A Computational Architecture for Coupling Heterogeneous Numerical Models and Computing Coupled Derivatives
A Computational Architecture for Coupling Heterogeneous Numerical Models and Computing Coupled Derivatives
复制标题
耦合异构数值模型和计算耦合导数的计算体系结构
DOI：
10.1145/3182393
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. Martins
中科院分区：
文献类型：
--
作者：
John T. Hwang;J. Martins
计算建模的挑战之一是耦合模型来解决多学科问题。基于流的计算框架通过模块化方法减轻了部分挑战，其中数据从组件流向组件。然而，现有的基于流的框架是低效的耦合导数时，需要进行优化。为了解决这个问题，我们开发了模块化分析和统一衍生物（MAUD）架构。MAUD将多学科模型制定为非线性方程组，这导致了统一所有计算导数的方法的线性方程。这使得使用MAUD架构的基于流的框架能够为链式规则、伴随方法、耦合伴随方法和混合方法提供公共接口; MAUD自动为问题使用适当的方法。一个分层的，矩阵的方法，使现代的解决方案，如牛顿-克雷洛夫求解器内使用这种单片配方，没有计算开销。使用Python实现的MAUD解决了两个演示问题：一个是具有超过200万个未知数和25，000个设计变量的纳米卫星优化，另一个是涉及6，000多个设计变量和23，000个约束的飞机优化。MAUD现在在开源框架OpenMDAO中实现，该框架已用于解决飞机、卫星、风力涡轮机和涡轮风扇发动机的设计问题。
One of the challenges in computational modeling is coupling models to solve multidisciplinary problems. Flow-based computational frameworks alleviate part of the challenge through a modular approach, where data flows from component to component. However, existing flow-based frameworks are inefficient when coupled derivatives are needed for optimization. To address this, we develop the modular analysis and unified derivatives (MAUD) architecture. MAUD formulates the multidisciplinary model as a nonlinear system of equations, which leads to a linear equation that unifies all methods for computing derivatives. This enables flow-based frameworks that use the MAUD architecture to provide a common interface for the chain rule, adjoint method, coupled adjoint method, and hybrid methods; MAUD automatically uses the appropriate method for the problem. A hierarchical, matrix-free approach enables modern solution techniques such as Newton--Krylov solvers to be used within this monolithic formulation without computational overhead. Two demonstration problems are solved using a Python implementation of MAUD: a nanosatellite optimization with more than 2 million unknowns and 25,000 design variables, and an aircraft optimization involving over 6,000 design variables and 23,000 constraints. MAUD is now implemented in the open source framework OpenMDAO, which has been used to solve aircraft, satellite, wind turbine, and turbofan engine design problems.