Compression Ignition Engine Research Articles

Investigation of biodiesel blends and hydrogen addition effects on CI engine characteristics through statistical analysis

This study investigates the performance, combustion, and emission characteristics of a single-cylinder compression ignition engine fueled with blends of Soy methyl ester (BS100) and macauba methyl ester (BM100) biodiesels, with hydrogen addition. Biodiesel blends (BSM10 - BSM50), and hydrogen addition (2–5%) were tested at various engine loads and speeds. The optimal blend, BSM20 (80% BS100 + 20% BM100), showed up to 7.8% higher brake thermal efficiency and 6.2% lower brake-specific fuel consumption than diesel. Emissions were significantly reduced: NOx by 12.9%, HC by 22.4%, CO by 27.5%, and smoke opacity by 18.7%. Adding 5% hydrogen to BSM20 further improved performance, reducing fuel consumption by up to 3.1% and increasing thermal efficiency by 2.5%, with substantial reductions in hydrocarbon and carbon monoxide emissions. This investigation highlights the potential of sustainable biodiesel blends and hydrogen as viable alternatives to conventional diesel fuels, offering significant environmental benefits and enhanced engine performance.

Optimization of intelligent charge compression ignition engine in hybrid electric powertrain by adaptive equivalent consumption management strategy

Dual fuel intelligent charge compression ignition (ICCI), taking advantage of two direct injection fuel supply systems, substituted low-carbon fuels for conventional fuels, improving fuel economy and emissions. This study focused on the application of the ICCI engine on the power-split hybrid powertrain and carried out the influence of the real-time energy management strategy on ICCI engine performance. Taking the ideal solution calculated by dynamic programming (DP) as the benchmark, equivalent fuel consumption minimum strategy (ECMS) and adaptive equivalent fuel consumption minimum strategy (AECMS) were compared in aspects of ICCI engine performance and state of charge (SOC) control robustness. The results showed that AECMS had better performance on SOC control, where the maximum deviation from the ideal control by DP was lower than 0.5 % during the charge sustaining stage, and the maximum deviation during the charging and discharging stages was no more than 1.5 %. In terms of engine performance control, the accumulative fuel consumption increased by no more than 20 %, and the average E85 (85 % ethanol in ethanol-gasoline blend) substitution ratio reduced by less than 8 %. Despite consuming a high proportion of E85, the accumulative fuel mass consumption of the hybrid powertrain was still 4 % lower than the conventional diesel vehicle.

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a neural network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network, coupled with traditional backpropagation, facilitates the nonlinear mapping of inputs to outcomes. In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, an RMSE of 0.0011, and an MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC. Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.

Split injection timing optimization in ammonia/biodiesel powered by RCCI engine

Employing carbon-neutral (NH3) and low carbon footprint (biodiesel) fuels in Reactivity Controlled Compression Ignition (RCCI) engine mode is one possible method for minimizing carbon emissions in diesel engines. In this study, a Compression Ignition (CI) engine was customized to run in RCCI mode, employing High Reactive Fuel (HRF) as biodiesel and Low Reactive Fuel (LRF) as ammonia (NH3). Based on our earlier findings, the proportion of ammonia in premixing is fixed at 40 %. In the first phase, the primary injection timing of the algal biodiesel varied between 12 and 21°CA bTDC at a preset pre-injection timing of 46°CA bTDC. In the next phase, the pre-injection time varied between 46 and 54°CA bTDC, with the optimal main injection time being 18°CA. The result suggested that the optimal main and pre-injection at 18 and 58°CA bTDC increased the Cylinder Pressure (CP) by 30.1 %, and peak HRR and combustion phase angle were advanced. Brake Thermal Efficiency exhibited an 11 % enhancement, with Brake Specific Energy Consumption decreasing by 24.1 %. Nitrogen Oxide levels saw an increase of around 39.5 %. In contrast, reductions of 19.2 %, 23.5 %, 39.7 %, and 21.7 % were observed in Hydrocarbons, Carbon Monoxide, smoke emissions, and Exhaust Gas Temperature, respectively, compared to the single injection under full load conditions.

