Methods, Apparatus And Systems For Real Time Identification And Control Of Modes Of Oscillation

A system for real time identification of modes of oscillation includes a sensor, an observer, a controller and an actuator. The sensor senses a controlled system such as a combustor, and generates a signal indicative of the modes of oscillation in the controlled system. For example, these modes of oscillation can be combustion instabilities. The observer receives the signal from the sensor, and uses the signal to determine modal functions and frequencies of the modes of interest with a pair of integrals with changing time limits. The controller receives the modal functions and frequency for each mode of interest from the observer, and effects a gain and phase shift for each mode. Based on the modal functions, the frequency, the gain and the phase shift, the controller generates and outputs a control signal, that is supplied to the actuator. The actuator controls the modes of oscillation of the controlled system, based on the control signal. The system of this invention can be used to damp or enhance oscillation modes of the controlled system, depending upon whether the oscillation modes are beneficial or detrimental to system performance.Georgia Tech Research Corporatio

Program user's manual for optimizing the design of a liquid or gaseous propellant rocket engine with the automated combustor design code AUTOCOM

This computer program manual describes in two parts the automated combustor design optimization code AUTOCOM. The program code is written in the FORTRAN 4 language. The input data setup and the program outputs are described, and a sample engine case is discussed. The program structure and programming techniques are also described, along with AUTOCOM program analysis

Large Eddy Simulations of gaseous flames in gas turbine combustion chambers

Recent developments in numerical schemes, turbulent combustion models and the regular increase of computing power allow Large Eddy Simulation (LES) to be applied to real industrial burners. In this paper, two types of LES in complex geometry combustors and of specific interest for aeronautical gas turbine burners are reviewed: (1) laboratory-scale combustors, without compressor or turbine, in which advanced measurements are possible and (2) combustion chambers of existing engines operated in realistic operating conditions. Laboratory-scale burners are designed to assess modeling and funda- mental flow aspects in controlled configurations. They are necessary to gauge LES strategies and identify potential limitations. In specific circumstances, they even offer near model-free or DNS-like LES computations. LES in real engines illustrate the potential of the approach in the context of industrial burners but are more difficult to validate due to the limited set of available measurements. Usual approaches for turbulence and combustion sub-grid models including chemistry modeling are first recalled. Limiting cases and range of validity of the models are specifically recalled before a discussion on the numerical breakthrough which have allowed LES to be applied to these complex cases. Specific issues linked to real gas turbine chambers are discussed: multi-perforation, complex acoustic impedances at inlet and outlet, annular chambers.. Examples are provided for mean flow predictions (velocity, temperature and species) as well as unsteady mechanisms (quenching, ignition, combustion instabil- ities). Finally, potential perspectives are proposed to further improve the use of LES for real gas turbine combustor designs

Gas turbine transient performance simulation, control and optimisation

A gas turbine engine is a complex and non-linear system. Its dynamic response changes at different operating points. The exogenous inputs: atmospheric conditions and Mach number, also add disturbances and uncertainty to the dynamic. To satisfy the transient time response as well as safety requirements for its entire operating range is a challenge for control system design in the gas turbine industry. Although the recent design of engine control units includes some advanced control techniques to increase its control robustness and adaptability to the changing environment, the classic scheduling technique still plays the decisive role in determining the control values due to its better reliability under normal circumstances. Producing the schedules requires iterative experiments or simulations in all possible circumstances for obtaining the optimal engine performance. The techniques, such as scheduling method or linear control methods, are still lack of development for control of transient performance on most commercial simulation tools. Repetitive simulations are required to adjust the control values in order to obtain the optimal transient performance. In this project, a generalised model predictive controller was developed to achieve an online transient performance optimisation for the entire operating range. The optimal transient performance is produced by the controller according to the predictions of engine dynamics with consideration of constraints. The validation was conducted by the application of the control system on the simulated engines. The engines are modelled to component-level by the inter-component volume method. The results show that the model predictive controller introduced in this project is capable of providing the optimal transient time response as well as operating the engine within the safety margins under constant or varying environmental conditions. In addition, the dynamic performance can be improved by introducing additional constraints to engine parameters for the specification of smooth power transition as well as fuel economy

Recursive least squares for online dynamic identification on gas turbine engines

Online identification for a gas turbine engine is vital for health monitoring and control decisions because the engine electronic control system uses the identified model to analyze the performance for optimization of fuel consumption, a response to the pilot command, as well as engine life protection. Since a gas turbine engine is a complex system and operating at variant working conditions, it behaves nonlinearly through different power transition levels and at different operating points. An adaptive approach is required to capture the dynamics of its performance

