Calibration Of Input Parameters Research Articles

A structural quality evaluation model for three-dimensional simulations

Abstract Purpose In recent years, computer simulations have become an innovative approach which enables research in the field of highly complicated physical phenomena and the study of the laws which govern the universe. The proper interpretation of the results of a visual simulation requires the highest quality of the generated image, as every distortion or mistake may have a significant influence on the readability, accuracy and even credibility of the presentation of the results. The aim of this presentation is to determine a model that enables precise quality evaluation of the three-dimensional visual simulations in the field of structural correctness. Design/methodology/approach The developed model is a solution that makes it possible to estimate the quality of stereoscopic image in the context of major three-dimensional structural dysfunctions, namely, vertical parallax, rotation mismatch and scale mismatch. Implementing the wrought theoretical model with the use of cost-effective mechanisms of image feature detection creates a robust method which enables the scalar grade of structural correctness of the studied three-dimensional simulation to be computed. Findings On the basis of the conducted research, in particular, taking into account three-dimensional simulations, it was stated that the formulated model with the developed method provides an efficient, structural quality estimation tool applicable for a wide variety of three-dimensional images. The obtained results indicate that the wrought method has huge potential in the application of high-resolution simulations by enabling a screening test of the structural quality of the stereoscopic view in quasi-real time. Practical implications The developed method may be used both in order to verify the quality of ready-to-use three-dimensional image and also at the stage of the calibration of input parameters of the simulation. Originality/value The paper takes into account the selection of the most significant distortions which occur in visual, three-dimensional simulations providing a cost-effective and versatile tool which allows for the detection and elimination of serious mistakes and dysfunctions as early as at the calibration stage.

Technology diffusion of Industry 4.0: an agent-based approach

Governmental interventions, such as public policies and programs, play a vital role in innovation diffusion, particularly if the application area is heterogeneous, like the German federal high-tech approach of Industry 4.0. Interventions can thus inhibit market failure and negative externalities or disseminate the technology and promote positive externalities. To analyse the impact of governmental intervention, considering the particularities of the Industry 4.0 approach, an agent-based model (ABM) is proposed, particularly to test the sensitivity of Industry 4.0 innovation diffusion speed and degree due to interventions such as promotion, educational support, technology networks (hubs), technology standardisation, and financial aid among manufacturing small and medium size enterprises (SMEs) in Germany. This paper describes a conceptual model structured along the overview, design concept, and details framework. Grounding and calibration of input parameters and agent behaviour are based on firm characteristics and adoption determinants (technology-organisation-environment model) from survey data and Industry 4.0 case studies.

Technology diffusion of Industry 4.0: an agent-based approach

Governmental interventions, such as public policies and programs, play a vital role in innovation diffusion, particularly if the application area is heterogeneous, like the German federal high-tech approach of Industry 4.0. Interventions can thus inhibit market failure and negative externalities or disseminate the technology and promote positive externalities. To analyse the impact of governmental intervention, considering the particularities of the Industry 4.0 approach, an agent-based model (ABM) is proposed, particularly to test the sensitivity of Industry 4.0 innovation diffusion speed and degree due to interventions such as promotion, educational support, technology networks (hubs), technology standardisation, and financial aid among manufacturing small and medium size enterprises (SMEs) in Germany. This paper describes a conceptual model structured along the overview, design concept, and details framework. Grounding and calibration of input parameters and agent behaviour are based on firm characteristics and adoption determinants (technology-organisation-environment model) from survey data and Industry 4.0 case studies.

Modelling the cyclic response of structural steel for FEM analyses

Modelling the cyclic response of structural steel plays an important role in the design and performance assessment of steel structures. Up to date, several mathematical models were developed to simulate metal plasticity, but only some of them were implemented in Finite Element Method (FEM) based software packages such as Abaqus, by using incremental plasticity procedures. Within this article, the “built-in” combined isotropic/kinematic hardening model is used to model metal plasticity under cyclic loading regime. A brief description of the constitutive model together with the calibration procedure of the material parameters based on experimental data are presented. Finite element analyseswere carried out on simplified FEM models to provide numerical predictions using the calibrated material parameters. Since the “built-in” combined model has several limitations (especially related to the isotropic component), adjustments of the material parameters were made to accommodate to different loading histories. The chosen material model and the calibrated input parameters are validated byanalysing the FEM predictions to be in good agreement with the experimental results with respect to cyclic behaviour and failure mode.

