A Universal MDO Framework Based on the Adaptive Discipline Surrogate Model

Abstract

High time-consuming computation has become an obvious characteristic of the modern multidisciplinary design optimization (MDO) solving procedure. To reduce the computing cost and improve solving environment of the traditional MDO solution method, this article introduces a novel universal MDO framework based on the support of adaptive discipline surrogate model with asymptotical correction by discriminative sampling. The MDO solving procedure is decomposed into three parts: framework level, architecture level, and discipline level. Framework level controls the MDO solving procedure and carries out convergence estimation; architecture level executes the MDO solution method with discipline surrogate models; discipline level analyzes discipline models to establish adaptive discipline surrogate models based on a stochastic asymptotical sampling method. The MDO solving procedure is executed as an iterative way included with discipline surrogate model correcting, MDO solving, and discipline analyzing. These are accomplished by the iteration process control at the framework level, the MDO decomposition at the architecture level, and the discipline surrogate model update at the discipline level. The framework executes these three parts separately in a hierarchical and modularized way. The discipline models and disciplinary design point sampling process are all independent; parallel computing could be used to increase computing efficiency in parallel environment. Several MDO benchmarks are tested in this MDO framework. Results show that the number of discipline evaluations in the framework is half or less of the original MDO solution method and is very useful and suitable for the complex high-fidelity MDO problem.