Premixed charge compression ignition (PCCI) presents an intriguing alternative to conventional diesel combustion (CDC), facilitating exceptionally low oxides of nitrogen (NO x ) and soot emissions by reducing local in-cylinder combustion temperatures and enhancing fuel-air mixing. PCCI combustion mode has been extensively investigated through high exhaust gas recirculation (EGR) levels and early or late injection timings to extend the ignition delay. One of the significant challenges in PCCI mode is controlling combustion initiation and a narrow engine operating load range due to early ignition and knocking combustion with high-reactivity diesel. The present work addresses this shortcoming by modifying injector orientation and utilizing a blend of high-reactivity diesel and low-reactivity gasoline. Two fuel blends are investigated, viz. 10% (D90G10) and 20% (D80G20) of gasoline mixed with diesel on a volume basis. The mechanical fuel injection system in the test engine is replaced with a common-rail direct injection (CRDi) system, and the compression ratio is reduced from 17.5 to 15 to achieve PCCI combustion. An initial parametric investigation revealed that early direct injection and high fuel injection pressure restricted the load range to 30% of the rated load in PCCI mode using diesel fuel. However, with diesel and EGR, the load range can extend to 60% of the rated load, resulting in high emissions of unburned hydrocarbons (HC) and carbon monoxide (CO). The engine cylinder head is modified to accommodate a vertical injector orientation, enabling operation with diesel-gasoline blends to address these shortcomings. The experiments were conducted at a constant rated speed with varying load conditions, and injection timing was optimized to achieve the maximum operable load range while maintaining stable combustion, maximizing brake thermal efficiency, and keeping NO x emissions below 100 ppm, at each load in PCCI mode to assess the effects of injector orientation and the diesel-gasoline blend. The results show that using a diesel-gasoline blend significantly improved the engine’s operable load range and reduced unburned emissions in PCCI. The longer ignition delay associated with the gasoline blend promotes enhanced premixed combustion, resulting in lower local equivalence ratios and lower in-cylinder combustion temperatures. This enables a substantial extension of the engine load range up to 73.4% of the rated load. Concurrently, NO x emissions are reduced to below 100 ppm, and soot emissions decrease by 69.4%. Improved combustion phasing and in-cylinder thermodynamic conditions at higher engine loads result in an 8.8% enhancement in brake thermal efficiency when using a modified injector operated with a diesel-gasoline blend, compared to the unmodified injector operated with diesel (reference case). Additionally, HC and CO emissions are reduced by 52.2% and 84.3%, respectively, at 60% of the rated engine load.
Intelligent perception and control of fuel injection characteristics represent a key cutting-edge technology for achieving controllable and efficient combustion in engines. Existing methods primarily rely on deterministic point estimates, lacking a quantitative assessment of the reliability of perception results, which limits their decision-making and application in closed-loop control under complex, time-varying operating conditions. To address this, this paper proposes an online injection rate perception method (VB-BiLSTM) that integrates variational Bayesian inference with a bidirectional long short-term memory (BiLSTM) network. This method uses the injector inlet pressure fluctuation signal and the solenoid valve drive current as coupled time-series inputs. By distributing network parameters and training the model to maximize the evidence lower bound (ELBO), combined with Monte Carlo sampling and Gaussian reparameterization, it achieves stable gradient optimization and real-time forward inference, thereby providing a posterior confidence interval alongside the output fuel injection rate curve. The offline-trained model was embedded into LabVIEW for online deployment and validated on a high-pressure common-rail system test bench. Test results demonstrate that the proposed VB-BiLSTM model achieves high-precision injection rate estimation, with an R 2 value for injection rate consistently exceeding 0.9811 and an injection volume error consistently below 3.7%, while providing a stable 95% posterior confidence interval. Uncertainty exhibits adaptive variation characteristics during the injection rise, peak, and fall phases, effectively reflecting confidence differences caused by dynamic pressure changes.
