Combustion of Fuel Surrogates: An Application to Gas Turbine Engines

The previously developed approaches for fuel droplet heating and evaporation processes, mainly using the Discrete Multi Component Model (DMCM), are investigated for the aerodynamic combustion simulation. The models have been recently improved and generalised for a broad range of bio-fossil fuel blends so that the application areas are broadened with an increased accuracy. The main distinctive features of these models are that they consider the impacts of species’ thermal conductivities and diffusivities within the droplets in order to account for the temperature gradient, transient diffusion of species and recirculation. A formulation of fuel surrogates is made using the recently introduced model, referred to as “Complex Fuel Surrogate Model (CFSM)”, and analysing their heating, evaporation and combustion characteristics. The CFSM is aimed to reduce the full composition of fuel to a much smaller number of components based on their mass fractions, and to formulate fuel surrogates. Such an approach has provided a proof of concept with the implementation of the developed model into a commercial CFD code ANSYS Fluent. A case study is made for the CFD modelling of a gas turbine engine using a kerosene fuel surrogate, which is the first of its kind. The surrogate is proposed using the CFSM, with the aim to reduce the computational time and improve the simulation accuracy of the CFD model.

Combustion of Fuel Surrogates: An Application to Gas Turbine Engines

The previously developed models for fuel droplet heating and evaporation processes, mainly the Discrete Multi Component Model (DMCM), and Multi-Dimensional Quasi-Discrete Model (MDQDM) are investigated for the aerodynamic combustion simulation. The models have been recently improved, and generalised for a broad range of bio-fossil fuel blends so that the application areas are broadened with increased accuracy. The main distinctive features of these models are that they consider the impacts of species thermal conductivities and diffusivities within the droplets to account for the temperature gradient, transient diffusion of species and recirculation. A formulation of fuel surrogates is made, using the recently introduced model, referred to as ‘’Complex Fuel Surrogate Model (CFSM)’’ and analysing their heating, evaporation, and combustion characteristics. The CFSM is aimed to reduce the full composition of fuel to a much smaller number of components based on their mass fractions, and to formulate fuel surrogates. Such approach has provided a proof of concept with the implementation of the developed model into a commercial CFD code ANSYS-Fluent. A case study is made for the CFD modelling of gas-turbine engine using kerosene fuel surrogate. The surrogate is proposed using the CFSM. The model is implemented into ANSYS-Fluent via a user-defined function to provide the first full simulation of the combustion process. Detailed chemical mechanism is also implemented into ANSYS Chemkin for the combustion study.

Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A Bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the ‘unicity’ of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the ‘unicity’ of the surrogate composition has revealed that (i) the unicity is strongly correlated with the accuracy and that (ii) both ‘unicity’ and accuracy of the prediction are very sensitive to the high presence of aromatics.