Calibration Of Model Parameters Research Articles

A numerical study on the influence of grain boundary oxides on dwell fatigue crack growth of a nickel-based superalloy

• A computational multi-physical modeling approach to oxide-assisted intergranular dwell crack growth of γ′ strengthened nickel-based superalloys is presented. The modeling framework explicitly couples mass transport of chemical species involved in oxide formation to the evolution of mechanical fields ahead of a crack tip. The viscoplastic response accounts for the influence of the γ′ dispersion. • Detailed Quantitative study of the interaction between the dynamic stress-assisted oxide formation ahead of the crack tip and the resulting near crack-tip stress fields was implemented. • A method for extracting crack growth rates based on failure of the oxide wedge ahead of a crack tip is presented • The predicted rates are shown to be in good agreement with available experimental data without any direct information or calibration of model parameters to crack growth rate data in advance. • Established model shows capability of reveal the effect of particle dispersion of nickel-based superalloy on the dwell fatigue crack growth rate. A theoretical treatment on the oxide-controlled dwell fatigue crack growth of a γ’ strengthened nickel-based superalloys is presented. In particular, this study investigates the influence of an externally applied load and variations in the γ’ dispersion on the grain boundary oxide growth kinetics. A dislocation-based viscoplastic constitutive description for high temperature deformation is used to simulate the stress state evolution in the vicinity of a crack at elevated temperature. The viscoplastic model explicitly accounts for multimodal γ’ particle size distributions. A multicomponent mass transport formulation is used to simulate the formation/evolution of an oxide wedge ahead of the crack tip, where stress-assisted vacancy diffusion is assumed to operate. The resulting set of constitutive and mass transport equations have been implemented within a finite element scheme. Comparison of predicted compositional fields across the matrix/oxide interface are compared with experiments and shown to be in good agreement. Simulations indicate that the presence of a fine γ’ size distribution has a strong influence on the predicted ow stress of the material and consequently on the relaxation in the vicinity of the crack-tip/oxide wedge. It is shown that a unimodal dispersion leads to reduced oxide growth rates (parabolic behavior) when compared to a bimodal one. Stability conditions for oxide formation are investigated and is associated with the prediction of compressive stresses within the oxide layer just ahead of the crack tip, which become progressively negative as the oxide wedge develops. However, mechanical equilibrium requirements induce tensile stresses at the tip of the oxide wedge, where failure of the oxide is predicted. The time taken to reach this critical stress for oxide failure has been calculated, from which dwell crack growth rates are computationally derived. The predicted rates are shown to be in good agreement with available experimental data.

A mechanistic evaluation relating microstructural morphology to a modified Mohr-Griffith compression-shear constitutive model for 3D printed concrete

The hardened-state mechanical characteristics of 3D printable concrete (3DPC) mixtures exhibit a strong dependence on the employed extrusion-based process, material, and design parameters and are predominantly anisotropic by nature. It has been shown that at the heart of the observed mechanical anisotropy lies the microstructural morphology of the manufactured component. Additionally, it is hypothesised that a linear Coulomb friction assumption misrepresents the interfacial compression-shear constitutive behaviour exhibited in 3DPC. Thus, additional calibration of the shear model parameters is sought, forming the basis for the current investigation. In this regard, the present contribution offers a comprehensive investigation of the constant compression-shear performance of a fibre-reinforced printable concrete (FRPC) mixture via a direct shear test (DST) methodology for concrete samples additively manufactured by extrusion-based 3D concrete printing (3DCP). The anisotropic material strength is studied in the three orthogonal material planes, then suitable failure criteria are considered, and a novel modified Mohr-Griffith criterion is proposed. X-ray computed tomography (CT) is employed to explore the microstructural morphology (pore size, shape, orientation, and total porosity content), fracture surface angle, and fracture surface area of 3DCP inter and intralayers compared to specimens cast from the same FRPC mixture. A mechanistic evaluation of the constant compression-shear performances relates the ensuing shear strength to the microstructural morphology observed in the experimentally assessed samples. Thereby, this contribution provides the basis for a fundamentally more detailed understanding of the hardened-state mechanical capacity of 3DPC, which is supported by a novel failure criterion and solid theoretical explanations of the influential microstructural features affecting the mechanical characteristics. Finally, it is postulated that improved mechanical performance and reduced anisotropy, conjuring less material complexity and uncertainty, is permitted by stabilising the microstructural morphology in 3DPC.

