Decoupling uncertainty quantification from robust design optimization

2019 ◽  
Vol 59 (6) ◽  
pp. 1969-1990 ◽  
Author(s):  
Tanmoy Chatterjee ◽  
Rajib Chowdhury ◽  
Palaniappan Ramu
Keyword(s):  
Design Optimization ◽  
Uncertainty Quantification ◽  
Robust Design ◽  
Robust Design Optimization

scholarly journals Uncertainty quantification and robust design optimization applied to aircraft propulsion systems

2018 ◽  
Vol 29 ◽  
pp. 289-302 ◽  
Author(s):  
Marco Panzeri ◽  
Andrey Savelyev ◽  
Kirill Anisimov ◽  
Roberto d’Ippolito ◽  
Artur Mirzoyan
Keyword(s):  
Design Optimization ◽  
Uncertainty Quantification ◽  
Robust Design ◽  
Robust Design Optimization ◽  
Propulsion Systems ◽  
Aircraft Propulsion

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

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.

ERGO: a new Robust Design Optimization Technique combining Multi-Objective Bayesian Optimization with Analytical Uncertainty Quantification

2021 ◽  
pp. 1-22
Author(s):  
Jolan Wauters
Keyword(s):  
Design Optimization ◽  
Uncertainty Quantification ◽  
Robust Design ◽  
Bayesian Optimization ◽  
Robust Design Optimization ◽  
The Novel ◽  
Analytical Uncertainty ◽  
Multi Objective ◽  
Optimization Framework ◽  
Quantities Of Interest

Abstract In this work, robust design optimization (RDO) is treated, motivated by the increasing desire to account for variability the design phase. The problem is formulated in a multi-objective setting with the objective of simultaneously minimizing the mean of the objective and its variance due to variability of design variables and/or parameters. This allows the designer to choose its robustness level without the need to repeat the optimization as typically encountered when formulated as a single objective. To account for the computational cost that is often encountered in RDO problems, the problem is fitted in a Bayesian optimization framework. The use of surrogate modeling techniques to efficiently solve problems under uncertainty has effectively found its way in the optimization community leading to surrogate-assisted optimization-under-uncertainty schemes. The surrogates are often considered cheap-to-sample black-boxes and are sampled to obtain the desired quantities of interest. However, since the analytical formulation of the surrogates is known, an analytical treatment of the problem is available. To obtain the quantities of interest without sampling an analytical uncertainty propagation through the surrogate is presented. The multi-objective Bayesian optimization framework and the analytical uncertainty quantification are linked together through the formulation of the robust expected improvement (REI), obtaining the novel efficient robust global optimization (ERGO) scheme. The method is tested on a series of test cases to examine its behavior for varying difficulties and validated on an aerodynamic test function which proves the effectiveness of the novel scheme.

Dimension reduction method for reliability-based robust design optimization

2008 ◽  
Vol 86 (13-14) ◽  
pp. 1550-1562 ◽  
Author(s):  
Ikjin Lee ◽  
K.K. Choi ◽  
Liu Du ◽  
David Gorsich
Keyword(s):  
Dimension Reduction ◽  
Design Optimization ◽  
Robust Design ◽  
Reduction Method ◽  
Robust Design Optimization ◽  
Dimension Reduction Method

Robust Design Optimization for Crashworthiness of Vehicle Side Impact

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Souvik Chakraborty ◽  
Tanmoy Chatterjee ◽  
Rajib Chowdhury ◽  
Sondipon Adhikari
Keyword(s):  
Design Optimization ◽  
New Product Development ◽  
Robust Design ◽  
Automobile Industry ◽  
Human Error ◽  
Differential Evolution Algorithm ◽  
Side Impact ◽  
Robust Design Optimization ◽  
Parameter Uncertainties ◽  
Design Engineers

Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, a deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modeling imperfections, parameter uncertainties, and/or human error. In this study, an operational model-based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). An adaptive framework for determining the optimum basis order in PCFE has also been presented. It is argued that the coupled DEA–PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphases on the two objectives have also been performed. For all the cases, DEA–PCFE is found to yield highly accurate results.

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