Calibrating the stress-time curve of a combined finite-discrete element method to a Split Hopkinson Pressure Bar experiment

We present a generic method for automatically calibrating a computer code to an experiment, with uncertainty, for a given “training” set of computer code runs. The calibration technique is general and probabilistic, meaning the calibration uncertainty is represented in the form of a probability distribution. We demonstrate the calibration method by calibrating a combined Finite-Discrete Element Method (FDEM) to a Split Hopkinson Pressure Bar (SHPB) experiment with a granite sample. The probabilistic calibration method combines runs of a FDEM computer simulation for a range of “training” settings and experimental uncertainty to develop a statistical emulator. The process allows for calibration of input parameters and produces output quantities with uncertainty estimates for settings where simulation results are desired. Input calibration and FDEM fitted results are presented. We find that the maximum shear strength σtmax and to a lesser extent maximum tensile strength σnmax govern the behavior of the stress-time curve before and around the peak, while the specific energy in Mode II (shear) Et largely governs the post-peak behavior of the stress-time curve. Good agreement is found between the calibrated FDEM and the SHPB experiment. Interestingly, we find the SHPB experiment to be rather uninformative for calibrating the softening-curve shape parameters (a, b, and c). This work stands as a successful demonstration of how a general probabilistic calibration framework can automatically calibrate FDEM parameters to an experiment.

A calibration framework for discrete element model parameters using genetic algorithms

In this research, a universal framework for automated calibration of microscopic properties of modeled granular materials is proposed. The proposed framework aims at industrial scale applications, where optimization of the computational time step is important. It can be generally applied to all types of DEM simulation setups. It consists of three phases: data base generation, parameter optimization, and verification. In the first phase, DEM simulations are carried out on a multi-dimensional grid of sampled input parameter values to generate a database of macroscopic material responses. The database and experimental data are then used to interpolate the objective functions with respect to an arbitrary set of parameters. In the second phase, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the calibration multi-objective optimization problem. In the third phase, the DEM simulations using the results of the calibrated input parameters are carried out to calculate the macroscopic responses that are then compared with experimental measurements for verification and validation.The proposed calibration framework has been successfully demonstrated by a case study with two-objective optimization for the model accuracy and the simulation time. Based on the concept of Pareto dominance, the trade-off between these two conflicting objectives becomes apparent. Through verification and validation steps, the approach has proven to be successful for accurate calibration of material parameters with the optimal simulation time.

Impact of model complexity and multi-scale data integration on the estimation of hydrogeological parameters in a dual-porosity aquifer

This study investigates the impact of model complexity and multi-scale prior hydrogeological data on the interpretation of pumping test data in a dual-porosity aquifer (the Chalk aquifer in England, UK). In order to characterize the hydrogeological properties, different approaches ranging from a traditional analytical solution (Theis approach) to more sophisticated numerical models with automatically calibrated input parameters are applied. Comparisons of results from the different approaches show that neither traditional analytical solutions nor a numerical model assuming a homogenous and isotropic aquifer can adequately explain the observed drawdowns. A better reproduction of the observed drawdowns in all seven monitoring locations is instead achieved when medium and local-scale prior information about the vertical hydraulic conductivity (K) distribution is used to constrain the model calibration process. In particular, the integration of medium-scale vertical K variations based on flowmeter measurements lead to an improvement in the goodness-of-fit of the simulated drawdowns of about 30%. Further improvements (up to 70%) were observed when a simple upscaling approach was used to integrate small-scale K data to constrain the automatic calibration process of the numerical model. Although the analysis focuses on a specific case study, these results provide insights about the representativeness of the estimates of hydrogeological properties based on different interpretations of pumping test data, and promote the integration of multi-scale data for the characterization of heterogeneous aquifers in complex hydrogeological settings.

Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic.

Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained.

Prediction of La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 cathode microstructures during sintering: Kinetic Monte Carlo (KMC) simulations calibrated by artificial neural networks

Abstract The Potts Kinetic Monte Carlo (KMC) model, proven to be a robust tool to study all stages of sintering process, is an ideal tool to analyze the microstructure evolution of electrodes in solid oxide fuel cells (SOFCs). Due to the nature of this model, the input parameters of KMC simulations such as simulation temperatures and attempt frequencies are difficult to identify. We propose a rigorous and efficient approach to facilitate the input parameter calibration process using artificial neural networks (ANNs). The trained ANN reduces drastically the number of trial-and-error of KMC simulations. The KMC simulation using the calibrated input parameters predicts the microstructures of a La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 cathode material during sintering, showing both qualitative and quantitative congruence with real 3D microstructures obtained by focused ion beam scanning electron microscopy (FIB-SEM) reconstruction.