Data driven low-bandwidth intelligent control of a jet engine combustor

This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system\u27s operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a good or bad operating mode. A data-driven predictor is also designed that monitors the combustion process in real time and provides a prediction of what operating mode the flame will be in for the next measurement. A path planning algorithm is then discussed for planning an input trajectory from the current operating condition to the desired operating condition that avoids regions of instability or blow-out in the input space. An adaptive layer is incorporated into the path planning algorithm to ensure that the path planner can update its trajectory when new information about the operating space becomes available

Modeling gaseous non-reactive flow in a lean direct injection gas turbine combustor through an advanced mesh control strategy

[EN] Fuel efficiency improvement and harmful emissions reduction are the main motivations for the development of gas turbine combustors. Numerical computational fluid dynamics (CFD) simulations of these devices are usually computationally expensive since they imply a multi-scale problem. In this work, gaseous non-reactive unsteady Reynolds-Averaged Navier-Stokes and large eddy simulations of a gaseous-fueled radial-swirled lean direct injection combustor have been carried out through CONVERGE (TM) CFD code by solving the complete inlet flow path through the swirl vanes and the combustor. The geometry considered is the gaseous configuration of the CORIA lean direct injection combustor, for which detailed measurements are available. The emphasis of the work is placed on the demonstration of the CONVERGE (TM) applicability to the multi-scale gas turbine engines field and the determination of an optimal mesh strategy through several grid control tools (i.e., local refinement, adaptive mesh refinement) allowing the exploitation of its automatic mesh generation against traditional fixed mesh approaches. For this purpose, the normalized mean square error has been adopted to quantify the accuracy of turbulent numerical statistics regarding the agreement with the experimental database. Furthermore, the focus of the work is to study the behavior when coupling several large eddy simulation sub-grid scale models (i.e., Smagorinsky, Dynamic Smagorinsky, and Dynamic Structure) with the adaptive mesh refinement algorithm through the evaluation of its specific performances and predictive capabilities in resolving the spatial-temporal scales and the intrinsically unsteady flow structures generated within the combustor. This investigation on the main non-reacting swirling flow characteristics inside the combustor provides a suitable background for further studies on combustion instability mechanisms.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partly sponsored by the program "Ayuda a Primeros Proyectos de Investigacion (PAID-06-18), Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), Spain.'' The support given to Mr. Mario Belmar by Universitat Politecnica de Valencia through the "FPI-Subprograma 2'' grant within the "Programa de Apoyo para la Investigacion y Desarrollo (PAID-01-18)'' is gratefully acknowledged.Payri, R.; Novella Rosa, R.; Carreres, M.; Belmar-Gil, M. (2020). Modeling gaseous non-reactive flow in a lean direct injection gas turbine combustor through an advanced mesh control strategy. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering. 234(11):1788-1810. https://doi.org/10.1177/0954410020919619S1788181023411Patel, N., Kırtaş, M., Sankaran, V., & Menon, S. (2007). Simulation of spray combustion in a lean-direct injection combustor. Proceedings of the Combustion Institute, 31(2), 2327-2334. doi:10.1016/j.proci.2006.07.232Luo, K., Pitsch, H., Pai, M. G., & Desjardins, O. (2011). Direct numerical simulations and analysis of three-dimensional n-heptane spray flames in a model swirl combustor. Proceedings of the Combustion Institute, 33(2), 2143-2152. doi:10.1016/j.proci.2010.06.077Masri, A. R., Pope, S. B., & Dally, B. B. (2000). Probability density function computations of a strongly swirling nonpremixed flame stabilized on a new burner. Proceedings of the Combustion Institute, 28(1), 123-131. doi:10.1016/s0082-0784(00)80203-9Johnson, M. R., Littlejohn, D., Nazeer, W. A., Smith, K. O., & Cheng, R. K. (2005). A comparison