Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatibility Models

2018 ◽  
pp. 157-172
Author(s):  
Nicola Toscani ◽  
Flavia Grassi ◽  
Giordano Spadacini ◽  
Sergio A. Pignari
Keyword(s):  
Uncertainty Quantification ◽  
Electromagnetic Compatibility ◽  
Probabilistic Approach

scholarly journals Probabilistic approach for deriving immunity levels against electromagnetic disturbances

2010 ◽  
Vol 23 (1) ◽  
pp. 45-53
Author(s):  
Bernd Jaekel ◽  
Darren Carpenter
Keyword(s):  
Electromagnetic Compatibility ◽  
Classification Scheme ◽  
Probabilistic Approach ◽  
General Description ◽  
Continuous Change ◽  
Electromagnetic Environment ◽  
Technical Literature ◽  
Communication Services ◽  
Electromagnetic Disturbances ◽  
Essential Prerequisite

Knowledge of electromagnetic environments existing at locations where equipment is intended to be operated is an essential prerequisite for achievement of electromagnetic compatibility. The necessity of such a knowledge results from the fact that suitable immunity requirements can be concluded from corresponding data regarding the electromagnetic environment. This information should include the type as well as the characteristics of electromagnetic phenomena occurring at those locations. Appropriate information can be obtained by various approaches, such as site surveys or technical assessments accompanied by evaluations of technical literature. It is obvious that the description of a general electromagnetic environment should consider a multitude of phenomena having a broad spectrum of parameters. However, in many cases such a general description is not helpful because it would imply that immunity against all such phenomena has to be taken into consideration. A more practical approach consists in introducing a classification scheme which gives a correlation between locations and the electromagnetic phenomena existing there. This concept forms the basis of the EMC publication IEC 61000-2-5. Because of continuous change of existing technologies and introduction of new ones, electromagnetic environments in a certain respect change fundamentally. This is very obvious in case of all the recently introduced radio and communication services with the generation of electromagnetic fields in the frequency range above 1 GHz. Hence the need arises to monitor continuously the electromagnetic environment and to adapt its description. Such adaptation is currently being done by a working group of IEC TC 77. The results of its work is object of the next edition of IEC 61000-2-5. The present status as well as expected changes are discussed in this paper. .

scholarly journals The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing

2019 ◽  
Vol 58 (9) ◽  
pp. 2019-2032 ◽  
Author(s):  
Jennie Molinder ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Peter Hessling
Keyword(s):  
Uncertainty Quantification ◽  
Wind Power ◽  
Probabilistic Approach ◽  
Power Production ◽  
Cold Climate ◽  
Empirical Modeling ◽  
Model Ensemble ◽  
Parameter Uncertainties ◽  
Deterministic Sampling ◽  
The Impact

AbstractA novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

Uncertainty Quantification of the Crosstalk in Multiconductor Transmission Lines via Degree Adaptive Stochastic Response Surface Method

2021 ◽  
Vol 36 (2) ◽  
pp. 174-183
Author(s):  
Quanyi Yu ◽  
Wei Liu ◽  
Kaiyu Yang ◽  
Xilai Ma ◽  
Tianhao Wang
Keyword(s):  
Uncertainty Quantification ◽  
Response Surface ◽  
Response Surface Method ◽  
Transmission Lines ◽  
Electromagnetic Compatibility ◽  
Probability Density Distribution ◽  
Stochastic Response ◽  
Multiconductor Transmission Lines ◽  
Stochastic Response Surface Method ◽  
Surface Method

The degree adaptive stochastic response surface method is applied to analyze statistically the crosstalk in multiconductor transmission lines (MTLs). The coefficient of polynomial chaos expansion (PCE) is obtained based on the least angle regression. The truncation degree of PCE is iterated using the degree adaptive truncation algorithm, and the optimal proxy model of the crosstalk of the original MTLs that satisfies the actual error requirements is calculated. The statistical properties of crosstalk in MTLs (such as mean, standard deviation, skewness, kurtosis, and probability density distribution) are obtained. The failure probability of the electromagnetic compatibility in the MTLs system is considered. The global sensitivity indices of crosstalk-related factors are analyzed. Finally, the proposed method is proved to be effective compared with the conventional Monte Carlo method. The uncertainty quantification of crosstalk in MTLs can be calculated efficiently and accurately.

