Bayesian optimization with active learning of design constraints using an entropy-based approach
Bayesian optimization with active learning of design constraints using an entropy-based approach
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DOI：
10.1038/s41524-023-01006-7
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发表时间：
2023-04
影响因子：
9.7
通讯作者：
Danial Khatamsaz;Brent Vela;Prashant Singh;Duane D. Johnson;D. Allaire;R. Arróyave
中科院分区：
文献类型：
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作者：
Danial Khatamsaz;Brent Vela;Prashant Singh;Duane D. Johnson;D. Allaire;R. Arróyave
设计用于燃气涡轮机发动机叶片的合金是一项复杂的任务，涉及平衡多个目标和约束。候选合金必须在室温下具有延展性，并在高温下保持其屈服强度，以及具有低密度、高导热性、窄凝固范围、高固相线温度和小的线性热膨胀系数。传统的集成计算材料工程（ICME）方法不足以探索组合巨大的合金设计空间，优化多个目标，也不能确保满足多个约束。在这项工作中，我们提出了一种方法来解决一个约束的多目标材料设计问题，在一个大的组成空间，特别是专注于Mo-Nb-Ti-V-W系统作为一个代表性的多主元素合金（MPEA）的潜在用途，在下一代燃气涡轮机叶片。我们的方法能够学习和适应设计空间中的未知约束，在流程的每个阶段做出最佳行动方案的决策。因此，我们确定了21帕累托最优合金，满足所有的约束。我们提出的框架是显着更有效和更快的比蛮力的方法。
The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.