|Reference : Explicit Thermodynamic Properties Using Radial Basis Functions Neural Networks|
|Scientific congresses and symposiums : Paper published in a book|
|Engineering, computing & technology : Mechanical engineering|
Engineering, computing & technology : Aerospace & aeronautics engineering
|Explicit Thermodynamic Properties Using Radial Basis Functions Neural Networks|
|Adam, Olivier [ > > ]|
|Léonard, Olivier [Université de Liège - ULg > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale >]|
|Proceedings of the second SIAM international Conference on Data Mining|
|2nd SIAM International Conference on Data Mining|
|[en] Gas turbine ; numerical simulations ; transient engine performance ; thermodynamic properties ; chemical equilibrium|
|[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.
|Researchers ; Professionals ; Students|
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