A multidisciplinary approach to the development of digital twin models of critical care delivery in intensive care units

To investigate critical care delivery in intensive care units (ICUs), we propose a qualitative and quantitative coupling approach to developing an ICU digital twin model. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model was adapted to conceptualise the current ICU system. A hybrid simulation model was developed to characterise major care delivery processes as discrete-time events, feature patients, clinicians, and other artifacts as autonomous agents, and integrate them in the same simulation environment to capture their interactions under a variety of ICU production conditions. Electronic health record (EHR) data from a medical ICU of Mayo Clinic Rochester, Minnesota, were used to calibrate model parameters. Upon iterative refinement and validation, the model has the potential to be integrated with the hospital information system to simulate real-life events as a full-fledged digital twin of the system. It can be used as an in-silico testbed to investigate the real-time allocation of ICU resources such as medical equipment, flexible staffing, workflow change, and support decisions of patient admission, discharge, and transfer, for healthcare delivery innovation. The interdisciplinary nature of this framework demonstrates and promotes the partnership between healthcare and engineering communities to building a better delivery system.

Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm

In practical engineering, experimental data is not fully in line with the true system response due to various uncertain factors, e.g., parameter imprecision, model uncertainty, and measurement errors. In the presence of mixed sources of aleatory and epistemic uncertainty, stochastic model updating is a powerful tool for model validation and parameter calibration. This paper investigates the use of Bray-Curtis (B-C) distance in stochastic model updating and proposes a Bayesian approach addressing a scenario where the dataset contains multiple outliers. In the proposed method, a B-C distance-based uncertainty quantification metric is employed, that rewards models for which the discrepancy between observations and simulated samples is small while penalizing those which exhibit large differences. To improve the computational efficiency, an adaptive binning algorithm is developed and embedded into the Bayesian approximate computation framework. The merit of this algorithm is that the number of bins is automatically selected according to the difference between the experimental data and the simulated data. The effectiveness and efficiency of the proposed method is verified via two numerical cases and an engineering case from the NASA 2020 UQ challenge. Both static and dynamic cases with explicit and implicit propagation models are considered.

Integration of Genomics with Crop Modeling for Predicting Rice Days to Flowering: A Multi-Model Analysis

The ability of crop models to decompose complex traits and integrate the underlying processes enables them to capture genotype-environment interactions in diverse environments. Integrating genomics with biophysical crop models represents a potential breakthrough technology for improving our understanding of genotype-environment interactions across the biological organization. We present the results of a multi-model analysis on integrating crop modeling with genomic prediction. Seven rice models were evaluated on their ability to predict days to flowering in ten environments from parameters estimated through genome-wide association and genomic prediction, using a 13-fold cross-validation scheme. Phenotypic data were based on a rice diversity panel of 169 accessions with 700k markers. Significant associations with known flowering genes were identified for several model parameters. Although high accuracy was achieved for genomic prediction of model parameters in calibration, prediction accuracy was low for untested genotypes. We observed divergent model performance using genomic-predicted model parameters, which was attributed to photoperiod and temperature response curves, and number of calibrated model parameters. Several areas were identified for further research that could lead to better understanding the genetic control of complex trait formation and improved integration of genomics with crop modeling.

