This study investigates the performance, exergy behaviour, and environmental impact of diesel–butanol blends in a Tempest four-stroke, water-cooled, direct-injection CI engine operating over a wide speed range (1200–3600 rpm). Engine simulations were performed for butanol concentrations from 0 to 100%, with constant injection timing and an equivalence ratio of 0.85. Previous research used second-law sustainability metrics sparingly and lacked data-driven multi-parameter analysis; this work integrates hierarchical clustering, RSM, and advanced exergy indicators, including SI, EPC, and DN. The models were statistically validated using ANOVA, demonstrating strong significance (p < 0.0001) and predictive accuracy (R 2 > 0.94). This study demonstrates that low butanol substitutions (2.5–10% by mass) in diesel fuel offer a favourable compromise for compression-ignition engine performance, exergy-based sustainability, and emissions when operated at low-to-moderate speeds (∼1200–2400 rpm). Blends in this range achieve up to 11% higher useful work exergy output, improved thermal and exergetic efficiencies, substantially enhanced Sustainability Index (SI) and Exergy Performance Coefficient (EPC), and 38–70% lower NO x emissions (especially pronounced at 1800 rpm) compared with neat diesel. Higher butanol fractions (>20–30%) degrade performance, markedly increasing HC emissions and exergy destruction primarily due to elevated latent heat of vaporization, prolonged ignition delay, and poorer fuel–air mixing. Engine speed emerges as the overwhelmingly dominant parameter governing power, efficiency, and sustainability metrics (confirmed by ANOVA F-values orders of magnitude larger than fuel-property terms), with maximum first-law efficiency and work output occurring near an intermediate speed (∼1800 rpm), while best second-law sustainability is consistently achieved at the lowest practical speeds.

This study investigates the impact of hydrogen enrichment (3, 6, and 9 LPM) and cerium oxide (CeO2) nanoparticle doping (50 and 100 ppm) on the combustion, performance, and emission behavior of a compression ignition (CI) engine fueled with a 20% eucalyptus biodiesel–diesel blend (BD20). Experimental trials were conducted under variable engine load conditions to comprehensively evaluate ignition delay (ID), cylinder pressure, heat release rate (HRR), brake thermal efficiency (BTE), brake specific energy consumption (BSEC), and key exhaust emissions. Hydrogen induction significantly improved combustion characteristics, with peak pressure rising to 85 bar and HRR increasing by 63.7 J/°CA. The shortest ignition delay (10.0°CA) was achieved with BD20 + H2 (9 LPM) + Ce100 at full load, reflecting a 44% reduction compared to diesel. BTE improved by up to 14.5%, while BSEC decreased by 23.9% over BD20. Emission analysis showed notable reductions in CO (−24.0%), HC (−13.2%), and smoke (−11.3%) with increasing hydrogen and CeO2 concentrations. However, NOx emissions increased by 20.9%, attributed to elevated combustion temperatures from hydrogen’s high reactivity. The novelty of this work lies in its integrated investigation of eucalyptus biodiesel, hydrogen, and CeO2 nanoparticles in a dual-fuel mode, which has been sparsely addressed in earlier literature.

The progressive incorporation of sustainable aviation fuels to achieve zero emissions in 2050 in the aviation industry implies the evaluation of properties and compatibility of these new fuels with the fuel injection systems existing in current aircraft. Although lubricity is more critical for fuels used in compression ignition engines, in small turbojet engines lubrication fluid is directly blended with fuels, which makes mandatory the good lubricating behavior of kerosene-lube oil blends. For this reason, apart from evaluating lubricity of neat alternative jet fuels, two additives are selected for this study. One is a typical synthetic lube oil used in this type of jet engine, and the other is a biodiesel renewable fuel known for its good lubricating properties. Results show similar results of wear scar diameters of neat kerosene fuels, with a clearly abrasive wear. The incorporation of these additives improves notably the lubricity, biodiesel being more effective than lube oil, appearing as a tribocorrosive wear when this renewable additive is used. The improved lubricating properties of biodiesel and kerosene blends open the possibility of replacing the lubricating oil added to small turbojet engines or directly using these blends in reciprocating engines used in light aircraft or unmanned aerial vehicles. Furthermore, this study has developed a comparison between the ball on cilinder lubricity evaluator and high frequency reciprocating rig methodologies (HFRR), results indicating a higher lubrication with biodiesel blends in HFRR tests.

