Multidisciplinary Aircraft Design Optimisation

Nickolay Jelev (Meng) 20-22nd November 2017 N.Jelev@soton.ac.uk

Academic supervisors: Prof. Andy Keane Dr András Sóbester Industrial supervisor: Dr Carren Holden

Jointly funded by Airbus UK and the University of Southampton.

Overview of the Design Process

Overview of Multidisciplinary Design Optimisation (MDO) Architectures

The Blackboard Architecture:

• Multidisciplinary Pattern Search

• Application UAV wing design problem

Future Work:

• Team Based design activity to test the proposed MDO framework

• Use data mining to speed up convergence

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Contents

3

Design Process

Multidisciplinary Design Optimisation (MDO)

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• Methods that solve problems

consisting of a number of domains.

They can better exploit the interactions

between the disciplines, thus in theory

arrive at a superior design than by

optimizing each discipline sequentially.

Overview of the Research Field

Monolithic Method

• Simultaneous Analysis and Design, (SAND)

• Multiple Discipline Feasible, (MDF)

• Individual Discipline Feasible, (IDF)

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Distributed Method

• Concurrent Subspace Optimisation, (CSSO)

• Collaborative Optimisation, (CO)

• Enhanced Collaborative Optimisation, (ECO)

• Bi-Level Integrated System Synthesis, (BLISS)

• Analytical Target Cascading, (ATC)

• Exact and Inexact Penalty Decompositions, (EPD/IPD)

• Quasi-Separable Decomposition, (QSP)

• MDO of Independent Subspaces, (MDOIS)

• Etc…

Abstract Method

• Bayesian Based Methods

• Game Theory Methods

• Blackboard Methods

• Fuzzy Logic Methods

• Etc…

Why Multidisciplinary Design Optimisation? • There is ample evidence and a shared consensus among academics that MDO methods

produce superior results than sequential one at the time domain optimisation.

• Two accepted categories of MDO approaches in Academia:

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Monolithic Distributed

All analyses routines are combined under a single optimiser.

Domain level optimisers are coupled with analyses routines to optimise local objectives. A system level optimiser coordinates the disciplines to a single optimal design.

Advantages: Advantages:

Generally faster to converge and more robust Designed to fit the already existing organisational structure in a company

Domains can operate independently of other domains and take advantage of low cost distributed computing

Disadvantages: Disadvantages:

Maintenance difficulties of merging numerous analysis tools under a single optimiser

Human out of the loop process Difficult to implement in an organisational

structure. Non trivial gradient computation

Generally much slower to converge Some require a non trivial problem decomposition

Change in Design Process

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The Blackboard Framework User Interface

Final Design Rule base

Blackboard

Data Mining

Database

To domains: Search Space From domains: Preferred Designs

Aerodynamics Group,

Minimising Drag

Weights Group,

Minimising mass

Structures Group,

Minimising Stresses

Starting Design and

Search Space

Controls Group,

Improving Stability

Manufacturing Group, Ease of Manufacturing

User Interface

Final Design Rule base

Blackboard

Data Mining

Database

To domains: Search Space From domains: Preferred Designs

Aerodynamics Group,

Minimising Drag

Weights Group,

Minimising mass

Starting Design and

Search Space

Manufacturing Group, Ease of Manufacturing

Structures Group,

Minimising Stresses

Controls Group,

Improving Stability

Multidisciplinary Pattern Search

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The Hooke and Jeeves Pattern

Search

The Multidisciplinary Pattern

Search

Simplified UAV Wing Design

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System:

Shared Variables: 𝑏, 𝑐, Λ,𝑡

𝑐 𝑚𝑎𝑥

Structures Local Variables: 𝑡𝑠𝑝𝑎𝑟 , 𝑑𝑜𝑢𝑡𝑒𝑟

Multidisciplinary Pattern Search

Minimise Wing Mass

Minimise Wing Drag

Database

𝑚𝑤𝑖𝑛𝑔

𝐷

𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠 𝑆𝑡𝑎𝑡𝑢𝑠

𝐵𝑀𝑟𝑜𝑜𝑡 𝐵𝑀𝑟𝑜𝑜𝑡 𝑚𝑤𝑖𝑛𝑔

𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠 𝑆𝑡𝑎𝑡𝑢𝑠

𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠 𝑆𝑡𝑎𝑡𝑢𝑠

𝒍𝒃

𝒖𝒃

𝒍𝒃

𝒖𝒃

Weighted Global Objective: 𝑎1𝐷 + 𝑎2𝑚𝑤𝑖𝑛𝑔

𝑂𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒𝑠 𝑆𝑡𝑎𝑡𝑢𝑠

Four Constraints

The Blackboard in Operation

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Results

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MDPS was tested on 150 starting points and the results were compared against 2 competing MDO architectures.

