The preliminary design of an aero-engine combustor is a multidisciplinary process that involves an extensive and systematic analysis of the design space. Simulation-driven approaches, in which several design configurations are numerically analyzed, may lead to heterogeneous models interacting with each other, sharing miscellaneous information within the process. Iterative and user-defined approaches, moreover, are inefficient when multiple and conflicting requirements are in place. To rely on integrated design methodologies has been demonstrated to be beneficial, especially if adopted in a structured approach to design optimization. In this paper, the application of the Combustor Design System Integration (DSI) to the definition of an optimal combustor preliminary configuration will be presented. Given a combustor baseline design, the multi-objective optimization problem has been defined by targeting an optimal distribution for temperature profiles and patterns at the combustor’s exit. Dilution port characteristics, such as hole number and dimension as well as the axial position of the row have been selected as design variables. To guarantee a water-tight design process while minimizing the user effort, the DSI tools were included in a dedicated framework for driving the optimization tasks. Here, a proper CFD domain for RANS, constituted by the flame tube region extended to the dilution port feeds, was adopted for imposing the air split designed for the combustor. Concerning a “complete” combustor sector, this allows a reduction in the computational effort while still being representative for its aero-thermal behavior. The optimization task was performed using a Response Surface Method (RSM), in which multiple, specific combustor configurations were simulated and the CFD result elaborated to build a meta-model of the combustor itself. Finally, the suitability of the resulting optimized configuration has been evaluated through an “a posteriori” analysis for thermal conditions and emission levels (NOx and CO). A lean combustion concept developed by Avio Aero with the aim of the homonymous EU research project, the NEWAC combustor, has been considered as test case.
In the development of an aero-engine combustor, the definition of a preliminary design is a practice in which knowhow, product experience and design rules are focal in deriving a configuration able to meet the functional requirements. Several configurations, and hence multiple geometries resulting in different behaviours, are iteratively analysed in this phase to extensively explore the design space. In this context, an automated procedure ranging from preliminary design to life estimation is necessary and crucial. A framework in which the tools employed in the design workflow are integrated and the low-added-value tasks are automated can allow the reduction of time per analysis within the loop and the enhancement of the procedure's robustness. In this paper will be presented the Combustor Design System Integration (DSI), a methodology aimed at easing and streamlining the design process of aero-engine combustors. To do this, digitization has been taken as the common thread for developing a data-centric approach. The logic behind the procedure will be reported, to focus then on the aero-thermal preliminary design. The procedure, for this phase, is composed of three main integrated components: a CAD generation system, which collects all the geometries for creating an exportable 3D model, a 1D thermal solver for the positioning and sizing of the aero feature on liners (i.e. cooling, dilution…) and a CFD environment with automated pre/post processing operations for reacting-flow analysis. The aim of this work is to contextualize the DSI approach in the combustor design process and to provide a first description of the methodology designed and developed in GE Avio. For that purpose, a straight-through configuration -the lean combustor NEWAC developed in the homonymous EU project-will be exploited as a test case. The development of the procedure is still in progress, so a validation through test cell data comparison, as well as highly-resolved CFD results, will be the subject for future papers.
In the development of an aero-engine combustor, the definition of a preliminary design is a practice in which know-how, product experience and design rules are focal in deriving a configuration able to meet the functional requirements. Several configurations, and hence multiple geometries resulting in different behaviours, are iteratively analysed in this phase to extensively explore the design space. In this context, an automated procedure ranging from preliminary design to life estimation is necessary and crucial. A framework in which the tools employed in the design workflow are integrated and the low-added-value tasks are automated can allow the reduction of time per analysis within the loop and the enhancement of the procedure’s robustness. In this paper will be presented the Combustor Design System Integration (DSI), a methodology aimed at easing and streamlining the design process of aero-engine combustors. To do this, digitization has been taken as the common thread for developing a data-centric approach. The logic behind the procedure will be reported, to focus then on the aero-thermal preliminary design. The procedure, for this phase, is composed of three main integrated components: a CAD generation system, which collects all the geometries for creating an exportable 3D model, a 1D thermal solver for the positioning and sizing of the aero feature on liners (i.e. cooling, dilution...) and a CFD environment with automated pre/post processing operations for reacting-flow analysis. The aim of this work is to contextualize the DSI approach in the combustor design process and to provide a first description of the methodology designed and developed in GE Avio. For that purpose, a straight-through configuration — the lean combustor NEWAC developed in the homonymous EU project — will be exploited as a test case. The development of the procedure is still in progress, so a validation through test cell data comparison, as well as highly-resolved CFD results, will be the subject for future papers.
As far as the preliminary thermal design of gas turbine components is concerned, 1-D codes are still widely used in standard industrial practice. Among the different components, the combustor is one of the most critical ones and its thermal design still greatly affects the reliability and life of the entire engine. During the initial phases of the design process, parameters are often roughly known. For this preliminary phase, a low-order approach is preferred instead of a high-fidelity simulation: the exploration of the whole space is extremely important to better understand the behavior of the system and to focus on the design objectives. Uncertainty quantification (UQ) methods, mainly developed in recent years and applied in many fields, are useful tools for the preliminary design phase and provide support during the whole design process. The objective of this work is to estimate the main sources of uncertainties in the design phase of an aeroengine effusion cooled combustor. The test case is based on a full annular lean-burn combustor, tested during the LEMCOTEC (Low EMissions COre-engine TEChnologies) European project. Among the test points investigated in the experimental campaign, the Approach condition is here analyzed. The inner liner is taken into consideration to investigate the metal temperature. Therm-1D, a 1-D in-house simulation code, is used to model the combustor and the open-source tool DAKOTA is adopted for the uncertainty quantification analysis. The baseline case of the combustor is studied and several uncertainty analyses are investigated. They are divided into 3 main groups: geometrical, tuning modelling parameters and thermal loads. For each group, the most relevant parameters are considered as a source of input uncertainty. In particular, a classical Monte Carlo approach is compared with four innovative polynomial-chaos approaches for each group: Gauss quadrature, total order with LHS sampling, stochastic collocation, and Smolyak. The analyses proved how the last two methods give the best results with a sensible lower amount of simulation (depending on the number of input variables). Lastly, results are compared with experimental data to achieve a better understanding of the most relevant input parameters and the propagation of their uncertainty on the results.
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