Improving the thermal-efficiency limit of ammonia-diesel dual-fuel (ADDF) marine engines is critical for low-carbon and practically deployable marine propulsion. This study presents an integrated experimental-numerical framework that combines a calibrated GT-POWER ADDF combustion model with a MATLAB-based Organic Rankine Cycle (ORC) model to quantify waste-heat-recovery effects under explicitly defined energy-boundary assumptions. The combustion model was calibrated using SJTU95 ADDF data and then transfer-guided and checked against V240 engine constraints over a wide range of ammonia energy ratios (0–83% AER); The SJTU175 platform was used for engine-ORC calibration. The engine outputs supplied to the ORC model include exhaust mass flow, exhaust temperature/enthalpy, jacket-water heat transfer and coolant temperature under different engine loads and AERs. The calibrated ADDF model predicts indicated thermal efficiency and peak cylinder pressure within ±5% of the available V240 measurements across the investigated AER range, while the ORC unit delivers 105.3–145.5 kW of net recovered power, corresponding to cycle efficiencies of 8.8%–9.3% and total system output gains of 5.2%–7.2%. At 65% AER, the combined engine-ORC system reaches a thermal efficiency of 37.6%, corresponding to 91.0% of the pure-diesel baseline, while at 83% AER, it retains 86.8% of the baseline performance. These results define a realistic thermal-efficiency benchmark for ORC-assisted ADDF marine engines and show that waste heat recovery can substantially mitigate, though not fully eliminate, the efficiency penalty associated with high ammonia substitution.
This study experimentally examines dimethyl ether (DME) spray combustion under engine-relevant conditions utilizing a single-hole research injector “Spray D” from the Engine Combustion Network (ECN). DME is a promising substitute for diesel fuel, due to its high cetane number and low level of particulate emissions, but also because it is manageable to produce DME with low lifecycle carbon emissions. Using an optically accessible chamber, DME sprays injected through an ECN Spray D injector (0.189 mm diameter) are examined using a range of advanced optical and laser diagnostic techniques. High-speed (50 kHz) diffuse back-illumination extinction imaging (DBI-EI) is utilized to assess both the spray liquid phase and any soot formation. High-speed 355 nm-planar laser-induced fluorescence (PLIF) and line-of-sight OH* chemiluminescence imaging are also performed in a quasi-simultaneous manner to evaluate the low- and high-temperature reactions during different ignition stages as well as under steady-state combustion. The PLIF measurements show formaldehyde (CH 2 O) as a low-temperature product or Polycyclic aromatic hydrocarbons (PAHs) where soot formation may occur at higher temperature. At regular oxidizing environments ranging from 12% to 21% O 2 , we observe no PAH and soot formation from DME, rendering the CH 2 O PLIF measurements unaffected by PAHs. The two-stage ignition process demonstrates the formation and consumption of CH 2 O, followed by increased OH* production under varying oxygen concentrations and temperatures. Consistent with a lower cetane number, the high-temperature ignition delay of DME is found to be slightly longer than that of n-dodecane, accompanied by a longer lift-off length. Under a 0% O 2 environment created to study fuel pyrolysis, mild PAH and soot formation of DME was observed, in contrast to the oxidative experiments where soot was not detectible. This research aims to enhance the global understanding of DME spray combustion by providing comprehensive datasets on low- and high-temperature ignition and combustion processes, as well as soot formation, which may be used to advance computational models specific for DME.
Direct injection of hydrogen in internal combustion engines generates extremely under-expanded jets characterized by complex shock structures, necessitating the use of high-resolution computational fluid dynamics (CFD) simulations for accurate prediction. However, such simulations are computationally prohibitive for model-based development. This study developed and validated a versatile and computationally efficient inflow boundary model for jets from an inward-opening injector with a straight cylindrical orifice. In this methodology, the upstream flow—including the intricate shock-wave structures up to the Mach disk—is represented by flow quantities at the Mach disk, which are then applied as inlet boundary conditions for downstream simulations. High-resolution CFD simulations were first validated against shadowgraph imaging to accurately capture jet structure, which subsequently informed the boundary conditions for the inflow boundary model. The inflow boundary was divided into three concentric regions: a stagnant core, a uniform jet, and an outer edge region. To accurately represent shear-layer development without empirical calibration, a radially decreasing velocity profile and an initial turbulence distribution were prescribed in the outer edge region. The model was validated across a broad range of operating conditions. Simulation results demonstrated strong agreement with the macroscopic jet characteristics observed via shadowgraph imaging and with hydrogen distributions measured by negative laser-induced fluorescence. Compared with high-resolution simulations, model-based simulations decreased the computational load by approximately 400-fold while maintaining practical accuracy. This demonstrates their suitability for efficient model-based development and optimization of hydrogen internal combustion engines.
