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This paper delves into the intricate realm of Formula 1 race car aerodynamics, focusing on the pivotal role played by floor flow structures in contemporary racing. The aerodynamic design of the floor of a Formula 1 car is a fundamental component that connects the flow structures from the front wing to the rear end of the car through the diffuser, thus significantly influencing the generation of lift and drag. In this work, CFD was used to predict the structure of the vortices and flow pattern underneath a Formula 1 car using a CAD model that mimicked the modern Red Bull Racing Team’s car in recent years. Through comprehensive analysis and simulation, a detailed understanding of the complex flow patterns and aerodynamic phenomena occurring beneath the floor of the car and its vicinity is presented.

The proposed Euro 7 regulation aims to substantially reduce the NOx emissions to 0.03 g/km, a trend also seen in upcoming China 6b and US EPA regulations. Meeting these stringent requirements necessitates advancements in Urea/Selective Catalytic Reduction (SCR) aftertreatment systems, with the urea deposit formation being a key challenge to its design. It’s proven that Computational Fluid Dynamics (CFD) can be an effective tool to predict Urea deposits. Transient wall temperature prediction is crucial in Urea deposit modeling. Additionally, fully understanding the kinetics of urea decomposition and by-products solidification are also critical in predicting the deposit amount and its location. In this study, we introduce (i) a novel film boiling model (IFPEN-BRT model) and (ii) a new urea by-product solidification model in the CONVERGE CFD commercial solver, and validate the results against the recent experiments.

The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

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.

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.

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.

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.

The Extended Coherent Flamelet Model (ECFM) is limited to lower order upwinding schemes to minimize the numerical discrepancy between species and tracers, which can lead to inaccurate estimates of the progress variable and consequently negative conditional mass fractions in the burned gases after ignition. The recently developed Species-Based ECFM (SB-ECFM) removes the species tracers from the definition of the progress variable, and allows the use of higher order schemes. In this study, SB-ECFM is coupled with the Imposed Stretch Spark Ignition Model (ISSIM) to simulate a spark-ignition engine, the transparent combustion chamber (TCC) engine. To examine the spatial discretization effect and demonstrate the improvement due to using higher order schemes, Reynolds-Averaged-Navier-Stokes (RANS) simulations performed with a first-order upwinding scheme and a second-order central differencing scheme are compared.

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.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.

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 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.

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.

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.

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 current trend of downsizing used in gasoline engines, while reducing fuel consumption and CO2 emissions, imposes severe thermal loads inside the combustion chamber. These critical thermodynamic conditions lead to the possible auto-ignition (AI) of fresh gases hot-spots around Top-Dead-Center (TDC). At this very moment where the surface to volume ratio is high, wall heat transfer influences the temperature field inside the combustion chamber. The use of a realistic wall temperature distribution becomes important in the case of a downsized engine where fresh gases hot spots found near high temperature walls can initiate auto-ignition. This paper presents a comprehensive numerical methodology for an accurately prediction of thermodynamic conditions inside the combustion chamber based on Conjugate Heat Transfer (CHT).

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.

This work involves modeling internal and near-nozzle flows of a gasoline direct injection (GDI) nozzle. The Engine Combustion Network (ECN) Spray G condition has been considered for these simulations using the nominal geometry of the Spray G injector. First, best practices for numerical simulation of the two-phase flow evolution inside and the near-nozzle regions of the Spray G injector are presented for the peak needle lift. The mass flow rate prediction for peak needle lift was in reasonable agreement with experimental data available in the ECN database. Liquid plume targeting angle and liquid penetration estimates showed promising agreement with experimental observations. The capability to assess the influence of different thermodynamic conditions on the two-phase flow nature was established by predicting non-flashing and flashing phenomena.

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.