6 resultados para Dynamic control

em Greenwich Academic Literature Archive - UK


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SMARTFIRE, an open architecture integrated CFD code and knowledge based system attempts to make fire field modeling accessible to non-experts in Computational Fluid Dynamics (CFD) such as fire fighters, architects and fire safety engineers. This is achieved by embedding expert knowledge into CFD software. This enables the 'black-art' associated with the CFD analysis such as selection of solvers, relaxation parameters, convergence criteria, time steps, grid and boundary condition specification to be guided by expert advice from the software. The user is however given the option of overriding these decisions, thus retaining ultimate control. SMARTFIRE also makes use of recent developments in CFD technology such as unstructured meshes and group solvers in order to make the CFD analysis more efficient. This paper describes the incorporation within SMARTFIRE of the expert fire modeling knowledge required for automatic problem setup and mesh generation as well as the concept and use of group solvers for automatic and manual dynamic control of the CFD code.

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This paper describes two new techniques designed to enhance the performance of fire field modelling software. The two techniques are "group solvers" and automated dynamic control of the solution process, both of which are currently under development within the SMARTFIRE Computational Fluid Dynamics environment. The "group solver" is a derivation of common solver techniques used to obtain numerical solutions to the algebraic equations associated with fire field modelling. The purpose of "group solvers" is to reduce the computational overheads associated with traditional numerical solvers typically used in fire field modelling applications. In an example, discussed in this paper, the group solver is shown to provide a 37% saving in computational time compared with a traditional solver. The second technique is the automated dynamic control of the solution process, which is achieved through the use of artificial intelligence techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxation using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate the potential for enhanced solution reliability due to obtaining acceptable convergence within each time step, unlike some of the comparison simulations.

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SMARTFIRE is a fire field model based on an open architecture integrated CFD code and knowledge-based system. It makes use of the expert system to assist the user in setting up the problem specification and new computational techniques such as Group Solvers to reduce the computational effort involved in solving the equations. This paper concentrates on recent research into the use of artificial intelligence techniques to assist in dynamic solution control of fire scenarios being simulated using fire field modelling techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxations using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate enhanced solution reliability due to obtaining acceptable convergence within each time step unlike some of the comparison simulations.

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This paper proposes a vehicular control system architecture that supports self-configuration. The architecture is based on dynamic mapping of processes and services to resources to meet the challenges of future demanding use-scenarios in which systems must be flexible to exhibit context-aware behaviour and to permit customization. The architecture comprises a number of low-level services that provide the required system functionalities, which include automatic discovery and incorporation of new devices, self-optimisation to best-use the processing, storage and communication resources available, and self-diagnostics. The benefits and challenges of dynamic configuration and the automatic inclusion of users' Consumer Electronic (CE) devices are briefly discussed. The dynamic configuration and control-theoretic technologies used are described in outline and the way in which the demands of highly flexible dynamic configuration and highly robust operation are simultaneously met without compromise, is explained. A number of generic use-cases have been identified, each with several specific use-case scenarios. One generic use-case is described to provide an insight into the extent of the flexible reconfiguration facilitated by the architecture.

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In the casting of reactive metals, such as titanium alloys, contamination can be prevented if there is no contact between the hot liquid metal and solid crucible. This can be achieved by containing the liquid metal by means of high frequency AC magnetic field. A water cooled current-carrying coil, surrounding the metal can then provide the required Lorentz forces, and at the same time the current induced in the metal can provide the heating required to melt it. This ‘attractive’ processing solution has however many problems, the most serious being that of the control and containment of the liquid metal envelope, which requires a balance of the gravity and induced inertia forces on the one side, and the containing Lorentz and surface tension forces on the other. To model this process requires a fully coupled dyna ic solution of the flow fields, magnetic field and heat transfer/melding process to account for. A simplified solution has been published previously providing quasi-static solutions only, by taking the irrotational ‘magnetic pressure’ term of the Lorentz force into account. The authors remedy this deficiency by modelling the full problem using CFD techniques. The salient features of these techniques are included in this paper, as space allows.