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This paper implements a coupled approach to integrate the internal nozzle flow and the ensuing fuel spray using a Volume-of-Fluid (VOF) method in the CONVERGE CFD software. A VOF method was used to model the internal nozzle two-phase flow with a cavitation description closed by the homogeneous relaxation model of Bilicki and Kestin [1]. An Eulerian single velocity field approach by Vallet et al. [2] was implemented for near-nozzle spray modeling. This Eulerian approach considers the liquid and gas phases as a complex mixture with a highly variable density to describe near nozzle dense sprays. The mean density is obtained from the Favreaveraged liquid mass fraction. The liquid mass fraction is transported with a model for the turbulent liquid diffusion flux into the gas.

Shock waves have been recently observed in high-pressure diesel sprays. In this paper, three-dimensional numerical simulations of supersonic diesel spray injection have been performed to investigate the underlying dynamics of the induced shock waves and their interactions with the spray. A Volume-of-Fluid based method in the CFD software (CONVERGE) is used to model this multiphase phenomena. An adaptive Mesh Refinement (AMR) scheme is employed to capture the front of the spray and the shock waves with high fidelity. Simulation results are compared to the available experimental observations to validate the numerical procedure. Parametric studies with different injection and ambient conditions are conducted to examine the effect of these factors on the generation of shock waves and their dynamics.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

The Reynolds stress turbulence model (RSM) has been developed to go beyond the Boussinesq hypothesis and to improve turbulence modeling of flows with significant mean streamline curvature and secondary flow. In this paper the RSM in commercial CFD software CONVERGE is tested for its performance and robustness when applying to complex flows. Several validation cases including flow over flat plate, vortex combustor, diesel engine spray and combustion were selected to test the RSM. The swirling flow in vortex combustor, non-reacting but vaporizing ECN Spray A (free jet) and Sandia small bore diesel engine case are used to demonstrate the benefits of the RSM over the widely used RNG k-epsilon model without model tuning. The vortex combustor case shows the RSM can provide good prediction for strong swirling flow. ECN spray A case was used to demonstrate that the RSM can accurately predict the liquid and vapor penetration lengths of a free jet under diesel engine conditions.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

The internal structure of Diesel fuel injectors is known to have a significant impact on the nozzle flow and the resulting spray emerging from each hole. In this paper the three-dimensional transient flow structures inside a Diesel injector is studied under nominal (in-axis) and realistic (including off-axis lateral motion) operating conditions of the needle. Numerical simulations are performed in the commercial CFD code CONVERGE, using a two-phase flow representation based on a mixture model with Volume of Fluid (VOF) method. Moving boundaries are easily handled in the code, which uses a cut-cell Cartesian method for grid generation at run time. First, a grid sensitivity study has been performed and mesh requirements are discussed. Then the results of moving needle calculations are discussed. Realistic radial perturbations (wobbles) of the needle motion have been applied to analyze their impact on the nozzle flow characteristics.

The 3-Zones Extended Coherent Flame Model (ECFM3Z) and the Tabulated Kinetics for Ignition (TKI) auto-ignition model are widely used for RANS simulations of reactive flows in Diesel engines. ECFM3Z accounts for the turbulent mixing between one zone that contains compressed air and EGR and another zone that contains evaporated fuel. These zones mix to form a reactive zone where combustion occurs. In this mixing zone TKI is applied to predict the auto-ignition event, including the ignition delay time and the heat release rate. Because it is tabulated, TKI can model complex fuels over a wide range of engine thermodynamic conditions. However, the ECFM3Z/TKI combustion modeling approach requires an efficient predictive spray injection calculation. In a Diesel direct injection engine, the turbulent mixing and spray atomization are mainly driven by the liquid/gas coupling phenomenon that occurs at moving liquid/gas interfaces.

Interest in operational cost reduction is driving engine manufacturers to consider low-cost fuel substitution in heavy-duty diesel engines. These dual-fuel (DF) engines could be operated either in diesel-only mode or operated with premixed natural gas (NG) ignited by a pilot flame of compression-ignited direct-injected diesel fuel. Under certain conditions, dual-fuel operation can result in increased cycle-to-cycle variability (CCV) during combustion. CFD can greatly help in understanding and identifying critical parameters influencing CCV. Innovative modelling techniques and large computing resources are needed to investigate the factors affecting CCV in dual-fuel engines. This paper discusses the use of the High Performance Computing resource Titan, at Oak Ridge National Laboratory, to investigate CCV of a dual-fuel engine.