of the flowfields and emissions of high-swirl injectors and low-swirl injectors for lean premixed gas turbines. Proceedings of the Combustion Institute, 30(2), 2867-2874. doi:10.1016/j.proci.2004.07.040Sankaran, V., & Menon †, S. (2002). LES of spray combustion in swirling flows. Journal of Turbulence, 3, N11. doi:10.1088/1468-5248/3/1/011Jones, W. P., Marquis, A. J., & Vogiatzaki, K. (2014). Large-eddy simulation of spray combustion in a gas turbine combustor. Combustion and Flame, 161(1), 222-239. doi:10.1016/j.combustflame.2013.07.016Ding, G., He, X., Xue, C., Zhao, Z., & Jin, Y. (2015). Preliminary design and experimental verification of a triple swirler combustor. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 229(12), 2258-2271. doi:10.1177/0954410015573555Menon, S., & Patel, N. (2006). Subgrid Modeling for Simulation of Spray Combustion in Large-Scale Combustors. AIAA Journal, 44(4), 709-723. doi:10.2514/1.14875Wang, P., Platova, N. A., Fröhlich, J., & Maas, U. (2014). Large Eddy Simulation of the PRECCINSTA burner. International Journal of Heat and Mass Transfer, 70, 486-495. doi:10.1016/j.ijheatmasstransfer.2013.11.025Cordier, M., Vandel, A., Cabot, G., Renou, B., & Boukhalfa, A. M. (2013). Laser-Induced Spark Ignition of Premixed Confined Swirled Flames. Combustion Science and Technology, 185(3), 379-407. doi:10.1080/00102202.2012.725791Patel, N., & Menon, S. (2008). Simulation of spray–turbulence–flame interactions in a lean direct injection combustor. Combustion and Flame, 153(1-2), 228-257. doi:10.1016/j.combustflame.2007.09.011Bang, B.-H., Kim, Y.-I., Jeong, S., Yoon, Y., Yarin, A. L., & Yoon, S. S. (2019). Theoretical model for swirling thin film flows inside nozzles with converging-diverging shapes. Applied Mathematical Modelling, 76, 607-616. doi:10.1016/j.apm.2019.06.025Linne, M., Paciaroni, M., Hall, T., & Parker, T. (2006). Ballistic imaging of the near field in a diesel spray. Experiments in Fluids, 40(6), 836-846. doi:10.1007/s00348-006-0122-0Desantes, J. M., Salvador, F. J., López, J. J., & De la Morena, J. (2010). Study of mass and momentum transfer in diesel sprays based on X-ray mass distribution measurements and on a theoretical derivation. Experiments in Fluids, 50(2), 233-246. doi:10.1007/s00348-010-0919-8Reddemann, M. A., Mathieu, F., & Kneer, R. (2013). Transmitted light microscopy for visualizing the turbulent primary breakup of a microscale liquid jet. Experiments in Fluids, 54(11). doi:10.1007/s00348-013-1607-2Chen, R.-H., & Driscoll, J. F. (1989). The role of the recirculation vortex in improving fuel-air mixing within swirling flames. Symposium (International) on Combustion, 22(1), 531-540. doi:10.1016/s0082-0784(89)80060-8Presser, C., Gupta, A. K., & Semerjian, H. G. (1993). Aerodynamic characteristics of swirling spray flames: Pressure-jet atomizer. Combustion and Flame, 92(1-2), 25-44. doi:10.1016/0010-2180(93)90196-aBulzan, D. L. (1995). Structure of a swirl-stabilized combusting spray. Journal of Propulsion and Power, 11(6), 1093-1102. doi:10.2514/3.23946Sommerfeld, M., & Qiu, H.-H. (1998). Experimental studies of spray evaporation in turbulent flow. International Journal of Heat and Fluid Flow, 19(1), 10-22. doi:10.1016/s0142-727x(97)10002-9Hadef, R., & Lenze, B. (2005). Measurements of droplets characteristics in a swirl-stabilized spray flame. Experimental Thermal and Fluid Science, 30(2), 117-130. doi:10.1016/j.expthermflusci.2005.05.002Soltani, M. R., Ghorbanian, K., Ashjaee, M., & Morad, M. R. (2005). Spray characteristics of a liquid–liquid coaxial swirl atomizer at different mass flow rates. Aerospace Science and Technology, 9(7), 592-604. doi:10.1016/j.ast.2005.04.004Tratnig, A., & Brenn, G. (2010). Drop size spectra in sprays from pressure-swirl atomizers. International Journal of Multiphase Flow, 36(5), 349-363. doi:10.1016/j.ijmultiphaseflow.2010.01.008Asgari, B., & Amani, E. (2017). A multi-objective CFD optimization of liquid fuel spray injection in dry-low-emission gas-turbine combustors. Applied Energy, 203, 696-710. doi:10.1016/j.apenergy.2017.06.080Moureau, V., Domingo, P., & Vervisch, L. (2011). From Large-Eddy Simulation to Direct Numerical Simulation of a lean premixed swirl flame: Filtered laminar flame-PDF modeling. Combustion and Flame, 158(7), 1340-1357. doi:10.1016/j.combustflame.2010.12.004Caraeni, D., Bergström, C., & Fuchs, L. (2000). Flow, Turbulence and Combustion, 65(2), 