Experimental analysis of cycle tire pyrolysis oil doped with 1-decanol + TiO2 additives in compression ignition engine using RSM optimization and machine learning approach

This study investigates the effect of TiO2 nano additives in conjunction with 1-decanol on the performance and emission characteristics of biodiesel. The analysis involves four different blends: 90D7TO3DO+25 ppm TiO2 (90 % Diesel+7 % Tire oil + 3 % decanol and 25 parts per minute Titanium dioxide), 80D14TO6DO+50 ppm TiO2 (80 % Diesel+14 % Tire oil + 6 % decanol and 50 parts per minute Titanium dioxide), 70D22TO8DO+75 ppm TiO2, (70 % Diesel+22 % Tire oil + 8 % decanol and 75 parts per minute Titanium dioxide), and 100TO+100 ppm TiO2.(100 % Tire oil + 100 parts per minute Titanium dioxide), (DOE), design of experiments approach is used in this analysis. BTE is high for 90D7TO3DO+25 ppm TiO2 (36.12 %), compared to Diesel (30 %), i.e. 90D7TO3DO+25 ppm TiO2 possesses 20.67 % higher than diesel. The blend 90D7TO3DO+25 ppm TiO2 exhibited the lowest fuel consumption (0.25 kg/kWh), compared to diesel (0.28 kg/kWh), i.e. it was 12 % less than diesel. Inline cylinder pressures were optimal at 70 bar for the blend 90D7TO3DO+25 ppm TiO2, indicating favorable combustion properties. In terms of emissions, diesel emits NOx (1850 ppm), and the blend 100TO+100 ppm TiO2 achieved the lowest NOX emission of 1550 ppm, representing a 16.3 % reduction compared to diesel. The RSM model's coefficient of determination (R-squared) for all the parameters considered was remarkably high (nearly 0.98), meaning that it accounts for 98.00 % of the variability in the parameters. The corrected R-squared value validates the model's robustness. The results of the coefficient of determination (R2) for Bayesian Ridge regression clearly illustrate that the method is capable of making accurate predictions across the majority of the measurements as compared to Random Forest (RF).

The Effect of Combustion Phase According to the Premixed Ethanol Ratio Based on the Same Total Lower Heating Value on the Formation and Oxidation of Exhaust Emissions in a Reactivity-Controlled Compression Ignition Engine

A compression ignition engine generates power by using the auto-ignition characteristics of fuel injected into the cylinder. Although it has high fuel efficiency, it discharges a lot of exhaust emissions such as NOX and PM. Therefore, there is much ongoing research aiming to reduce the exhaust emissions by using the technologies applied in this regard, such as PCCI, HCCI, etc. However, these methods still discharge large exhaust emissions. The RCCI method, which combines the spark ignition method and compression ignition method, is attracting attention. So, in this work, the objective of this study is to numerically investigate the effect of combustion phase according to the premixed ethanol ratio based on the same total heating value in-cylinder by changing the initial air composition on the formation and oxidation of exhaust emissions in the RCCI engine. The heating value of the premixed ethanol ratio varied from 0% to 40% based on the same total lower heating value in-cylinder in steps of 10%. It was assumed that the ethanol introduced into the cylinder through the premixing chamber was evaporated, and the initial air composition in the cylinder was changed and set. It was revealed that when the premixed ratio based on the same total lower heating value was increased, the introduced fuel amount into the crevice volume with advancing the start of energizing timing was decreased, which increased the peak cylinder pressure. In addition, the ignition delay was also longer due to the low cylinder temperature by the evaporation latent heat of the ethanol, which reduced the compression loss, so the IMEP value was increased. The rich equivalence ratio had a narrow distribution in the cylinder, which caused a reduction in cylinder temperature, so the NO formation amount was reduced. The ISCO value increased the increase in premixed ethanol ratio based on the same total lower heating value in-cylinder because the flame propagation of ethanol by combustion of diesel did not work well, and the CO formed by combustion was slowly oxidized due to the cylinder’s low temperature as a result of the evaporation latent heat of ethanol. From these results, the optimal operating conditions for simultaneously reducing the exhaust emissions and improving the combustion performance were judged such that the start of energizing timing was BTDC 23 deg, and the premixed ethanol ratio based on the same total lower heating value in-cylinder was 40%.

