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Aerospace
Special Issues
Multidisciplinary Design Optimization in Aerospace Engineering
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Multidisciplinary Design Optimization in Aerospace Engineering

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 27144

Special Issue Editor

Dr. Vladimir Balabanov

E-Mail Website
Guest Editor
Associate Technical Fellow, Structures Engineering Design Analysis and Support, Boeing Commercial Airplanes, Boeing, Seattle, WA 98108, USA
Interests: Optimization, Design, Finite Element Analysis and Optimizatoin, Composite Structures

Special Issue Information

Dear Colleagues,

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines.

MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines. However, including all disciplines simultaneously significantly increases the complexity of the problem.

These techniques have been used in a number of fields, including automobile design, naval architecture, electronics, architecture, computers, etc. However, the largest number of applications have been in the field of aerospace engineering, such as aircraft and spacecraft design. For example, the proposed Boeing blended wing body (BWB) aircraft concept has used MDO extensively in the conceptual and preliminary design stages. The disciplines considered in the BWB design are aerodynamics, structural analysis, propulsion, control theory, and economics.

This Special Issue on “Multidisciplinary Design Optimization in Aerospace Engineering” aims to provide an overview of the state-of-the-art and the main challenges in the field of aerospace engineering. Related approaches include design of experiments, numerical surrogate modeling, machine learning, and others. Application to single and multiple aerospace engineering disciplines will be considered.

Full research articles and review manuscripts that will make considerable contribution in the following topics are welcome:

Optimization;

Multidisciplinary Design Analysis and Optimization

Dr. Vladimir Balabanov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

36 pages, 751 KiB  
Article
pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles
by Eric S. Hendricks and Justin S. Gray
Aerospace 2019, 6(8), 87; https://doi.org/10.3390/aerospace6080087 - 8 Aug 2019
Cited by 57 | Viewed by 13107
Abstract
Aviation researchers are increasingly focusing on unconventional vehicle designs with tightly integrated propulsion systems to improve overall aircraft performance and reduce environmental impact. Properly analyzing these types of vehicle and propulsion systems requires multidisciplinary models that include many design variables and physics-based analysis [...] Read more.
Aviation researchers are increasingly focusing on unconventional vehicle designs with tightly integrated propulsion systems to improve overall aircraft performance and reduce environmental impact. Properly analyzing these types of vehicle and propulsion systems requires multidisciplinary models that include many design variables and physics-based analysis tools. This need poses a challenge from a propulsion modeling standpoint because current state-of-the-art thermodynamic cycle analysis tools are not well suited to integration into vehicles level models or to the application of efficient gradient-based optimization techniques that help to counteract the increased computational costs. Therefore, the objective of this research effort was to investigate the development a new thermodynamic cycle analysis code, called pyCycle, to address this limitation and enable design optimization of these new vehicle concepts. This paper documents the development, verification, and application of this code to the design optimization of an advanced turbofan engine. The results of this study show that pyCycle models compute thermodynamic cycle data within 0.03% of an identical Numerical Propulsion System Simulation (NPSS) model. pyCycle also provides more accurate gradient information in three orders of magnitude less computational time by using analytic derivatives. The ability of pyCycle to accurately and efficiently provide this derivative information for gradient-based optimization was found to have a significant benefit on the overall optimization process with wall times at least seven times faster than using finite difference methods around existing tools. The results of this study demonstrate the value of using analytic derivatives for optimization of cycle models, and provide a strong justification for integrating derivatives into other important engineering analyses. Full article
(This article belongs to the Special Issue Multidisciplinary Design Optimization in Aerospace Engineering)
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32 pages, 10164 KiB  
Article
The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAV
by Yousef Azabi, Al Savvaris and Timoleon Kipouros
Aerospace 2019, 6(4), 42; https://doi.org/10.3390/aerospace6040042 - 8 Apr 2019
Cited by 5 | Viewed by 6691
Abstract
In this work, an interactive optimisation framework—a combination of a low fidelity flow solver, Athena Vortex Lattice (AVL), and an interactive Multi-Objective Particle Swarm Optimisation (MOPSO)—is proposed for aerodynamic shape design optimisation of any aerial vehicle platform. This paper demonstrates the benefits of [...] Read more.
In this work, an interactive optimisation framework—a combination of a low fidelity flow solver, Athena Vortex Lattice (AVL), and an interactive Multi-Objective Particle Swarm Optimisation (MOPSO)—is proposed for aerodynamic shape design optimisation of any aerial vehicle platform. This paper demonstrates the benefits of interactive optimisation—reduction of computational time with high optimality levels. Progress towards the most preferred solutions is made by having the Decision Maker (DM) periodically provide preference information once the MOPSO iterations are underway. By involving the DM within the optimisation process, the search is directed to the region of interest, which accelerates the process. The flexibility and efficiency of undertaking optimisation interactively have been demonstrated by comparing the interactive results with the non-interactive results of an optimum design case obtained using Multi-Objective Tabu Search (MOTS) for the Aegis UAV. The obtained results show the superiority of using an interactive approach for the aerodynamic shape design, compared to posteriori approaches. By carrying out the optimisation using interactive MOPSO it was shown to be possible to obtain similar results to non-interactive MOTS with only half the evaluations. Moreover, much of the usual complexity of post-data-analysis with posteriori approaches is avoided, since the DM is involved in the search process. Full article
(This article belongs to the Special Issue Multidisciplinary Design Optimization in Aerospace Engineering)
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20 pages, 1868 KiB  
Article
Multi-Disciplinary Design Optimisation of the Cooled Squealer Tip for High Pressure Turbines
by Stefano Caloni, Shahrokh Shahpar and Vassili V. Toropov
Aerospace 2018, 5(4), 116; https://doi.org/10.3390/aerospace5040116 - 6 Nov 2018
Cited by 10 | Viewed by 5259
Abstract
The turbine tip geometry can significantly alter the performance of the turbine stage; its design represents a challenge for a variety of reasons. Multiple disciplines are involved in its design and their requirements limit the creativity of the designer. Multi-Disciplinary Design Optimisation (MDO) [...] Read more.
The turbine tip geometry can significantly alter the performance of the turbine stage; its design represents a challenge for a variety of reasons. Multiple disciplines are involved in its design and their requirements limit the creativity of the designer. Multi-Disciplinary Design Optimisation (MDO) offers the capability to improve the performance whilst satisfying all the design constraints. This paper presents a novel design of a turbine tip achieved via MDO techniques. A fully parametrised Computer-Aided Design (CAD) model of the turbine rotor is used to create the squealer geometry and to control the location of the cooling and dust holes. A Conjugate Heat Transfer Computational Fluid Dynamics (CFD) analysis is performed for evaluating the aerothermal performance of the component and the temperature the turbine operates at. A Finite Element (FE) analysis is then performed to find the stress level that the turbine has to withstand. A bi-objective optimisation reduces simultaneously the aerodynamic loss and the stress level. The Multipoint Approximation Method (MAM) recently enhanced for multi-objective problems is chosen to solve this optimisation problem. The paper presents its logic in detail. The novel geometry offers a significant improvement in the aerodynamic performance whilst reducing the maximum stress. The flow associated with the new geometry is analysed in detail to understand the source of the improvement. Full article
(This article belongs to the Special Issue Multidisciplinary Design Optimization in Aerospace Engineering)
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