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    Journal of Verification, Validation and Uncertainty Quantification

    EISSN: 2377-2166
    ISSN: 2377-2158
    Priority: 4
    Publisher: American Society of Mechanical Engineers
    Description: The Journal of Verification, Validation and Uncertainty Quantification (JVVUQ) disseminates original and applied research, illustrative examples, and high quality validation experimental data from leaders in the field of VVUQ as applied to: Design of More ...

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    Integrating Bayesian Calibration, Bias Correction, and Machine Learning for the 2014 Sandia Verification and Validation Challenge Problem 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2016:;volume( 001 ):;issue: 001:;page 11004
    Author(s): Unknown author
    Publisher: The American Society of Mechanical Engineers (ASME)
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    A Validation of Flare Combustion Efficiency Predictions From Large Eddy Simulations 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2016:;volume( 001 ):;issue: 002:;page 21001
    Author(s): Unknown author
    Publisher: The American Society of Mechanical Engineers (ASME)
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    Verification Assessment of Piston Boundary Conditions for Lagrangian Simulation of Compressible Flow Similarity Solutions 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2016:;volume( 001 ):;issue: 002:;page 21003
    Author(s): Unknown author
    Publisher: The American Society of Mechanical Engineers (ASME)
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    Special Issue: Sandia V&V Challenge Problem 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2016:;volume( 001 ):;issue: 001:;page 10201
    Author(s): Unknown author
    Publisher: The American Society of Mechanical Engineers (ASME)
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    A New Extrapolation-Based Uncertainty Estimator for Computational Fluid Dynamics 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2016:;volume( 001 ):;issue: 004:;page 41006
    Author(s): Phillips, Tyrone S.; Roy, Christopher J.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A new Richardson extrapolation-based uncertainty estimator is developed which utilizes a global order of accuracy. The most significant difference between the proposed uncertainty estimator (referred to as the global ...
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    Multiscale Validation and Uncertainty Quantification for Problems With Sparse Data 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2017:;volume( 002 ):;issue: 001:;page 11001
    Author(s): Jatale, Anchal; Smith, Philip J.; Thornock, Jeremy N.; Smith, Sean T.; Spinti, Jennifer P.; Hradisky, Michal
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Quantification of uncertainty in the simulation results becomes difficult for complex real-world systems with little or no experimental data. This paper describes a validation and uncertainty quantification (VUQ) approach ...
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    Mitigating Gibbs Phenomena in Uncertainty Quantification With a Stochastic Spectral Method 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2017:;volume( 002 ):;issue: 001:;page 11003
    Author(s): Tagade, Piyush M.; Choi, Han-Lim
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The use of spectral projection-based methods for simulation of a stochastic system with discontinuous solution exhibits the Gibbs phenomenon, which is characterized by oscillations near discontinuities. This paper investigates ...
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    Definition and Implementation of a Method for Uncertainty Aggregation in Component-Based System Simulation Models 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2017:;volume( 002 ):;issue: 001:;page 11006
    Author(s): Eek, Magnus; Gavel, Hampus; Ölvander, Johan
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Component-based system simulation models are used throughout all development phases for design and verification of both physical systems and control software, not least in the aeronautical industry. However, the application ...
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    Dynamics Model Validation Using Time-Domain Metrics 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2017:;volume( 002 ):;issue: 001:;page 11004
    Author(s): Ao, Dan; Hu, Zhen; Mahadevan, Sankaran
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Validation of dynamics model prediction is challenging due to the involvement of various sources of uncertainty and variations among validation experiments and over time. This paper investigates quantitative approaches for ...
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    A Robust Approach to Quantification of Margin and Uncertainty 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2017:;volume( 002 ):;issue: 001:;page 11005
    Author(s): Segalman, Daniel J.; Paez, Thomas L.; Bauman, Lara E.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A systematic approach to defining margin in a manner that incorporates statistical information and accommodates data uncertainty but does not require assumptions about specific forms of the tails of distributions is ...
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