Multiple statistical methods were evaluated to determine the most suitable approach to gauge whether a gasoline deposit control additive (DCA) causes a statistically significant increase in rate of stochastic preignition (SPI) in a direct injection, turbocharged, spark ignition (SI) engine. SPI is an abnormal combustion phenomenon observed at highly boosted conditions in turbocharged SI engines. The high in-cylinder pressures observed during an SPI event have the potential to cause physical damage to the engine. The SPI rate is a function of engine operating conditions as well as fuel and oil properties. DCAs are commonly used in commercial gasoline at very low concentrations (20–200 ppm m/m) to prevent the buildup of carbon deposits in the combustion chambers and fuel systems. Since the effect of DCAs on SPI rate can be subtle and the data inherently stochastic, a controlled engine test methodology with robust statistical analysis is required to conclusively determine whether a gasoline detergent increases or mitigates the rate of SPI occurrence. Therefore, the TOP TIER™ Standardized Dynamometer Test Method to Evaluate Additized Detergent Gasoline for Stochastic Pre-Ignition (hereafter referred to as the TOP TIER™ Additive SPI Test Protocol) described herein is designed to gauge whether a gasoline additive causes a statistically significant change in the SPI rate at a controlled operating condition as compared to unadditized gasoline. This study focuses on the statistical analysis portion of the test protocol. Multiple statistical approaches were evaluated using data from the TOP TIER™ Additive SPI Test Protocol to determine the most appropriate and robust approach for detecting a statistically significant difference in SPI rates. The analysis included the two-sample (unpaired) t-test, linear mixed-effects models (LMMs), the Mann–Whitney U test, and the permutation test. The results indicated a combination of permutation paired with Wilkinson’s method as the most suitable approach.
As a part of the project, the homogeneous reactivity-controlled compression ignition (hRCCI) concept is being investigated along with the development of 0D/1D combustion simulation models. The proposed concept employs early direct injection of octanol as the high-reactivity fuel in combination with port fuel injection of ethanol as the low-reactivity fuel. In the previous parts of the publication, testing results and the in-cylinder fuel and temperature distribution predictions were studied. Low-temperature combustion (LTC) concepts such as hRCCI are strongly governed by autoignition chemistry of the fuels and oxidizer, making the explicit consideration of chemical kinetics essential for predictive simulations. In addition, the hRCCI combustion process exhibits high sensitivity to a wide range of control parameters, including intake temperature, intake pressure, excess air ratio, premixed fuel ratio, and direct injection parameters, posing further challenges to model robustness. To address these challenges, a predictive and robust quasi-dimensional multi-zone combustion model for LTC engines is developed. The model couples a 0D/1D flow framework implemented in GT-POWER with detailed chemical kinetics solved using a Python–Cantera interface for user-defined combustion modeling. The combustion framework incorporates dedicated sub-models to account for fuel evaporation, interzonal heat transfer, wall heat transfer, spatial distribution of directly injected octanol, and temperature stratification across the zones. The model’s predictive capability is validated against experimental data from a single-cylinder research engine under variations in intake pressure, intake temperature, premixed ratio, and the start of direct octanol injection. The simulation results show strong agreement with experimental measurements, demonstrating the robustness and predictive accuracy of the developed modeling approach. The proposed simulation framework provides a valuable tool for the future development and optimization of low-temperature combustion engine concepts aimed at improving efficiency and reducing emissions.
