University of Liege, Belgium

Turbomachinery Group

Activity Report

1 Address

Olivier LEONARD, ProfessorTurbomachines et propulsionUniversite de Liege (ULg)Institut de Mecanique et Genie civil (B52)Chemin des chevreuils 1B-4000 LiegeTel : +32 (0)4 366 91 87Fax : +32 (0)4 366 91 36O.Leonard@ulg.ac.behttp ://www.ulg.ac.be/turbo

2 Brief description of the group

The Turbomachinery Group of the University of Liege is part of the Department of Ae-rospace, Mechanical and Material Engineering Sciences which involves 22 professors andabout 100 researchers. The Department is devoted to teaching and research in the fields ofapplied mathematics, optimization, vibrations, infography, metallic and polymeric mate-rials, fracture mechanics, solid mechanics, fluid mechanics, aerodynamics, turbomachines,propulsion, thermodynamics, power generation, theoretical and applied mechanics, ve-hicles, mechatronics and metrology.

The staff of the Turbomachinery Group involves 1 professor, 5 research engineers and 1technician :

Olivier ADAM (o.adam@ulg.ac.be) PhD StudentSebastien BORGUET (s.borguet@ulg.ac.be) PhD StudentPierre DEWALLEF (p.dewallef@ulg.ac.be) Post DocVincent KELNER (v.kelner@ulg.ac.be) PhD StudentOlivier LEONARD (o.leonard@ulg.ac.be) ProfessorJean-Francois SIMON (jf.simon@ulg.ac.be) PhD StudentJean-Marie VAN ONACKER (jmvonacker@ulg.ac.be) Technician

Before heading the group, Olivier LEONARD carried out the main part of his researchactivities at the von Karman Institute, within the Turbomachinery Department.

The group is devoted to teaching in the fields of turbomachinery (compressors, gas and

steam turbines, hydraulic machines) and aerospace propulsion (turbojets and rocket en-gines). Its research activities are organized along 3 major axis :

– The numerical simulation of flows in turbomachines for design purpose, with the de-velopment of a throughflow finite-volume computer code and a multi-stage, blade rowper blade row, finite-volume mean-line code.

– The development of optimization techniques using genetic algorithms for the optimaldesign of pumps, heat pipes, fans or blowers, and for the optimal pump scheduling.

– The development of methods for measurements validation in a turbine engine test cell,for health monitoring and model-based adaptive control of gas turbine engines.

The Turbomachinery Group has developed relationships with the von Karman Institute,CENAERO, ONERA, Ecole Centrale de Lyon, National Technical University of Athens,Chalmers, Snecma, Techspace Aero and others.

Equipment :

– Jet engine (Turbine Technologies SR30) on a test bench with data acquisition andfeedback control

– Test loop for hydraulic pumps (up to 15 kW) including detection of cavitation viaacoustic data processing

– FLUENT, FINE/TURBO

3 Description of the research activities

3.1 Health Monitoring and Model-Based Controlof Gas Turbine Engines

The first objective of this project is to develop and validate parametric and adaptivemodels for the health monitoring of gas turbine engines, so as to enable condition-basedmaintenance. The identification of the health parameters of the engines is based on mea-surements taken on the process and includes their validation. It makes it possible to followthe evolution with respect to time (a possible degradation) of the condition of the engine.The adaptation of the non linear model and the estimation of the health of the engine arebased on the use of Kalman filters, modified to take into account the nonlinear characterof the operation of the gas turbine engines and to allow the detection of erroneous mea-surements. The most recent versions of the developed algorithms are based on dynamicmodels and can benefit from the transients followed by the engines.

These tools can also provide an estimate of non measurable variables which are fundamen-tal for control such as maximum temperature or surge margin. The second objective ofthe project is to use these adaptive models and the diagnosis strategy to carry out actionsof control and adaptation of the gas turbine operation which would take into account itsreal, modified or degraded condition.