The Green Energy Effect on an HCCI Engine from Used Cooking Oil-based Biodiesel from Malaysia

Emissions from internal combustion engines (ICEs) significantly impact the environment, leading continents worldwide to work towards reducing them. The industry is increasingly leaning towards electric powertrains. However, power plants still utilize ICEs as generators, contributing to global pollution. Consequently, ICE emissions are garnering international attention. Alternatives like the Homogeneous Charge Compression Ignition (HCCI) engine and biodiesel fuels are being explored. HCCI engines have not been extensively tested with Used Cooking Oil (UCO) biodiesel. This study investigates the performance and emissions of HCCI engines using UCO-based biodiesel. This study tested an air-cooled, single-cylinder, 4-stroke diesel engine operating at 3600 rpm with a displacement of 0.219 liters. The HCCI mode was activated during preheating and run at 2700 rpm under varying biodiesel blend percentages and intake temperatures. In HCCI mode, brake-specific fuel consumption (BSFC) increased, peaking at a 90°C intake temperature. Diesel fuel in-cylinder pressure reached a maximum of 81 bars at 90°C, decreasing to 79 bars at 70°C. The HCCI mode resulted in lower NOx, CO, and UHC emissions. Higher biodiesel blend ratios further reduced CO emissions. Raising the intake air temperature to 90°C lowered NOx emissions by 96.66%, from 150 ppm to 5 ppm. Using green energy sources as fuel in HCCI engines significantly reduced emissions in this study, suggesting their potential as a future fuel for advanced engines.

The combustion and emission characteristics of pure methanol as a substitute fuel for compression ignition engines

ABSTRACT Methanol, as a carbon-neutral resource, exhibits significant potential in the automotive sector. The objectives of this study are to investigate the fuel economy, emission characteristics, and combustion properties of methanol in compression-ignition engines. The research findings indicated that the combustion duration of methanol significantly extended with increasing speed and load. In comparison to diesel, methanol exhibited a relatively lower peak heat release rate under various operating conditions. With an increase in injection pressure or an advancement in injection timing, the brake specific fuel consumption (BSFC) decreased initially and then increased. The optimal injection pressure for methanol was lower than that for diesel, while the optimal injection timing was advanced. As the EGR rate increased, BSFC gradually decreased at each speed. When comparing optimized methanol with diesel, the brake thermal efficiency (BTE) of methanol was 37.54%, slightly lower than that of diesel. Methanol’s NOx, soot, and CO emissions were significantly lower than diesel, while methanol and formaldehyde emissions were higher, especially with a methanol emission of 0.83 g/kW.h. Overall, applying pure methanol as fuel in compression-ignition engines can significantly reduce emissions while maintaining the BTE as compared with that of diesel fuel, but the methanol emissions in the exhaust increase significantly.

Simulation Studies of Pollutant Emission from Passenger Cars

<div>The article presents the results of simulation studies of pollutant emissions from passenger cars. The characteristics of emissions were determined using the vehicle driving test procedures, in consideration of differentiated average velocities as well as model traffic conditions: urban traffic jam, urban traffic with no congestion, rural, motorways, and highways. This article also presented the possibility of determining the characteristics of pollutant emission based on a singular realization of the vehicle velocities processes, as well as the intensity of pollutant emission, with the use of the Monte Carlo method. The pollutant emission characteristics enable specification of pollutant emission intensity, which can be used for the inventory of pollutant emissions from road transport (COPERT software applied as standard) and can be useful in the assessment of a degree of environmental hazard by modeling pollutant dispersion. In this article, the results related to pollutant emission characteristics were obtained with the use of the software HBEFA INFRAS AG for the cumulative category of passenger cars, in terms of their representative structure in Europe in 2020. The simulation driving tests were carried out separately for cars with spark ignition engines and for those with compression ignition engines. It was concluded that using specialized software to simulate emissions from road vehicles is an effective way to determine the characteristics of emissions from road transport.</div>

The role of charge reactivity in ammonia/diesel dual fuel combustion in compression ignition engine

This study investigates the combustion characteristics of diesel–NH3–air mixture in compression ignition engine. Three different levels of ammonia energy ration (AER) were used for the investigation: 0 % AER (pure diesel), 30 % AER, and 50 % AER. Increasing AER leads to a rise in the initial peak of apparent heat release rate and promotes the prevalence of premixed combustion. Numerical simulations effectively capture the abrupt increase in maximum cylinder pressure (Pmax) from 7.4 to 7.9 MPa as AER increases from 0 to 30 %. Additionally, raising AER from 0 to 30 % extends a high-temperature region externally in the squish zone, attributed to the higher knocking propensity of the 30 % AER premixed charge. The ammonia-assisted diesel accomplish advanced diesel direct injection timing (SOID) at both heavy and medium load conditions. Under light load conditions, an increase in ITE is observed for AER = 50 % as SOID varies from −4 °CA to −10 °CA (ATDC). For medium and high load conditions, ITE monotonically increases with advancing SOID. The maximum ITE of 44.5 % is achieved at SOID = −12 °CA (ATDC) and AER = 30 %.

