Calibration Framework Research Articles

Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models.

Abstract This study presents a novel Bayesian framework for statistical calibration of spatially distributed physical-based landslide prediction models. The calibration process is formulated in a statistical setting with the model parameters simulated as spatially variable with random fields and the model calibration defined within the Bayesian framework. The implementation of such calibration process is challenging due to large numbers of calibration parameters and high-dimensional likelihood functions, which are central in establishing a relation between observations and the corresponding model predictions. The former challenge was resolved by reformulating the Bayesian updating problem as an equivalent reliability problem and solving it with efficient reliability methods. The latter challenge was resolved by developing novel lower-dimensional approximate likelihood formulations, suitable for the interpretation of landslide initiation zones, based on the Approximate Bayesian Computation method. The novelties of the proposed approach stem from describing landslide model parameters as spatially variable, development of a statistical framework to calibrate landslide prediction models, and introduction of approximate likelihood formulations.

A sequential calibration and validation framework for model uncertainty quantification and reduction

This paper aims to provide new insights into model calibration, which plays an essential role in improving the validity of Modeling and Simulation (M&S) in engineering design and analysis. Various existing model calibration approaches including the direct Bayesian calibration method, the well-known Kennedy and O’Hagan (KOH) framework and its variants, and the optimization-based calibration method, are first investigated. It is observed that the direct Bayesian calibration and optimization-based calibration may be misled by potentially wrong information if the computer model cannot adequately capture the underlying true physics, while the effectiveness of the KOH framework and its variants is significantly affected by the prior distributions of the unknown model parameters. Based on this observation, a sequential model calibration and validation (SeCAV) framework is proposed to improve the efficacy of both model parameter calibration and bias correction for the purpose of uncertainty quantification and reduction. In the proposed method, the model validation and Bayesian calibration are implemented in a sequential manner, where the former serves as a filter to select the best experimental data for the latter, and provides the latter with a confidence probability as a weight factor for updating the uncertain model parameters. The parameter calibration result is then integrated with model bias correction to improve the prediction accuracy of the M&S. A mathematical example and an engineering example are employed to demonstrate the advantages and disadvantages of different approaches. The results show that the SeCAV framework, in general, performs better than the existing methods.

A ROS framework for the extrinsic calibration of intelligent vehicles: A multi-sensor, multi-modal approach

This paper proposes a general approach to the problem of extrinsic calibration of multiple sensors of varied modalities. This is of particular relevance for intelligent vehicles, which are complex systems that often encompass several sensors of different modalities. Our approach is seamlessly integrated with the Robot Operating System (ROS) framework, and allows for the interactive positioning of sensors and labelling of data, facilitating the calibration procedure. The calibration is formulated as a simultaneous optimization for all sensors, in which the objective function accounts for the various sensor modalities. Results show that the proposed procedure produces accurate calibrations, on par with state of the art approaches which operate only for pairwise setups.

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this work, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for the reduction of atmospheric contributions in unmanned aerial systems (UAS) imagery and for the production of scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions were used to develop and test an ELM radiometric calibration framework designed for the reduction of atmospheric contributions from UAS imagery in studies with limited site accessibility or data acquisition time constraints. The ELM radiometric calibration framework was developed specifically for water-based operations and the efficacy of using generalized study area calibration equations averaged across variable illumination and atmospheric conditions was assessed. The framework was effective in reducing atmospheric and other external contributions in UAS imagery. Unique to the proposed radiometric calibration framework is the radiance-to-reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equation development. While image-by-image calibrations are still preferred for high accuracy applications, this paper provides an ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in UAS imagery in situations with limited site accessibility or data acquisition constraints.

Comparison of material measures for areal surface topography measuring instrument calibration

The calibration of areal surface topography measuring instruments is a topic that is currently under discussion in international standard committees and a specification standard that defines the so-called ‘metrological characteristics’ has been published. For the broad industrial adoption of the metrological characteristics for calibration, however, clear and easy-to-apply calibration guidelines are required. Thus, a single-sample calibration artefact has been developed which allows the determination of the standardised metrological characteristics. In order to promote the adoption of the metrological characteristics calibration framework, measurements of the material measures with different instruments have been conducted and the results are presented. We report early work on the uncertainty of the comparison results and discuss systematic deviations between the various surface topography measuring instruments.