Robust Unsupervised Gaze Calibration Using Conversation and Manipulation Attention Priors

Gaze estimation is a difficult task, even for humans. However, as humans, we are good at understanding a situation and exploiting it to guess the expected visual focus of attention of people, and we usually use this information to retrieve people’s gaze. In this article, we propose to leverage such situation-based expectation about people’s visual focus of attention to collect weakly labeled gaze samples and perform person-specific calibration of gaze estimators in an unsupervised and online way. In this context, our contributions are the following: (i) we show how task contextual attention priors can be used to gather reference gaze samples, which is a cumbersome process otherwise; (ii) we propose a robust estimation framework to exploit these weak labels for the estimation of the calibration model parameters; and (iii) we demonstrate the applicability of this approach on two human-human and human-robot interaction settings, namely conversation and manipulation. Experiments on three datasets validate our approach, providing insights on the priors effectiveness and on the impact of different calibration models, particularly the usefulness of taking head pose into account.

Evaluation of surface soil moisture models over heterogeneous agricultural plots using L-band SAR observations

The goal of this study is to evaluate the efficiency of surface soil moisture models based on L-band SAR data at two different crop stages in typical Indian agricultural plots. Agricultural fields examined include paddy, tomato, sugarcane, at two distinct crop stages, and a reference fallow field. Among the evaluated models, X-Bragg model underestimates soil moisture in all agricultural fields, whereas the Oh 2004 model fits into three agricultural plots for two crop stages without any necessity of auxiliary field information. All models underperformed in the case of sugarcane at the grand growth stage. Although WCM gave best result, it came at the cost of field data utilized to calibrate model parameters. Overall, the Oh 2004 model outperforms other models across crop types and growth stages. To the best of our knowledge, this is the only study that deals with soil moisture estimations at the plot scale across different crops.

Prediction of permanent settlements of an upstream faced earth dam

In upstream faced earth dams all possible seismic-induced damages may be expressed using crest settlement as indicator, which may be predicted using complete dynamic analyses, or more traditional/simplified approaches such as sliding-block based methods and predictive formulas. The present paper compares results yielded by different analysis procedures applied to investigate the seismic response of an existing Italian earth dam. It shows how the use of both complete dynamic analyses and traditional simplified approaches, rather than resulting alternative each other, can be synergic in supporting the interpretation of the dam seismic response, somehow allowing a mutual check of result reliability. The paper also deal with a critical aspect in carrying out complete dynamic analyses of dam behavior, which is the calibration of model parameters. It shows how a back-analysis-based calibration procedure processing static observations may conveniently be implemented to set part of the high number of parameters characterizing complete dynamic approaches.

A modified pressure dependent multi-yield surface model for simulation of LEAP-Asia-2019 centrifuge experiments

A modified flow rule has been introduced into a pressure dependent multi-yield surface model for predicting the liquefaction of laboratory soil element tests and centrifuge experiments. The plastic potential function within the contractive phase in the flow rule of the constitutive model has been adjusted for better estimating the accumulation rate of plastic volumetric strain and the generation rate of excess pore water pressure. The modification of flow rule was mainly employed for achieving successful calibration of model parameters with the laboratory soil element tests and obtaining better predictions of the seismic response of LEAP-Asia-2019 centrifuge model tests. A calibration process of the constitutive model was firstly performed through numerical simulation of cyclic torsional shear tests of Ottawa F-65 sand samples with different relative densities under various cyclic stress ratios, and the estimation procedures of new parameters are provided. Then Type-B and Type-C numerical simulations were carried out for centrifuge experiments of a mildly sloping ground of Ottawa F-65 sand subjected to ramped sinusoidal input motions. The modified constitutive model could reasonably simulate the seismic response for almost all centrifuge model tests using the same set of model parameters.

Modelling the effects of antimicrobial metaphylaxis and pen size on bovine respiratory disease in high and low risk fattening cattle

Bovine respiratory disease (BRD) dramatically affects young calves, especially in fattening facilities, and is difficult to understand, anticipate and control due to the multiplicity of factors involved in the onset and impact of this disease. In this study we aimed to compare the impact of farming practices on BRD severity and on antimicrobial usage. We designed a stochastic individual-based mechanistic BRD model which incorporates not only the infectious process, but also clinical signs, detection methods and treatment protocols. We investigated twelve contrasted scenarios which reflect farming practices in various fattening systems, based on pen sizes, risk level, and individual treatment vs. collective treatment (metaphylaxis) before or during fattening. We calibrated model parameters from existing observation data or literature and compared scenario outputs regarding disease dynamics, severity and mortality. The comparison of the trade-off between cumulative BRD duration and number of antimicrobial doses highlighted the added value of risk reduction at pen formation even in small pens, and acknowledges the interest of collective treatments for high-risk pens, with a better efficacy of treatments triggered during fattening based on the number of detected cases.

