Robust Design and Uncertainty Quantification for Managing Risks in Engineering

AbstractComputer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.

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Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization

Journal of the Global Power and Propulsion Society ◽

10.33737/jgpps/138902 ◽

2021 ◽

pp. 1-16

Author(s):

Dirk Wunsch ◽

Charles Hirsch

Keyword(s):

Design Optimization ◽

Uncertainty Quantification ◽

Robust Design ◽

Principal Component ◽

Robust Design Optimization ◽

Marine Propeller ◽

Reconstruction Accuracy ◽

The Impact ◽

Deformation Modes

Methodologies to quantify the impact of manufacturing uncertainties in 3D CFD based design strategies have become increasingly available over the past years as well as optimization under uncertainties, aiming at reducing the systems sensitivity to manufacturing uncertainties. This type of non-deterministic simulation depends however strongly on a correct characterization of the manufacturing variability. Experimental data to characterize this variability is not always available or in many cases cannot be sampled in sufficiently high numbers. Principal Component Analysis (PCA) is applied to the sampled geometries and the influence of tolerances classes, sample size and number of retained deformation modes are discussed. It is shown that the geometrical reconstruction accuracy of the deformation modes and reconstruction accuracy of the CFD predictions are not linearly related, which has important implications on the total geometrical variance that needs to be retained. In a second application the characterization of manufacturing uncertainties to a marine propeller is discussed. It is shown that uncertainty quantification and robust design optimization of the marine propeller can successfully be performed on the basis of the derived uncertainties. This leads to a propeller shape that is less sensitive to the manufacturing variability and therefore to a more robust design.

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Uncertainty Quantification for a Flapping Airfoil with Chordwise Flexure

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.308-310.1448 ◽

2011 ◽

Vol 308-310 ◽

pp. 1448-1453

Author(s):

Liang Yu Zhao ◽

Cheng You Xing ◽

Xia Qing Zhang

Keyword(s):

Normal Distribution ◽

Uncertainty Quantification ◽

Robust Design ◽

Polynomial Chaos ◽

Flapping Wing ◽

Thrust Coefficient ◽

Propulsive Efficiency ◽

Point Collocation ◽

Flapping Airfoil ◽

Gauss Distribution

The uncertainty quantification for a flexible flapping airfoil was investigated using the point-collocation non-intrusive polynomial chaos method. The chordwise flexible amplitude was assumed to obey a normal distribution. It is observed that the time-averaged thrust coefficient obeys a Gauss-like but not the exact Gauss distribution, while the probability of the propulsive efficiency is much different from the exact Gauss distribution. The effect of the chordwise flexure on the time-averaged thrust coefficient is much larger than the effect on the propulsive efficiency. This work could be a preparation for the robust design of a flexible flapping wing with respect to a stochastic chordwise flexure.

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