Robert Morgan

Computational Fluid Dynamics (CFD) frameworks of supercritical cryogenic fluids need to employ Real Fluid models such as cubic Equations of State (EoS) to account for thermal and inertial driven mechanisms of fluid evolution and disintegration. Accurate estimation of the non-linear variation in density, thermodynamic and transport properties is required to computationally replicate the relevant thermo and fluid dynamics involved. This article reviews the availability, performance and the implementation of common Real Fluid EoS and data-based models in CFD studies of supercritical cryogenic fluids. A systematic analysis of supercritical cryogenic fluid (N₂, O₂ and CH₄) thermophysical property predictions by cubic (PR and SRK) and non-cubic (SBWR) Real Fluid EoS, along with Chung’s model, reveal that: (a) SRK EoS is much more accurate than PR at low temperatures of liquid phase, whereas PR is more accurate at the pseudoboiling region and (b) SBWR EoS is more accurate than PR and SRK despite requiring the same input parameters; however, it is limited by the complexity in thermodynamic property estimation. Alternative data-based models, such as tabulation and polynomial methods, have also been shown to be reliably employed in CFD. At the end, a brief discussion on the thermophysical modelling of cryogenic fluids affected by quantum effects is included, in which the unsuitability of the common real fluid EoS models for the liquid phase of such fluids is presented.

Cryogenic supercritical fluids represent an intriguing category of fluids that combine mechanical and thermophysical properties of both ultralow temperature conditions and phenomena taking place well above the critical point. Recent research has demonstrated that it is a common misconception to consider the supercritical state as one homogeneous state. Instead, these fluids consist of two to four liquid and gas like phases, each with their own unique characteristics. In our work, we investigate numerically single-specie cryogenic fluid jets—initially at subcritical temperatures—which are injected into a supercritical environment (both the pressure and temperature exceed the thermodynamic critical state). For the investigation, a new solver, namely, “CoolFoam,” has been developed, which is designed for compressible non-isothermal two-fluid simulations where diffusive transport of heat and/or mass is accounted for. Real fluid thermodynamics are modeled using a polynomial fitting approach developed in our previous work. We introduce also a new phase characterization framework based on the association of phases with specific temperature ranges (rather than using a single line like the Widom line), which allowed us to better identify the similarity effects between the various conditions. We analyze the inter-dependence of the underlying phenomena: density gradient and diffusive mass transport [molecular and thermo(Soret)-diffusion] and turbulence. We also compared supercritical N₂ with subcritical liquid and gas jets to highlight potential differences with respect to how these jets behave. We find that the jet dynamics are largely dictated by the thermodynamic transition of the injected fluid and the associated variation in thermophysical properties.

In this work air fuel mixing and combustion dynamics in the recuperated split cycle engine (RSCE) are investigated through new theoretical analysis and complementary optical experiments of the flow field. First, a brief introduction to the basic working principles of the RSCE cycle will be presented, followed by recent test bed results relevant to pressure traces and soot emissions. These results prompted fundamental questioning of the air-fuel mixing and combustion dynamics taking place. Hypotheses of the mixing process are then presented, with differences to that of a conventional Diesel engine highlighted. Moreover, the links of the reduced emissions, air transfer processes and enhanced atomisation are explored. Initial experimental results and Schlieren images of the air flow through the poppet valves in a flow rig are reported. The Schlieren images display shockwave and Mach disk phenomena. Demonstrating supersonic air flow in the chamber is consistent with complementary CFD work. The results from the initial experiment alone are inconclusive to suggest which of the three suggested mixing mechanism hypotheses are dominating the air–fuel dynamics in the RSCE. However, one major conclusion of this work is the proof for the presence of shockwave phenomena which are atypical of conventional engines.

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.

Traffic related NOx and particle emission remain a significant concern particularly in the urban environment. Electrification offers a medium to long term solution, but there remains a need to significantly reduce internal combustion engine emissions in the short and medium term, and potentially in the long term for long range inter city transportation. Late injection low temperature combustion (LTC) has the potential to achieve ultra-low emissions levels in a compression ignition engine by increasing the lean pre-mixed burn fraction. However, significant quantities of diluent are normally required to achieve the required delay in ignition and pre-mixing to achieve LTC. This results in high boost requirements, increased pumping work and the complexity of the air handling system and potentially adversely impacting fuel economy. In this paper, results from a single cylinder light duty research engine are presented using a novel ramped combustion chamber focused at mid to high engine loads. The ramped combustion chamber improves mixing and enables more retarded injection timings than those possible on conventional bowl designs. This combustion strategy has enabled LTC conditions to be achieved at lower dilution rates, typically 20-30% at loads up to 15bar IMEP. CFD analysis of the air-fuel interaction indicates the ramped bowl effectively deflects fuel away from the squish region enabling very late injection timings. One dimensional analysis of the engine system was used to investigate the potential of late exhaust valve opening in improving work recovery, resulting in improved fuel consumption over the baseline LTC valve timings.

The Direct Numerical Simulation (DNS) approach to solving the fundamental transport equations down to the smallest scales of motion is favorable should the requirement be a truly predictive solution of fluid dynamic problems, but the simulation run times are unacceptable for most practical industrial applications. Despite the steadily increasing computational capabilities, Reynolds Averaged Navier-Stokes (RANS) based frameworks remain the most commercially viable option for high volume sectors, like automotive. The sub models within RANS simplify the description of key physical phenomena and include several numerical constants. These so-called “tuning constants” introduce multivariable dependencies that are almost impossible to untangle with local sensitivity studies. This paper addresses the prevailing difficulties in setting up an adequate diesel spray simulation which arise from the mentioned multi-variable interactions of these “tuning constants”, by applying a statistical approach named Design of Experiments (DoE).Often combined with an optimizer, DoE is commonly used to find an optimum set of engine parameters for set criteria at reduced experimental effort. In this case, the methodology was applied to determine an optimal set of “tuning constants” for the simulations which best matched experimental data at five conditions taken from the Engine Combustion Network (ECN) database. Multi-variable DoE were run for each condition. Stochastic response models (SPM’s) highlighted crucial simulation sensitivities of the turbulent dissipation constant C2 and liquid/gas-phase momentum transfer at injection pressure swings. Further, a comparison of the breakup models which produced matching simulations exhibited patterns which correspond with physical processes. Lastly, it was shown that while a single set of the constants can give reasonable results in the space explored, there is merit in adjusting key constants to suit the operating condition in the search for accuracy.