Alcohol-diesel blends fueled off-road compression ignition engine development: Part 1 – Assessing the influence of methanol-diesel blends on performance, combustion, and emissions

The effect of internal exhaust gas recirculation strategy on spark assisted compression ignition engine with methanol and ethanol gasoline

Investigation of the effects of hydrogen and ammonia addition on a marine auxiliary compression ignition engine

Multi-Parameter Co-Optimization of a Methanol/Diesel Dual-Fuel direct injection compression ignition engine focusing on operation stability

Experimental and Exergy Analysis of a Hydrogen-Assisted Compression Ignition Engine Powered by Jatropha and Pine Oil Blends

Impact of using plastic oil biodiesel on the thermal radiation characteristics of compression ignition engine

Due to depletion of fossil fuels and increase of Global Warming, the importance of renewable resources and emission regulations is getting increased, many developing countries are focused on emission regulations and usage of renewable energy sources in automobile sector, Compression Ignition (C.I) Engine is the main source of energy conservation device used in automobiles, which has good thermal efficiency and less specific fuel consumption. In this study, an attempt is made to determine the optimal injection pressure for C. I Engine, to regulate the emissions of C. I engine by using Canola oil Biodiesel as a fuel and to improve the performance of C.I Engine by adding Graphene Oxide Nanoparticle additive in Canola Biodiesel. Experiments are conducted on C. I Engine at 200 bar, 220 bar, and 240 bar injection pressures with diesel as a fuel; results show that 220 bar is the best injection pressure for C. In the engine, canola oil biodiesel blends of B20, B30, and B40 are tested at 220 bar injection pressure; results show a significant reduction in engine emissions, but the B30 blend decreases engine performance. So, to obtain best performance from engine B30 biodiesel blend is mixed with graphene oxide Nanoparticle additive in 40PPM, 60PPM and 80PPM by using ultrasonication process with Sodium dodecyl sulfate (SDS) as a surfactant, results shown that B30+60PPM of graphene oxide Nanoparticle additive blend has increased Brake The mall Efficiency(BTE) of 20% and reduced Brake Specific Fuel Consumption (BSFC) of 16.12% compared to B30, there is a reduction in unburned hydrocarbon (HC) and Carbon Monoxide (CO) emissions and Slight increment in Carbon Dioxide (CO2) and Oxides of Nitrogen (NOx) emissions.

The use of ammonia and natural gas blends in Homogeneous Charge Compression Ignition ( HCCI) engines offers a promising pathway for efficient ammonia utilization while reducing emissions such as soot and NO x . In this study, a detailed kinetic mechanism for NH 3 and CH 4 combustion is first constructed by combining the KONNOV mechanism with Aramco Mech 3.0. The mechanism is then progressively reduced under HCCI conditions using the Directed Relation Graph with Error Propagation (DRGEP), Directed Relation Graph with Path Flux Analysis (DRGPFA), and Full Species Sensitivity Analysis (FSSA). To enhance prediction accuracy, sensitivity analysis is conducted to identify the elementary reactions that have the greatest influence on ignition delay time (IDT) and laminar flame speed (LFS). Using response surface methodology (RSM), the Arrhenius parameters including the pre-exponential factor A, the temperature exponent b, and the activation energy Ea of the key reactions R9 ( H 2 O 2 + M ⇄ 2 OH + M ) and R5 ( O 2 + H ⇄ OH + O ) are optimized. The optimal Arrhenius parameters A, b, and Ea for R9 and R5 are 9.162 × 10 13 , 0.536, and 5.4 × 10 4 cal/mol, and 2.25 × 10 14 , 0.22, and 1.88 × 10 4 cal/mol, respectively. The resulting optimized mechanism, which contains 48 species and 192 reactions, shows significantly improved predictive accuracy. The detailed, reduced, and optimized mechanisms are subsequently coupled with an HCCI engine model to evaluate combustion and emission characteristics. Among the three mechanisms, the optimized mechanism achieves the best agreement with simulated ignition timing and heat release behavior. Its reliability is further confirmed through comparison with two independent natural gas fueled HCCI experimental datasets, where it provides the closest match to measured in-cylinder pressure and heat release rate. These findings show that the optimized mechanism offers a robust kinetic foundation for NH 3 and CH 4 HCCI combustion modeling, even when dedicated dual fuel experimental data are limited.

A Cantera-based combustion-kinetics framework that maps the operating space of hydrogen compression ignition (H2-CI) engines and establishes a structured charter to guide experiments. Beginning with a diesel (n-dodecane) baseline at an intake temperature of 300 K, the model is virtually converted to neat hydrogen and evaluated across intake temperatures of 400–600 K, compression ratios (CR) of 20–28, and exhaust gas recirculation (EGR) levels of 0–15%. Hydrogen demonstrates stable operation across a broad equivalence ratio window (ϕ = 0.45–2.1), achieving power outputs of 16–22 kW and higher efficiencies with substantially lower fuel mass than diesel. The optimal operating region is identified at an approximately 400 K intake temperature, CR = 24–28, and EGR between 5% and 10%, where power remains high (20–18 kW), efficiency increases (above 50%), and NOx emissions are markedly reduced (from 332 ppm at zero EGR to 48 ppm at 5% EGR and 10 ppm at 10% EGR), with only modest hydrogen slip (0.07–0.11). The kinetics-based framework thus provides a systematic and validated roadmap for experimental calibration, research, and development of compression ignition engines working on pure hydrogen.

