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The increasing need for cleaner and more efficient combustion systems has promoted a paradigm shift in the automotive industry. Virtual hardware and engine calibration screening at the early development stage, has become the most effective way to reduce the time necessary to bring new products to market. Virtual engine development processes need to provide realistic engine combustion rate responses for the entire engine map and for different engine calibrations. Quasi Dimensional (Q-D) combustion models have increasingly been used to predict engine performance at multiple operating conditions. The physics-based Q-D turbulence models necessary to correctly model the engine combustion rate within the Q-D combustion model framework are a computationally efficient means of capturing the effect of port and combustion chamber geometry on performance. A rigorous method of correlating the effect of air motion on combustion parameters such as heat release is required to enable novel geometric architectures to be assessed to deliver future improvements in engine performance.A previously assessed process using a combination of a 0-D combustion Stochastic Reactor Model (SRM), provided by LOGESoft, a 1-D engine system model and non-combusting, 'cold' CFD is used. The approach uses a single baseline CFD run and a user developed scalar mixing time (τSRM) response to quickly predict the Rate of Heat Release (RoHR). In this work, the physically-based response for τSRM has been further developed to consider the effect of Variable Valve Timing (VVT) for a variety of engine operating conditions. Cold CFD and 1-D engine simulations have initially been carried out to investigate changes in Turbulent Kinetic Energy (k) and its dissipation (ε) caused by VVT changes, allowing the engine Rate of Heat Release (RoHR) to be predicted. The change in the intake flow velocity was correlated to the scalar mixing time, τSRM resulting in a good engine RoHR prediction at the explored conditions.

Electrical energy storage will play a key role in the transition to a low carbon energy network. Liquid air energy storage (LAES) is a thermal–mechanical energy storage technology that converts electricity to thermal energy. This energy is stored in three ways: as latent heat in a tank of liquid air, as warm sensible heat in a hot tank and as cold sensible heat in a packed bed regenerator (PBR), which is the focus of this paper. A PBR was selected because the temperature range (−196 °C to 10 °C) prohibits storage in liquid media, as most fluids will undergo a phase change over a near 200 °C temperature range. A change of phase in the storage media would result in exergy destruction and loss of efficiency of the LAES device. Gravel was selected as the storage media, as (a) many gravels are compatible with cryogenic temperatures and (b) the low cost of the material if it can be used with minimal pre-treatment. PBRs have been extensively studied and modelled such as the work by Schumann, described by Wilmott and later by White. However, these models have not been applied to and validated for a low temperature store using gravel. In the present research, a comprehensive modelling and experimental program was undertaken to produce a validated model of a low-temperature PBR. This included a study of the low-temperature properties of various candidate gravels, implementation of a modified Schumann model and validation using a laboratory scale packed bed regenerator. Two sizes of gravel at a range of flow rates were tested. Good agreement between the predicted and measured temperature fields in the PBR was achieved when a correlation factor was applied to account for short circuiting of the storage media through flow around the interface between the walls of the regenerator and storage media.

In recent years, the exploration of new combustion technologies has accelerated in response to increasingly stringent emissions regulations and fuel economy demands. Virtual engineering tools, that enable the screening of novel hardware and engine calibrations at the early stage of engine development, have become imperative to meet new emission regulations. One-dimensional engine simulations are used at the start of the design of a new engine to define the overall combustion system geometries. Later, more complex three-dimensional computational fluid dynamics calculations are coupled to one-dimensional engine system codes to optimise initial concept geometries and define a system design ready for prototyping. To provide meaningful results, one-dimensional engine system codes often use empirical-based combustion models to calculate the engine burn rate. Moreover, realistic engine burn rates responses, for the entire engine map and for different calibrations, are required to provide three-dimensional computational fluid dynamics codes with correct boundary conditions during the design optimisation phase. Thus, the burn characteristic of new non-traditional combustion solution, for which little experimental data are available, needs to be initially assumed. To improve virtual development and reduce this uncertainty, the industry’s attention shifted towards quasi-dimensional combustion models capable of providing engine burn rate predictions. Within the quasi-dimensional 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 zero-dimensional turbulence models for different engine concepts and calibrations is therefore needed to enable quasi-dimensional combustion models to predict the engine burn rate. An alternative methodology, with limited dependency on previous test data, is required to enhance the exploration of novel combustion strategies and geometric architectures. An available process, based on a quasi-dimensional combustion stochastic reactor model, a one-dimensional engine system model and non-combusting three-dimensional computational fluid dynamics calculations, was used for this work. The approach uses limited non-combusting computational fluid dynamics calculations and a previously developed scaling factor response for the stochastic reactor model turbulence input ( τSRM) to quickly predict the engine rate of heat release. In this work, the scaling factor response was assessed against two different engine variants over a variety of engine operating conditions. Moreover, the same response was used to predict the effect of different bore-to-stroke ratios on the engine combustion rate and knock tolerance. Non-combusting computational fluid dynamics and one-dimensional engine system simulations have been carried out to investigate changes in turbulence characteristics due to different engine variants and bore-to-stroke ratios. It was shown that limited number of non-combusting computational fluid dynamics runs is required to characterise the in-cylinder turbulence for each explored engine variant. The scaling factor response was used to manipulate the turbulence input ( τSRM) resulting in good engine burn rates predictions for the explored engine variants and bore-to-stroke ratios. The presented methodology showed augmented predictive capabilities and has potential to move the engine development towards a less hardware dependent virtual approach, offering a practical solution for the exploration of new engine concepts.