Sensitivity analysis of Biome-BGCMuSo for gross and net primary productivity of typical forests in China

Process-based models are widely used to simulate forest productivity, but complex parameterization and calibration challenge the application and development of these models. Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation. However, parameters are not dependent on each other, and the results of sensitivity analysis usually vary due to different forest types and regions. Hence, global and representative sensitivity analysis would provide reliable information for simple calibration. To determine the contributions of input parameters to gross primary productivity (GPP) and net primary productivity (NPP), regression analysis and extended Fourier amplitude sensitivity testing (EFAST) were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX. Generally, GPP and NPP were highly sensitive to C:Nleaf (C:N of leaves), Wint (canopy water interception coefficient), k (canopy light extinction coefficient), FLNR (fraction of leaf N in Rubisco), MRpern (coefficient of linear relationship between tissue N and maintenance respiration), VPDf (vapor pressure deficit complete conductance reduction), and SLA1 (canopy average specific leaf area in phenological phase 1) at all observation sites. Various sensitive parameters occurred at four observation sites within different climate zones. GPP and NPP were particularly sensitive to FLNR, SLA1 and Wint, and C:Nleaf in temperate, alpine and subtropical zones, respectively. The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient. We found that parameter calibration should be performed according to plant functional type (PFT), and more attention needs to be paid to the differences in climate and environment. These findings contribute to determining the target parameters in field experiments and model calibration.

Effects of typical corn kernel shapes on the forming of repose angle by DEM simulation

Corn kernel shape is an important feature to improve the accuracy of simulation results, also the repose angle is often used to calibrate model parameters in simulation. In this study, the effects of the corn kernel shapes on the behavior during corn kernels accumulation were investigated in detail. Firstly, the DEM models of three typical shapes of corn kernels were developed. Secondly, the influences of the corn kernel shapes on the process for forming repose angle were investigated, such as corn kernels distance change, energy conversion, and the contact number between corn kernels. Results show that the corn kernel shape has a significant effect on the repose angle formation. The irregular shape of corn kernels limit their rolling when adding the corn kernel length in one or two directions. In addition, the irregular shapes of corn kernels increase the contact number and extend simulation time. Regular shape corn kernels need to be mixed with irregular shape corn kernels to improve its flowability. Finally, observation of the trajectory of corn kernel repose angles indicates that spherical corn kernels contact the bottom plate early, and forming small cone first, then the cone becomes bigger to change the direction of other corn kernels from top to bottom. Keywords: corn kernel shape, energy conversion, repose angle, DEM DOI: 10.25165/j.ijabe.20221502.5670 Citation: Shi L R, Zhao W Y, Yang X P. Effects of typical corn kernel shapes on the forming of repose angle by DEM simulation. Int J Agric & Biol Eng, 2022; 15(2): 248–255.

Automatic Correction of Parameters of Rice Phenology Prediction Model Based on Random Forest Algorithm

With the rapid development of various Internet of Things and cloud computing, a large amount of data is rapidly accumulated in various fields, and the resulting data dimension is getting higher and higher. Evolutionary algorithm has been widely used in the field of crop model parameter calibration. Because the structure of crop model includes many sets of ordinary differential equations, which are nonlinear, discontinuous and multi peak, it is particularly important to choose an appropriate evolutionary algorithm according to the characteristics of crop model. At the same time, the parameters of evolutionary algorithm have a great impact on the performance of the algorithm. The improved differential evolution algorithm with adaptive control parameters shows good convergence speed and stability in the parameter calibration of rice phenology model. Variable selection is particularly important in high-dimensional data processing, and the importance rating of variables is the key problem. Aiming at the problem of low classification accuracy and generalization ability of the model in the single variable selection algorithm, this paper proposes a hybrid variable selection algorithm. The algorithm is divided into two stages: the filtering stage, which uses mutual information to quickly eliminate some irrelevant variables and reduce the dimension of the sample space; In the encapsulation stage, under the framework of replacement theory, the residual variables are selected by random forest, which shows that the algorithm has higher classification accuracy and generalization ability than the comparison algorithm.