Abstract This study examines the performance and emission characteristics of a four‐stroke compression ignition engine fueled with STBD25, a blend comprising 25% Sapindus trifoliatus biodiesel and 75% diesel. The fuel was emulsified using magnesium‐doped calcium oxide, Span 80, and Tween 80. The research evaluated diesel and five biodiesel blends, focusing on TN80 concentrations of 15, 30, and 45 ppm, with SP80–TN80 ratios of 1:0.5, 1:1, and 1:1.5. The introduction of SP80 and TN80 improved several critical physicochemical properties, including the cetane number and heating value. However, changes in density, flash point, and viscosity were minimal. Among the tested blends, STBD25 emulsified with 30 ppm MDC and an SP80–TN80 ratio of 1:1 demonstrated the best performance. This blend achieved higher in‐cylinder pressure (7.11%–12.6%), heat release rate (6.09%–35.52%), and brake thermal efficiency (10.05%–16.01%). Additionally, it significantly reduced emissions of hydrocarbons (27.77%–38.8%), carbon monoxide (33.33%–66.66%), oxides of nitrogen (2.41%–18.02%), and smoke (20.12%–28.8%). An artificial neural network (ANN) was utilized to model engine characteristics under various load conditions, yielding a high correlation coefficient of 0.99956, which indicates excellent agreement with experimental results. The TOPSIS method was applied to identify optimal input conditions, resulting in higher brake thermal efficiency and reduced exhaust emissions. Under full load conditions, the blend of STBD25, 30 ppm SP80, 30 ppm TN80, and 30 ppm MDC was the most effective combination, achieving a relative closeness value of 0.999, signifying its superior performance.

This study investigates the performance and emission behaviour of a Compression Ignition (CI) engine fuelled with neat Diesel (D), a diesel-microalgae biodiesel blend (DA30), and hydrogen-enriched biodiesel blends (DA30+H4 and DA30+H8) at varying load conditions. The results show that DA30 exhibited lower BTE and higher BSFC compared to diesel, primarily attributed to biodiesel’s lower calorific value and higher viscosity. However, hydrogen enrichment significantly improved performance, with DA30+H8 achieving a 7% higher BTE and up to 3.1% lower BSFC than diesel at medium to high loads. Emission analysis revealed that biodiesel and hydrogen supplementation lessened CO, UBHC, and smoke opacity relative to diesel, with DA30+H8 achieving maximum reductions of 8.1%, 15.9%, and 12.4%, respectively, at full load. Although biodiesel blending increased NOx, hydrogen addition moderated this effect, lowering NOx emissions by up to 5% compared with diesel. Overall, DA30+H8 achieved the most balanced trade-off between performance and emissions, underscoring the promise of hydrogen-enriched microalgae biodiesel as a sustainable and cleaner substitute to conventional diesel fuel in CI engines. Major Findings: 1. Hydrogen enrichment improved performance, raising BTE by 7% and reducing BSFC by 3.1%. 2. The DA30+H8 blend cut CO, UBHC, and smoke emissions by 8.1%, 15.9%, and 12.4%, respectively. 3. Hydrogen-assisted microalgae biodiesel showed a cleaner and more efficient alternative to diesel.

ABSTRACT Three-dimensional CFD simulations are used to investigate the effects of the n-octanol substitution percentage (OSP, 0–40%) on combustion and emission tendencies in an n-octanol/diesel dual-injection (DI2) compression ignition engine. Simulations are performed at a fixed EGR rate of 40% with constant total injected fuel energy and fixed injection sequence and timings. With increasing OSP, the main heat-release event shifts toward TDC with a reduced HRR peak and a smoother heat-release. The peak in-cylinder pressure generally increases, indicating a more constant-volume-like heat-release distribution and enhanced pressure rise during early expansion. Combustion phasing and duration exhibit a non-linear response, CA50 moves toward TDC and the late-combustion tail shortens at higher OSP, while the overall burn duration slightly increases at low-to-medium OSP but decreases at higher OSP. Emission trends are pollutant-specific: soot generally decreases, NOₓ shows limited sensitivity but a mild upward tendency at high OSP, and incomplete-combustion products diverge with monotonically increasing HC and CO reversal at high OSP correlating with the shortened late-combustion tail. These results clarify OSP-dependent trade-offs under high-EGR constraints and support further optimization of injection splitting and combustion organization under the present operating constraints.

