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Mitigating Error and Uncertainty in Partitioned Analysis: A Review of Verification, Calibration and Validation Methods for Coupled Simulations

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Abstract

Partitioned analysis involves coupling of constituent models that resolve different scales or physics by allowing them to exchange inputs and outputs in an iterative manner. Through partitioning, simulations of complex physical systems are becoming evermore present in the scientific modeling community, making the Verification and Validation (V&V) of partitioned models to quantifying the predictive capability of their simulations increasingly important. Partitioning presents unique challenges, as well as opportunities, for the V&V community. Verification gains a new level of complexity in partitioned models, as numerical errors can easily be introduced at the coupling interface where non-matching domains and models are integrated together. For validation, partitioned analysis allows the quantification of the uncertainties and errors in constituent models through comparison against separate-effect experiments conducted in independent constituent domains. Such experimental validation is important as uncertainties and errors in the predictions of constituents can be transferred across their interfaces, either compensating for each other or accumulating during iterative coupling operations. This paper reviews published literature on methods for assessing and improving the predictive capability of strongly coupled models of physical and engineering systems with an emphasis on advancements made in the last decade.

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Notes

  1. Verification procedures are further broken down into code verification (ensuring the computer code has been written correctly) and solution verification (quantifying numerical errors introduced by factors such as round-off, iterations, and discretization) [5]. Herein, we focus on solution verification.

  2. “Constituent” is used to define a model representing an isolated physical phenomena or behavior within a scale. “Component” involves the coupling of some constituents, but does not resolve the full system.

  3. The term “numerical error” is an umbrella term indicating three main sources of error: round-off, truncation, and discretization. We note that when one does not have a “truth” to compare to, the term numerical error can be referred to as “numerical uncertainty.” Herein the focus of the following sections is relative to discretization as this is the factor most influential in introducing errors in coupled models.

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Acknowledgments

This works has been supported by the Department of Energy Office of Nuclear Energy’s Nuclear Energy University Programs through Grant number 00101999 and by the Department of Education Graduate Assistance in Areas of National Need through Grant number P200A120222.

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  1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA

    Garrison Stevens & Sez Atamturktur

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Dr. Atamturktur has received research grants from 4SE Structural Engineers company and serves as a member of the ASME Verification & Validation 10 committee and the Society of Experimental Mechanics Model Validation and Uncertainty Quantification committee.

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Stevens, G., Atamturktur, S. Mitigating Error and Uncertainty in Partitioned Analysis: A Review of Verification, Calibration and Validation Methods for Coupled Simulations. Arch Computat Methods Eng 24, 557–571 (2017). https://doi.org/10.1007/s11831-016-9177-0

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  • DOI: https://doi.org/10.1007/s11831-016-9177-0

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