Browse Publications Technical Papers 2013-01-1384
2013-04-08

Reliability-Based Design Optimization with Model Bias and Data Uncertainty 2013-01-1384

Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties. The proposed technique is demonstrated through a vehicle design problem aiming at minimizing the vehicle weight through gauge optimization while satisfying reliability constraints. The RBDO result considering model uncertainty is compared with the one from conventional RBDO to demonstrate the benefits of the proposed method.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 18% off list price.
Login to see discount.
We also recommend:
TECHNICAL PAPER

Peak Pressure Position Estimation from Structure-Borne Sound

2005-01-0039

View Details

TECHNICAL PAPER

Bionic Optimization of Air-Guiding Systems

2004-01-1377

View Details

JOURNAL ARTICLE

Model-Based Optimization of a Hydraulic Backhoe using Multi-Attribute Utility Theory

2009-01-0565

View Details

X