inverse problem. An objective function gives a value (error) that indicates how well a parameter set makes the equation fits the experimental results. This error value is minimized by applying optimization techniques; in this case, by using a Genetic Algorithm followed by a gradient-based strategy. Two objective functions are used; the first is based on the well-established least square approach, and the second is proposed. The proposed objective function shows a faster performance than the least square. In addition, the proposed objective function also gives the best fit for the Hensel-Spittel equation. The Johnson-Cook equation is fitted using only the proposed function. The Hensel-Spittel equation shows several local minimums, whereas the Johnson-Cook shows a global one. Keywords: Hot forging, Parameter identification, constitutive equations, finite element analysis.

Parameter identification of ductile timber beam-to-steel column connections: Procedure and assessment

Ductile beam–column connections in a moment-resistant timber frame are essential part that can affect the seismic performance of the timber frame. As the first step, this study presents a ductile timber beam-to-steel column joint connected by a steel link, herein designed as a box-shaped steel bracket. A robust unscented Kalman filter (UKF) algorithm was presented to identify the model parameters of the proposed the connection. In addition, a dual-scale assessment method is proposed to evaluate the accuracy of the identified results. UKF algorithm is limited to the problem of nonpositive definite matrices in high dimensional parameter identification. To prevent the interruption of the program and improve the stability of the program, the Cholesky decomposition in the UKF was superseded by singular value decomposition (SVD), a noise estimator was embedded into the UKF to self-adapt the noise covariance matrix during the iteration. The assessment was carried out for both the steel links and timber beam-to-steel column connections. Subsequently the framework was introduced for the parameter identification of the steel links. For the hysteretic curves, performance indices, and energy dissipation, there was good agreement between the experimental and numerical results. The steel bracket improves the ductility of the timber beam-to-steel column connection. The average ductility coefficient of the steel bracket with ribs can reach 7.11. The proposed parameter identification procedure is verified to be applicable in the field of ductile timber beam-to-steel column connections. The program stability is improved and the error of most of the performance parameters is within 10%. However, this method still need to be verified for dynamic analysis in the whole building.

A rate-dependent constitutive model predicting the double yield phenomenon, self-heating and thermal softening in semi-crystalline polymers

The double yield (DY) phenomenon observed in a wide variety of semi-crystalline polymers (SCP) adds difficulties in the material characterization. In this paper, a constitutive model, termed as explicit semi-crystalline polymer (ESCP) model, is proposed to study DY phenomenon as well as the rate- and temperature-dependent thermomechanical response below the glass transition temperature. The underlying yield kinetics due to the morphological changes of the spherulite micro-structure is represented by a rheological analogue described by a physically-based amorphous intermolecular resistance and a rate-independent crystalline interlamellar resistance. Independently-identified viscoelastic response and network resistance are also implemented to complete the model description. The activation and disclosure of the crystalline component depend on the saturated state of amorphous phase. The proposed model is validated against experimental data obtained from different authors for three commonly used SCPs: nylon 101, LDPE and PA6. A straightforward parameter identification procedure, requiring a minimum number of calibration tests, is presented to illustrate the model usage. The thermomechanical-coupled analyses provide satisfactory predictions using simulated models of a cylinder compression and dogbone tensile tests at different rates, where the self-heating and thermal softening effects are naturally captured by the model.