LHC-ATLAS Phase-I upgrade: calibration and simulation of a new trigger readout system for the Liquid Argon calorimeter

The ATLAS Liquid Argon Calorimeter group has been working on an upgrade of its trigger readout electronics. As part of this upgrade, new trigger “Super Cells” will be introduced, increasing the granularity of the trigger readout by a factor of ten with respect to Run 2. While the production and installation of the new electronics is underway, a calibration framework for the energy and timing measurement of the Super Cell readout is also being developed. The framework is created by using data taken in calibration runs with a pulsing system to establish the Super Cell pulse shape and other parameters, i.e. pedestal level, equivalent transverse energy for one ADC bit, and coefficients of an optimal filtering algorithm for energy calculation. The new hardware has been verified to meet the design specifications using the framework. A new simulation of the calorimeter, using the calibrated parameters obtained during the commissioning of the new trigger readout system, is also being developed. The simulation takes into consideration the long bipolar pulse shape for any number of interaction per bunch crossing. This accounts for highly realistic out-of-time pileup effect.

Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling

This paper demonstrates and validates the application of a recently proposed archetype modeling and calibration framework for setting up 11 stochastic archetype building energy models of Danish detached single-family houses (SFH’s). For this task, the municipal district heating system of Aarhus, Denmark, and its associated building stock were employed as case study, together comprising a dataset of 18 475 SFH’s with hourly time series heating data for two years (2017–2018). The 11 physics-based archetype models were each calibrated using a training building sample with data from a one-year calibration period (2017) and tested for their ability to forecast the heat load of another building sample in a previously unseen one-year validation period (2018). The calibrated archetype models were further tested for their joint forecasting ability to match the aggregated heat load of six suburban dwelling areas of different composition and location within the city region of Aarhus, and finally for their ability to forecast the entire citywide dataset of 18 475 SFH’s. The modeling framework performs very well for the aggregated citywide predictions with a practically non-existent bias of the overall heat load during the validation period (NMBE < 0.5%) and with only moderate inaccuracies present in hourly load predictions (MAPE < 12%). The high forecasting accuracy validates the application of the demonstrated archetype modeling framework for long-term urban-scale predictions; however, analysis of the time series errors indicate that the performance could be further improved by focusing on a better representation of the holiday periods and by ensuring the training data to be adequately informative to enable a good calibration of the model parameters. The simplicity of the archetype models coupled with the applied physics-based model structure makes the framework suitable for general energy planning purposes. By adapting a more dynamic model structure, it would also be possible to apply the framework for more complex analysis of, for instance, the urban-scale demand response and the general heating flexibility of the building stock.

Phasor measuring unit calibration considering topology expansion of electric power utilities

Phasor Measuring Units (PMU) is considered as a feasible alternative for power system data communication and for the preparation of smart grid. To operate multiple PMUs in stable mode, synchronization is a much essential procedure. After examining the concise literature survey on topical PMU applications, this paper step ahead by considering prevailing objectives such as: extension of network topology and PMU calibration considering bias errors. The appropriate deployment of PMUs and intimidating situation influence the reliance on physical configuration. Hence, it is obligatory to extend the topology of the networks. Also, calibrating the PMUs in a definite trail is crucial as the phasor measurements will be corrupted by bias error. This paper proposes a novel framework for bias error detection and calibration of PMU while expanding the topology network. With a whale optimization algorithm based method to improve the accuracy of PMU measurements for enhancing power system state estimation, monitoring and control operations. Case studies on the standard test systems have been conducted to test the efficacy of the intended tool.

Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images

AbstractPhysically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow.