• SAND is a monolithic

architecture and stands for

“Simultaneous Analysis and

Design”

• CO is a distributed architecture

and stands for Collaborative

Optimisation.

• MDPS stands for Multidisciplinary

Pattern Search and represents the

results obtained from the

distributed Blackboard method.

Future Work – (Surrogate Assisted) Data Mining

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Future Work – User Controlled Bounds

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Thank you!

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Nickolay Jelev n.jelev@soton.ac.uk

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Min:

Such that:

𝑊𝑡𝑜𝑡𝑆≤ 60

Aerodynamics:

Where:

𝐶𝐷 = 𝐶𝐷𝑖 + 𝐶𝐷𝑝

𝑅𝑒 =𝜌𝑉𝑐 1 + Λ

2𝜇

𝐶𝑓 =1.328

𝑅𝑒

𝑓𝑡𝑐 = 1 + 2.7𝑡

𝑐 𝑚𝑎𝑥+ 100

𝑡

𝑐 𝑚𝑎𝑥

4

𝑆𝑤𝑒𝑡 = 2 1 + 0.5𝑡

𝑐 𝑚𝑎𝑥𝑏𝑐

𝐶𝐷𝑝 =𝐶𝑓𝑓𝑡𝑐𝑆𝑤𝑒𝑡

𝑆

𝐶𝐷𝑖 =𝐶𝐿2

𝜋𝐴𝑅𝑒

Profile Drag:

Induced Drag:

Simplified UAV Wing Design

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𝑚𝑓𝑜𝑎𝑚 =1

3𝑏 𝐴𝑟𝑜𝑜𝑡 + 𝐴𝑟𝑜𝑜𝑡𝐴𝑡𝑖𝑝 + 𝐴𝑡𝑖𝑝 𝜌𝑓𝑜𝑎𝑚𝑝𝑐𝑢𝑡

Min:

Structures:

𝐴𝑡𝑖𝑝 = Λ2𝐴𝑟𝑜𝑜𝑡

𝑚𝑤𝑖𝑛𝑔 = 𝑚𝑓𝑜𝑎𝑚 +𝑚𝑠𝑝𝑎𝑟 +𝑚𝑎𝑢𝑥 Total Wing mass:

Mass of a frustum:

Estimated profile areas:

Mass of a spar: 𝑚𝑠𝑝𝑎𝑟 = 𝑏𝜋𝑑𝑠𝑝𝑎𝑟2

4−𝑑𝑠𝑝𝑎𝑟 − 2𝑡𝑠𝑝𝑎𝑟

2

4

Where:

𝑝𝑐𝑢𝑡 = 1 − 2𝑡

𝑐 𝑚𝑎𝑥 Estimated Area cut-out:

𝐴𝑟𝑜𝑜𝑡 = 𝑝𝑐𝑢𝑡𝜋𝑐2𝑡%4

𝑡

𝑐 𝑚𝑎𝑥+𝑐2

21 − 𝑡%

𝑡

𝑐 𝑚𝑎𝑥1.03 +

𝑡%10

− 𝐴𝑠𝑝𝑎𝑟

Simplified UAV Wing Design

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Such that:

Structures:

𝑞 𝑦 =𝑁𝑊𝑓𝑢𝑠𝑒

𝑏 1 + Λ1 + Λ − 1

𝑦

𝑏 Wing Loading:

Span-wise Moment: 𝑀 𝑦 = 𝑞 𝑦 𝑑𝑦 𝑏

0

𝑑𝑦𝑏

0

Axial Stress: 𝜎 =

𝑀𝑧

𝐼𝑦

𝐹𝑂𝑆𝜎𝑟𝑜𝑜𝑡 < 𝜎𝑈𝑇𝑆

Geometric Constraints:

𝑡𝑠𝑝𝑎𝑟 −𝑑𝑠𝑝𝑎𝑟

2≤ 0

𝑡𝑐Λ − 𝑡𝑠𝑝𝑎𝑐𝑒 −𝑑𝑠𝑝𝑎𝑟

2≤ 0

Simplified UAV Wing Design

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Global Objective Function:

𝑓0 = 0.4𝐷 +𝑚𝑤𝑖𝑛𝑔 + 𝑝

Where:

if: constraints are satisfied

𝑝 = 0 else:

𝑝 =𝜎 − 𝜎𝑚𝑎𝑥𝜎𝑚𝑎𝑥

+

𝑊𝑆−𝑊𝑆 𝑚𝑎𝑥

𝑊𝑆 𝑚𝑎𝑥

+ (𝑡𝑠𝑝𝑎𝑟

−𝑑𝑠𝑝𝑎𝑟

2) + (𝑡𝑐Λ − 𝑡𝑠𝑝𝑎𝑐𝑒 −

𝑑𝑠𝑝𝑎𝑟

2)