multidisciplinary aircraft design optimisationmdf) β’ individual discipline feasible, ......
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Multidisciplinary Aircraft Design Optimisation
Nickolay Jelev (Meng) 20-22nd November 2017 [email protected]
Academic supervisors: Prof. Andy Keane Dr AndrΓ‘s SΓ³bester 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:
β’ Multidisciplinary Pattern Search
β’ Application UAV wing design problem
Future Work:
β’ Team Based design activity to test the proposed MDO framework
β’ Use data mining to speed up convergence
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Contents
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Design Process
Multidisciplinary Design Optimisation (MDO)
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β’ 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
β’ Simultaneous Analysis and Design, (SAND)
β’ Multiple Discipline Feasible, (MDF)
β’ Individual Discipline Feasible, (IDF)
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Distributed Method
β’ Concurrent Subspace Optimisation, (CSSO)
β’ Collaborative Optimisation, (CO)
β’ Enhanced Collaborative Optimisation, (ECO)
β’ Bi-Level Integrated System Synthesis, (BLISS)
β’ Analytical Target Cascading, (ATC)
β’ Exact and Inexact Penalty Decompositions, (EPD/IPD)
β’ Quasi-Separable Decomposition, (QSP)
β’ MDO of Independent Subspaces, (MDOIS)
β’ Etcβ¦
Abstract Method
β’ Bayesian Based Methods
β’ Game Theory Methods
β’ Blackboard Methods
β’ Fuzzy Logic Methods
β’ Etcβ¦
Why Multidisciplinary Design Optimisation? β’ There is ample evidence and a shared consensus among academics that MDO methods
produce superior results than sequential one at the time domain optimisation.
β’ 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: π, π, Ξ,π‘
π πππ₯
Structures Local Variables: π‘π πππ , πππ’π‘ππ
Multidisciplinary Pattern Search
Minimise Wing Mass
Minimise Wing Drag
Database
ππ€πππ
π·
πΆπππ π‘πππππ‘π ππ‘ππ‘π’π
π΅πππππ‘ π΅πππππ‘ ππ€πππ
πΆπππ π‘πππππ‘π ππ‘ππ‘π’π
πΆπππ π‘πππππ‘π ππ‘ππ‘π’π
ππ
ππ
ππ
ππ
Weighted Global Objective: π1π· + π2ππ€πππ
ππππππ‘ππ£ππ ππ‘ππ‘π’π
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.
β’ SAND is a monolithic
architecture and stands for
βSimultaneous Analysis and
Designβ
β’ CO is a distributed architecture
and stands for Collaborative
Optimisation.
β’ MDPS stands for Multidisciplinary
Pattern Search and represents the
results obtained from the
distributed Blackboard method.
Future Work β (Surrogate Assisted) Data Mining
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Future Work β User Controlled Bounds
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Min:
Such that:
ππ‘ππ‘πβ€ 60
Aerodynamics:
Where:
πΆπ· = πΆπ·π + πΆπ·π
π π =πππ 1 + Ξ
2π
πΆπ =1.328
π π
ππ‘π = 1 + 2.7π‘
π πππ₯+ 100
π‘
π πππ₯
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ππ€ππ‘ = 2 1 + 0.5π‘
π πππ₯ππ
πΆπ·π =πΆπππ‘πππ€ππ‘
π
πΆπ·π =πΆπΏ2
ππ΄π π
Profile Drag:
Induced Drag:
Simplified UAV Wing Design
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πππππ =1
3π π΄ππππ‘ + π΄ππππ‘π΄π‘ππ + π΄π‘ππ ππππππππ’π‘
Min:
Structures:
π΄π‘ππ = Ξ2π΄ππππ‘
ππ€πππ = πππππ +ππ πππ +πππ’π₯ Total Wing mass:
Mass of a frustum:
Estimated profile areas:
Mass of a spar: ππ πππ = ππππ πππ2
4βππ πππ β 2π‘π πππ
2
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Where:
πππ’π‘ = 1 β 2π‘
π πππ₯ Estimated Area cut-out:
π΄ππππ‘ = πππ’π‘ππ2π‘%4
π‘
π πππ₯+π2
21 β π‘%
π‘
π πππ₯1.03 +
π‘%10
β π΄π πππ
Simplified UAV Wing Design
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Such that:
Structures:
π π¦ =ππππ’π π
π 1 + Ξ1 + Ξ β 1
π¦
π Wing Loading:
Span-wise Moment: π π¦ = π π¦ ππ¦ π
0
ππ¦π
0
Axial Stress: π =
ππ§
πΌπ¦
πΉπππππππ‘ < ππππ
Geometric Constraints:
π‘π πππ βππ πππ
2β€ 0
π‘πΞ β π‘π ππππ βππ πππ
2β€ 0
Simplified UAV Wing Design
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Global Objective Function:
π0 = 0.4π· +ππ€πππ + π
Where:
if: constraints are satisfied
π = 0 else:
π =π β ππππ₯ππππ₯
+
ππβππ πππ₯
ππ πππ₯
+ (π‘π πππ
βππ πππ
2) + (π‘πΞ β π‘π ππππ β
ππ πππ
2)