Kinematic Calibration of Serial and Parallel Robots Based on Finite and Instantaneous Screw Theory

In current robot calibration approaches, the error propagation and identification of serial and parallel robots fail to be solved intuitively and generically, resulting in an inefficient calibration implementation and a low accuracy improvement. In this article, we present a generic error modeling method of serial robots and extend to parallel robots by finite and instantaneous screw (FIS) theory. The differential map and the explicit description of FIS on the robot motions enable a concise error modeling of the serial robot. The identifiability of errors in serial robot is discussed. The maximum independent errors are proved to be 4 r + 2 p + 6, where r and p are the numbers of revolute and prismatic joints, respectively. Based on the error mapping of serial limbs, reciprocal twist and wrench are introduced to consider the interaction among limbs and reveal the error propagation of the parallel robot. Then, the identification algorithms with high robustness and efficiency are investigated for the serial and parallel robots. Specifically, the conventional ill-conditioning problems of parallel robots are addressed. Finally, the proposed kinematic calibration framework for both types of robots are compared with the existing methods, and verified by simulations and experiments. The results show that our calibration approach improves the robot accuracy in a robust and efficient manner.

Calibration of parallel bond parameters in bonded particle models via physics-informed adaptive moment optimisation

This study proposes an automated calibration procedure for bond parameters in bonded discrete element modelling. By exploring the underlying physical correlations between microscopic parameters of bonds and macroscopic strength parameters of the continuum to be modelled, the microscopic shear strength and tensile strength are identified as independent variables for calibration purpose. Then a physics-informed iterative scheme is proposed to automatically approximate the bond parameters by viewing the micro-macro relation as an implicitly defined mathematical mapping function. As a result of highly non-convex features of this implicit mapping, the adaptive moment estimation (Adam), which is especially suitable for problems with noisy gradients, is adopted as the basic iterative scheme, in conjunction with other numerical techniques to approximately evaluate the partial derivatives involved. The whole procedure offers a simple and effective framework for bond parameter calibration. A numerical example of SiC ceramic is provided for validation. By compared with some existing calibration methods, the proposed method shows significant advantages in terms of calibration efficiency and accuracy.

On‐site surrogates for large‐scale calibration

AbstractMotivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limitations in the canonical apparatus stemming from several factors. We propose a new strategy of on‐site design and surrogate modeling for a computer simulator acting on a high‐dimensional input space that, although relatively speedy, is prone to numerical instabilities, missing data, and nonstationary dynamics. Our aim is to strike a balance between data‐faithful modeling and computational tractability in a calibration framework—tailoring the computer model to a limited field experiment. Situating our on‐site surrogates within the canonical calibration apparatus requires updates to that framework. We describe a novel yet intuitive Bayesian setup that carefully decomposes otherwise prohibitively large matrices by exploiting the sparse blockwise structure. Empirical illustrations demonstrate that this approach performs well on toy data and our motivating honeycomb example.

DEM calibration of cohesive material in the ring shear test by applying a genetic algorithm framework

This research demonstrates capturing different stress states and history dependency in a cohesive bulk material by DEM simulations. An automated calibration procedure, based on the Non-dominated Sorting Genetic Algorithm, is applied. It searches for the appropriate simulation parameters of an Elasto-Plastic Adhesive contact model such that its response is best fitted to the shear stress measured in experiments. Using this calibration procedure, the optimal set of DEM input parameters are successfully found to reproduce the measured shear stresses of the cohesive coal sample in two different pre-consolidation levels. The calibrated simulation resembles the stress history dependent values of shear stress, bulk density and wall friction. Through the case study of the ring shear tester, this research demonstrates the robustness and accuracy of the calibration framework using multi-objective optimization on multi-variable calibration problems irrespective of the chosen contact model.

A hybrid calibration approach to Hertz‐type contact parameters for discrete element models

SummaryThis study aims at providing a hybrid calibration framework to estimate Hertz‐type contact parameters (particle‐scale shear modulus and Poisson ratio) for both two‐dimensional and three‐dimensional discrete element modelling (DEM). On the basis of statistically isotropic granular packings, a set of analytical formulae between macroscopic material parameters (Young modulus and Poisson ratio) and particle‐scale Hertz‐type contact parameters for granular systems are derived under small‐strain isotropic stress conditions. However, the derived analytical solutions are only estimated values for general models. By viewing each DEM modelling as an implicit mathematical function taking the particle‐level parameters as independent variables and employing the derived analytical solutions as the initial input parameters, an automatic iterative scheme is proposed to obtain the calibrated parameters with higher accuracies. Considering highly nonlinear features and discontinuities of the macro‐micro relationship in Hertz‐based discrete element models, the adaptive moment estimation algorithm is adopted in this study because of its capacity of dealing with noise gradients of cost functions. The proposed method is validated with several numerical cases including randomly distributed monodisperse and polydisperse packings. Noticeable improvements in terms of calibration efficiency and accuracy have been made.

