Reference : Explicit Thermodynamic Properties Using Radial Basis Functions Neural Networks

	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Mechanical engineering Engineering, computing & technology : Aerospace & aeronautics engineering
	To cite this reference:	http://hdl.handle.net/2268/20927

Title :	Explicit Thermodynamic Properties Using Radial Basis Functions Neural Networks
Language :	English
Author, co-author :	Adam, Olivier [ > > ]
	Léonard, Olivier [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]
Publication date :	2002
Main document title :	Proceedings of the second SIAM international Conference on Data Mining
Collection and collection volume :	PR108
Audience :	International
ISBN :	0-89871-517-2
Event name :	2nd SIAM International Conference on Data Mining
Event place (city) :	Arlington, Virginia
Event country :	USA
Keywords :	[en] Gas turbine ; numerical simulations ; transient engine performance ; thermodynamic properties ; chemical equilibrium
Abstract :	[en] Gas turbine design, development, monitoring and maintenance are widely based on numerical simulations of the steady and transient engine performance. Most of the equations that are solved in the simulation programs involve the thermodynamic properties of the fluid flowing through the engine. These properties depend on temperature, pressure, humidity and fuel dosage. As the solution of chemical equilibrium is not compatible with real-time computations, a chemical solver is used off-line to generate a large database which neural networks are trained on. These networks are built on radial basis functions such as multiquadrics. A forward selection approach is used to select data points from the training set as the centers of the transfer functions. The selection stops when the prediction error starts growing. The resulting networks for specific heat and enthalpy of the gas mixture are 3 orders of magnitude faster than the chemical solver. In order to further increase the efficiency and the generalization capabilities of the model, an external optimization solver has been used to tune the shape of the transfer functions. Several solutions are proposed and preliminary results are presented.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/20927

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