Study on the integration of hydrogen in a multi-cylinder low heat rejection diesel engine using a ternary blend

Among alternative fuels, hydrogen (H2) holds significant promise as both a fuel and an energy carrier. It is expected to become a key alternative fuel in the near future to meet stringent pollution standards. H2 is used in internal combustion (IC) engines, gas turbines, and the aerospace industry due to its non-toxic and odorless nature, high calorific value (CV), and wide temperature range of combustibility. Additionally, it is a long-term renewable energy source that produces fewer pollutants. This study explores the impact of different H2 ratios on combustion behavior, engine performance, and emission characteristics in a dual-fuel compression ignition (CI) diesel engine. The current research focuses on the effect of TB of ethanol, Jatropha methyl ester, and diesel with the induction of H2 with different flow rates in diesel engine in dual fuel mode (DFM). The tests were performed at different engine loads, 25%, 50%, 75%, and 100% at a constant engine speed of 2500 rpm. H2 is inducted at different flow rates like 2, 4, and 6 Litres per minute (lpm). Partially stabilized zirconia (PSZ) is used to coat the cylinder head, piston crown, and valves with a thickness of 0.5 mm. The combined effect of TB fuel and H2 in a CI engine improves combustion and engine performance. At full load condition, brake thermal efficiency (BTE) is improved by 11.5% for TB fuel with H2 at 6 lpm, and exhaust gas temperature (EGT) increased by 12.7%. The cylinder pressure (CP) and heat release rate (HRR) are increased by 7.5% and 10.6% at TB fuel with 6 lpm of H2 induction. The utilization of H2 as an alternative energy source has significant consequences for the future of green energy production.

An efficient data-driven approach for modeling and optimization of reactivity-controlled compression ignition engine fueled with polyoxymethylene dimethyl ethers and hydrogen

This paper presents an efficient data-driven approach for optimizing the utilization of PODEn and hydrogen in reactivity-controlled compression ignition (RCCI) engines. A novel high dimensional model representation (HDMR) technique is adopted to construct a highly accurate surrogate model for the RCCI engine based on the dataset generated by a detailed CFD model that has been calibrated against experimental data. The HDMR model is then coupled with Non-dominated Sorting Genetic Algorithm III (NSGA III), a multi-objective genetic algorithm, to search for the optimal operating conditions where the RCCI engine fueled by PODEn and H2 achieves the highest combustion performance. Results suggest that the constructed HDMR model is highly accurate, being able to predict the engine performance within milliseconds. The coupled HDMR-NSGA III approach successfully identifies the optimal engine operating condition, which significantly improves the combustion performance and reduces pollutant emissions compared with the base condition.

INVESTIGATION OF THE EFFECTS OF THE DIFFERENT BIODIESELS’ BLENDS ON ENGINE PERFORMANCE

Internal combustion (IC) compression ignition (CI) engines running on diesel, play a dominant role of today’s economies, particularly in the agriculture and transport sectors. However, because of diesel associated concerns greenhouse gases (GHG) emissions, coupled with depletion of its reserves together and fluctuations in prices, the biodiesel has gained popularity as a promising alternative fuel. Unfortunately, engines running on biodiesel are associated with decreased power output, poor fuel atomization and increased nitrogen oxides emissions. Biodiesels have been blended with diesel to improve on its properties, however, it has been a difficult to obtain the best blending level, since they are sourced from a variety of vegetable oils whose fuel parameters and interactions differ considerably, causing variation in their combustion processes. Thus, the research aimed to investigate the effects of biodiesels’ blends with diesel on engine performance parameters. Five biodiesels from waste vegetable oil, canola, sunflower, oleander, and coconut oil were characterized and blended with diesel at 10, 15, 20, 25 and 30% by volume. The blends were tested in a 3.5 kW CI engine at maximum speeds and loads. Experiments indicated the biodiesel blends lead to reduced lower heating value, increased density and kinematic viscosity compared to diesel. The blends resulted into increased fuel consumption and nitrogen oxides, while, reducing on brake thermal efficiency and carbon monoxide emissions.