Calibration of KCC model for UHPC under impact and blast loadings

Ultra-high performance concrete (UHPC) is a promising material for both civil and military protective structures against the potential impact and blast loadings. However, the existing numerical simulation studies related to the material model calibrations for precisely describing the dynamic behaviors of UHPC structural members under both impact and blast loadings are limited, and the calibration procedures are also not in consensus. This paper aims to propose a comprehensive Karagozian & Case Concrete (KCC) model parameter calibration procedure and reliable parameter generation method to well predict the dynamic behavior of UHPC members subjected to dynamic loadings. First, the failure surface, equation of state (EOS), damage evolution and strain rate effect parameters were comprehensively calibrated based on the existing experimental and analytical data of UHPC, and general guidelines on the determination of key KCC model parameters for UHPC are given. Then, the numerical simulations with both the autogenerated and the calibrated parameters were carried out to predict the dynamic response and damage evolution of UHPC members, i.e., reinforced UHPC beam and column, UHPC-FST beam, and reinforced UHPC panel, under drop hammer impact and field explosion. By comparing the test data, the validation of the proposed parameter generation approach is verified. The present work can provide helpful references for designing and evaluating the UHPC structural members under potential impact and blast loadings.

A Micromechanical Anisotropic Damage Model for Brittle Rocks With Non-Associated Plastic Flow Rule Under True Triaxial Compressive Stresses

A micromechanical anisotropic damage model with a non-associated plastic flow rule is developed for describing the true triaxial behaviors of brittle rocks. We combine the Eshelby’s solution to the inclusion problem with the framework of irreversible thermodynamics. The main dissipative mechanisms of inelastic deformation due to the frictional sliding and damage by microcrack propagation are strongly coupled to each other. A Coulomb-type friction criterion is formulated in terms of the local stress applied onto the microcracks as the yielding function. The back-stress term contained in this local stress plays a critical role in describing the material’s hardening/softening behaviors. With a non-associated flow rule, a potential function is involved. Some analytical analysis of the non-associated micromechanical anisotropic damage model are conducted, which are useful for the model parameters calibration. The proposed model is used to simulate the laboratory tests on Westerly granite under true triaxial stresses. Comparing the numerical simulation results provided by the models with associated/non-associated plastic flow rule and experimental results, it is clear that the proposed non-associated model gives a better prediction than the previous associated model.

Extending a physics-based constitutive model using genetic programming

In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions. A major drawback is the calibration of model parameters that depend on processing conditions. Currently, these parameters must be optimized to fit measured data since their relations to processing conditions (e.g. deformation temperature, strain rate) are not fully understood. We present a new approach that identifies the functional dependency of calibration parameters from processing conditions based on genetic programming. We propose two (explicit and implicit) methods to identify these dependencies and generate short interpretable expressions. The approach is used to extend a physics-based constitutive model for deformation processes. This constitutive model operates with internal material variables such as a dislocation density and contains a number of parameters, among them three calibration parameters. The derived expressions extend the constitutive model and replace the calibration parameters. Thus, interpolation between various processing parameters is enabled. Our results show that the implicit method is computationally more expensive than the explicit approach but also produces significantly better results.

A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps.

Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell–cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.

