论文标题
具有经验确定的输入域的混合实验的顺序设计，并应用了对核燃料棒的损害信用惩罚
Sequential Design of Mixture Experiments with an Empirically Determined Input Domain and an Application to Burn-up Credit Penalization of Nuclear Fuel Rods
论文作者
论文摘要
本文提出了一种在未知优化域上最大化随机计算机模拟器输出y（x）的顺序设计。用于估计优化域的培训数据通常是由模拟器建模的物理系统组成的（历史）输入。提供了两种估计模拟器输入域的方法。提出了众所周知的有效全局优化算法的扩展，以最大化y（x）。域估计/最大化程序应用于两个很容易理解的分析示例。它也用于通过最大化二维燃料棒的K效率“关键性系数”（被认为是一维异质裂隙介质）来解决核安全问题。两个域估计方法之一依赖于专业知识类型的约束。我们表明，这些约束最初被选为解决用过的燃料杆示例，因为它们在第二个分析优化示例中也可以取得良好的结果。当然，在其他应用中，有必要设计更适合这些应用程序的替代约束。
This paper proposes a sequential design for maximizing a stochastic computer simulator output, y(x), over an unknown optimization domain. The training data used to estimate the optimization domain are a set of (historical) inputs, often from a physical system modeled by the simulator. Two methods are provided for estimating the simulator input domain. An extension of the well-known efficient global optimization algorithm is presented to maximize y(x). The domain estimation/maximization procedure is applied to two readily understood analytic examples. It is also used to solve a problem in nuclear safety by maximizing the k-effective "criticality coefficient" of spent fuel rods, considered as one-dimensional heterogeneous fissile media. One of the two domain estimation methods relies on expertise-type constraints. We show that these constraints, initially chosen to address the spent fuel rod example, are robust in that they also lead to good results in the second analytic optimization example. Of course, in other applications, it could be necessary to design alternative constraints that are more suitable for these applications.