Investigation of Split Diesel Injections in Methanol/Diesel Dual-Fuel Combustion in an Optical Engine

Methanol is a promising alternative fuel due to its wide availability of raw materials, mature production processes, and low production cost. However, because of the low cetane number, methanol must include a more reactive fuel to assist with combustion when used in compression ignition (CI) engines. In this study, based on the optical CI engine platform, methanol is injected into the intake port, and diesel is directly injected into the cylinder to achieve dual-fuel combustion. The effects of the methanol energy ratios and diesel split injection strategies on combustion are investigated. The results show that the premixed blue flame was mainly concentrated in the near wall region, whereas the yellow flame produced by diesel combustion tended to concentrate in the central region as the methanol energy ratio increased. When the methanol energy ratio exceeded 50%, the ignition delay was significantly prolonged, while the flame area was greatly reduced. Meanwhile, the peak values for the cylinder pressure and heat release rate decreased significantly, indicating a significant deterioration in combustion. At the earlier diesel pre-injection timing at −58°, the overall dual-fuel combustion at each main injection timing exhibited low-temperature premixed combustion characteristics, with a lower peak exothermic rate and flame brightness. At the later pre-injection timing at −33°, the spray flame at all main injection timings could be observed, with higher peak heat release rates and indications of thermal efficiency. Combustion at later main injection timings was characterized by diffusion combustion, and the main injection timing could effectively regulate the combustion process through phase adjustment.

The conjoint effect of lab-grown nano-graphene dispersant and omega-9 fatty acid surfactant on performance of CI engine

This article aims to assess the performance characteristics of lab-grown graphene nanoparticles via electrolytic exfoliation as nano additives in engine oil. Physical characterization confirmed the morphology and purity of the prepared graphene nanoparticles. Nanolubricants were prepared at 0.1%, 0.2%, and 0.3% volume fraction of the nanoparticles with the addition of oleic acid as surfactant. Measurements of density and viscosity for the nanolubricants were recorded at varying concentrations and temperatures. The performance parameters of a 4-stroke single-cylinder compression ignition (CI) engine were computed using the formulated nanolubricants. The emission of toxic gases into the environment post-engine performance evaluation was also analyzed for different formulations of nanolubricant samples. Results showed that the relative viscosity variation with concentration for the experimental data was in well agreement with other available predictive models. The nanolubricant with 0.1% concentration yielded a reduction of 13.96 ± 0.39% in total fuel consumption (TFC), a reduction of 5.55 ± 0.17% in brake-specific fuel consumption (BSFC) and an augmentation of 3.96 ± 0.13% in brake thermal efficiency (BTE) in comparison to plain engine oil and other prepared formulations. Also, a marginal reduction in emission of exhaust toxic gases i.e., (2.13 ± 0.1%) ppm in NOx and a significant maximum reduction of 7.46 ± 0.2% in smoke opacity has been obtained with 0.1% nanolubricant sample as compared to other test samples. The results from the present study may be useful for developing sustainable nanolubricants for environmental viability and energy savings.

Validation of effect of composite additive on optimized combustion characteristics of CI engine using AHP and GRA method

The primary focus of this study is the validation of composite additives with the help of additional optimization methods and the analysis of its effect on the combustion characteristics of compression ignition (CI) engines. Previous work on the identification of the correct multi additive combination by Taguchi and the TOPSIS optimization method has shown substantial improvements in the performance and emission characteristics of CI engines. The same work was extended using the GRA Optimization method with the Multi-Criteria Decision-Making (MCDM) optimization technique known as the Analytic Hierarchy Process (AHP) to validate the optimization results from the previous optimization work. Remarkably, all optimization methods yielded consistent results, pointing to the superiority of the composite additive sample ‘D8EH6E4 hence supporting the outcome of previous work. Subsequent testing and comparison of this novel composite additive with baseline diesel fuel for combustion characteristics analysis demonstrated notable improvements in combustion parameters, including a 25 % reduction in the rate of pressure rise, an 18 % decrease in net heat release rate, and a 6 % decrease in mean gas temperature.

The Influence of Powering a Compression Ignition Engine with HVO Fuel on the Specific Emissions of Selected Toxic Exhaust Components

The aim of the research was to determine the potential of hydrotreated vegetable oil (HVO) in reducing nitrogen oxides and particulate matter emissions from the Perkins 854E-E34TA compression ignition engine. The concentrations of these toxic exhaust gas components were measured using the following analyzers: AVL CEB II (for NOx concentration measurement) and Horiba Mexa 1230 PM (for PM measurement). The measurements were carried out in the ESC test on a compression ignition engine with direct fuel injection and a turbocharger. The engine had a common rail fuel supply system and met the Stage IIIB/Tier 4 exhaust emission standard. Two fuels were used in the tests: diesel fuel (DF) and hydrotreated vegetable oil (HVO). As part of the experiment, the basic indicators of engine operation were also determined (torque, effective power, and fuel consumption) and selected parameters of the combustion process, such as the instantaneous pressure of the working medium in the combustion chamber, maximum pressures and temperatures in the combustion chamber, and the heat release rate (HRR), were calculated. The tests were carried out in accordance with the ESC test because the authors wanted to determine how the new generation HVO fuel, powering a modern combustion engine with a common rail fuel system, would perform in a stationary emission test. Based on the obtained research results, the authors concluded that HVO fuel can replace diesel fuel in diesel engines even without major modifications or changes in engine settings.

