In this work, the Techno-economic Environmental Risk Analysis framework, a multidisciplinary optimisation tool developed by Cranfield University, is utilised in conjunction with an in-house optimiser to carry out aircraft engine cycle optimisation processes. The central point here is the evaluation of the capabilities of the in-house optimiser for performing this type of optimisation processes. Simplifying hypotheses are thus considered when both defining the aircraft flight trajectory and modelling the different engine configurations analysed. Accordingly, several optimum engine cycles minimising separately three objective functions, (i) specific fuel consumption in cruise, (ii) fuel burned, and (iii) oxides of nitrogen emitted, are determined. The cycle optimisation processes carried out yield results reflecting the general trends expected when optimising according to these objective functions. It follows then that the in-house optimiser is suitable for carrying out gas turbine power plant optimisation processes. It is expected that this optimiser be utilised in future for both optimising the preliminary design of gas turbine engines and determining optimum and "greener" aircraft engine cycles.
This work focuses on the development and implementation of an emissions prediction model which allows the reliable calculation of emissions trends from current and potential future aircraft gas turbine combustors. The ultimate goal of the model developed involves its use in efficient aircraft trajectory optimisation processes, which eventually allow identifying aircraft “greener trajectories”, minimizing in that way both aircraft fuel consumption and the pollutants emitted. From the three broad strategies that can be adopted for combustor emissions prediction: empirical correlations, stirred reactor models, and comprehensive numerical simulations involving detailed Computational Fluid Dynamics calculations, the second one was chosen for the development of the model described in this work. Thus, critical zones within the combustor are represented by individual stirred reactors, incorporating the processes of mixing, combustion heat release, and pollutant formation. To take into account inhomogeneities in gas composition and temperature which influence directly the rates of pollutant formation, a stochastic representation of turbulent mixing in the combustor primary zone is utilized. Results obtained from the simulations of an actual combustor using the emissions prediction model developed in terms of NOx, CO, UHC, and soot/smoke emitted show reasonable agreement with experimental data, reproducing the trends observed in practice.
This work describes initial results obtained from an ongoing research involving the development of optimization algorithms which are capable of performing multi-disciplinary aircraft trajectory optimization processes. A short description of both the rationale behind the initial selection of a suitable optimization technique and the status of the optimization algorithms is firstly presented. The optimization algorithms developed are subsequently utilized to analyze different case studies involving one or more flight phases present in actual aircraft flight profiles. Several optimization processes focusing on the minimization of total flight time, fuel burned and oxides of nitrogen (NO x ) emissions are carried out and their results are presented and discussed. When compared with others obtained using commercially available optimizers, results of these optimization processes show satisfactory level of accuracy (average discrepancies ~2%). It is expected that these optimization algorithms can be utilized in future to efficiently compute realistic, optimal and 'greener' aircraft trajectories, thereby minimizing the environmental impact of commercial aircraft operations.
Compared to other simulation approaches utilized for modeling turbulent reacting flows with detailed chemistry, probability density function (PDF) methods offer several advantages. This is because the changes in fluid composition due to convection and reaction processes can be treated exactly. PDF methods require however closure models for the mixing process representing the transport of the PDF owing to molecular diffusion. Over the years several mixing models with different degrees of complexity have been developed. A review of the main Lagrangian mixing models for turbulent combustion developed so far is presented in this work. This review includes models where the composition of a particle changes (i) essentially independently of the composition associated with the other particles, and (ii) through direct interaction with other particles. The main advantages and shortcomings of the mixing models reviewed are highlighted accordingly. Because Lagrangian Monte Carlo techniques are usually used for solving PDF transport equations, a particular emphasis is put on their corresponding particle implementation. The mixing models review is preceded by a section highlighting the mathematical formulation associated with the use of PDF methods for turbulent reacting flows. In the last part of the article both comparative results of mixing models performance and prospects for the mixing models are discussed. Despite the effort that has been devoted to the development of more capable mixing models, currently there is no mixing model presenting all desirable characteristics. Even more, there are significant differences in the results obtained when different mixing models are utilized, especially when higher order scalar statistics are accounted for. Therefore work still needs to be carried out in order to develop a mixing model satisfying all desirable characteristics expected from these models. Several avenues can be further explored in order to achieve this goal. These potential routes include those accounting for spatial scalar structures and both scalar length and turbulent frequency scales distributions. Other approaches based on competitive mixing, manifold-based features and Lagrangian coherent structures have also the potential to further improve upon existing mixing models. The development of a sound mixing model will allow eventually removing one of the largest sources of modeling uncertainty in PDF-based computations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.