An automated selection algorithm for nonlinear solvers in MDO

Abstract

There are two major types of approaches that are used for the multidisciplinary analysis (MDA) of coupled systems: fixed-point-iteration-based approaches and coupled Newton-based approaches. Fixedpoint-iteration approaches are easier to implement, but can require a large number of iterations or diverge for strongly coupled problems. On the other hand, coupled-Newton approaches have superior convergence orders, but generally require more effort to implement and have more expensive iterations. Additionally, these two major approaches have many variations, including hybrid approaches where the MDA begins with a fixed-point iteration and then switches to a coupled-Newton approach after a certain number of iterations. However, there is a lack of criteria to govern how to select between these approaches, and when to switch between them in a hybrid approach. This paper compares these approaches and provides an algorithm that can be used to automatically select and switch between them. The proposed algorithm is implemented using OpenMDAO, NASA's open-source framework for multidisciplinary analysis and optimization, and is tested using OpenAeroStruct, an open-source low-fidelity tool for aerostructural optimization. The results show that the proposed algorithm provides a balance of improved robustness and speed.

Static aeroelasticity analysis of rotor blade using a Gauss-Seidel fluid-structure interaction method

“…A widely used strategy for solving MDA is the nonlinear block Gauss-Seidel (NLBGS) approach [2], in which the fluid and structure fields are solved sequentially with mutual iteration until convergence. Compared with weak coupling methods, NLBGS is more in line with the physical mechanism and has been extensively studied in external flow [3,4,5,6,7,8]. As for internal flow, some developments in numerical modeling of FSI are described as follows.…”

Cloud-Based CAD Parametrization for Design Space Exploration and Design Optimization in Numerical Simulations

“…Things can get even more complicated, as in some cases we might need to deal with design optimization problems incorporating many disciplines (e.g., aerodynamics, propulsion, structures, and performance). In this case, we say we are dealing with a multi-disciplinary design optimization problem (MDO) [11][12][13][14][15][16]. MDO allows designers and engineers to incorporate all relevant disciplines simultaneously.…”

Analysis of architectural variants for auditable blockchain-based private data sharing

“…For example, the exterior of an engine's physical structure is optimized based on the current design of the propulsion system. Such a process is referred to as a multi-interdisciplinary optimization (MDO) process [9,20]. As depicted in Figure 2, in an MDO process, the results of one task have to be shared as input data to the optimization process of the subsequent tasks.…”