The Monte Carlo Method for evaluating measurement uncertainty: Application for determining the properties of materials

Motra, Hem Bahadur; Hildebrand, Jörg; Wuttke, Frank

doi:10.1016/j.probengmech.2016.04.005

Cited by 20 publications

(8 citation statements)

References 23 publications

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“…Monte Carlo techniques are already common practice in different areas (e.g. Dobney et al, 2000;Hackett et al, 2006;Breivik and Allen, 2008;Melsom et al, 2012;Motra et al, 2016;Duraisamy and Iaccarino, 2017) and a common tool for sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Rabatel¹,

Rampal²,

Carrassi³

et al. 2018

The Cryosphere

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Abstract. We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the freedrift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.

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Section: Introductionmentioning

confidence: 99%

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Rabatel¹,

Rampal²,

Carrassi³

et al. 2018

The Cryosphere

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“…Guilleminot et al [77] used Monte Carlo method in order to take into account the fluctuation in mechanical properties of random interphase in polymeric materials reinforced by nanoparticles. Various other uncertainty analyses have also been carried out thanks to the numerical solver such as contact mechanics [78], dynamical systems [79], measurement of mechanical properties [80] or viscoelastic composite structures [81]. This method has an ability to propagate variability of input variables to the output results by repeating randomly input sampling [82].…”

Section: Methods Usedmentioning

confidence: 99%

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Pham

et al. 2019

Materials

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This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

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“…But MCM uses the random sampling of probability distributions for distributed propagation and its scope of application is more extensive than GUM. 7 Bernstein and Weckenmann 8 introduced the uncertainty analysis of a new optical multi-sensor measurement system, used GUM to test the uncertainty of four typical industrial environments and explored the potential of multi-sensor measurement methods. Shi et al 9,10 studied the uncertainty of the different design parameters in different microelectromechanical system (MEMS) pressure sensors with slit structure and analyzed the influence of phase drift and different slit sizes on performance, which provided a reference for the optimal design of sensors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of measurement uncertainty of a surface acoustic wave micro-pressure sensor

Huang

Jiang

et al. 2019

Measurement and Control

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As an important support for test and control projects, sensor's performance is directly related to the accuracy of the measurement. To fully analyze the sources of measurement uncertainty for a surface acoustic wave micro-pressure sensor, in this study the Monte Carlo method and Guide to the Expression of Uncertainty in Measurement to evaluate measurement uncertainty of sensors are used, the sensing experiment was conducted and the measurement addition model was established. We determined the source of measurement uncertainty for a surface acoustic wave micro-pressure sensor. The results show that the Monte Carlo method can obtain a more reliable and accurate inclusion interval in the measurement uncertainty evaluation of a surface acoustic wave micro-pressure sensor.

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The Monte Carlo Method for evaluating measurement uncertainty: Application for determining the properties of materials

Cited by 20 publications

References 23 publications

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Analysis of measurement uncertainty of a surface acoustic wave micro-pressure sensor

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