A Framework for Aerodynamic and Structural Optimization in Conceptual Design

Amadori, Kristian; Jouannet, Christopher; Krus, Petter

doi:10.2514/6.2007-4061

Cited by 13 publications

(13 citation statements)

References 7 publications

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“…22 A previous study indicated hyper-heuristic parameter control performed well when coupled with deterministic control. 17 As a result, hyper-heuristic control is performed by varying within the deterministic rule, equation (6), to adapt the rate at which model fidelity changes, i.e. becomes (t).…”

Section: Hyper-heuristic Controlmentioning

confidence: 99%

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Dynamically controlled variable-fidelity modelling for aircraft structural design optimisation

Allen

Coates

Trevelyan

2013

Proceedings of the Institution of Mechanical Engineers, Part G:

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Structural mass optimisation of an aircraft design is important in increasing the likelihood that a high quality airframe is designed of minimal weight whilst providing necessary resistance to load. Analysis of such structures is often performed at a single level of model fidelity, the selection of which can lead to either excessive computational time or reduced accuracy of results. Alternatively, variable-fidelity modelling may be employed to reduce such computational expense whilst maintaining accuracy, traditionally performed using predetermined levels of fidelity for specific periods of the optimisation process. This paper investigates dynamically controlled variable-fidelity modelling during aircraft conceptual design optimisation wherein fidelity is controlled as a dynamic parameter of the optimisation process. Consequently, model fidelity is adapted during optimisation to promote early discovery of promising design characteristics prior to detailed analysis of the best designs available. Models are constructed through the grouping of similar structural members within elements, thus reducing the number of degrees of freedom and subsequent computational effort required for analysis of each design. A case study is performed to verify the results of analysis and obtain benchmark results for optimisation with static model fidelity prior to the investigation of various set-ups of dynamically controlled variablefidelity modelling. The results of this study indicate improved design quality using dynamically controlled variable-fidelity modelling compared to using static model fidelity whilst reducing the necessary computation time.

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Section: Hyper-heuristic Controlmentioning

confidence: 99%

“…Variables v 3 and v 7 indicate the number of ribs in each horizontal lifting surface whilst v 4 , v 6 and v 8 indicate the number of stringers on each face of each lifting surface. Variables v 9 to v 11 define the spanwise distribution the rth rib as…”

Section: Optimisation With Static Model Fidelitymentioning

confidence: 99%

Dynamically controlled variable-fidelity modelling for aircraft structural design optimisation

Allen

Coates

Trevelyan

2013

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…In addition to the above, a second set of models was also identified as common; however, the main difference here is that they are typically used as support elements to either complement or to enhance the calculations and thus close the optimization loop in a way that is meaningful to the design team. The first example of this is a dedicated model for the geometry which aims to provide a central representation of the aircraft in order to be used by other analyses like for instance in aerodynamics or structures [12,41,45,[57][58][59]. Overall, a fast and robust geometry model that is also compatible with the other disciplines is often stressed as one of the main enablers for a seamless MDO [17,35,60,61], while at the same time, it is important to be able to capture the given problem [27] and have a flexible parametrization that can not only cover the design space but also offer the desired level of detail [32,55,56,62].…”

Section: Disciplinary Modelsmentioning

confidence: 99%

Multidisciplinary Design Optimization of Aerial Vehicles: A Review of Recent Advancements

Παπαγεωργίου

Tarkian

Amadori

et al. 2018

International Journal of Aerospace Engineering

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The aim of this paper is to present the most common practices in multidisciplinary design optimization (MDO) of aerial vehicles over the past decade. The literature sample is identified through established internet search engines, and a stringent review methodology is implemented in order to ensure the selection of the most relevant sources. In this work, the primary emphasis is on the assessment of the state-of-the-art framework development strategies, while at a secondary level, the objective is to identify the possible improvement directions by evaluating the research trends and gaps. As an additional contribution, statistical studies are also provided, and it is shown how MDO of aerial vehicles has evolved in terms of problem formulation, disciplinary modeling, analysis capabilities, tool implementation, and general applicability. Given this foundation as well as the results of the review, this work concludes by presenting a roadmap for guiding academia and industry in respect to the application of MDO on aerial vehicles. Overall, the roadmap together with the literature review is not only expected to serve as a guide for newcomers into the MDO field but also as an elementary basis which will allow researchers to conduct additional studies in this important and constantly evolving area of design.

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“…Other researchers have found other solutions to filling the gap. Amadori et al [59][60][61] has developed templates that work with CATIA from the start, and Haimes and Drela 21 use openCASCADE in a similar way. Providing the connection between lofting and CFD or CSM grids improves both the dataflow and the workflow, and hence the efficiency and agility of the design process.…”

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confidence: 99%

Towards a Unified Framework using CPACS for Geometry Management in Aircraft Design

Rizzi

Zhang

Nagel

et al. 2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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The performance requirements for the next generations of airliners are stringent and require invention and design of unconventional configurations departing from the classical Cayley functional decomposition. The break with tradition calls for higher fidelity physicsbased predictions of performance early on in the project. The paper makes the case for a unified, open, data-centric software environment for aircraft design and describes the merge of the CEASIOM conceptual design software package, developed by a number of partners including KTH, with the CPACS formalized data management system developed at DLR. The system provides multi-fidelity and multi-disciplinary analysis capabilities for concurrent design by geographically distributed expert teams. The data-centric architecture uses the CPACS schema and access mechanisms for management of design data across all disciplines and fidelity levels. This makes the system extensible and mitigates the problems encountered in handing over the model to later design phases. The concepts have been tested by interfacing external modules to CEASIOM/CPACS through a graphical CPACS XML editor, the ACbuilder gateway. Results of comparative analyses on models imported in this way from the RDS and VAMPzero conceptual design packages are reported here. CPACS will be released to the general public in spring '12. The CEASIOM team experience of joining forces via CPACS with DLR is altogether positive and further in-house development of software for aircraft performance prediction and design by the CEASIOM team will use the CPACS system. I. Introduction & Overview A. Classical Conceptual-Preliminary DesignPresent trends in aircraft design towards lighter more flexible airframes flying expanded flight envelopes with augmented stability call for more tightly-coupled multi-disciplinary design procedures to efficiently evaluate the flying qualities of the aero-servo-elastic aircraft. Numerous textbooks 1-4 explain in detail how the entire aircraft design process can be categorized into three distinct phases, each with its own distinct objectives and tasks: (1) conceptual definition at the aircraft level; (2) preliminary definition of its components; and (3), detailed definition for flight. The tools and how they are used, and even the people using them, usually change between the phases.Conceptual design is primarily a search process to formulate a set of design variable quantities, which, according to appropriate modeling principles, define a vehicle that fulfills a set of minimum requirements determined by its mission. The tools for the search are provided by mathematical modeling (usually empirically based) to find a quantified description of the aircraft concept and its size, weight, and performance. The first step sizes the aircraft, i.e calculates the aircraft takeoff gross weight, empty weight and fuel weight so that it achieves the range called for in the design specification. This results in a quantification of design parameters that determine the aircraft configuration involvin...

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A Framework for Aerodynamic and Structural Optimization in Conceptual Design

Cited by 13 publications

References 7 publications

Dynamically controlled variable-fidelity modelling for aircraft structural design optimisation

Dynamically controlled variable-fidelity modelling for aircraft structural design optimisation

Multidisciplinary Design Optimization of Aerial Vehicles: A Review of Recent Advancements

Towards a Unified Framework using CPACS for Geometry Management in Aircraft Design

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