Calibration and validation of a continuum damage mechanics model in aid of axial crush simulation of braided composite tubes

We present the details of a combined experimental and numerical study used to calibrate the parameters of a macro-mechanical damage model that is incorporated as a user material model in the explicit finite element program, LS-DYNA, to represent the progressive damage behaviour of composites at the level of the representative volume element. Specifically, the model parameters defining the transverse tensile and axial compressive response of a triaxially-braided carbon fibre/epoxy composite are determined based on results from notched tensile, 4-point bend and eccentric compression tests. To demonstrate the validity of the material model and its calibrated input parameters, the response of notched tensile coupon and eccentric compression tests are simulated and shown to correlate well with experimental measurements. Further validation of the model is provided here and in Ref. [1] through successful simulation of the axial crushing of square tubes made of the same material.

Landslide prediction system for rainfall induced landslides in Slovenia (Masprem)

In this paper we introduce a landslide prediction System for modelling the probabilities of landslides through time in Slovenia (Masprem). The System to forecast rainfall induced landslides is based on the landslide susceptibility map, landslide triggering rainfall threshold values and the precipitation forecasting model. Through the integrated parameters a detailed framework of the System, from conceptual to operational phases, is shown. Using fuzzy logic the landslide prediction is calculated. Potential landslide areas are forecasted on a national scale (1: 250,000) and on a local scale (1: 25,000) for five selected municipalities where the exposure of inhabitants, buildings and different type of infrastructure is displayed, twice daily. Due to different rainfall patterns that govern landslide occurrences, the System for landslide prediction considers two different rainfall scenarios (M1 and M2). The landslides predicted by the two models are compared with a landslide inventory to validate the Outputs. In this study we highlight the rainfall event that lasted from the 9th to the 14th of September 2014 when abundant precipitation triggered over 800 slope failures around Slovenia and caused large material damage. Results show that antecedent rainfall plays an important role, according to the comparisons of the model (M1) where antecedent rainfall is not considered. Although in general the landslides areas are over-predicted and largely do not correspond to the landslide inventory, the overall Performance indicates that the system is able to capture the crucial factors in determining the landslide location. Additional calibration of input parameters and the landslide inventory as well as improved spatially distributed rainfall forecast data can further enhance the model's prediction.

Automation of RELAP5 input calibration and code validation using genetic algorithm

Validation of system thermal-hydraulic codes is an important step in application of the codes to reactor safety analysis. The goal of the validation process is to determine how well a code can represent physical reality. This is achieved by comparing predicted and experimental system response quantities (SRQs) taking into account experimental and modelling uncertainties. Parameters which are required for the code input but not measured directly in the experiment can become an important source of uncertainty in the code validation process. Quantification of such parameters is often called input calibration. Calibration and uncertainty quantification may become challenging tasks when the number of calibrated input parameters and SRQs is large and dependencies between them are complex. If only engineering judgment is employed in the process, the outcome can be prone to so called “user effects”.The goal of this work is to develop an automated approach to input calibration and RELAP5 code validation against data on two-phase natural circulation flow instability. Multiple SRQs are used in both calibration and validation.In the input calibration, we used genetic algorithm (GA), a heuristic global optimization method, in order to minimize the discrepancy between experimental and simulation data by identifying optimal combinations of uncertain input parameters in the calibration process. We demonstrate the importance of the proper selection of SRQs and respective normalization and weighting factors in the fitness function.In the code validation, we used maximum flow rate as the SRQ of primary interest. The ranges of the input parameter were defined based on the experimental data and results of the calibration process. Then GA was used in order to identify combinations of the uncertain input parameters that provide maximum deviation of code prediction results from the experimental data. Such approach provides a conservative estimate of the possible discrepancy between the code result and the experimental data.

To screen or not to screen for breast cancer? How do modelling studies answer the question?

Breast cancer screening is a topic of hot debate, and currently no general consensus has been reached on starting and ending ages and screening intervals, in part because of a lack of precise estimations of the benefit-harm ratio. Simulation models are often applied to account for the expected benefits and harms of regular screening; however, the degree to which the model outcomes are reliable is not clear. In a recent systematic review, we therefore aimed to assess the quality of published simulation models for breast cancer screening of the general population. The models were scored according to a framework for qualitative assessment. We distinguished seven original models that utilized a common model type, modelling approach, and input parameters. The models predicted the benefit of regular screening in terms of mortality reduction; and overall, their estimates compared well to estimates of mortality reduction from randomized controlled trials. However, the models did not report on the expected harms associated with regular screening. We found that current simulation models for population breast cancer screening are prone to many pitfalls; their outcomes bear a high overall risk of bias, mainly because of a lack of systematic evaluation of evidence to calibrate the input parameters and a lack of external validation. Our recommendations concerning future modelling are therefore to use systematically evaluated data for the calibration of input parameters, to perform external validation of model outcomes, and to account for both the expected benefits and the expected harms so as to provide a clear balance and cost-effectiveness estimation and to adequately inform decision-makers.