The standard capability of engine experimental studies is that ensemble averaged quantities like in-cylinder pressure from multiple cycles and emissions are reported and the cycle to cycle variation (CCV) of indicated mean effective pressure (IMEP) is captured from many consecutive combustion cycles for each test condition. However, obtaining 3D spatial distribution of all the relevant quantities such as fuel-air mixing, temperature, turbulence levels and emissions from such experiments is a challenging task. Computational Fluid Dynamics (CFD) simulations of engine flow and combustion can be used effectively to visualize such 3D spatial distributions. A dual fuel engine is considered in the current study, with manifold injected natural gas (NG) and direct injected diesel pilot for ignition.

The accurate modeling of fuel spray behavior under diesel engine conditions requires well-characterized boundary conditions. Among those conditions, the spray cone angle is important due to its impact on the spray mixing process, flame lift-off locations and subsequent soot formation. The spray cone angle is a highly dynamic variable, but existing correlations have been developed mainly for diesel fuels at quasi-steady state and relatively low injection pressures. The objective of this study was to develop spray cone angle correlations for both diesel and a light-end gasoline fuel over a wide range of diesel-engine operating conditions that are capable of capturing both the transient and quasi-steady state processes. Two important macroscopic characteristics of solid cone sprays, the spray cone angle and spray penetration, were measured using a single-hole heavy-duty injector using two fuels at diesel engine conditions in an optical constant volume vessel.

Detailed chemical kinetics, although preferred due to increased accuracy, can significantly slow down CFD combustion simulations. Chemistry solutions are typically the most computationally costly step in engine simulations. The calculation time can be significantly accelerated using a multi-zone combustion model. The multi-zone model is integrated into the CONVERGE CFD code. At each time-step, the CFD cells are grouped into zones based on the cell temperature and equivalence ratio. The chemistry solver is invoked only on each zone. The zonal temperature and mass fractions are remapped onto the CFD cells, such that the temperature and composition non-uniformities are preserved. Two remapping techniques published in the literature are compared for their relative performance. The accuracy and speed-up of the multi-zone model is improved by using variable bin sizes at different temperature and equivalence ratios.

A set of reduced chemical mechanisms was developed for use in multi-dimensional engine simulations of premixed gasoline combustion. The detailed Primary Reference Fuel (PRF) mechanism (1034 species, 4236 reactions) from Lawrence Livermore National Laboratory (LLNL) was employed as the starting mechanism. The detailed mechanism, referred to here as LLNL-PRF, was reduced using a technique known as Parallel Direct Relation Graph with Error Propagation and Sensitivity Analysis. This technique allows for efficient mechanism reduction by parallelizing the ignition delay calculations used in the reduction process. The reduction was performed for a temperature range of 800 to 1500 K and equivalence ratios of 0.5 to 1.5. The pressure range of interest was 0.75 bar to 40 bar, as dictated by the wide range in spark timing cylinder pressures for the various cases. In order to keep the mechanisms relatively small, two reductions were performed.

Urea/SCR systems have been proven effective at reducing NOx over a wide range of operating conditions on mid/heavy duty diesel vehicles. However, design changes due to reduction in the size of modern compact Urea/SCR systems and lower exhaust temperature have increased the possibility of urea deposit formation. Urea deposits are formed when urea in films and droplets undergoes undesirable secondary reactions and generate by-products such as ammelide, biuret and cyanuric Acid (CYA). Ammelide and CYA are difficult to decompose which lead to the formation of solid deposits on the surface. This phenomenon degrades the performance of the after treatment system by decreasing overall mixing efficiency, lowering de-NOx efficiency and increasing pressure drop. Therefore, mitigating urea deposits is a primary design goal of modern diesel after-treatment systems.

