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ACT - Case Studies |
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Technology Applications Services Software Case Studies About Us Information Contact |
The following sections provide some examples
of a variety of different implementations of Evolutionary and Advanced
Computational technologies.
These Case Studies have all shown effective results, and have delivered benefits in terms of Time and Cost savings to the organisations involved.
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ACT - Thermal Power System redesign |
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This project involved the
redesign of elements of a nuclear power station thermal systems. The
objective was to improve the overall performance of the thermal cycle of
the nuclear power plant by optimising the station design as well as the
operation, using a combination of Genetic Algorithms and conventional
numerical optimisation techniques. The project resulted in an increase of
predicted net electricity output of over 0.3%.
for details of the Thermal Power System redesign - click here |
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ACT - Gas Turbine Blade Cooling Hole geometry |
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Collaboration with Rolls Royce resulted in a research programme using a Genetic Algorithm to explore the design of Gas Turbine engine cooling hole geometries. The problem involved the development of a simple, one-dimensional steady-state model of a cooling passage situated at the leading edge of a high-pressure gas turbine blade. for details of the Gas Turbine Blade Cooling Hole project - click here |
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ACT - Wing Design with conflicting constraints |
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A project in conjunction with British
Aerospace (BAe), developed an interactive evolutionary design environment
for a military airframe preliminary design problem. The problem has three
main components - Aerodynamics, Performance and Configuration. Each of
these areas has a number of different parameters, and many of the
objectives are highly conflicting
for details of the Wing Design project - click here |
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ACT - DIPSO : Distributed Problem Solving Environment |
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DIPSO is a problem solving environment
(PSE) which integrates modelling and optimisation services located
at geographically distributed centres. Integration is supported through
Grid technologies and software components relating to system modelling and
design search, exploration and analysis. These processes return
high-quality design solutions and relevant design information to support
conceptual design decision-making.
for details of the DIPSO project - click here |
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ACT - Fault Coverage Test Code Generation using Evolutionary Computation |
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The following case study, relating to work carried out for Rolls Royce Associates,
illustrates the manner in which evolutionary computing can manage constraints whilst
also avoiding convergence upon local optima and identifying
high performance solutions. The work relates to the design of industrial test and monitoring systems (TAMS)
widely used for real-time testing of the functionality of electronic circuits. The application of an Inductive Genetic Algorithm achieved a fault coverage of 71.5%, which was a very significant improvement over previous work within Rolls Royce Associates where the best produced fault coverage was 57% which was below the 70% threshold for the circuitry to pass design specification. for details of the Fault Coverage Test Code project - click here |
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ACT - Preliminary Airframe and Flight Trajectory Design |
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The following case study relates to work carried out for BAE Systems (Warton) investigating the
preliminary air-frame design and definition of flight trajectory for an air-launched winged vehicle
that achieves orbit before returning to atmosphere for a conventional landing.
The problem is extremely sensitive to five implicit non-linear constraints relating to air-speed,
climb angle, and physical parameters describing the air-frame and engine configuration.
The task was to minimize the empty weight of the vehicle.
for details of the Preliminary Airframe Design project - click here |
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ACT - Collaborative Evolutionary Multi-Project Resource Scheduling |
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Many real world scheduling problems, particularly those which arise in the large-scale construction, corporation wide logistics, or large-scale software development, are multi-project resource constrained problems, with a particular emphasis on the word multi. Such problems typically involve many thousands or tens of thousands of tasks, grouped into several hundred or more sub-projects. The individual sub-projects are tightly linked by project-wide resources and other logistic interdependencies. This project developed a process to deal with multi-project resource-constrained scheduling problems, and was designed to combine standard automated scheduling with scheduler guidance. A collaborative evolutionary multi-project scheduler was developed, which uses an evolutionary algorithm to evolve schedules according partly to standard criteria of due date slippage and makespan, but also includes the scheduler's input as part of a schedule's selective fitness. The scheduler regularly views example GANTT charts from the current population, and assigns coarse relative fitnesses to them by considering the overall ‘shape’ of the developing schedules. The resultant system can be integrated with a wide range of existing project management packages. for details of Collaborative Evolutionary Multi-Project Resource Scheduling - click here |
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