Partners : National Technical University of Athens

Recent related publications :

– Robust Validation of Measurements on Jet Engines, P. Dewallef, O. Leonard, EuropeanJournal of Mechanical and Environmental Engineering, Vol 46, No 4, 2001

– On-Line Validation of Measurements on Jet Engines Using Automatic Learning Me-thods, P. Dewallef, O. Leonard, Proceedings of the XV International Symposium on

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Airbreathing Engines, Bangalore, 2001

– On-Line Measurement Validation and Performance Monitoring Using Robust KalmanFiltering Techniques, P. Dewallef, O. Leonard, Proceedings of the 5th European Confe-rence on Turbomachinery Fluid Dynamics and Thermodynamics, Prague, 2003

– On-Line Performance Monitoring and Engine Diagnostic Using Robust Kalman Filte-ring Techniques, P. Dewallef, O. Leonard, ASME Paper GT-2003-38379, 2003

– On-Line Aircraft Engine Diagnostic Using a Soft-Constrained Kalman Filter, P. De-wallef, O. Leonard, K. Mathioudakis, ASME Paper GT2004-53539, 2004

– Combining Classification Techniques with Kalman Filters for Aircraft Engine Diagnos-tics, P. Dewallef, O. Leonard, K. Mathioudakis, C. Romessis, ASME Paper GT2004-53541, 2004, Journal of Engineering for Gas Turbines and Power, Vol. 128, January2006

– On-Line Transient Engine Diagnostic in a Kalman Filtering Framework, S. Borguet, P.Dewallef, O. Leonard, ASME Paper GT2005-68013, 2005

– Application of the Kalman Filter to Health Monitoring of Gas Turbine Engines - ASequential Approach to Robust Diagnosis, P. Dewallef, PhD Thesis, ISSN 0075-9333,D/2005/0480/44, 2005.

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Fig. 2 – Detection of problem on the low pressure turbine of a turbofan

3.2 Optimization using genetic algorithms and metamodels

The purpose of this project is to develop robust metaheuristic methods for solving optimi-zation, design or process control problems which utilizes simultaneously binary, discreteand continuous parameters. This situation results in a great number of configurationsto be considered, which must satisfy a important number of constraints. Moreover “realworld” problems lead to the optimzation of several but contradictory objectives.

This project brings a response to this type of problems, by combining the advantagesof genetic algorithms and nonlinear mathematical programming. The genetic algorithmsallow a broad and systematic exploration of the design space, the various possible solutionsbeing subjected to a Darwinian natural selection process. The nonlinear programmingbrings the efficiency of a local and fast exploration around a promising configuration. Theresulting optimization tool can be coupled to any metamodel of the application to beoptimized (data bases, response surfaces, neural networks, analytical models, parametricmodels...). The multiplicity of the objectives is addressed following the Pareto approach.

Up to now these optimization tools were applied to several problems such as the optimalsizing of lubrication pumps for turbojets, the optimization of pump scheduling, the designand the operation of blowers and the optimization of heat pipes geometries.

Partners : Group of Stochastic Methods (University of Liege), Cenaero, Fluorem, EuroHeat Pipes

Recent related publications :

– Application of Genetic Algorithms to Lubrication Pumps Stacking Design, V. Kelner,O. Leonard, Journal of Computational and Applied Mathematics, Vol 168/1-2 pp 255-265, 2003

– Optimal Pump Scheduling for Water Supply Using Genetic Algorithms, V. Kelner and

Fig. 3 – Optimal blade geometry for maximum pressure rise and minimum losses

O. Leonard, Proceedings of the 5th International Conference on Evolutionary Compu-ting for Industrial Applications - EUROGEN’03, Barcelona, 2003

– An Hybrid Optimization Technique Coupling Evolutionary and Local Search Algo-rithms, V. Kelner, F. Capitanescu, O. Leonard, and L. Wehenkel, Proceedings of the3rd International Conference on Advanced Computational Methods in Engineering -ACOMEN’05, Ghent, 2005

– Multi Objective of a Fan Blade by Coupling a Genetic Algorithm and a ParametricFlow Solver, V. Kelner, G. Grondin, O. Leonard, and P. Ferrand, Proceedings of the6th International Conference on Evolutionary Computing for Industrial Applications -EUROGEN’05, Munich, 2005