Reduced-order models typically assume that the flow through the injector orifice is quasi-steady. The current study investigates to what extent this assumption is true and what factors may induce large-scale variations. Experimental data were collected from a single-hole metal injector with a smoothly converging hole and from a transparent facsimile. Gas, likely indicating cavitation, was observed in the nozzles. Surface roughness was a potential cause for the cavitation. Computations were employed using two engineering-level Computational Fluid Dynamics (CFD) codes that considered the possibility of cavitation. Neither computational model included these small surface features, and so did not predict internal cavitation. At steady state, it was found that initial conditions were of little consequence, even if they included bubbles within the sac. They however did modify the initial rate of injection by a few microseconds.

Urea water solution-based Selective Catalytic Reduction (SCR) of NOx emissions from vehicular diesel engines is now widely used world-wide to meet strict health and environmental protection regulations. While urea-based SCR is proven effective, urea-derived deposits often form near injectors, on mixers and pipes, and on the SCR catalyst face. Further understanding of these deposit-formation processes is needed to design aftertreatment system hardware and control systems capable of avoiding severe urea-derived deposits. Computational Fluid Dynamics (CFD) is widely used in SCR aftertreatment design. Film formation, movement, solid wall cooling and deposit initiation/growth time-scales are in the range of minutes to hours, but traditional CFD simulations take too long to reach these time-scales. Here, we propose and demonstrate the frozen flow approach for pulsed sprays and conjugate heat transfer to reduce computation time while maintaining accuracy of key physics.

Combustion is governed by only two phenomena: chemical reactions and mixing (i.e., transport of energy, species, and momentum). A Reynolds Averaged Navier-Stokes (RANS) turbulence model is commonly employed to account for the enhanced mixing due to the presence of turbulence in fluid flow. A RANS turbulence model enhances mixing by introducing a turbulent viscosity. The addition of a turbulent viscosity not only enhances mixing but it also eliminates smaller scales in the CFD simulation. Even though the turbulent viscosity eliminates smaller scales, it is common for RANS engine combustion simulations to be under-resolved. The lack of sufficient mesh resolution to resolve the remaining scales in a RANS combustion simulation may result in a significant sub-grid term that needs to be modeled. In the context of combustion simulation, it is shown that frequently this sub-grid term is significantly more important than Turbulent Chemistry Interaction terms (TCI).

The Computational Chemistry Consortium (C3) is dedicated to leading the advancement of combustion and emissions modeling. The C3 cluster combines the expertise of different groups involved in combustion research aiming to refine existing chemistry models and to develop more efficient tools for the generation of surrogate and multi-fuel mechanisms, and suitable mechanisms for CFD applications. In addition to the development of more accurate kinetic models for different components of interest in real fuel surrogates and for pollutants formation (NOx, PAH, soot), the core activity of C3 is to develop a tool capable of merging high-fidelity kinetics from different partners, resulting in a high-fidelity model for a specific application. A core mechanism forms the basis of a gasoline surrogate model containing larger components including n-heptane, iso-octane, n-dodecane, toluene and other larger hydrocarbons.

Urea-water solution (UWS) injection combined with Selective Catalytic Reduction (SCR) has developed as an effective method of meeting EPA and EURO NOx emissions regulations for diesel engines. Urea/SCR systems encompass a wide range of engine sizes, from light duty vehicles to large ship or power generation engines. One key challenge faced by modern urea/SCR systems is the formation of solid deposits of urea decomposition by-products that are difficult to remove. These deposits are proven to be detrimental to urea/SCR systems by decreasing ammonia uniformity, clogging injector nozzles and increasing pressure drop of the whole system. Urea deposits only form in a narrow range of wall temperatures and take many minutes to hours to form. The decomposition of urea into deposits begins with the formation of biuret and then progresses into the crystalline species cyanuric acid (CYA), ammelide, and ammeline.

Fluid-Structure Interaction (FSI) simulation approach can be used to simulate a turbocharger. However, this predictive 3D simulation encounters the challenge of a long computational time. The impeller speed can be above 100,000 rpm, and generally a CFD solver limits the maximum movement of the impeller surface per time step. The maximum movement must be a fraction (~0.3) of the cell length, thus the time step will be very small. A Multiple Reference Frame (MRF) approach can reduce computational time by eliminating the need to regenerate the mesh at each time-step to accommodate the moving geometry. A static local reference zone encompassing the impeller is created and the impact of the impeller movement is modeled via a momentum source. However, the MRF approach is not a predictive simulation because the impeller speed must be given by the User. A new simulation approach was introduced that coupled the FSI and MRF approach.