Model Calibration Parameter Using Optimization Trial in HEC-HMS for Unda Watershed

In water resource planning, information on water availability is needed. Nowadays, data on water availability is still difficult to obtain. With technology in the form of a rainfall-runoff simulation model that can predict water availability in the Unda watershed. It can add information about the potential for water in the Unda watershed. It can be used to prepare water resources management in the Unda watershed so that the existing potential can be used sustainably. Based on the rainfall simulation model results in the Unda watershed, it can be concluded that after running the initial model and calibration. The results are obtained R2 value was 0.68 and increased by 9.81% to 0.754. Both the initial model and the calibration model show an efficient R2 value, NASH value increases by 49.93% to 0.713, which includes satisfactory criteria, RMSE value of 1.135 and decreased by 49.47% to 0.758, and the PBIAS value was 44.70% which was classified as unsatisfactory and decreased from 80.24% to 24.80% at the time of calibration which was classified as satisfactory. In general, the overall simulation results are quite good for representing the watershed’s efficient hydrological process.

Targeting arterial partial pressure of carbon dioxide in acute respiratory distress syndrome patients using extracorporeal carbon dioxide removal.

A retrospective analysis of SUPERNOVA trial data showed that reductions in tidal volume to ultraprotective levels without significant increases in arterial partial pressure of carbon dioxide (PaCO2 ) for critically ill, mechanically ventilated patients with acute respiratory distress syndrome (ARDS) depends on the rate of extracorporeal carbon dioxide removal (ECCO2 R). We used a whole-body mathematical model of acid-base balance to quantify the effect of altering carbon dioxide (CO2 ) removal rates using different ECCO2 R devices to achieve target PaCO2 levels in ARDS patients. Specifically, we predicted the effect of using a new, larger surface area PrismaLung+ device instead of the original PrismaLung device on the results from two multicenter clinical studies in critically ill, mechanically ventilated ARDS patients. After calibrating model parameters to the clinical study data using the PrismaLung device, model predictions determined optimal extracorporeal blood flow rates for the PrismaLung+ and mechanical ventilation frequencies to obtain target PaCO2 levels of 45 and 50 mm Hg in mild and moderate ARDS patients treated at a tidal volume of 3.98 ml/kg predicted body weight (PW). Comparable model predictions showed that reductions in tidal volumes below 6 ml/kg PBW may be difficult for acidotic highly severe ARDS patients with acute kidney injury and high CO2 production rates using a PrismaLung+ device in-series with a continuous venovenous hemofiltration device. The described model provides guidance on achieving target PaCO2 levels in mechanically ventilated ARDS patients using protective and ultraprotective tidal volumes when increasing CO2 removal rates from ECCO2 R devices.

Predictive Capabilities of 2D and 3D Block Propagation Models Integrating Block Shape Assessed from Field Experiments

Block propagation models have been used for years for rockfall hazard assessment. However, the calibration of model parameters that allow the simulations to accurately predict rockfall trajectories for a given study site remains a key issue. This research aims at investigating the predictive capabilities of block propagation models after a preliminary calibration phase. It is focused on models integrating the shape of blocks since, despite their sound physical bases, they remain less used than lumped-mass approaches due to their more recent popularisation. Benefiting from both a recently built model integrating block shape, usable in 2D and 3D, and from recent experimental results at the slope scale, we first performed a calibration based on the use of the 2D model, and then we evaluated the predictive capabilities of the calibrated model in 2D and in 3D using the remaining part of the experimental results. The calibrated model simulations predict the main characteristics of the propagation, that is the preferential deposit zones and the ranges of velocities at specific locations. Good matches between simulations and experimental results in both the calibration and validation phases emphasizes the wide applicability of the model: after a calibration phase on a sufficient number of different soil types, the model may be used in a predictive manner. The good match between 2D and 3D simulations also highlights the ease-of-use of the model for field applications, as the 2D model produces sufficiently accurate results while also being easier and faster to calibrate. As classically observed for block propagation models, the model is not sufficient to predict the details of the velocity and stopping points but provides accurate predictions of the global ranges of these quantities, in particular of the extreme values. To lift these limitations in terms of predictive capabilities, more advanced calibration procedures based on optimization techniques constitute a promising path forward.