Ammonia-diesel dual-fuel engines have garnered increasing interests due to their potential for low-carbon emissions. However, under high ammonia energy ratio conditions, they face the challenge of elevated unburned ammonia (NH 3 ) and nitrogen oxides (NO x ) emissions. This study developed an “upstream selective catalytic reduction (SCR) + ammonia oxidation catalyst (AOC) + downstream SCR” aftertreatment system, which aimed at simultaneously mitigating both unburned ammonia and NO x emissions. The upstream SCR primarily reduces NO x formation, while AOC controls excessive NH 3 . The downstream SCR is incorporated to further reduce NO x generated by the AOC. The system was evaluated using a one-dimension transient model based on validated catalyst kinetics and measured engine-out conditions. The results indicate that the proposed strategy, featuring an upstream SCR stage, can reduce the required supplemental ammonia injection and decrease N 2 O emissions. The one-stage configuration (SCR0 + AOC1 + SCR1) exhibited the most favorable overall emissions control performance. Parametric analyses showed that the 300°C temperature condition led to higher NH 3 slip from SCR0 and higher predicted N 2 O formation in AOC1. In addition, the mass flow rate mainly affects the transient response, and lower flow rates are more favorable for reducing residual NO x and transient emission peaks.
Energy transition in road transportation for developing countries is usually related with the implementation of the best available technologies, the improvement of energy efficiency, and the use of fuels with less carbon content than diesel and gasoline. One the most promoted alternative fuel worldwide has been natural gas, and most recently, hydrogen and ammonia are playing important roles like energy transition fuels and carriers. This study presents an experimental research of a Diesel Euro-4 vehicle N2-category tested on a chassis dynamometer for stationary and transient operations according to WLTP protocol, by using two dual-fueling strategies with natural gas (NG) and hydrogen ( H 2 ). The experimental phase was carried out in Medelín-Colombia, vehicle dynamic was determined by applying coast-down methodology at representative conditions. Stationary tests were performed to show the effect of vehicle speed and transmission gear on the engine load and pollutants. Gaseous and particles emissions, as well as fuel consumption and energy efficiency were measured for both dual-fueling systems and for conventional diesel operation. Compared with diesel mode, dual-NG operation increased CO emissions by a factor of 3.1, HC emissions by 6.7, and PM2.5 emissions by 10.96%. Furthermore, NOx were reduced in 44.32% and PN2.5 emissions in 3.53%, whereas traveled distance per gallon was extended in 23.35% and overall energy intensity was increased in 41.42%. Dual- H 2 operation reduced in 22.42% and 12.33% the THC and PM2.5 emissions respectively, when compared with diesel mode. The PN2.5 were reduced in 60.83%, whereas NOx, C O 2 , and overall energy tensity were increased in 11.10%, 3.97%, and 2.47% respectively, for WLTC cycle Class 1. The results obtained enable a greater understanding of the effects of dual-fuel operation on the energy efficiency and pollutant emissions of CI engines, at representative transient conditions for cities located at high altitude above sea level, and the best available technologies for road load transportation in developing countries.
