A process for an Efficient Heat Release Prediction at the Concepts Screening Stage of Gasoline Engine Development

  • Christian Rota

    Student thesis: Doctoral Thesis


    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.
    Date of Award2020
    Original languageEnglish
    Awarding Institution
    • University of Brighton
    SupervisorRobert Morgan (Supervisor) & David Mason (Supervisor)

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