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

Jatale, Anchal; Smith, Philip J.; Thornock, Jeremy; Smith, Sean; Spinti, Jennifer P.; Hradisky, Michal

doi:10.1007/s10494-015-9612-6

Cited by 9 publications

(5 citation statements)

References 44 publications

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“…34 According to the modeling technique in different domains, a lot of verification and validation (V&V) frameworks have been proposed for domain model assessment. [35][36][37] They summarized the critical issues of a domain model, integrated particular verification tools, and included several statistical tests to implement a complete evaluation process. Basically, these frameworks are domain-dependent.…”

Section: The Overall Framework Of Model Credibility Evaluationmentioning

confidence: 99%

A pattern-based validation method for the credibility evaluation of simulation models

2019

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Measuring the credibility of a simulation model has always been challenging due to its growing uncertainty and complexity. During the past decades, plenty of metrics and evaluation procedures have been developed for evaluating different sorts of simulation models. Most of the existing research focuses on the direct comparison of numerical results with a group of reference data. However, it is sometimes unsuitable for evolving dynamic models such as the multi-agent models. With the same condition, both the practical system and the simulation model perform highly dynamic actions. The credibility of the model with insufficient information, non-stationary states and changing environment is unable to acquire through a direct pair comparison. This paper presents a pattern-based validation method to complementarily extract hidden patterns that exist in both a simulation model and its reference data, and assess the model performance in a different aspect. Firstly, multi-dimensional perceptually important points strategy is modified to find the patterns exist in time-serial data. Afterward, a pattern organizing topology is applied to automatically depict required pattern from reference data and assess the performance of the corresponding simulation model. The extensive case study on three simulation models shows the effectiveness of the proposed method.

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Section: The Overall Framework Of Model Credibility Evaluationmentioning

confidence: 99%

A pattern-based validation method for the credibility evaluation of simulation models

2019

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“…The minimum grid spacing used by Maragkos et al [10] was 1.23 cm, with a maximum spacing of 5.39 cm, resulting in a puffing frequency of 1.41 Hz with an SGS model and 1.31 Hz without an SGS model. More recently, Jatale et al [11] presented and applied a framework for uncertainty quantification to the 1 m diameter helium plume using the experimental data and LES.…”

Section: A Backgroundmentioning

confidence: 99%

“…Buoyancy-driven flows are ubiquitous in nature and engineering, spanning natural phenomena as diverse as thermals in the ocean, volcanic plumes, and wildland fires, as well as engineering applications such as flue gas systems and burners for industrial processing. With the increasing availability and decreasing cost of high performance computing resources, numerical simulations are being used more and more to study these flows, in particular using large eddy (e.g., [1][2][3][4][5][6][7][8][9][10][11]) and direct numerical (e.g., [1,6]) simulations (LES and DNS, respectively). However, prior attempts to reproduce experimental results for larger-scale buoyancy-driven flows, particularly in the context of LES for meterscale plumes and pool fires [12], have proven difficult, limiting the use of simulations for the understanding and design of many real-world systems.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Numerical Simulations of a Large-Scale Helium Plume Using Adaptive Mesh Refinement

Wimer,

Day,

Lapointe

et al. 2019

Preprint

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The physical characteristics and evolution of a large-scale helium plume are examined through a series of numerical simulations with increasing physical resolution using adaptive mesh refinement (AMR). The five simulations each model a 1 m diameter circular helium plume exiting into a (4 m) 3 domain, and differ solely with respect to the smallest scales resolved using the AMR, spanning resolutions from 15.6 mm down to 0.976 mm. As the physical resolution becomes finer, the heliumair shear layer and subsequent Kelvin-Helmholtz instability are better resolved, leading to a shift in the observed plume structure and dynamics. In particular, a critical resolution is found between 3.91 mm and 1.95 mm, below which the mean statistics and frequency content of the plume are altered by the development of a Rayleigh-Taylor instability near the centerline in close proximity to the base of the plume. This shift corresponds to a plume "puffing" frequency that is slightly higher than would be predicted using empirical relationships developed for buoyant jets. Ultimately, the high-fidelity simulations performed here are intended as a new validation dataset for the development of subgrid-scale models used in large eddy simulations of real-world buoyancy-driven flows.

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“…The approach developed by the American Society of Mechanical Engineers (ASME) is the standard for verification and validation in computational fluid dynamics and heat transfer [7]. The boundto-bound consistency analysis approach was developed by Frenklach and coworkers [8] and used by Oberkampf and Roy [1], Jatale et al [9,10], and Schroeder [11]. A six-step framework for validation of computer models called simulator assessment and validation engine (SAVE) was developed by the National Institute of Statistical Sciences group [12].…”

Section: Description Of Validation and Uncertainty Quantification Appmentioning

confidence: 99%

A Validation/Uncertainty Quantification Analysis for a 1.5 MW Oxy-Coal Fired Furnace: Sensitivity Analysis

Oscar

Spinti

Fry

et al. 2018

Journal of Verification, Validation and Uncertainty Quantification

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A validation/uncertainty quantification (VUQ) study was performed on the 1.5 MWth L1500 furnace, an oxy-coal fired facility located at the Industrial Combustion and Gasification Research Facility at the University of Utah. A six-step VUQ framework is used for studying the impact of model parameter uncertainty on heat flux, the quantity of interest (QOI) for the project. This paper focuses on the first two steps of the framework. The first step is the selection of model outputs in the experimental and simulation data that are related to the heat flux: incident heat flux, heat removal by cooling tubes, and wall temperatures. We describe the experimental facility, the operating conditions, and the data collection process. To obtain the simulation data, we utilized two tools, star-ccm+ and Arches. The star-ccm+ simulations captured flow through the complex geometry of the swirl burner while the Arches simulations captured multiphase reacting flow in the L1500. We employed a filtered handoff plane to couple the two simulations. In step two, we developed an input/uncertainty (I/U) map and assigned a priority to 11 model parameters based on prior knowledge. We included parameters from both a char oxidation model and an ash deposition model in this study. We reduced the active parameter space from 11 to 5 based on priority. To further reduce the number of parameters that must be considered in the remaining steps of the framework, we performed a sensitivity analysis on the five parameters and used the results to reduce the parameter set to two.

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

Cited by 9 publications

References 44 publications

A pattern-based validation method for the credibility evaluation of simulation models

A pattern-based validation method for the credibility evaluation of simulation models

High-Resolution Numerical Simulations of a Large-Scale Helium Plume Using Adaptive Mesh Refinement

A Validation/Uncertainty Quantification Analysis for a 1.5 MW Oxy-Coal Fired Furnace: Sensitivity Analysis

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