End-gas autoignition and the resulting rapid pressure oscillations are widely regarded as the primary mechanisms underlying knocking in internal combustion (IC) engines, which directly limits the thermal efficiency and operating load of natural gas engines. This study investigates the flame propagation and end-gas autoignition of a lean natural gas-air mixture (phi = 0.8) in a micro-pilot diesel-ignited dual-fuel engine using combustion visualization and in-cylinder pressure analysis in a compression-expansion machine. The effect of advancing the pilot diesel injection timing on auto-ignition and knock-limited operation was examined. The results show that progressive injection timing shifts combustion from normal operation to Premixed Mixture Ignition in the End-gas Region (PREMIER) and eventually to knocking with strong pressure oscillations. Both PREMIER and knocking exhibited a secondary peak in the cylinder pressure and heat release rate; however, autoignition occurred later in the PREMIER combustion phase, corresponding to a smaller end-gas mass and moderated volumetric heat release, limiting pressure-oscillation amplification. The knock intensity was markedly higher in knocking, with dominant pressure oscillation frequencies of 6.4, 10.0, and 14.5 kHz, compared with no dominant frequency for normal combustion. Flame propagation analysis revealed a faster growth of the auto-ignited flame area during knocking, with end-gas velocities increasing monotonically with knock intensity. Thermodynamic interpretation using Bradley's epsilon-xi framework provided mechanistic differentiation between the PREMIER and knocking regimes by mapping onto the CH4-air detonation peninsula framework. The results suggest that controlled end-gas autoignition in PREMIER combustion can help extend the lean-burn operating range while maintaining the combustion stability and high efficiency of natural gas dual-fuel engines.
Ammonia blending in diesel is an efficient combustion strategy that overcomes the low reactivity of ammonia while maintaining high engine adaptability. A smaller and accurate chemical kinetic mechanism is crucial for exploring the application of ammonia/diesel in engine. Building upon a previously developed reduced mechanism, this study presents a newly developed four-component skeletal mechanism for ammonia/diesel dual-fuel combustion. The mechanism includes 123 species and 669 reactions, with 134 specifically identified as cross-reactions between fuel components. It was progressively validated and optimized through detailed kinetic analyses involving ammonia, n-dodecane, cyclohexane, toluene, diesel, and their ammonia/diesel mixtures. To investigate the impact of ammonia-diesel cross-reactions on engine emissions, a CFD model of an RCCI engine was established, demonstrating the applicability of the skeletal mechanism in three-dimensional simulations. The results show that incorporating cross-reactions significantly improves the prediction accuracy for in-cylinder pressure, heat release rate, and emission trends. Under 40% ammonia energy fraction, the presence of cross-reactions notably enhanced radical formation and chain-initiation processes, leading to advanced combustion phasing and higher in-cylinder temperatures. This, in turn, promoted the formation of NO2 and N2O, accelerated NH3 consumption and CO oxidation, but also intensified local soot formation due to incomplete combustion. The proposed skeletal mechanism offers a favorable balance between computational efficiency and predictive accuracy, providing a robust kinetic foundation for optimizing combustion and emission control in ammonia/diesel dual-fuel engines.
Hydrogen direct injection internal combustion engines (H2 ICE) present a practical and cost-effective alternative for medium-term energy transition applications, leveraging principles similar to spark ignition engines (SI). However, optimizing H2 ICE requires addressing hydrogen’s unique properties, including low molecular weight, high diffusivity, and distinct combustion characteristics. Existing research on hydrogen combustion often fails to replicate in-engine conditions, particularly regarding thermodynamic states and turbulence levels. This study investigates the entire in-engine process, including hydrogen jet injection, mixing, combustion, and heat transfer, under conditions representative of H2 ICE. Experiments were conducted using an optically accessible high-pressure, high-temperature vessel, allowing independent variation of parameters such as chamber gas and injector temperature, chamber pressure, and hydrogen injection pressure. A prototype injector for heavy-duty applications (PHINIA DI CHG15) was used to inject hydrogen into a quiescent environment during 7.5 ms, achieving a global equivalence ratio of 0.39. A slide-shaped deflector preserved the jet’s kinetic energy, generating a transient tumble motion that enhanced turbulence and mixing. Combustion behavior was analyzed using schlieren imaging, negative laser-induced fluorescence, and in-chamber high-speed pressure measurements. The results reveal a strong correlation between turbulence levels, flame front speed, and heat flux. Turbulence generated during injection influenced combustion up to 150 ms post-injection, enhancing mixing and flame propagation. Delaying ignition timing from 40 to 150 ms reduced the heat release rate fivefold and wall heat flux from 3.5 to 1 MW/m 2 , with sensitivity diminishing beyond 150 ms. Hydrogen exhibited significantly higher flame front speeds, even under quiescent conditions, compared to conventional fuels like methane (CH4), due to thermo-diffusive instabilities. These findings highlight hydrogen’s distinct combustion dynamics and providing a quantitative database for numerical models validation.
