1Substantial progress has been made in understanding and reducing temperature inhomogeneity in rapid compression machines (RCMs) with the help of computational modelling. To date, however, it has not been possible to investigate and map the full range of possible RCM designs, working gases and operating conditions. In this article, we present a framework which simplifies the task of comprehensive and general RCM performance prediction. A set of thermophysical and geometrical parameters have been defined to characterize the design and operating conditions of a general RCM. Dimensional analysis was applied to reduce the number of variables and a sensitivity analysis, based on computational simulations, was used to rank the dimensionless parameters and eliminate unimportant ones. The results of this analysis show that Reynolds number, Prandtl number, aspect ratio, and crevice volume ratio are the most important parameters determining temperature inhomogeneity.A further set of computational simulations was conducted to predict post-compression temperature inhomogeneity over the full range of RCM design and operating parameters. These results are well represented by a simple power law equation that correlates a dimensionless temperature inhomogeneity parameter (mass-averaged over the main chamber) as a function of post-compression time with just three parameters -Peclet number (the product of Reynolds and Prandtl numbers), aspect ratio, and crevice volume ratio. This equation can serve as a simple and general tool for RCM designers and users who wish to determine optimal configurations that minimise temperature inhomogeneity for combustion experiments.2
The design and operation of premixers for gas turbines must deal with the possibility of relatively rare events causing dangerous autoignition. Rare autoignition events may occur in the presence of fluctuations of operational parameters, such as temperature and fuel composition, and must be understood and predicted. This work presents a methodology based on Incompletely Stirred Reactor (ISR) and surrogate modelling to increase efficiency and feasibility in premixer design optimisation for rare events. For a representative premixer, a space-filling design is used to sample the variability of three influential operational parameters. An ISR is then reconstructed and solved in a post-processing fashion for each sample, leveraging a well-resolved CFD solution of the non-reacting flow inside the premixer. Via detailed chemistry and reduced computational costs, the evolution of autoignition precursors and temperature, conditioned on a mixture fraction, is tracked, and accurate surrogate models are trained on all samples. The final quantification of the autoignition probability is achieved by querying the surrogate models via Monte Carlo sampling of the random parameters. The approach is fast and reliable so that user-controllable, independent variables can be optimised to maximise system performance while observing a constraint on the allowable probability of autoignition.
There is a need for fast and reliable emissions prediction tools in the design, development and performance analysis of gas turbine combustion systems to predict emissions such as NOx, CO. Hybrid emissions prediction tools are defined as modelling approaches that (1) use computational fluid dynamics (CFD) or component modelling methods to generate flow field information, and (2) integrate them with detailed chemical kinetic modelling of emissions using chemical reactor network (CRN) techniques. This paper presents a review and comparison of hybrid emissions prediction tools and uncertainty quantification (UQ) methods for gas turbine combustion systems. In the first part of this study, CRN solvers are compared on the bases of some selected attributes which facilitate flexibility of network modelling, implementation of large chemical kinetic mechanisms and automatic construction of CRN. The second part of this study deals with UQ, which is becoming an important aspect of the development and use of computational tools in gas turbine combustion chamber design and analysis. Therefore, the use of UQ technique as part of the generalized modelling approach is important to develop a UQ-enabled hybrid emissions prediction tool. UQ techniques are compared on the bases of the number of evaluations and corresponding computational cost to achieve desired accuracy levels and their ability to treat deterministic models for emissions prediction as black boxes that do not require modifications. Recommendations for the development of UQ-enabled emissions prediction tools are made.
Rapid compression machines (RCMs) are widely used by the fuel research community to provide engine-relevant conditions to study ignition delay time (IDT), which is a crucial target for validating chemical kinetic mechanisms for fuels. Creviced piston heads are routinely used to ensure temperature homogeneity within the combustion chambers of RCMs. However, due to the exponential dependence of kinetic rate coefficients on temperature, homogeneity of the *
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