scholarly journals Model-form and predictive uncertainty quantification in linear aeroelasticity

2017 ◽  
Vol 73 ◽  
pp. 137-161 ◽  
Author(s):  
C.T. Nitschke ◽  
P. Cinnella ◽  
D. Lucor ◽  
J.-C. Chassaing
Keyword(s):  
Uncertainty Quantification ◽  
Predictive Uncertainty ◽  
Model Form

Global Optimization Under Uncertainty and Uncertainty Quantification Applied to Tractor-Trailer Base Flaps

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Jacob A. Freeman ◽  
Christopher J. Roy
Keyword(s):  
Global Optimization ◽  
Wind Speed ◽  
Uncertainty Quantification ◽  
Numerical Approximation ◽  
Epistemic Uncertainty ◽  
Optimization Under Uncertainty ◽  
Optimized Design ◽  
Predictive Uncertainty ◽  
Model Form ◽  
Design Variables

Using a global optimization evolutionary algorithm (EA), propagating aleatory and epistemic uncertainty within the optimization loop, and using computational fluid dynamics (CFD), this study determines a design for a 3D tractor-trailer base (back-end) drag reduction device that reduces the wind-averaged drag coefficient by 41% at 57 mph (92 km/h). Because it is optimized under uncertainty, this design is relatively insensitive to uncertain wind speed and direction and uncertain deflection angles due to mounting accuracy and static aeroelastic loading. The model includes five design variables with generous constraints, and this study additionally includes the uncertain effects on drag prediction due to truck speed and elevation, steady Reynolds-averaged Navier–Stokes (RANS) approximation, and numerical approximation. This study uses the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) optimization and uncertainty quantification (UQ) framework to interface the RANS flow solver, grid generator, and optimization algorithm. The computational model is a simplified full-scale tractor-trailer with flow at highway speed. For the optimized design, the estimate of total predictive uncertainty is +15/−42%; 8–10% of this uncertainty comes from model form (computation versus experiment); 3–7% from model input (wind speed and direction, flap angle, and truck speed); and +0.0/−28.5% from numerical approximation (due to the relatively coarse, 6 × 106 cell grid). Relative comparison of designs to the no-flaps baseline should have considerably less uncertainty because numerical error and input variation are nearly eliminated and model form differences are reduced. The total predictive uncertainty is also presented in the form of a probability box, which may be used to decide how to improve the model and reduce uncertainty.

Case studies of predictive uncertainty quantification for geothermal models

Geothermics ◽  
2021 ◽  
Vol 97 ◽  
pp. 102263
Author(s):  
Jericho Omagbon ◽  
John Doherty ◽  
Angus Yeh ◽  
Racquel Colina ◽  
John O'Sullivan ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Case Studies ◽  
Predictive Uncertainty

scholarly journals Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

2017 ◽  
Vol 93 ◽  
pp. 351-367 ◽  
Author(s):  
Kendra L. Van Buren ◽  
Morvan Ouisse ◽  
Scott Cogan ◽  
Emeline Sadoulet-Reboul ◽  
Laurent Maxit
Keyword(s):  
Uncertainty Quantification ◽  
Frequency Range ◽  
Coupled Models ◽  
Model Form

scholarly journals Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models

2021 ◽  
pp. 1-35
Author(s):  
Rishabh Singh ◽  
Jose C. Principe
Keyword(s):  
Uncertainty Quantification ◽  
Quantum Physics ◽  
Reproducing Kernel ◽  
Epistemic Uncertainty ◽  
Reproducing Kernel Hilbert Space ◽  
Gradient Flow ◽  
Network Models ◽  
Neural Network Models ◽  
Decomposition Problem ◽  
Predictive Uncertainty

This letter introduces a new framework for quantifying predictive uncertainty for both data and models that rely on projecting the data into a gaussian reproducing kernel Hilbert space (RKHS) and transforming the data probability density function (PDF) in a way that quantifies the flow of its gradient as a topological potential field quantified at all points in the sample space. This enables the decomposition of the PDF gradient flow by formulating it as a moment decomposition problem using operators from quantum physics, specifically Schrödinger's formulation. We experimentally show that the higher-order modes systematically cluster the different tail regions of the PDF, thereby providing unprecedented discriminative resolution of data regions having high epistemic uncertainty. In essence, this approach decomposes local realizations of the data PDF in terms of uncertainty moments. We apply this framework as a surrogate tool for predictive uncertainty quantification of point-prediction neural network models, overcoming various limitations of conventional Bayesian-based uncertainty quantification methods. Experimental comparisons with some established methods illustrate performance advantages that our framework exhibits.

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