IMTHUA Methods for Thermal-Hydraulics Code Structure Uncertainty Assessment

2008 ◽  
Author(s):  
Mohammad Pourgol-Mohammad ◽  
Ali Mosleh ◽  
Mohammad Modarres
Keyword(s):  
Model Uncertainty ◽  
Uncertainty Assessment ◽  
Assessment Model ◽  
Test Facility ◽  
System Component ◽  
Thermal Hydraulics ◽  
Model Uncertainties ◽  
True Value ◽  
Code Structure ◽  
Model Mixing

A successful treatment of model uncertainty results in an expression of uncertainty that includes the true value at some stated level of confidence. Code structure uncertainties (model uncertainty) are a crucial source of uncertainty quantification for thermal-hydraulics (TH) system codes such as RELAP5, TRAC, and recently consolidated TRACE code. These codes are an assembly of models and correlations for simulation of physical phenomena and behavior of system component. In some cases there are alternative sub-models, or several different correlations for calculation of a single phenomenon of interest. There are also “user options” for choosing one of several models or correlations in performing a specific code computation. Dynamic characteristics of TH add more complexity to the code calculation, meaning for example, that specific code models and correlations invoked are sequence-dependent, and based certain (dynamic) conditions being satisfied. This paper discusses the techniques developed in the Integrated Methodology for Thermal-Hydraulics Uncertainty Analysis (IMTHUA), specifically for the treatment of uncertainties due to code structure and models. The methodology comprehensively covers various aspects of complex code uncertainty assessments for important accident transients. It considers the TH code structural uncertainties explicitly by treating internal sub-model uncertainties, and by propagating such model uncertainties in the code calculations (including uncertainties about input parameters.) Structural uncertainty assessment (model uncertainty) for a single model will be discussed by considering “correction factor”, “bias”, and also through sub-model output updating with available experimental evidence. In case of multiple alternative models, techniques of dynamic model switching, user controlled model selection, model mixing, and model maximization/minimization will be discussed. Examples from different applications including, Marviken test facility blowdown, LOFT LBLOCA and a typical PWR LOCA scenario calculations will be provided for greater clarification of the proposed techniques.

Thermal Hydraulics Structural Uncertainty Analysis: Approaches and Challenges

2010 ◽  
Author(s):  
Mohammad Pourgol-Mohamad
Keyword(s):  
Uncertainty Analysis ◽  
Model Uncertainty ◽  
Uncertainty Assessment ◽  
Assessment Model ◽  
Structural Uncertainty ◽  
Thermal Hydraulics ◽  
Model Uncertainties ◽  
Uncertainty Sources ◽  
Model Mixing ◽  
Structural Uncertainties

Model uncertainty is a relatively new topic of discussion in TH code calculations, despite being often the major contributor to the overall uncertainty and a challenging practice in uncertainty analysis. The Integrated thermal-hydraulics uncertainty analysis (IMTHUA) methodology, developed by the authors, treats the TH code structural uncertainties (generally known as model uncertainty) explicitly by treating internal sub-model uncertainties, and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. This paper presents systematic model uncertainty of thermal-hydraulics system codes as part of IMTHUA methodology. The objective is to demonstrate effectiveness and practicality of the methodology on complex thermal-hydraulics system codes calculations and discuss the challenges dealing with these types of uncertainty sources. TH codes are an assembly of models and correlations for simulation of physical phenomena and behavior of system parameters in temporal domain. In some cases, there are alternative sub-models, or several different correlations for calculation of a specific phenomenon of interest. There are also “user options” for choosing one of several models or correlations in performing a specific code computation. Dynamic characteristics of TH calculations add more complexity to the code calculation, meaning for example, that specific code models and correlations invoked are sequence-dependent, and based certain (dynamic) conditions being satisfied. Structural uncertainty assessment (model uncertainty) for a single model will be discussed by considering “correction factor”, “bias”, and also through Bayesian sub-model output updating with available experimental evidence. In case of multiple alternative models, several techniques including dynamic model switching, user controlled model selection, model mixing, will be discussed. This paper discusses the challenges in treatment of the structural uncertainties in Thermal-Hydraulics system codes. Subjectivity and dependency on expert judgment in some of the solutions leaves some concerns on context of such systematic solutions to utilize imperfect and partially relevant data and information.

