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A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis
Optimization and Engineering ( IF 2.1 ) Pub Date : 2020-05-18 , DOI: 10.1007/s11081-020-09504-z
Spyridon G. Kontogiannis , Mark A. Savill

The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and reliable. For such problems, minimum computational cost while providing global optimality and extensive design information at an early conceptual stage is desired. However, such a complex problem consisting of various objectives and interacting disciplines is associated with a challenging design space. This provides a large pool of possible designs, requiring an efficient exploration scheme with the ability to provide sufficient feedback early in the design process. This paper demonstrates a generalized optimization framework with rapid design space exploration capabilities in which a Multifidelity approach is directly adjusted to the emerging needs of the design. The methodology is developed to be easily applicable and efficient in computationally expensive multidisciplinary problems. To accelerate such a demanding process, Surrogate Based Optimization methods in the form of both Radial Basis Function and Kriging models are employed. In particular, a modification of the standard Kriging approach to account for Multifidelity data inputs is proposed, aiming to increasing its accuracy without increasing its training cost. The surrogate optimization problem is solved by a Particle Swarm Optimization algorithm and two constraint handling methods are implemented. The surrogate model modifications are visually demonstrated in a 1D and 2D test case, while the Rosenbrock and Sellar functions are used to examine the scalability and adaptability behaviour of the method. Our particular Multiobjective formulation is demonstrated in the common RAE2822 airfoil design problem. In this paper, the framework assessment focuses on our infill sampling approach in terms of design and objective space exploration for a given computational cost.

中文翻译:

使用代理建模和多保真度分析的多学科设计优化的通用方法

多学科设计的优势已广为人知,但尚未被方法应快速且可靠的行业完全采用。对于这样的问题,需要最小的计算成本,同时在概念的早期阶段提供全局最优性和广泛的设计信息。但是,这种由各种目标和相互作用的学科组成的复杂问题与富有挑战性的设计空间有关。这提供了大量可能的设计,因此需要一种有效的探索方案,并且必须能够在设计过程的早期提供足够的反馈。本文演示了一种具有快速设计空间探索功能的通用优化框架,其中可以根据设计的新兴需求直接调整Multifidelity方法。该方法被开发为在计算上昂贵的多学科问题中易于应用和有效。为了加快这种苛刻的过程,采用了基于径向基函数和Kriging模型形式的基于替代的优化方法。特别是,提出了对标准Kriging方法进行修改以解决多保真数据输入的问题,目的是在不增加培训成本的情况下提高其准确性。通过粒子群优化算法解决了代理优化问题,并实现了两种约束处理方法。在1D和2D测试用例中直观地演示了替代模型的修改,而Rosenbrock和Sellar函数用于检查该方法的可伸缩性和适应性行为。我们常见的RAE2822机翼设计问题证明了我们特殊的多目标公式。在本文中,对于给定的计算成本,框架评估着重于我们在设计和目标空间探索方面的填充采样方法。
更新日期:2020-05-18
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