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Ammonia, which is one of the most produced inorganic chemicals worldwide, has gained significant attention in recent years as a carbon-free fuel due to its significant energy density in maritime and power plant applications. This fuel offers several advantages including low production costs and being safe for storage and transport. Reactivity controlled compression ignition (RCCI) combustion mode is considered as a promising strategy reducing the level of nitrogen oxides (NOx) emissions and particulate matters (PM) in internal combustion engines (ICEs) due to the lower combustion temperatures and charge homogeneity. Ammonia-based RCCI combustion strategy can offer a simultaneous reduction of CO2 and NOx. In this study, a RCCI engine fuelled by ammonia and diesel is numerically simulated considering chemical reactions kinetics mechanism of the combustion.

Chemical-kinetics-based multizone models (MZM) are effective tools for performance-oriented simulations of low-temperature combustion concepts. It demonstrates a better trade-off between simulation speed and predictivity than both high-fidelity computational fluid dynamics (CFD) and low-fidelity data-driven models. This study applies a newly developed MZM, referred to as UVATZ, to simulate reactivity-controlled compression ignition (RCCI) combustion, fueled by natural gas (NG) and diesel. In such a concept, in-cylinder conditions at intake valve closing (IVC) largely define the kinetically dominated combustion predictions. To secure IVC predictions accurately, UVATZ is for the first time coupled with a detailed air/fuel path dynamics model created in commercial engine modeling software (GT-Suite), forming a 1D simulation framework.

In this work neural network models are used to reconstruct in-cylinder pressure from a vibration signal measured from the engine surface by a low-cost accelerometer. Using accelerometers to capture engine combustion is a cost-effective approach due to their low price and flexibility. The paper describes a virtual sensor that re-constructs the in-cylinder pressure and some of its key parameters by using the engine vibration data as input. The vibration and cylinder pressure data have been processed before the neural network model training. Additionally, the correlation between the vibration and in-cylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI). The experimental matrix covers multiple load/rpm steady-state operating points with different start of injection and lambda setpoints.

With low-temperature combustion engine research reaching an applicable level, physics-based control-oriented models regain attention. For reactivity controlled combustion concepts, chemical kinetics-based multizone models have been proven to reproduce the governing physics for performance-oriented simulations. They offer accuracy levels similar to high-fidelity computational fluid dynamics (CFD) models but with a fraction of their computational effort. Nevertheless, state-of-the-art reactivity controlled compression ignition (RCCI) simulations with multizone model toolchains still face challenges related to predictivity and calculation speed. This study introduces a new multizone modelling framework that addresses these challenges. It includes a C++ code, deeply integrated with open-source, thermo-kinetic libraries, and coupled to an industry standard 1-D modelling framework.

To comply with the ambitious CO2 targets of the European Union, greenhouse gas emissions from the transport sector should be eliminated by 2050. Incremental powertrain improvement and electrification are only a part of the solution and need to be supplemented by carbon-neutral fuels. Due to the high technology readiness level, biofuels offer a short-term decarbonization measure. The high process energy demand for transesterification or hydrotreating however, hinders the well-to-wheel CO2 reduction potential of current market biodiesels. An often-raised, economically and energetically feasible alternative is to use unprocessed oils with viscosity and cold-properties improvers instead. The present work investigates the suitability of one such biofuel (PlantanolTM) for advanced common rail engines operating in a partially premixed compression ignition mode. Preliminary investigations are carried out on a Euro VIb light-duty car engine.

The Reactivity Controlled Compression Ignition concept for dual-fuel engines has multiple challenges of which some can be overcome using Variable Valve Actuation approaches. For various fuel combinations, the engine research community has shown that running dual-fuel engines in RCCI mode, improves thermal efficiency and results in ultra-low engine-out nitrous oxides and soot. However, stable RCCI combustion is limited to a certain load range, depending on available hardware. At low loads, the combustion efficiency can drop significantly, whereas at high loads, the maximum in-cylinder pressure can easily exceed the engine design limit. In this paper, three VVA measures to increase load range, improve combustion efficiency, and perform thermal management are presented. Simulation results are used to demonstrate the potential of these VVA measures for a heavy-duty engine running on natural gas and diesel.