scholarly journals Model Uncertainty: A Challenge in Nonlinear Coupled Multidisciplinary System Design

2018 ◽  
Author(s):  
Alexander W. Feldstein ◽  
David Lazzara ◽  
Norman Princen ◽  
Karen E. Willcox
Keyword(s):  
System Design ◽  
Model Uncertainty ◽  
Multidisciplinary System

Presenting a Multi-Level Superstructure Optimization Approach for Mechatronic System Design

2010 ◽  
Author(s):  
Henrik C. Pedersen ◽  
Torben O. Andersen ◽  
Michael R. Hansen ◽  
Michael M. Bech
Keyword(s):  
System Design ◽  
Integrated Design ◽  
Design Approach ◽  
Optimization Approach ◽  
Mechatronic System ◽  
Mechatronic Systems ◽  
Analysis And Design ◽  
Basic Set ◽  
Multi Level ◽  
Multidisciplinary System

Synergism and integration in the design process is what sets apart a Mechatronic System from a traditional, multidisciplinary system. However the typical design approach has been to divide the design problem into sub problems for each technology area (mechanics, electronics and control) and describe the interface between the technologies, whereas the lack of well-established, systematic engineering methods to form the basic set-off in analysis and design of complete mechatronic systems has been obvious. The focus of the current paper is therefore to present an integrated design approach for mechatronic system design, utilizing a multi-level superstructure optimization based approach. Finally two design examples are presented and the possibilities and limitations of the approach are outlined.

AGILE Satellite Thermal Control System Design: Thermal Model Uncertainty Reduction

2007 ◽  
Author(s):  
Marco Molina ◽  
Matteo Giacomazzo ◽  
Paolo Sabatini ◽  
Christian Vettore ◽  
Giovanni Annoni ◽  
...  
Keyword(s):  
Control System ◽  
System Design ◽  
Model Uncertainty ◽  
Thermal Model ◽  
Thermal Control ◽  
Uncertainty Reduction ◽  
Control System Design ◽  
Thermal Control System ◽  
Agile Satellite

Design Space Exploration for Quantifying a System Model’s Feasible Domain

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Brad J. Larson ◽  
Christopher A. Mattson
Keyword(s):  
System Design ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
System Model ◽  
Design Decisions ◽  
System Models ◽  
Model Domain ◽  
Compositional System ◽  
Multidisciplinary System

A major challenge in multidisciplinary system design is predicting the effects of design decisions at the point these decisions are being made. Because decisions at the beginning of system design, when the least is known about the new system, have the greatest impact on its final behavior, designers are increasingly interested in using compositional system models (system models created from independent models of system components) to validate design decisions early in and throughout system design. Compositional system models, however, have several failure modes that often result in infeasible or failed model evaluation. In addition, these models change frequently as designs are refined, changing the model domain (set of valid inputs and states). To compute valid results, the system model inputs and states must remain within this domain throughout simulation. This paper develops an algorithm to efficiently quantify the system model domain. To do this, we (1) present a formulation for system model feasibility and identify types of system model failures, (2) develop a design space exploration algorithm that quantifies the system model domain, and (3) illustrate this algorithm using a solar-powered unmanned aerial vehicle model. This algorithm enables systematic improvements of compositional system model feasibility.

A Spatial-Random-Process Based Multidisciplinary System Uncertainty Propagation Approach With Model Uncertainty

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Zhen Jiang ◽  
Wei Li ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Uncertainty Quantification ◽  
Random Process ◽  
Model Uncertainty ◽  
Electronic Packaging ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Statistical Moments ◽  
Robust Design Optimization ◽  
System Uncertainty ◽  
Multidisciplinary System

The performance of a multidisciplinary system is inevitably affected by various sources of uncertainties, usually categorized as aleatory (e.g., input variability) or epistemic (e.g., model uncertainty) uncertainty. In the framework of design under uncertainty, all sources of uncertainties should be aggregated to assess the uncertainty of system quantities of interest (QOIs). In a multidisciplinary design system, uncertainty propagation (UP) refers to the analysis that quantifies the overall uncertainty of system QOIs resulting from all sources of aleatory and epistemic uncertainty originating in the individual disciplines. However, due to the complexity of multidisciplinary simulation, especially the coupling relationships between individual disciplines, many UP approaches in the existing literature only consider aleatory uncertainty and ignore the impact of epistemic uncertainty. In this paper, we address the issue of efficient uncertainty quantification of system QOIs considering both aleatory and epistemic uncertainties. We propose a spatial-random-process (SRP) based multidisciplinary uncertainty analysis (MUA) method that, subsequent to SRP-based disciplinary model uncertainty quantification, fully utilizes the structure of SRP emulators and leads to compact analytical formulas for assessing statistical moments of uncertain QOIs. The proposed method is applied to a benchmark electronic packaging design problem. The estimated low-order statistical moments of the QOIs are compared to the results from Monte Carlo simulations (MCSs) to demonstrate the effectiveness of the method. The UP result is then used to facilitate the robust design optimization of the electronic packaging system.

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