17,660 research outputs found
Exploiting the Design Freedom of RM
This paper details how Rapid Manufacturing (RM) can overcome the restrictions imposed by the
inherent process limitations of conventional manufacturing techniques and become the enabling
technology in fabricating optimal products. A new design methodology capable of exploiting
RM’s increased design freedom is therefore needed. Inspired by natural world structures of trees
and bones, a multi-objective, genetic algorithm based topology optimisation approach is
presented. This combines multiple unit cell structures and varying volume fractions to create a
heterogeneous part structure which exhibits a uniform stress distribution.Mechanical Engineerin
Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines
Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods
Stochastic level-set method for shape optimisation
We present a new method for stochastic shape optimisation of engineering
structures. The method generalises an existing deterministic scheme, in which
the structure is represented and evolved by a level-set method coupled with
mathematical programming. The stochastic element of the algorithm is built on
the methods of statistical mechanics and is designed so that the system
explores a Boltzmann-Gibbs distribution of structures. In non-convex
optimisation problems, the deterministic algorithm can get trapped in local
optima: the stochastic generalisation enables sampling of multiple local
optima, which aids the search for the globally-optimal structure. The method is
demonstrated for several simple geometrical problems, and a proof-of-principle
calculation is shown for a simple engineering structure.Comment: 17 pages, 10 fig
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
- …