A Systematic Approach for Severe Accident Uncertainty Analysis

2016 ◽  
Author(s):  
Seyed Mohsen Hoseyni ◽  
Mohammad Pourgol-Mohammad
Keyword(s):  
Uncertainty Analysis ◽  
Model Uncertainty ◽  
Uncertainty Assessment ◽  
Severe Accident ◽  
Test Facility ◽  
Model Uncertainties ◽  
Uncertainty Sources ◽  
Output Uncertainty ◽  
Input Parameters ◽  
Physical And Chemical

Uncertainty exists in every modeling process especially in those areas with complexity of the calculations like severe accident (SA) code which cover a broad range of physical and chemical phenomena. A systematic framework is proposed here for effective uncertainty assessment of SA computations by efficient use of available data and information. Available methodologies are either input-based or output based. The proposed methodology takes the advantages of both approaches and introduces an integrated one which quantifies the uncertainty of code input parameters (parameter uncertainty), code internal structure (model uncertainty) and code outputs (output uncertainty). The proposed methodology is comprisd of a hybrid qualitative and quantitative approach for identification of uncertainty sources. Using a Bayesian ensemble of sensitivity measures, identified severe accident phenomena are ranked according to their effect on the figure of merit. The other feature of the proposed methodology is the consideration of the SA code structural uncertainties (generally known as model uncertainty) explicitly by treating internal sub-model uncertainties and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. The code output is further updated through additional Bayesian updating with available experimental data from the integrated test facilities. In this paper, the key elements are discussed for the uncertainty analysis methodology and its application is demonstrated on the LP-FP2 experiment of LOFT test facility.

Uncertainty Propagation in Complex Codes Calculations

2013 ◽  
Author(s):  
Mohammad Pourgol-Mohammad
Keyword(s):  
Uncertainty Analysis ◽  
Extreme Values ◽  
Research Question ◽  
Uncertainty Propagation ◽  
Tolerance Limit ◽  
Test Facility ◽  
Second Phase ◽  
Thermal Hydraulics ◽  
Model Uncertainties ◽  
Time Step

The uncertainty propagation is an important segment of quantitative uncertainty analysis for complex computational codes (e.g., RELAP5 thermal-hydraulics) computations. Different sampling techniques, dependencies between uncertainty sources, and accurate inference on results are among the issues to be considered. The dynamic behavior of the system codes executed in each time step, results in transformation of accumulated errors and uncertainties to next time step. Depending on facility type, availability of data, scenario specification, computing machine and the software used, propagation of uncertainty results in considerably different results. This paper discusses the practical considerations of uncertainty propagation for code computations. The study evaluates the implications of the complexity on propagation of the uncertainties through inputs, sub-models and models. The study weighs different techniques of propagation, their statistics with considering their advantages and limitation at dealing with the problem. The considered methods are response surface, Monte Carlo (including simple, Latin Hypercube, and importance sampling) and boot-strap techniques. As a case study, the paper will discuss uncertainty propagation of the Integrated Methodology on Thermal-Hydraulics Uncertainty Analysis (IMTHUA). The methodology comprehensively covers various aspects of complex code uncertainty assessment for important accident transients. It explicitly examines the TH code structural uncertainties by treating internal sub-model uncertainties and by propagating such model uncertainties along with parameters in the code calculations. The two-step specification of IMTHUA (input phase following with the output updating) makes it special case to make sure that the figure of merit statistical coverage is achieved at the end with target confidence level. Tolerance limit statistics provide confidence a level on the level of coverage depending on the sample size, number of output measures, and one-sided or two-sided type of statistics. This information should be transferred to the second phase in the form of a probability distribution for each of the output measures. The research question is how to use data to develop such distributions from the corresponding tolerance limit statistics. Two approaches of using extreme values method and Bayesian updating are selected to estimate the parametric distribution parameters and compare the coverage in respect to the selected coverage criteria. The analysis is demonstrated on the large break loss of coolant accident for the LOFT test facility.

scholarly journals Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257849
Author(s):  
Muhammad Wasim ◽  
Ahsan Ali ◽  
Mohammad Ahmad Choudhry ◽  
Faisal Saleem ◽  
Inam Ul Hasan Shaikh ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Precision Agriculture ◽  
Model Uncertainty ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Wind Disturbance ◽  
Model Uncertainties ◽  
Nonlinear Simulation ◽  
Lumped Model ◽  
Disturbance Estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty

2019 ◽  
Author(s):  
Yanjun Zhang ◽  
Tingting Xia ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Real World ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Parameter Uncertainties ◽  
True Value ◽  
Uncertainty Model ◽  
The Difference

