Application of Bayesian Theory to Interval Based Representation of Epistemic Uncertainty for a Decomposed Multilevel Optimization Framework

2016 ◽  
Author(s):  
Ian D. Dettwiller ◽  
Masoud Rais-Rohani
Keyword(s):  
Epistemic Uncertainty ◽  
Bayesian Theory ◽  
Multilevel Optimization ◽  
Optimization Framework

Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map Using a Bayesian Approach

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
David Gorsich ◽  
Paramsothy Jayakumar ◽  
Monica Majcher
Keyword(s):  
Design Optimization ◽  
Epistemic Uncertainty ◽  
Uncertainty Reduction ◽  
Soil Conditions ◽  
Mobility Prediction ◽  
Soil Maps ◽  
Optimization Framework ◽  
Uncertainty Sources ◽  
Vehicle Mobility ◽  
Testing Design

Abstract The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role in vehicle mobility prediction and mission planning. The complicated vehicle–terrain interactions and the presence of heterogeneous uncertainty sources in the modeling and simulation (M&S) result in epistemic uncertainty/errors in the vehicle mobility prediction for given terrain and soil conditions. In this paper, the uncertainty sources that cause the uncertainty in mobility prediction are first partitioned into two levels, namely uncertainty in the M&S and uncertainty in terrain and soil maps. With a focus on the epistemic uncertainty in the M&S, this paper presents a testing design optimization framework to effectively reduce the uncertainty in the M&S and thus increase the confidence in generating off-road mobility maps. A Bayesian updating approach is developed to reduce the epistemic uncertainty/errors in the M&S using mobility testing data collected under controllable terrain and soil conditions. The updated models are then employed to generate the off-road mobility maps for any given terrain and soil maps. Two types of design strategies, namely testing design for model selection and testing design for uncertainty reduction, are investigated in the testing design framework to maximize the information gain subject to limited resources. Results of a numerical example demonstrate the effectiveness of the proposed mobility testing design optimization framework.

Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map

2019 ◽  
Author(s):  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
David Gorsich ◽  
Paramsothy Jayakumar ◽  
Monica Majcher
Keyword(s):  
Design Optimization ◽  
Epistemic Uncertainty ◽  
Uncertainty Reduction ◽  
Soil Conditions ◽  
Mobility Prediction ◽  
Soil Maps ◽  
Optimization Framework ◽  
Uncertainty Sources ◽  
Vehicle Mobility ◽  
Testing Design

Abstract The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role in vehicle mobility prediction and mission planning. The complicated vehicle-terrain interactions and the presence of heterogeneous uncertainty sources in the modeling and simulation (M&S) result in epistemic uncertainty/errors in the vehicle mobility prediction for given terrain and soil conditions. In this paper, the uncertainty sources that cause the uncertainty in mobility prediction are first partitioned into two levels, namely uncertainty in the M&S and uncertainty in terrain and soil maps. With a focus on the epistemic uncertainty in the M&S, this paper presents a testing design optimization framework to effectively reduce the uncertainty in the M&S and thus increase the confidence in generating off-road mobility maps. A Bayesian updating approach is developed to reduce the epistemic uncertainty/errors in the M&S using mobility testing data collected under controllable terrain and soil conditions. The updated models are then employed to generate off-road mobility maps for any given terrain and soil maps. Two types of design strategies, namely testing design for model selection and testing design for uncertainty reduction, are investigated in the testing design framework to maximize the information gain subject to limited resources. Results of a numerical example demonstrate the effectiveness of the proposed mobility testing design optimization framework.

Multilevel Optimization Framework for Hierarchical Stiffened Shells Accelerated by Adaptive Equivalent Strategy

2016 ◽  
Vol 24 (3) ◽  
pp. 575-592 ◽  
Author(s):  
Bo Wang ◽  
Kuo Tian ◽  
Haixin Zhao ◽  
Peng Hao ◽  
Tianyu Zhu ◽  
...  
Keyword(s):  
Multilevel Optimization ◽  
Stiffened Shells ◽  
Optimization Framework

STRUCTURAL RELIABILITY ANALYSIS BASED ON P-BOXES

2020 ◽  
Vol 92 (6) ◽  
pp. 51-58
Author(s):  
S.A. SOLOVYEV ◽  
Keyword(s):  
Probability Distribution ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Epistemic Uncertainty ◽  
Random Variable ◽  
Strength Criterion ◽  
Structural Elements ◽  
Structural Element ◽  
Limit States ◽  
Distribution Shape

The article describes a method for reliability (probability of non-failure) analysis of structural elements based on p-boxes. An algorithm for constructing two p-blocks is shown. First p-box is used in the absence of information about the probability distribution shape of a random variable. Second p-box is used for a certain probability distribution function but with inaccurate (interval) function parameters. The algorithm for reliability analysis is presented on a numerical example of the reliability analysis for a flexural wooden beam by wood strength criterion. The result of the reliability analysis is an interval of the non-failure probability boundaries. Recommendations are given for narrowing the reliability boundaries which can reduce epistemic uncertainty. On the basis of the proposed approach, particular methods for reliability analysis for any structural elements can be developed. Design equations are given for a comprehensive assessment of the structural element reliability as a system taking into account all the criteria of limit states.

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