75,458 research outputs found
A multi-objective genetic algorithm for the design of pressure swing adsorption
Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA
processes would be beneficial for the development of the technology, but their development is
a difficult task due to the complexity of the simulation of PSA cycles and the computational
effort needed to detect the performance at cyclic steady state.
We present a preliminary investigation of the performance of a custom multi-objective genetic
algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of
air for N2 production. The simulation requires a detailed diffusion model, which involves coupled
nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA
to handle this complex problem has been assessed by comparison with direct search methods.
An analysis of the effect of MOGA parameters on the performance is also presented
Real Coded Genetic Algorithm with Enhanced Abilities for Adaptation Applied to Optimisation of MIMO Systems
This article presents an investigation of real coded Genetic Algorithm Blend
Crossover Alpha modification, with enhanced ability for adaptation, applied to minimisation of transmit power in multiple-input multiple-output (MIMO) systems beamforming. The goal is to formulate transmit power minimisation task as a black box software object and evaluate an alternative to currently existing methods for optimisation of transmit energy in multicast system constrained by signal to noise ratio. The novelty of this adaptive methodology for determination of minimal power level within certain Quality of Service criteria is that it guarantees satisfaction of the constraint and 100% feasibility of achieved solutions. In addition this methodology excludes retuning algorithms parameters by using black box model for the problem definition. Experiments are conducted for identification of weight vectors assigned for signal strength and direction. Achieved experimental results are presented and analysed
Preliminary space mission design under uncertainty
This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take the min to account. In this paper, Evidence Theory is proposed to crystallise the inherent uncertainties. The design process is then formulated as an optimisation under uncertainties(OUU). Three techniques are proposed to solve the OUU problem: (a) an evolutionary multi-objective approach, (b) a step technique consisting of maximising the belief for different levels of performance, and (c) a clustering method that firstly identifies feasible regions.The three methods are applied to the Bepi Colombo mission and their effectiveness at solving the OUU problem are compared
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Global Trajectory Optimisation : Can We Prune the Solution Space When Considering Deep Space Manoeuvres? [Final Report]
This document contains a report on the work done under the ESA/Ariadna study 06/4101 on the global optimization of space trajectories with multiple gravity assist (GA) and deep space manoeuvres (DSM). The study was performed by a joint team of scientists from the University of Reading and the University of Glasgow
Space activities in Glasgow; advanced microspacecraft from Scotland
The City of Glasgow is renowned for its engineering and technological innovation; famous Glaswegian
inventors and academics include James Watt (Steam Engine) and John Logie Baird (television), amongst many
others. Contemporary Glasgow continues to pioneer and invent in a multitude of areas of science and
technology and has become a centre of excellence in many fields of engineering; including spacecraft
engineering.
This paper will discuss how Clyde Space Ltd and the space groups at both Glasgow and Strathclyde
Universities are combining their knowledge and expertise to develop an advanced microspacecraft platform that
will enable a step change in the utility value of miniature spacecraft. The paper will also explore how the
relationship between the academic and industrial partners works in practice and the steps that have been taken
to harness resulting innovation to create space industry jobs within a city that was, until recently, void of any
commercial space activity
Preliminary space mission design under uncertainty
This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take them into account. In this paper, Evidence Theory is proposed to crystallise the inherent uncertainties. The design process is then formulated as an Optimisation Under Uncertainties (OUU). Three techniques are proposed to solve the OUU problem: (a) an evolutionary multi-objective approach, (b) a step technique consisting of maximising the belief for different levels of performance, and (c) a clustering method that
firstly identifes feasible regions. The three methods are applied to the BepiColombo mission and their
effectiveness at solving the OUU problem are compared
Enhancing service requirements of technical product-service systems
Due to the integration of product and services as a new business model, product reliability and strategies for cost reduction at the early design stage have become important factors for many manufacturing firms. It is, therefore, critical at this phase to analyse the risk involved with Service Requirements noncompliance in order to help designers make informed decisions; as these decisions have a large impact on the Product Life Cycle (PLC).
An investigation has been performed into how Service Requirements are analysed in a service orientated business to achieve reduced Life Cycle Cost (LCC) and improvements of existing Service Requirements. Weibull distribution and Monte Carlo principle have been proposed to do so; as they are considered as the most widely used in product reliability studies in the industry sector. A generic methodology for risk evaluation of failure to deliver a new product against Service Requirements is presented in this paper. This is part of the ongoing research project which aims to, apart from comparing current and targeted Service Requirements, it also facilitates an optimisation of them at the minimum risk of nonconformity
Stochastic axial compressor variable geometry schedule optimisation
The design of axial compressors is dictated by the maximisation of flow
efficiency at on design conditions whereas at part speed the requirement for
operation stability prevails. Among other stability aids, compressor variable
geometry is employed to rise the surge line for the provision of an adequate
surge margin. The schedule of the variable vanes is in turn typically obtained
from expensive and time consuming rig tests that go through a vast combination
of possible settings. The present paper explores the suitability of stochastic
approaches to derive the most flow efficient schedule of an axial compressor for
a minimum variable user defined value of the surge margin. A genetic algorithm
has been purposely developed and its satisfactory performance validated against
four representative benchmark functions. The work carries on with the necessary
thorough investigation of the impact of the different genetic operators employed
on the ability of the algorithm to find the global extremities in an effective
and efficient manner. This deems fundamental to guarantee that the algorithm is
not trapped in local extremities. The algorithm is then coupled with a
compressor performance prediction tool that evaluates each individual's
performance through a user defined fitness function. The most flow efficient
schedule that conforms to a prescribed surge margin can be obtained thereby fast
and inexpensively. Results are produced for a modern eight stage high bypass
ratio compressor and compared with experimental data available to the research.
The study concludes with the analysis of the existent relationship between surge
margin and flow efficiency for the particular compressor under scrutiny. The
study concludes with the analysis of the existent relationship between surge
margin and flow efficiency for the particular compressor under scrutiny
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Design, implementation and testing of an integrated branch and bound algorithm for piecewise linear and discrete programming problems within an LP framework
A number of discrete variable representations are well accepted and find regular use within LP systems. These are Binary variables, General Integer variables, Variable Upper Bounds or Semi Continuous variables, Special Ordered Sets of type One and type Two. The FortLP system has been extended to include these representations. A Branch and Bound algorithm is designed in which the choice of sub-problems and branching variables are kept general. This provides considerable scope of experimentation with tree development heuristics and the tree search can then be guided by search parameters specified by user subroutines. The data structures for representing the variables and the definition of the branch and bound tree are described. The results of experimental investigation for a few test problems are reported
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