Abstract Various types of uncertainties, such as parameter uncertainty, model uncertainty, metamodeling uncertainty may lead to low robustness. Parameter uncertainty can be either epistemic or aleatory in physical systems, which have been widely represented by intervals and probability distributions respectively. Model uncertainty is formally defined as the difference between the true value of the real-world process and the code output of the simulation model at the same value of inputs. Additionally, metamodeling uncertainty is introduced due to the usage of metamodels. To reduce the effects of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering problems, both interval and probabilistic parameter uncertainties are likely to exist simultaneously in a single problem. However, few works have considered mixed interval and probabilistic parameter uncertainties together with other types of uncertainties. In this work, a general RO framework is proposed to deal with mixed interval and probabilistic parameter uncertainties, model uncertainty, and metamodeling uncertainty simultaneously in design optimization problems using the intervals-of-statistics approaches. The consideration of multiple types of uncertainties will improve the robustness of optimal designs and reduce the risk of inappropriate decision-making, low robustness and low reliability in engineering design. Two test examples are utilized to demonstrate the applicability and effectiveness of the proposed RO approach.

Hydrological model uncertainty assessment in southern Africa

2010 ◽  
Vol 387 (3-4) ◽  
pp. 221-232 ◽  
Author(s):  
D.A. Hughes ◽  
E. Kapangaziwiri ◽  
T. Sawunyama
Keyword(s):  
Southern Africa ◽  
Model Uncertainty ◽  
Hydrological Model ◽  
Uncertainty Assessment

Uncertainty Analysis Plus Importance in Severe Accident Calculations: Quantitative Evaluation of Important Parameters

2013 ◽  
Author(s):  
Seyed Mohsen Hoseyni ◽  
Mohammad Pourgol-Mohammad ◽  
Ali Abbaspour Tehranifard ◽  
Faramarz Yousefpour
Keyword(s):  
Uncertainty Analysis ◽  
Epistemic Uncertainty ◽  
Input Parameter ◽  
Uncertainty Assessment ◽  
Severe Accident ◽  
Test Facility ◽  
Sources Of Uncertainty ◽  
Output Uncertainty ◽  
Quantitative Results ◽  
Systematic Framework

This paper describes a systematic framework for quantifying the degree of contribution of each parameter to the uncertainty of the output in severe accident assessment. This research is an extension of the recent work of the authors on uncertainty assessment of severe accident calculations where the main sources of uncertainty are identified through the so-called modified PIRT approach. The proposed methodology here utilizes uncertainty importance measures for the quantification of the effect of each input parameter on the output uncertainty. A response surface fitting approach is proposed for estimating the associated uncertainties with less calculation cost. The quantitative results are used to plan in reducing epistemic uncertainty in the input variable(s). The application of the proposed methodology is demonstrated for the ACRR MP-2 severe accident test facility.

Validation and Uncertainty Analysis of the Thermal Hydraulics Module of SOCRAT-BN Code on the Rod Bundle Experiment

2015 ◽  
Vol 1084 ◽  
pp. 717-721
Author(s):  
Yulia Vinogradova ◽  
Nikolai Ryzhov ◽  
Ruslan Chalyy
Keyword(s):  
Heat Transfer ◽  
Uncertainty Analysis ◽  
Reactor Core ◽  
Thermal Hydraulics ◽  
Fast Reactors ◽  
Model Uncertainties ◽  
Fuel Rod ◽  
Rod Bundle ◽  
Code Model ◽  
Design Basis

SOCRAT-BN code is developed for the analysis of design and beyond design basis accidents at sodium cooled fast reactors. To simulate the behavior of the coolant in the reactor core heat transfer and friction in rod bundle geometry are required to consider. The article describes the validation of the code SOCRAT-BN on the experiment with fuel rod imitators in the triangular geometry with wire-wound taking into account experiment and some code model uncertainties.

Equipment Uncertainty Assessment due to Voltage Sags Considering the Uncertainties of Invalidation Event and its Severity

2013 ◽  
Vol 321-324 ◽  
pp. 1347-1350
Author(s):  
Da Yang ◽  
Ying Wang ◽  
Xian Yong Xiao
Keyword(s):  
Uncertainty Assessment ◽  
Comprehensive Assessment ◽  
Fuzzy Entropy ◽  
Assessment Model ◽  
Cross Entropy ◽  
Its Sequence ◽  
Voltage Sags ◽  
Equipment Failure ◽  
Estimation Problems ◽  
Quantitative Assessments

Considering the complicated uncertainty of the occurrence of equipment failure and the serious level of its sequence due to voltage sags, this paper classifies the invalided samples into three categories. The corresponding uncertainty is measured by random entropy, fuzzy entropy and cross entropy respectively. A maximum hybrid entropy assessment model is proposed. Based on the quantitative assessments of equipment invalidation rates, the comprehensive assessment is carried out according to the corresponding weight. Compared with existing methods, the proposed method is able to overcome both over- and under-estimation problems, with the results in line with practice very well.

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