Evaluating the feasibility of machine learning algorithms for combustion regime classification in biodiesel-fueled homogeneous charge compression ignition engines

The present study explores the feasibility of machine-learning algorithms in classifying the combustion regimes in a homogenous charge compression ignition (HCCI) engine fueled with biodiesels from diverse sources. A distinctive feature of this research is the extensive database of HCCI combustion regimesgenerated for neat biodiesel fuels of different compositions. The engine experiments reveal significant variability in the load range of operation of HCCI engines across different biodiesel fuels. Strategies including compression ratio variation and charge dilution were employed to achieve higher engine loads, and the results confirm that the interplay of fuel reactivity and engine load, along with compression ratio and charge dilution, dictates the combustion stability. This complex phenomenon necessitates ascertaining the prevailing combustion type under a given engine load for biodiesel fuels derived from diverse sources. Thus, the present study aims to investigate the applicability of machine learning algorithms for developing models that classify combustion regimes in a neat biodiesel-fueled light-duty HCCI diesel engine. Models are developed to categorize biodiesel HCCI combustion into four groups: engine misfire, stable combustion without dilution, stable combustion with dilution, and knocking combustion. Biodiesel composition (13 different methyl esters), compression ratio and engine load are considered the input variables for the machine learning (ML) models. The results show that Naive Bayes and logistic regression models exhibit poor applicability among the investigated ML models due to a low F1 score (<0.8). The k-NN and SVM models demonstrate practical suitability with reasonably good F1 scores. However, the decision tree model outperforms all, precisely classifying 94 % of calibration and validation samples. It could precisely classify all the validation samples corresponding to misfire combustion, 21 of 22 samples to stable combustion, 4 of 6 samples to stable combustion with dilution, and 30 of 31 to knocking combustion. Thus, it emerges as the best approach to classify the combustion regimes of HCCI engines operated with neat biodiesel fuels.

Applying fischer tropsch and its pentanol blends into an aviation compression ignition engine for PM emissions control

The aviation industry is recovering from COVID-19 to regain rapid growth at an increasing rate of over 5 % annually. Aviation compression ignition (CI) engines attributed to their economic fuel consumption and adorable reliability, have been widely utilized in the general aviation (GA) industry. To fill in the knowledge gap of the compatibility of Fischer Tropsch (FT) blended with long-chain oxygenated fuel applied into the aviation engines, this research focuses on analyzing the combustion performance and particle matter (PM) emissions of an aviation CI engine by burning FT alternative fuel and its pentanol blends (FT80P20 - 80 % FT + 20 % pentanol), in comparison with baseline diesel/pentanol-diesel blends (D80P20 - 80 % diesel + 20 % pentanol). It was found that FT80P20 significantly increases the indicated thermal efficiency (ITE) by 6.5 %, and remarkably decreases the indicated specific fuel consumption (ISFC) by 12 % in contrast to neat diesel at high load (8.5 bar IMEP), which is due to better homogeneous charge that alternates heterogeneous combustion. Moreover, the PM size-resolved number distributions of emissions were characterized. It was observed that the integrated PM emissions from diesel were predominant (∼1.4 × 1014 #/kg∙fuel), while those from FT80P20 were significantly reduced by nearly 85 % (∼0.2 × 1014 #/kg∙fuel). Concurrently, the particulate geometric mean diameter (GMD) were decreased apparently from 80 nm to 35 nm correspondingly. The research findings reveal that “main diffusion combustion” was predominant as burning diesel, while “main premixed combustion” was prominent as using FT80P20, which has a promising impact on effective engine-work promotion with superior PM mitigation.

Impact of varying the injection angle on compression ignition (CI) engines fueled with hydrogen-enriched microalgae biodiesel

Impact of varying the injection angle on compression ignition (CI) engines fueled with hydrogen-enriched microalgae biodiesel