Verification, validation and uncertainty quantification in multi-physics modeling for nuclear reactor design and safety analysis

Avramova, Maria; Ivanov, Kostadin

doi:10.1016/j.pnucene.2010.03.009

Cited by 64 publications

(39 citation statements)

References 2 publications

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“…We estimate posterior distributions of these roughness parameters using the Metropolis Hastings algorithm and select their mode as the values to be used in the surrogate model defined in Equations (7), (8) and (9). Once the roughness parameters r have been estimated, the surrogate model in Equation (7) is fully defined.…”

Section: Multivariate Surrogate Modelmentioning

confidence: 99%

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Mahmoudi

Tapia

Karayagiz

et al. 2018

Integr Mater Manuf Innov

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Metal additive manufacturing (AM) typically suffers from high degree of variability in the properties/performance of the fabricated parts, particularly due to the lack of understanding and control over the physical mechanisms that govern microstructure formation during fabrication. This paper directly addresses an important problem in AM: the determination of the thermal history of the deposited material. Any attempts to link process to microstructure in AM would need to consider the thermal history of the material. In-situ monitoring only provides partial information and simulations may be necessary to have a comprehensive understanding of the thermo-physical conditions to which the deposited material is subjected. We address this in the present work through linking thermal models to experiments via a computationally efficient surrogate modeling approach based on multivariate Gaussian processes (MVGPs). The MVGPs are then used to calibrate the free parameters of the multi-physics models against experiments, sidestepping the use of prohibitively expensive Monte Carlo-based calibration. This framework thus makes it possible to efficiently evaluate the impact of varying process parameter inputs on the characteristics of the melt pool during AM. We demonstrate the framework on the calibration of a thermal model for Laser-Powder Bed Fusion AM of Ti-6Al-4V against experiments carried out over a wide window in the process parameter space. While this work deals with problems related to AM, its applicability is wider as the proposed framework could potentially be used in many other ICME-based problems where it is essential to link expensive computational materials science models to available experimental data.

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Section: Multivariate Surrogate Modelmentioning

confidence: 99%

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Mahmoudi

Tapia

Karayagiz

et al. 2018

Integr Mater Manuf Innov

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“…Other complete or partial verification and V/UQ frameworks can be found in the literature [30][31][32][33]. We have modified the framework presented by Bayarri et.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

Jatale

Smith

Thornock

et al. 2015

Flow Turbulence Combust

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A framework for verification and validation/uncertainty quantification of largescale, highly coupled, complex processes is presented. Steps in the framework include code and solution verification, development of an input/uncertainty map, definition of evaluation criteria, development of a surrogate model, performance of a consistency analysis (a mathematical statement limiting error), and the feed forward and feedback of information gain from the analysis. This framework is applied to a turbulent, buoyant, 1 m diameter helium plume. Experimental measurements are obtained from Sandia National Laboratory while the helium plume simulations are performed using Large Eddy Simulation. Both parameter and experimental uncertainty associated with the helium plume are reduced by requiring consistency.

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“…As a consequence of many meetings, the international expert community in reactor physics, thermal-hydraulics, and uncertainty and sensitivity analysis, had decided that a first step was to define an OECD benchmark for Uncertainty Analysis in Modelling (UAM) for design, operation, and safety analysis of LWRs. Polytechnic University of Madrid (UPM) has participated in the Phase 1 (Neutronics Phase) of this benchmark in the Exercise I-1 [1].…”

Section: Introductionmentioning

confidence: 99%

Importance of Nuclear Data Uncertainties in Criticality Calculations

Ceresio

Cabellos

Martínez

et al. 2012

EPJ Web of Conferences

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Abstract. The aim of this paper is to study the importance of nuclear data uncertainties in the prediction of the uncertainties in k eff for LWR (Light Water Reactor) unit-cells. The first part of this work is focused on the comparison of different sensitivity/uncertainty propagation methodologies based on TSUNAMI and MCNP codes; this study is undertaken for a fresh-fuel at different operational conditions. The second part of this work studies the burnup effect where the indirect contribution due to the uncertainty of the isotopic evolution is also analyzed.

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Verification, validation and uncertainty quantification in multi-physics modeling for nuclear reactor design and safety analysis

Cited by 64 publications

References 2 publications

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive Manufacturing

Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

Importance of Nuclear Data Uncertainties in Criticality Calculations

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