To address the combustion optimization of methanol-hydrogen dual-fuel engines, this study established a three-dimensional numerical model of methanol port fuel injection (PFI) and high-pressure hydrogen direct injection (HDI), combined with Taguchi orthogonal experimental design, to systematically investigate the effects of nozzle number, injection angle, and length-to-diameter ratio (L/D) on combustion characteristics. Results indicate that the nozzle number is the dominant factor, with a contribution rate of 89.2% to the indicated mean effective pressure (IMEP). Increasing the number of nozzles to six can significantly improve the in-cylinder hydrogen distribution, achieving an indicated thermal efficiency (ITE) of 45.77%. Optimizing the injection angle to 40° facilitates the formation of an ideal mixture distribution and promotes the combustion process; while a moderate length-to-diameter ratio (L/D = 2) can achieve an optimal balance between flow resistance and turbulent kinetic energy. Parameter optimization based on the Taguchi method determined that the optimal combination for improving IMEP is six nozzles, 40° injection angle, and L/D = 2. This study reveals the critical role of nozzle structural parameters in mixture formation, providing a theoretical basis for the nozzle design of dual-fuel engines.
Injection angle is a key parameter for the matching of fuel, air, and combustion chamber in engines, and cavitation characteristics inside the orifice with different injection angle will affect the subsequent spray and combustion processes. Based on real-size nozzle parameters, this study used a high-speed camera to investigate cavitation inside the orifice of optical transparent nozzles under different injection angle and fuel types, acquired backlit cavitation images, and analyzed cavitation intensity and spray cone angle under different needle movement states. The research results indicate that within the small injection angle range, cavitation intensity and spray cone angle gradually increase with the increase of injection angle, within the large injection angle range, cavitation intensity and spray cone angle gradually decrease as the injection angle increases. Cavitation tests were also conducted on gasoline and compared with diesel, and it was found that the variation law of the effect of injection angle on cavitation intensity and spray cone angle for gasoline is consistent with that for diesel. Due to the unique physical properties of gasoline, its cavitation intensity and spray cone angle are significantly higher than those of diesel. This study can provide a reference for the optimal design of nozzle structures and combustion chamber design in diesel high-pressure injection systems.
Ammonia-hydrogen (NH 3 -H 2 ) co-firing is an important technological path for achieving clean combustion, and passive pre-combustion chamber (PPC) has received widespread attention as a low-cost and easy to implement optimization method. However, research on PPC mainly focuses on the discussion of fuel supply strategies, with relatively little research on chemical reactions and structural boundaries, which undoubtedly limits the structural updates of PPC and the development of clean combustion technologies. Based on experiments, this study constructed a simulation model of NH 3 -H 2 engine and then proposed a multivariate structural study by changing the inclination angle (α), swirl angle (β), and nozzle numbers, combined with the nitrogen (N 2 ) labeling method. In the study, thermal pollutants are distinguished by labeling nitrogen that comes from air as N* 2 , and the results showed that Thermal N*O accounted for 50% of the total generated NO, mainly generated in the temperature range of 2400 K-2800 K. The study further clarified the N 2 O generation process, which consists of three stages: high temperature, medium-to-low temperature, and low temperature, and is dominated by Thermal N* 2 O (increased by 83.39%), Fuel N 2 O (increased by 50.75%), and Thermal N* 2 O (increased by 40.37%), respectively. Finally, it is confirmed that the structure with 4-nozzle α of 20° and β of 5° possesses the optimal thermodynamic properties, which can increase the indicated thermal efficiency (ITE) to 40.92%. This study provides new optimization ideas for the efficient utilization and pollutant control of NH 3 -H 2 engines and provides mathematical support and empirical reference for achieving clean combustion.