A Probabilistic Approach to the NASA Langley Multidisciplinary Uncertainty Quantification Challenge Problem

2014 ◽  
Author(s):  
Roger G. Ghanem ◽  
Charanraj Thimissetti ◽  
iman yadegaran ◽  
Vahid Kasharvazaddeh ◽  
Sami Masri ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Probabilistic Approach

A New Probabilistic Approach for Uncertainty Quantification in Well Performance of Shale Gas Reservoirs

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 2038-2048 ◽  
Author(s):  
Wei Yu ◽  
Xiaosi Tan ◽  
Lihua Zuo ◽  
Jenn-Tai Liang ◽  
Hwa C. Liang ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Shale Gas ◽  
Probabilistic Approach ◽  
Model Parameters ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Production Forecasting ◽  
Shale Gas Reservoirs ◽  
Decline Curve ◽  
Curve Model

Summary Over the past decade, technological advancements in horizontal drilling and multistage fracturing enable natural gas to be economically produced from tight shale formations. However, because of limited availability of the production data as well as the complex gas-transport mechanisms and fracture geometries, there still exist great uncertainties in production forecasting and reserves estimation for shale gas reservoirs. The rapid pace of shale gas development makes it important to develop a new and efficient probabilistic-based methodology for history matching, production forecasting, reserves estimates, and uncertainty quantification that are critical for the decision-making processes. In this study, we present a new probabilistic approach with the Bayesian methodology combined with Markov-chain Monte Carlo (MCMC) sampling and a fractional decline-curve (FDC) model to improve the efficiency and reliability of the uncertainty quantification in well-performance forecasting for shale gas reservoirs. The FDC model not only can effectively capture the long-tail phenomenon of shale gas-production decline curves but also can obtain a narrower range of production prediction than the classical Arps model. To predict the posterior distributions of the decline-curve model parameters, we use a more-efficient adaptive Metropolis (AM) algorithm in place of the standard Metropolis-Hasting (MH) algorithm. The AM algorithm can form the Markov chain of decline-curve model parameters efficiently by incorporating the correlation between the model parameters. With the predicted posterior distributions of the FDC model parameters generated by the AM algorithm, the uncertainty in production forecasts and estimated-ultimate-recovery (EUR) prediction can then be quantified. This work provides an efficient and robust tool that is based on a new probabilistic approach for production forecasting, reserves estimations, and uncertainty quantification for shale gas reservoirs.

Uncertainty Quantification of Load Effects under Stochastic Traffic Flows

2018 ◽  
Vol 19 (01) ◽  
pp. 1940009 ◽  
Author(s):  
He-Qing Mu ◽  
Qin Hu ◽  
Hou-Zuo Guo ◽  
Tian-Yu Zhang ◽  
Cheng Su
Keyword(s):  
Probability Distribution ◽  
Uncertainty Quantification ◽  
Optimal Parameter ◽  
Probability Model ◽  
Probabilistic Approach ◽  
Bridge Engineering ◽  
Traffic Load ◽  
Distribution Model ◽  
Load Effect ◽  
Load Effects

Load effect characterization under traffic flow has received tremendous attention in bridge engineering, and uncertainty quantification (UQ) of load effect is critical in the inference process. Bayesian probabilistic approach is developed to overcome the unreliable issue caused by negligence of uncertainty of parametric and modeling aspects. Stochastic traffic load simulation is conducted by embedding the random inflow component into the Nagel–Schreckenberg (NS) model, and load effects are calculated by stochastic traffic load samples and influence lines. Two levels of UQ are performed for traffic load effect characterization: at parametric level of UQ, not only the optimal parameter values but also the associated uncertainties are identified; at model level of UQ, rather than using a single prescribed probability model for load effects, a set of probability distribution model candidates is proposed, and model probability of each candidate is evaluated for selecting the most suitable/plausible probability distribution model. Analytic work was done to give closed-form solutions for the expression involved in both parametric and model UQ. In the simulated examples, the efficiency and robustness of the proposed approach are firstly validated, and UQ are performed to different load effect data achieved by varying the structural span length under the changing total traffic volume. It turns out that the uncertainties of load effects are traffic-specific and response-specific, so it is important to conduct UQ of load effects under different traffic scenarios by using the developed approach.

scholarly journals A probabilistic approach with built-in uncertainty quantification for the calibration of a superelastic constitutive model from full-field strain data

2021 ◽  
Vol 192 ◽  
pp. 110357
Author(s):  
Harshad M. Paranjape ◽  
Kenneth I. Aycock ◽  
Craig Bonsignore ◽  
Jason D. Weaver ◽  
Brent A. Craven ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Uncertainty Quantification ◽  
Probabilistic Approach ◽  
Field Strain ◽  
Full Field ◽  
Strain Data
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