China 
Africa 
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Unmanned Aircraft System Design
Published in R. Kurt Barnhart, Douglas M. Marshall, Eric J. Shappee, Introduction to Unmanned Aircraft Systems, 2021
Commercial process integration and design optimization (PIDO) tools such as ModelCenter [MCOPT1], ModeFrontier [MFOPT1], ISIGHT [ISOPT1], OPTISLANG [www.dynardo.de/en/software/optislang.html] and HEEDS [HEEDSOPT1] tie together varying fidelity, physics-based tools, and transfer data between the tools to enable a digital transformation in UAS engineering. These architectural tools are rapidly expanding to provide web-based collaboration interfaces (e.g. VOLTA) [www.esteco.com/volta] to cloud-computing resources that enable geographically distributed teams to design and analyze efficiently. Open-source tools like NASA’s OpenMDAO [OPENMDAO1, OPENMDAO2] provide a Python-based library of optimization solvers that can be used to run codes like OpenAeroStruct [OAS1]. The input/output process flow is visualized with an N2 diagram [XDSM1] in Figure 9.4. An N3 diagram may be used to combine insights from analysis results at multiple fidelity levels for each discipline where a higher fidelity analysis reduces the uncertainty for a part of the design space. Leveraging gradient-based and nongradient-based optimizers, curve fitting with response-surface methods in addition to machine learning and deep learning tools, will continue to enable better designs of UAS. They exploit the benefits of being able to address a larger number of design variables with multidisciplinary and multifidelity architectures. Outputs from these PIDO tools may validate compliance of the UAS design to system requirements that are systematically organized within model-based systems engineering tools such as TeamCenter [TCREQ1], DOORS [DREQ1], or JAMA [JREQ1]. The digital transformation is completed by communicating data to product life-cycle management tools that are already well integrated with computer-aided design tools.
Minimum lap time trajectory optimisation of performance vehicles with four-wheel drive and active aerodynamic control
Published in Vehicle System Dynamics, 2023
Pieter de Buck, Joaquim R. R. A Martins
The OCP is formulated and solved using the multidisciplinary design optimisation (MDO) framework OpenMDAO [21], combined with the optimal control package Dymos [22]. This package extends the capabilities of the OpenMDAO framework by allowing the user to input the governing state equations and their derivatives in a modular fashion. The phases of the OCP are discretised onto a grid of state, control, and collocation nodes, turning the OCP into a nonlinear programming (NLP) problem.