Historical perspectives of BEPU research in US

Rohatgi, U.S.; Kaizer, Joshua

doi:10.1016/j.nucengdes.2019.110430

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…When the QoI is timedependent, oscillatory or discontinuous, a significant deal of engineering judgment should be used to pinpoint numerous QoIs from the corresponding time series. (7) It is recommended that CIRCÉ should not be used for more than 3 parameters simultaneously [20]. As pointed in [105], when more than 1 parameter is considered, there are multiple ways to explain the discrepancies between the model and data, making IUQ an ill-posed problem.…”

Section: The Circé Methods Developed By Ceamentioning

confidence: 99%

“…In the late 1980s, best-estimate (BE) safety analysis strategy started to be embedded in the Code Scaling Applicability and Uncertainty (CSAU) [1] [2], and Evaluation Models Development and Assessment Procedure (EMDAP) [3] [4] methodologies, which were accepted by the U.S. Nuclear Regulatory Commission. This strategy is commonly referred to as Best Estimate plus Uncertainty (BEPU) [5] [6] [7]. The goal of BEPU aims to capture the physical phenomena as realistically as possible by implementing a wide range of modeling options and accurate calculation methods to capture physical phenomena at a greater fidelity.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

Wu,

Xie,

Alsafadi

et al. 2021

Preprint

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Uncertainty Quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions. The concept of UQ in the nuclear community generally means forward UQ (FUQ), in which the information flow is from the inputs to the outputs. Inverse UQ (IUQ), in which the information flow is from the model outputs and experimental data to the inputs, is an equally important component of UQ but has been significantly underrated until recently. FUQ requires knowledge in the input uncertainties which has been specified by expert opinion or user self-evaluation. IUQ is defined as the process to inversely quantify the input uncertainties based on experimental data.This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the IUQ methodologies that have been used on the physical models in system thermal-hydraulics codes. IUQ methods can be categorized by three main groups: frequentist (deterministic), Bayesian (probabilistic), and empirical (design-ofexperiments). We used eight metrics to evaluate an IUQ method, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. Twelve IUQ methods are reviewed, compared, and evaluated based on these eight metrics. Such comparative evaluation will provide a good guidance for users to select a proper IUQ method based on the IUQ problem under investigation.

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Section: The Circé Methods Developed By Ceamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

Wu,

Xie,

Alsafadi

et al. 2021

Preprint

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“…BEPU necessarily provides focus on the controlling physics and assures that codes have the necessary capabilities. Early applications of BEPU (Code Scaling, Applicability, and Uncertainty (CSAU) and Evaluation Model Development and Assessment Process (EMDAP), see Rohatgi and Kaizer [2020]) did not explicitly require evidence that code capabilities were implemented correctly (i.e., that they were free of defects). In the arena of modern engineering codes, we now require evidence that codes be developed and maintained according to accepted standards of software quality assurance (SQA).…”

Section: Introductionmentioning

confidence: 99%

Code and Solution Verification Assessment of the CTF Thermal Hydraulic Subchannel Code

Salko¹,

Pilch²,

Kumar³

2021

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ABBREVIATIONS BEPU Best Estimate Plus Uncertainty CASL Consortium for Advanced Simulation of Light Water Reactors CHF critical heat flux CILC crud-induced localized corrosion CIPS crud-induced power shift CSAU Code Scaling, Applicability, and Uncertainty DNB departure from nucleate boiling EMDAP Evaluation Model Development and Assessment Process LBLOCA large-break loss-of-coolant accident LHS left-hand side LOCA loss-of-coolant accident NPP nuclear power plant PWR pressurized water reactor RHS right-hand side RIA reactivity insertion accident SQA software quality assurance TKE turbulent kinetic energy VERA Virtual Environment for Reactor Applications

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“…Uncertainty Quantification (UQ) [1] is the process to quantify the uncertainties in Quantityof-Interest (QoIs) by propagating the uncertainties in input parameters through the computer model. The Best Estimate plus Uncertainty (BEPU) approach [2] [3] for nuclear systems M&S requires that the prediction uncertainty must be quantified in order to prove that the investigated designs stay within acceptance criteria.…”

Section: Introductionmentioning

confidence: 99%

Towards Improving the Predictive Capability of Computer Simulations by Integrating Inverse Uncertainty Quantification and Quantitative Validation with Bayesian Hypothesis Testing

Xie,

Alsafadi,

2021

Preprint

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The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems modeling and simulation requires that the prediction uncertainty must be quantified in order to prove that the investigated design stays within acceptance criteria. A rigorous Uncertainty Quantification (UQ) process should simultaneously consider multiple sources of quantifiable uncertainties: (1) parameter uncertainty due to randomness or lack of knowledge; (2) experimental uncertainty due to measurement noise; (3) model uncertainty caused by missing/incomplete physics and numerical approximation errors, and (4) code uncertainty when surrogate models are used. In this paper, we propose a comprehensive framework to integrate results from inverse UQ and quantitative validation to provide robust predictions so that all these sources of uncertainties can be taken into consideration.Inverse UQ quantifies the parameter uncertainties based on experimental data while taking into account uncertainties from model, code and measurement. In the validation step, we use a quantitative validation metric based on Bayesian hypothesis testing. The resulting metric, called the Bayes factor, is then used to form weighting factors to combine the prior and posterior knowledge of the parameter uncertainties in a Bayesian model averaging process. In this way, model predictions will be able to integrate the results from inverse UQ and validation to account for all available sources of uncertainties. This framework is a step towards addressing the ANS Nuclear Grand Challenge on "Simulation/Experimentation" by bridging the gap between models and data.

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Historical perspectives of BEPU research in US

Cited by 19 publications

References 11 publications

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

Code and Solution Verification Assessment of the CTF Thermal Hydraulic Subchannel Code

Towards Improving the Predictive Capability of Computer Simulations by Integrating Inverse Uncertainty Quantification and Quantitative Validation with Bayesian Hypothesis Testing

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