Cybermatrix: A Novel Approach to Computationally and Collaboration Intensive Multidisciplinary Optimization for Transport Aircraft Design

This paper presents an approach to multi-disciplinary optimization (MDO) of transport aircraft that attempts to strike a balance between two broad classes of MDO approaches: those arising from the formal optimization background, and those coming from the aircraft design background. It starts from the observation that any kind of numerical design process can be viewed as an approximation of a formal optimization process, where Jacobians of cost functions may be inexact and are often not explicitly computed. Based on that, a specific MDO problem representation and a highly parallel process assembly and execution protocol (the “cybermatrix” protocol) is defined, as well as one possible realization on high-performance computing (HPC) resources. The approach is applied to an optimization of a long-range transport aircraft, employing disciplinary subprocesses for high-fidelity aerodynamic design of wing airfoil shapes, structural sizing of lifting surfaces, and determination and evaluation of design loads.

[1]  René Liepelt,et al.  AEROELASTIC ANALYSIS MODELLING PROCESS TO PREDICT THE CRITICAL LOADS IN AN MDO ENVIRONMENT , 2015 .

[2]  William Gropp,et al.  CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences , 2014 .

[3]  Joaquim R. R. A. Martins,et al.  Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration , 2014 .

[4]  A. DeBlois,et al.  Development of a Multilevel Multidisciplinary-Optimization Capability for an Industrial Environment , 2013 .

[5]  Gianfranco La Rocca,et al.  Composing MDAO symphonies : Graph-based generation and manipulation of large multidisciplinary systems , 2017 .

[6]  Joaquim R. R. A. Martins,et al.  Automatic evaluation of multidisciplinary derivatives using a graph-based problem formulation in OpeNMDAO , 2014 .

[7]  Joaquim R. R. A. Martins,et al.  An asymmetric suboptimization approach to aerostructural optimization , 2009 .

[8]  Joaquim R. R. A. Martins,et al.  High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet , 2002 .

[9]  Mohammad Abu-Zurayk,et al.  Development and application of multi-disciplinary optimization capabilities based on high-fidelity methods , 2012 .

[10]  Gertjan Looye,et al.  UNIFYING MANOEUVRE AND GUST LOADS ANALYSIS MODELS , 2009 .

[11]  Thomas Klimmek,et al.  Parametric Set-Up of a Structural Model for FERMAT Configuration for Aeroelastic and Loads Analysis , 2014 .

[12]  Charlie Vanaret,et al.  GEMS: A Python Library for Automation of Multidisciplinary Design Optimization Process Generation , 2018 .

[13]  Pier Davide Ciampa,et al.  The AGILE Paradigm: the next generation of collaborative MDO , 2017 .

[14]  Arthur Stück,et al.  An Adjoint-based Aerodynamic Shape Optimization Strategy for Trimmed Aircraft with Active Engines , 2017 .

[15]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .