A multi-fidelity analysis selection method using a constrained discrete optimization formulation

Metaheuristic optimization;Analysis selection;Uncertainty;Model uncertainty;Epistemic uncertainty;Discrete optimization

Stults, Ian Collier ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Metaheuristic optimization;    Analysis selection;    Uncertainty;    Model uncertainty;    Epistemic uncertainty;    Discrete optimization;   

Others  :  https://smartech.gatech.edu/bitstream/1853/31706/1/stults_ian_c_200912_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method that will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results.