Calibration and Validation Framework for Selective Laser Melting Process Based on Multi-Fidelity Models and Limited Experiment Data

Abstract There are significant quality and reliability problems for components/products made by additive manufacturing (AM) due to various reasons. Selective laser melting (SLM) process is one of the popular AM techniques and it suffers from low quality and reliability issue as well. Among many reasons, the lack of accurate and efficient models to simulate the SLM process could be the most important one because reliability and quality quantification rely on accurate models; otherwise, a large number of experiments should be conducted for reliability and quality assurance. To date, modeling techniques for the SLM process are either computationally expensive based on finite element (FE) modeling or economically expensive requiring a significant amount of experiment data for data-driven modeling. This paper proposes the integration of FE and data-driven modeling with systematic calibration and validation framework for the SLM process based on limited experiment data. Multi-fidelity models are the FE model for the SLM process and a machine learning model constructed based on the FE model instead of real experiment data. The machine learning model, after incorporation of the learned physics from the FE model, is then further improved based on limited real experiment data through the calibration and validation framework. The proposed work enables the development of highly efficient and accurate models for melt pool prediction of the SLM process under various configurations. The effectiveness of the framework is demonstrated by real experiment data under 14 different printing configurations.

Calibration for Computer Experiments With Binary Responses and Application to Cell Adhesion Study

Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these parameters is important because it provides a scientific understanding of the underlying system which is not available in physical experiments. Most of the work in the literature is limited to the analysis of continuous responses. Motivated by a study of cell adhesion experiments, we propose a new calibration framework for binary responses. Its application to the T cell adhesion data provides insight into the unknown values of the kinetic parameters which are difficult to determine by physical experiments due to the limitation of the existing experimental techniques. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

A Novel Simultaneous Calibration and Localization Algorithm Framework for Indoor Scenarios

Under the navigational process of simultaneous beacon-calibrating and target-positioning, the real-time positioning and calibrating accuracy in indoor scenarios is significantly limited. To solve it, a novel Simultaneous Calibration and Localization (SCAL) algorithm framework for indoor scenarios is proposed. The proposed framework is mainly divided into the Target Localization and Beacon Calibration (TLBC) and the Global Optimization (GO) section: in the TLBC section, under the typical distributed beacon layout, a processing mechanism for synchronously locating the target and calibrating beacons is proposed; in GO section, parallel to TLBC section, a global optimization method based on Geometric Dilution of Precision based on Error-propagation (EGDOP) model is further proposed, and it can utilize the geometric trajectory of target and positioning error of target and beacons to efficiently and simultaneously optimize the positioning of target trajectory and beacons from TLBC section. The numerical simulation results show that the improvement of the GO section on the positioning performance is verified. Compared with the SCAL algorithm based on backward processing with inverse trajectory, the proposed framework has improved the accuracy of target and beacon positioning by 39.06% and 58.01%, respectively, and accuracy of positioning reaches 0.2557m and 0.2437m, respectively, in an actual indoor positioning scene.

Sensitivity Analysis‐Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China

AbstractModel parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO) algorithm. We use this framework to calibrate catchment‐specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25° spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters—the infiltration parameter (B) and two of the soil depth parameters (D1, D2)—are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash‐Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA‐based ASMO framework is an effective and efficient model‐optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions.

Analysis of the stress and deformation states in the vertical flat jack test

Performing a realistic assessment of unreinforced masonry structures involves designing and executing appropriate experimental tests on masonry components for determining the material model parameters to be used in structural analysis. Considering recent developments in which inverse analysis was used as calibration framework, a vertical flat-jack test is investigated in this paper. Two simplified models are described for the analysis of the stress state in the masonry due to the pressure transferred by the flat-jack. Furthermore, with the aim of designing the sensor setup for the test, a POD analysis on the deformation state of the structure is carried out, highlighting the basic deformation modes which govern the response. The results show that high stresses and local modes can occur in the proximity of the flat-jack, and thus local use of FRP reinforcement is recommended to avoid undesired brittle crack propagation which may prevent accurate calibration of mortar joint mechanical characteristics.