Driving efficiency in design for rare events using metamodeling and optimization
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Rare events have very low probability of occurrence but can have significant impact. Earthquakes, volcanoes, and stock market crashes can have devastating impact on those affected. In industry, engineers evaluate rare events to design better high-reliability systems. The objective of this work is to increase efficiency in design optimization for rare events using metamodeling and variance reduction techniques. Opportunity exists to increase deterministic optimization efficiency by leveraging Design of Experiments to build an accurate metamodel of the system which is less resource intensive to evaluate than the real system. For computationally expensive models, running many trials will impede fast design iteration. Accurate metamodels can be used in place of these expensive models to probabilistically optimize the system for efficient quantification of rare event risk. Monte Carlo is traditionally used for this risk quantification but variance reduction techniques such as importance sampling allow accurate quantification with fewer model evaluations. Metamodel techniques are the thread that tie together deterministic optimization using Design of Experiments and probabilistic optimization using Monte Carlo and variance reduction. This work will explore metamodeling theory and implementation, and outline a framework for efficient deterministic and probabilistic system optimization. The overall conclusion is that deterministic and probabilistic simulation can be combined through metamodeling and used to drive efficiency in design optimization. Applications are demonstrated on a gas turbine combustion autoignition application where user controllable independent variables are optimized in mean and variance to maximize system performance while observing a constraint on allowable probability of a rare autoignition event.
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