This work is investigation about the engine performance and emission of a single-cylinder variable compression ignition engine working on diesel blended with Waste Plastic Oil (WPO) under various load conditions. The blends containing 10%, 20%, 30%, 40%, and 50% WPO by volume were examined with pure diesel (D100). Results show a gradual decrease in Brake Thermal Efficiency (BTE) with increasing WPO volume while Exhaust Gas Temperature (EGT) rose with higher blends, peaking at 337 °C for D50WPO50.The emission of CO₂ found to be less by 33.3% at the highest blend, while CO and hydrocarbon (HC) increased by 32.7% and 51.3%. Emission of Nitrogen oxides (NOₓ) showed a non-linear trend, initially decreasing at D80WPO20. D80WPO20 is the best-balanced blend keeping in mind optimum performance and emission. The findings are aligned with the past studies conducted in same fields

Machine learning algorithms are often used to mathematically establish relationships between data sets. Successful results have been achieved in performance, production, consumption, fault, and wear prediction applications using learning algorithms. The high testing costs of Homogeneous Charge Compression Ignition (HCCI) engines, the determination of efficient operating ranges, and the challenges of performance prediction in untested regions have recently made the use of artificial intelligence technologies increasingly popular. In this study, a dataset (805 data) was created by varying the λ value in a single-cylinder HCCI engine (Ricardo Hydra) and conducting performance measurements at different engine speeds. Based on the input values of Compression Ratio, RON (Research Octane Number), Intake Air Temperature (K), Engine Speed (rpm), and Lambda (λ) within the dataset, the output variables IMEP(Bar), Effective Torque, Indicated Thermal Efficiency, and COVimep (%) were predicted. In this study, a prediction model was developed using the AdaBoost and Tree machine learning algorithms. The experimental results demonstrated that the AdaBoost algorithm achieved the highest accuracy in predicting IMEP (Bar) output values and the lowest error rates in predicting Indicated Thermal Efficiency output values. The highest performance was obtained with an R metric a value of 9.57×10-1, while the lowest error rates were calculated as 2.89×10-4 for the MSE error metric, 1.70×10-2 for the RMSE error metric, 1.30×10-2 for the MAE error metric, and 4.10×10-2 for the MAPE error metric. The results indicate that high-accuracy predictions can be made using the proposed model.

ABSTRACT This study presents a comprehensive computational investigation of hydrogen (H2)/diesel dual-fuel combustion strategies for heavy-duty compression-ignition (CI) engines, with a particular focus on clarifying the combustion physics and optimization potential of dual direct injection (DDI) relative to port fuel injection of H2 with diesel direct injection (PFI/DI). A systematic comparison under identical energy and operating conditions reveals that, although PFI/DI yields lower nitrogen oxides (NOx) emissions due to ultra-lean premixed combustion, it suffers from uncontrollably high maximum pressure rise rates (MPRR) driven by end-gas autoignition, severely limiting its applicability at high loads. In contrast, DDI enables diffusion-controlled combustion with improved mixture stratification, resulting in lower MPRR and higher indicated thermal efficiency (ITE), albeit with increased NOx formation caused by localized high-temperature regions. Building on this comparison, an extensive parametric investigation of the DDI mode is conducted to identify the dominant factors governing performance and emissions. The results demonstrate that intake boosting and compression ratio primarily enhance ITE through reductions in wall heat transfer and exhaust losses, respectively. Injector jet angle and piston bowl geometry are shown to critically control jet-flame-wall interactions, with an intermediate jet angle of 72.5° combined with a toroidal piston bowl yielding the highest ITE by minimizing wall heat losses. Furthermore, the study reveals that diesel pilot injection timing and quantity have a negligible influence once reliable ignition is established, confirming that the pilot fuel primarily serves as an ignition source rather than a combustion-phasing control mechanism. Excessive swirl, however, is found to deteriorate combustion stability by promoting premixed heat release and elevated MPRR. Overall, this work provides new physical insight into the combustion dynamics of H2/diesel DDI operation and identifies key design and operating parameters for achieving high efficiency while mitigating combustion instability, offering practical guidance for the development of next-generation hydrogen-fueled heavy-duty CI engines.

Performance and emission characteristics of reverse reactivity-controlled compression ignition (R-RCCI) engines: A comparative CFD/Chemical kinetics study with standard RCCI configurations

Application of ignition assistant plug to a compression ignition engine fuelled with a blend of jet fuel and synthetic fuel