Evaluating the fidelity and robustness of calibrated numerical model predictions

Purpose – Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters. Design/methodology/approach – Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model predictiveness are evaluated in the development of a simplified finite element beam model of the CX-100 wind turbine blade. Findings – Findings of this study show that calibrating the input parameters of a numerical model with the sole objective of improving fidelity to available measurements degrades the robustness of model predictions at both tested and untested settings. A more optimal model may be obtained by calibration methods considering both fidelity and robustness. Multi-criteria Decision Making further confirms the conclusion that the optimal model performance is achieved by maintaining a balance between fidelity and robustness during calibration. Originality/value – Current methods for model calibration focus solely on fidelity while the authors focus on the trade-off between fidelity and robustness.

Green vehicle routing in urban zones – A neuro-fuzzy approach

Local city authorities are making a serious effort to expand the number of low-greenhouse gas vehicles (green vehicles) at home. There is no reliable methodology, however, to support the implementation of this passenger transportation concept. In order to optimize the green capacity, a system has been developed to support decision making in urban green vehicle routing. The objective of this paper is to propose a green vehicle distribution model in a public transportation network. The problem has been defined as a problem of non-linear optimization with dispersed input parameters, requiring neuro-fuzzy logic. An adaptive neural network was developed, taking into account the costs to be borne by operators and users, and the environmental parameters along the observed vehicle route. Each input parameter of the neuro-fuzzy model has been placed in a complex context. They were divided into the elements describing in more detail the environmental status, the operator and passenger costs. The advantage of the model is that several factors shaping the input parameters have been taken into consideration. On the other hand, the complexity of urban systems management makes it a considerable challenge, and the surrounding circumstances are difficult to predict accurately. Accordingly, the inputs of the green vehicle model were fuzzified. The Index of Performance (IP) is the output, associated with each branch of the passenger transportation network. The model has been tested on a part of the public transport system in central Belgrade. The results have proven a practical application possible, and a calibration of input parameters allows for full implementation in public transport vehicle routing.

Improving prediction accuracy of thermal analysis for weld-based additive manufacturing by calibrating input parameters using IR imaging

In experiments, it is usually difficult to accurately determine simulation input parameters such as heat source parameters, material properties at high temperature, etc. The uncertainty of such input parameters is responsible for the large error of thermal simulation for weld-based additive manufacturing. In this paper, a new approach is presented to calibrate uncertain input parameters. The approach is based on the solution of the inverse heat conduction problem of small-scale five-layer deposition and the application of the infrared (IR) imaging technique. The calibration of heat source parameters involves a multivariate optimization search using the pattern search method, whereas the calibration of the combined radiation and convection model includes a number of one-dimensional searches using the Fibonacci search method. Based on an in-depth analysis of IR images, thermal characteristics such as mean layer temperature and cooling rate are selected as the comparison results and included in cost functions. Lastly, the validity of the approach is demonstrated by a simulation case of 15-layer deposition with calibrated input parameters. The comparison between the simulated and experimental results verifies the improved prediction accuracy.

Parameter calibration framework for environmental emergency models

Simulation systems that predict the propagation of environmental emergencies have to satisfy hard real-time constraints to prevent tragedy. To obtain more reliable forecasts, input parameter calibration mechanisms should be integrated because it is often impossible to measure highly dynamic input parameters in a correct and timely manner. Evolutionary optimisation methods showed promising calibration potential but involve numerous expensive fitness evaluations. This makes their application to time-restricted real-world problems impractical, especially when only limited computing resources are available. We therefore propose a framework for efficient parameter calibration based on evolutionary intelligent systems. A genetic algorithm is joined with a case-based reasoner to form a hybrid calibration approach. The suggested framework allows the user to select the configuration of the calibration process according to emergency and prediction characteristics and available computing resources. The possibility to generate quick calibration estimates thus minimising the additional computational effort caused by the introduction of parameter calibration is highlighted. The framework was tested in the area of forest fire spread prediction. A case base was generated from real historical and synthetical forest fires. Experiments show that case-based reasoning generates results comparable to pure evolutionary optimisation approaches, clearly outperforming the latter in runtime.