As part of the dtec.bw MORE project, serial hybrid powertrains featuring alternative combustion regimes, specifically homogeneous reactivity-controlled compression ignition (hRCCI), are under investigation to maximize thermal efficiency while minimizing NOx and soot emissions. The proposed hRCCI concept utilizes dual-fuel stratification, employing early direct injection (DI) of octanol (high-reactivity fuel) alongside port fuel injection (PFI) of ethanol (low-reactivity fuel). While 0D/1D engine modeling is essential for developing predictive powertrain simulation frameworks, conventional models often lack the robustness required to capture the complexities of low-temperature combustion (LTC). This study addresses this limitation by developing a multi-zone “onion skin” quasi-dimensional model. Since for LTC, ignition and heat release rates are highly sensitive to thermal and chemical stratification, capturing these gradients is critical. 3D computational fluid dynamics (CFD) simulations are capable of capturing thermal and chemical stratifications to a high degree of accuracy, whereas 0D simulation models, in general, do not consider them. The primary contribution of this work is the translation of high-fidelity 3D CFD data into a computationally efficient quasi-dimensional environment. A simplified 3D CFD architecture was utilized to map the effects of various operating parameters on mixture distribution. Using these results, a regression learning model was trained to predict octanol and temperature stratification within the combustion chamber. Validation against simulation data demonstrates that this coupled machine learning and multi-zone approach provides a robust, predictive tool for evaluating advanced LTC concepts within larger system-level simulations.
High exhaust gas recirculation (EGR) rates in dedicated hybrid engines (DHEs) cause combustion instability and fuel economy degradation. This study investigates a 2.0L turbocharged DHE with a high-tumble combustion system optimized by computational fluid dynamics (CFD) simulation. At the brake thermal efficiency (BTE) operating point (2750 r/min, brake mean effective pressure [BMEP] 11 bar), engine bench tests evaluated the effects of three ignition energy levels (120 mJ, 150 mJ, 200 mJ) and two spark plug configurations (1.1 mm nickel alloy and 0.7 mm pin-to-pin iridium) on combustion characteristics, fuel economy, the misfire-limited EGR rate (hereinafter the EGR misfire limit), and engine-out emissions. Results show that above 23% EGR, intake condensate weakens ignition and destabilizes combustion. Raising ignition energy to 200 mJ extends the EGR misfire limit from 23% to 28%, shortens ignition delay (CA0-10) and combustion duration (CA10-90) by 4.5°CA and 2.8°CA and reduces BSFC by 3.2 g/kWh. The 0.7-mm iridium plug outperforms the 1.1-mm nickel alloy plug above 27% EGR: CA0-10 and CA10-90 shorten by 2.9°CA and 2.5°CA, the EGR misfire limit extends by 0.5 percentage points, and BSFC improves by 0.9 g/kWh. The combined optimization extends the EGR misfire limit to 28.5% with a cumulative BSFC reduction of 4.5 g/kWh. HC emissions decrease with reduced spark plug gap, while NOx and CO remain insensitive to the ignition strategy. The coupled optimization of high-energy ignition and small-gap spark plug effectively overcomes ignition degradation under high EGR. The reduced breakdown voltage of the small gap and the sufficient energy reserve of high-energy ignition form a synergistic effect, providing quantitative design guidance for ignition systems in next-generation high-efficiency DHEs.
Cashew nut shell oil–based biodiesel (BD) is an environmentally friendly and sustainable alternative energy source that can help decrease the depletion of fossil fuels and reduce environmental pollution. In this research, the BD extracted from cashew nut shell was enriched with green-synthesized nanoparticles with various blends and evaluated for its performance. The BD20A blend recorded the best thermal efficiency of the brake, 29.5%, which was a boost of about 20.4% over diesel when using a medium load of 2.7 kW. Furthermore, the decrease in brake-specific fuel consumption was 36.2%, and exhaust gas temperature improved by 26.1% due to enhanced combustion, indicating better combustion and utilization of heat. The BD10A and BD20A recorded a considerable decrease in emissions compared to diesel under full-load conditions, with carbon monoxide and hydrocarbons reducing by 35.7% and 33.3%, respectively, and a moderate increase of nitrogen oxides. Among the multi-objective optimization approaches, the Jaya algorithm exhibited the fastest convergence rate and identified the optimum operating condition that achieved the best trade-off between engine performance and exhaust emissions. BD blends, particularly BD20A, provide greater thermal performance and better combustion behavior as well as lower exhaust emissions, making them viable as green alternatives to the traditional diesel fuel.