223-244. doi:10.1023/a:1011428926494Icardi, M., Gavi, E., Marchisio, D. L., Olsen, M. G., Fox, R. O., & Lakehal, D. (2011). Validation of LES predictions for turbulent flow in a Confined Impinging Jets Reactor. Applied Mathematical Modelling, 35(4), 1591-1602. doi:10.1016/j.apm.2010.09.035Sankaran, V., & Menon, S. (2002). Vorticity-scalar alignments and small-scale structures in swirling spray combustion. Proceedings of the Combustion Institute, 29(1), 577-584. doi:10.1016/s1540-7489(02)80074-8Lebas, R., Menard, T., Beau, P. A., Berlemont, A., & Demoulin, F. X. (2009). Numerical simulation of primary break-up and atomization: DNS and modelling study. International Journal of Multiphase Flow, 35(3), 247-260. doi:10.1016/j.ijmultiphaseflow.2008.11.005Zhou, Y., Huang, Y., & Mu, Z. (2017). Large eddy simulation of the influence of synthetic inlet turbulence on a practical aeroengine combustor with counter-rotating swirler. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(3), 978-990. doi:10.1177/0954410017745900Torregrosa, A. J., Broatch, A., García-Tíscar, J., & Gomez-Soriano, J. (2018). Modal decomposition of the unsteady flow field in compression-ignited combustion chambers. Combustion and Flame, 188, 469-482. doi:10.1016/j.combustflame.2017.10.007Xu, L., Bai, X.-S., Jia, M., Qian, Y., Qiao, X., & Lu, X. (2018). Experimental and modeling study of liquid fuel injection and combustion in diesel engines with a common rail injection system. Applied Energy, 230, 287-304. doi:10.1016/j.apenergy.2018.08.104Broatch, A., Olmeda, P., Margot, X., & Gomez-Soriano, J. (2019). Numerical simulations for evaluating the impact of advanced insulation coatings on H2 additivated gasoline lean combustion in a turbocharged spark-ignited engine. Applied Thermal Engineering, 148, 674-683. doi:10.1016/j.applthermaleng.2018.11.106Esclapez, L., Riber, E., & Cuenot, B. (2015). Ignition probability of a partially premixed burner using LES. Proceedings of the Combustion Institute, 35(3), 3133-3141. doi:10.1016/j.proci.2014.07.040Rhie, C. M., & Chow, W. L. (1983). Numerical study of the turbulent flow past an airfoil with trailing edge separation. AIAA Journal, 21(11), 1525-1532. doi:10.2514/3.8284Gousseau, P., Blocken, B., & van Heijst, G. J. F. (2013). Quality assessment of Large-Eddy Simulation of wind flow around a high-rise building: Validation and solution verification. Computers & Fluids, 79, 120-133. doi:10.1016/j.compfluid.2013.03.006Hanna, S. ., Tehranian, S., Carissimo, B., Macdonald, R. ., & Lohner, R. (2002). Comparisons of model simulations with observations of mean flow and turbulence within simple obstacle arrays. Atmospheric Environment, 36(32), 5067-5079. doi:10.1016/s1352-2310(02)00566-6Hanna, S. R., Hansen, O. R., & Dharmavaram, S. (2004). FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations. Atmospheric Environment, 38(28), 4675-4687. doi:10.1016/j.atmosenv.2004.05.041Yakhot, V., Orszag, S. A., Thangam, S., Gatski, T. B., & Speziale, C. G. (1992). Development of turbulence models for shear flows by a double expansion technique. Physics of Fluids A: Fluid Dynamics, 4(7), 1510-1520. doi:10.1063/1.858424Blazek, J. (2015). Turbulence Modeling. Computational Fluid Dynamics: Principles and Applications, 213-252. doi:10.1016/b978-0-08-099995-1.00007-5Pope, S. B. (2004). Ten questions concerning the large-eddy simulation of turbulent flows. New Journal of Physics, 6, 35-35. doi:10.1088/1367-2630/6/1/035Celik, I. B., Cehreli, Z. N., & Yavuz, I. (2005). Index of Resolution Quality for Large Eddy Simulations. Journal of Fluids Engineering, 127(5), 949-958. doi:10.1115/1.1990201Celik, I., Klein, M., & Janicka, J. (2009). Assessment Measures for Engineering LES Applications. Journal of Fluids Engineering, 131(3). doi:10.1115/1.3059703Ivanic, T., Foucault, E., & Pecheux, J. (2003). Dynamics of swirling jet flows. Experiments in Fluids, 35(4), 317-324. doi:10.1007/s00348-003-0646-5Huang, Y., & Yang, V. (2009). Dynamics and stability of lean-premixed swirl-stabilized combustion. Progress in Energy and Combustion Science, 35(4), 293-364. doi:10.1016/j.pecs.2009.01.002Syred, N., & Beér, J. M. (1974). Combustion in swirling flows: A review. Combustion and Flame, 23(2), 143-201. doi:10.1016/0010-2180(74)90057-

Aeronautical engineering: A special bibliography with indexes, supplement 82, April 1977

This bibliography lists 311 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1977