The conversion of heavy-duty diesel engines to natural gas spark-ignition engines offers a promising solution for improving engine performance and sustainability. However, it also introduces the challenge of high NO x emissions because of high compression ratios. This study aims to achieve ultra-low emissions by investigating the combined effects of equivalence ratio and Colorless Distributed Combustion (CDC) strategy with a three-dimensional numerical simulation. Specifically, the research focuses on the performance and emissions characteristics of a heavy-duty diesel engine converted to spark-ignition operation using methane fuel. To address this objective, three equivalence ratios (0.6, 0.7, and 0.8) were examined under varying oxygen concentrations (23%, 21%, 19%, 17%, and 15% by mass), where dilution was achieved by introducing nitrogen (N 2 ) as an inert diluent to promote distributed combustion conditions. A three-dimensional computational fluid dynamics (CFD) model incorporating a reduced methane chemical mechanism was employed to capture in-cylinder flow, heat release, and pollutant formation. The G-equation combustion model was coupled with the RNG k-ϵ turbulence model to simulate the propagation of lean premixed turbulent flames. The CDC regime effectively minimized NO x emissions across all equivalence ratios, with the most favorable balance between efficiency and emissions observed at φ = 0.7 and an oxygen concentration of 19%. These findings provide insights into the optimization of lean CDC strategies in compression ignition engines converted to gas-fueled spark-ignition systems for cleaner combustion.
Decarbonising the heavy-duty sector requires concepts that surpass the diesel cycle in efficiency while meeting increasingly stringent emissions standards. The recuperated split-cycle engine (RSCE) offers a potential pathway, combining quasi-isothermal compression (enabled with secondary working fluids, SWF) with internal exhaust heat recuperation. This work assesses hydrogen-diesel dual fuelling using single-cylinder RSCE experiments at a range of loads, supported by a validated Chemkin-Pro framework. The modelling extended the analysis using a reactor network and multizone model to evaluate high-load conditions, pre-ignition mixing and SWF carry-over. Experiments show stable dual-fuel operation, with light and controllable pre-ignition linked to elevated HO 2 /H 2 O 2 /OH radicals. Model predictions at higher load indicate that, relative to a neat diesel baseline, BMEP is predicted to be maintained, with BSFC and CO 2 reduced by 33% and 43% at 10%Vol H 2 . NOx is predicted to decrease by 23% at 5%Vol H 2 , but to increase by 42% at 10%Vol H 2 , as the H 2 energy share approaches 40%, consistent with faster premixed heat release and elevated O/OH driving thermal-NO. With efficient aftertreatment conversion, the 5%Vol case is predicted to meet 0.2 g/kWh NOx, whilst 10%Vol remains within the 0.26 g/kWh on-road benchmark. Furthermore, the model predicts that adding H 2 O to the charge (SWF carry-over) could reduce NOx by 30% at 0.10% H 2 O with a 3% BMEP penalty alongside an increase in HC due to temperature-limited oxidation. This combined experimental demonstration of expander port-injected H 2 in an RSCE, supported by a physically informed spatially resolved multizone modelling framework, supports the combustion feasibility for hydrogen-diesel dual fuelling and identifies the calibration and hardware levers required to achieve practical impact for Euro 7-class NOx alongside EU CO 2 reductions. Overall, these results position RSCE + H 2 as a potential mid-term route towards sustainable heavy-duty propulsion.
To meet the development requirements of ultra-high efficiency, ultra-low emissions, and strong working condition adaptability for internal combustion engines, the collaborative optimization of engine performance and emissions has become an urgent problem to be solved. Nevertheless, several performance indicators of engines are mutually restricted, and there is a complex coupling relationship between controllable variables and performance parameters. All these make it difficult to rely on experience to achieve the multi-objective performance optimization for engines, promoting the research on model-based multi-objective engine performance optimization. Since the effectiveness of optimization relies on fast and accurate predictive models, the advantages of machine learning (ML) algorithms have been fully exploited. In this paper, the literature on engine performance prediction models based on ML algorithms is reviewed. Moreover, the technical fields and significant achievements with regard to multi-objective engine performance optimization based on intelligent optimization algorithms are analyzed and summarized. The most commonly used intelligent algorithms in engine performance optimization are evaluated and compared in detail, which provides guidance for the analysis and selection of proper optimization strategies, modeling methods, and optimization algorithms. Finally, this paper discusses the research hotspots and future development direction in this field, in order to provide more ideas for the future research on engine multi-objective performance optimization.
