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Title: A process for an efficient heat release prediction at the concepts screening stage of gasoline engine development
Author: Rota, Christian
ISNI:       0000 0004 9359 3289
Awarding Body: University of Brighton
Current Institution: University of Brighton
Date of Award: 2020
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In recent years, the exploration of new combustion technologies has accelerated due to new stringent emissions regulations and fuel economy requirements. Virtual engineering tools, that enable the screening of non-traditional hardware and engine calibration at the early stage of engine development, have become imperative to meet new emission regulations. In the current engine development process benchmarking and historical test data, are used to carry out simple 1-D engine system calculations and define the overall engine concept design. Later, to provide a definitive design ready for prototyping, more complex Computational Fluid Dynamics (CFD) calculations are coupled to 1-D engine system codes to optimise initial concept geometries and high-level calibrations. However, to provide meaningful results, 1-D engine system codes often use empirical based combustion models that require an initial input, called engine burn rate. Realistic engine burn rate responses, for the entire engine map and for different design concepts, are also required to provide 3D CFD codes with correct boundary conditions during the design optimisation phase. Thus, the engine burn rate of new combustion technologies, for which little experimental data is available, need to be initially assumed. To improve the predictive capabilities virtual engine development processes, the industry’s attention shifted towards Quasi-Dimensional (Q-D) combustion models capable of providing engine burn rate predictions. However, within the Q-D modelling framework, turbulence models, adding extra user-input variables, are required to capture the effect of different combustion chamber geometries on the engine combustion rate. Rigorous validation of Q-D turbulence models for different engine concepts and engine maps is needed to enable Q-D combustion models to predict the engine burn rate. Therefore, an alternative methodology characterised by limited dependency on previous test data is required to enhance the exploration of novel combustion strategies and geometric architectures. In this thesis, an alternative engine development process that uses a combination of a Q-D combustion Stochastic Reactor Model (SRM), a 1-D engine system model and noncombusting, “cold” CFD calculations, is proposed. The SRM code captures the combustion chemistry in a computationally efficient manner but does not capture in isolation geometric variables such as port and piston geometry. To account for that, the approach uses limited non-combusting CFD baseline calculations to characterise the engine in-cylinder flow of each screened engine concepts. A physics-based scaling factor response was developed and used to provide the SRM with the correct turbulence input, known as scalar mixing time (τSRM). The response was assessed against four different engine variants over a variety of engine operating conditions. The same response was used to predict the effect of different bore to stroke ratios (B/S) on the engine combustion rate and knock tolerance. Non-combusting CFD and 1-D engine system simulations have been carried out to investigate the effect of different engine variants and operating conditions on the in-cylinder turbulence. It was shown that τSRM of different operating conditions can be scaled to the intake flow velocity predicted by 1-D engine system analysis. This allows to predict the engine RoHR at the explored engine variants and operating conditions within the experimental standard deviation. The presented methodology showed augmented predictive capabilities and has potential to move the engine development towards a less hardware dependent approach for the exploration of new engine concepts.
Supervisor: Morgan, Robert ; Mason, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available