– Robust Design of a Fan Blade by Coupling Multi Objective Genetic Optimization andFlow Parameterization, V. Kelner, G. Grondin, O. Leonard, P. Ferrand, Proceedings ofthe International Congress on Fluid Dynamics Applications in Ground Transportation,Lyon, 2005

– Geometric Optimization of Grooved Heat Pipes by a Genetic Algorithm Technique, C.Goffaux, S. Pierret, S. Rossomme, V. Kelner, S. Van Oost and L. Barremaecker, Procee-dings of the 6th International Conference on Heat Pipes, Heat Pumps and Refrigerators,Minsk, 2005

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Fig. 4 – Optimal pumping schedule for minimum costs and maximum lifespan

3.3 Throughflow simulations in turbomachines

The objective of this project is to introduce non stationary effects due to the rotor-statorinteractions into a throughflow model for compressors and turbines. Following the ap-proach of Adamczyk, several averages of the unsteady 3-D Navier-Stokes equations areperformed to end up with a steady axisymmetric set of equations. In this final set of equa-tions, different stresses and forces appear. They introduce the (mean) effect of the flowphenomena that have been averaged. They are similar to the Reynolds stresses but trans-lating non stationary but non stochastic effects correlated to the rotor rotational speed.The inclusion of these deterministic stresses in a stationary model makes it possible to pre-dict the radial mixing process observed in experiments and in non stationary simulations,but which are not reproducible in stationary simulations using a mixing plane.

This set of equations represent the ultimate throughflow model, provided that one couldfind a way to model the stresses and forces. In the present research, the results of unsteady3-D simulations are used to evaluate the effects of the different stresses and forces (andtheir modelization) inside the throughflow environment. This throughflow model differsfrom classical streamline curvature method as it is directly based on the Navier-Stokesequations solved by finite volume techniques.

Partners : ONERA, Ecole Centrale de Lyon

Recent related publications :

– A Throughflow Analysis Tool Based on the Navier-Stokes Equations, J.-F. Simon, O.Leonard, Proceedings of the 6th European Conference on Turbomachinery Fluid Dy-namics and Thermodynamics, Lille, 2005

Fig. 5 – Entropy distribution in a compressor stage

3.4 Quasi-1D simulations in turbomachines

The performances of a gas turbine engine must be analyzed well before the engine istested on the bench or in flight. This study provides the designer with guidelines for thechoice of the many design parameters and for optimizing the final configuration of the jetengine. It also allows to test (in a virtual way) the correct operation of the engine duringcritical manœuvres. The present project is aimed at providing a quasi-1D modern CFDtool for the numerical modeling of the operation of a compressor and a whole jet engine.

Thanks to the progress of the simulation methods and computing power, it is now possibleto develop and to use models of jet engines based on the application of the laws of thefluid mechanics to a great number of cells. This approach makes it possible to describewith a high degree of accuracy the exchanges of mass, energy and momentum within themachine, while reducing to the bare minimum the quantity of information of empiricalnature. It takes advantage of the high precision and efficiency of the CFD methods forspace and time discretization, with CPU times of a few seconds.

This quasi-1D tool tool may be applied to the a large number of problems such as waterand hail ingestion, bleeds and cooling flows, mechanical and thermal transients, reversedflows and surge, characteristic map extrapolation, building global compressor model fromstage/partial results, coupling with cycle calculations and global modeling of a jet engine.

Recent related publications :

– Explicit Thermodynamic Properties Using Radial Basis Functions Neural Networks, O.Adam, O. Leonard, Proceedings of the 2nd SIAM International Conference on DataMining, Arlington, 2002

– A Quasi-One-Dimensional Model for Axial Compressors, O. Adam, O. Leonard, Pro-ceedings of the XVII International Symposium on Airbreathing Engines, Munich, 2005

Fig. 6 – Comparison of the quasi-1D and experimental results on a 3-stage compressor