This study mechanistically defines a novel physicochemical synergy between Al 2 O 3 and TiO 2 nanoparticles used as fuel additives in a Gasoline Direct Injection (GDI) engine, resolving their inherent performance-emission trade-offs. Using Response Surface Methodology (RSM), we first quantified the distinct roles and conflicts of the additives. Al 2 O 3 acted as a physical combustion enhancer, leveraging its high thermal conductivity to improve fuel vaporization, which yielded significant gains in engine torque (up to 10.9%) and power (up to 5.0%). However, this physical enhancement comes at a critical cost: severely elevated in-cylinder temperatures, as proven by a 16.3% increase in thermal NO x emissions. Conversely, TiO 2 acts as a chemical catalyst, promoting late-stage oxidation to effectively reduce incomplete combustion products, including carbon monoxide (CO) and unburned hydrocarbons (HC) by 12.5% and 17.3%, respectively. The central discovery of this study, visible only through multivariate analysis, is that these two mechanisms are powerfully synergistic. We demonstrate that the primary drawback of Al 2 O 3 (high temperature) serves as the primary enabler for TiO 2 (thermal activation), exponentially accelerating its catalytic efficiency according to the Arrhenius principle. This “physicochemical activation” where the physical problem solves the chemical one, is validated by the net thermodynamic gain: the energy recovered from improved combustion completeness (reduced CO/HC) outweighed the increased thermal losses (evidenced by NO x ). This net positive balance was quantified as a 3.1% reduction in the specific fuel consumption (SFC). Multi-response optimization confirmed this mechanism, identifying the 2500–3500 rpm range not merely as a statistical optimum, but as the critical “sweet spot” in which in-cylinder temperatures are sufficiently high to unlock this synergistic pathway. This study provides a new framework for designing multi-additive packages based on synergistic thermal activation.
The mechanism of gasoline cycle-by-cycle variation (CCV) is investigated in this study using a combined experimental and simulation approach. Experimental data analysis reveals that flame kernel variation is not the sole source of CCV. A strong linear correlation exists between the standard deviation of combustion process timing fluctuations (represented by CA05, CA10, CA50, and CA90) and their mean elapsed combustion times from spark timing. This correlation is independent of engine speeds, loads, dilution ratios and engine type. A quasi-dimensional combustion model is employed for single-parameter simulation study to identify the sources of CCV. The results indicate that fluctuations in turbulence velocity and initial kernel size have the dominant impact on combustion CCV. Based on these findings, a new CCV model has been developed and calibrated. The model demonstrates excellent predictive accuracy for the standard deviation of combustion CCV and indicated mean effective pressure (IMEP) CCV. Specifically, the average absolute error for IMEP CCV prediction is as low as 0.423% and 0.419% across the entire operating range of two different engines.
n-Butanol can reduce pollutant emissions while also alleviating dependence on fossil fuels. This study employed numerical simulation methods, keeping the total fuel injection volume constant, to investigate the effects of two-stage injection and three-stage injection (7% pre-injection + 15% main injection + 15% post-injection) strategies on the combustion and emission performance of n-butanol/diesel engines. The model’s accuracy was verified through bench testing. Results indicate that when the pre-injection ratio increases from 3% to 9%, the peak cylinder pressure during the main injection combustion phase gradually rises, increasing by 5.98% compared to single injection. At a pre-injection ratio of 7%, the combustion duration is shortest, the indicated mean effective pressure is highest, NO x emissions slightly increase, and soot emissions decrease by 39.75% compared to single injection. As the proportion of post-injection ratio increased from 5% to 20%, the secondary combustion heat release rate rose by 16.98%. At 15% post-injection, the longest ignition delay was observed, with the highest indicated mean effective pressure and a 70.19% reduction in soot emissions. The three-stage injection strategy effectively combined the advantages of both pre-injection and post-injection approaches. Compared to other injection strategies, it improved indicated mean effective pressure, achieved NO x emissions comparable to post-injection, and delivered better soot emission performance than pure pre-injection.