Parallel Multi-level Genetic Ensemble for Numerical Weather Prediction Enhancement

The need for reliable predictions in environmental modelling is well-known. Particularly, the predicted weather and meteorological information about the future atmospheric state is crucial and necessary for almost all other areas of environmental modelling. Additionally, right decisions to prevent damages and save lives could be taken depending on a reliable meteorological prediction process. Lack and uncertainty of input data and parameters constitute the main source of errors for most of these models. In recent years, evolutionary optimization methods have become popular to solve the input parameter problem of environmental models. We propose a new parallel meteorological prediction scheme that uses evolutionary optimization methods based on Multi-Chromosome Genetic Algorithm to enhance the quality of weather forecasts by focusing on the calibration of input parameters. This new scheme is parallelized and executed on a HPC environment in order to reduce the time needed to obtain the final prediction. The new approach is called Multi-Level Genetic Ensemble (M-Level G-Ensemble) and it has been tested using historical data of a wellknown weather catastrophe: Hurricane Katrina that occurred in 2005 in the Gulf of Mexico. Results obtained with our approach provide both significant improvements in weather prediction and a significant reduction in the execution time.

Development of a SWAT Patch for Better Estimation of Sediment Yield in Steep Sloping Watersheds1

Abstract: The watershed scale Soil and Water Assessment Tool (SWAT) model divides watersheds into smaller subwatersheds for simulation of rainfall‐runoff and sediment loading at the field level and routing through stream networks. Typically, the SWAT model first needs to be calibrated and validated for accurate estimation through adjustment of sensitive input parameters (i.e., Curve Number values, USLE P, slope and slope‐length, and so on). However, in some instances, SWAT‐simulated results are greatly affected by the watershed delineation and Digital Elevation Models (DEM) cell size. In this study, the SWAT ArcView GIS Patch II was developed for steep sloping watersheds, and its performance was evaluated for various threshold values and DEM cell size scenarios when delineating subwatersheds using the SWAT model. The SWAT ArcView GIS Patch II was developed using the ArcView GIS Avenue program and Spatial Analyst libraries. The SWAT ArcView GIS Patch II improves upon the SWAT ArcView GIS Patch I because it reflects the topographic factor in calculating the field slope‐length of Hydrologic Response Units in the SWAT model. The simulated sediment value for 321 subwatersheds (watershed delineation threshold value of 25 ha) is greater than that for 43 subwatersheds (watershed delineation threshold value of 200 ha) by 201% without applying the SWAT ArcView GIS Patch II. However, when the SWAT ArcView GIS Patch II was applied, the difference in simulated sediment yield decreases for the same scenario (i.e., difference in simulated sediment with 321 subwatersheds and 43 subwatersheds) was 12%. The simulated sediment value for DEM cell size of 50 m is greater than that for DEM cell size of 10 m by 19.8% without the SWAT ArcView GIS Patch II. However, the difference becomes smaller (3.4% difference) between 50 and 10 m with the SWAT ArcView GIS Patch II for the DEM scenarios. As shown in this study, the SWAT ArcView GIS Patch II can reduce differences in simulated sediment values for various watershed delineation and DEM cell size scenarios. Without the SWAT ArcView GIS Patch II, variations in the SWAT‐simulated results using various watershed delineation and DEM cell size scenarios could be greater than those from input parameter calibration. Thus, the results obtained in this study show that the SWAT ArcView GIS Patch II should be used when simulating hydrology and sediment yield for steep sloping watersheds (especially if average slope of the subwatershed is >25%) for more accurate simulation of hydrology and sediment using the SWAT model. The SWAT ArcView GIS Patch II is available at http://www.EnvSys.co.kr/~swat for free download.

Two numerical models for landslide dynamic analysis

Two microcomputer-based numerical models (Dynamic ANalysis (DAN) and three-dimensional model DAN (DAN3D)) have been developed and extensively used for analysis of landslide runout, specifically for the purposes of practical landslide hazard and risk assessment. The theoretical basis of both models is a system of depth-averaged governing equations derived from the principles of continuum mechanics. Original features developed specifically during this work include: an open rheological kernel; explicit use of tangential strain to determine the tangential stress state within the flowing sheet, which is both more realistic and beneficial to the stability of the model; orientation of principal tangential stresses parallel with the direction of motion; inclusion of the centripetal forces corresponding to the true curvature of the path in the motion direction and; the use of very simple and highly efficient free surface interpolation methods. Both models yield similar results when applied to the same sets of input data. Both algorithms are designed to work within the semi-empirical framework of the “equivalent fluid” approach. This approach requires selection of material rheology and calibration of input parameters through back-analysis of real events. Although approximate, it facilitates simple and efficient operation while accounting for the most important characteristics of extremely rapid landslides. The two models have been verified against several controlled laboratory experiments with known physical basis. A large number of back-analyses of real landslides of various types have also been carried out. One example is presented. Calibration patterns are emerging, which give a promise of predictive capability.