Compression Ratio Engine Research Articles

Applications of machine learning techniques to broaden operating envelope of biodiesel-fueled HCCI engines

A significant shortcoming of Homogeneous charge compression ignition (HCCI) engines is their narrow operating envelope, particularly with high-reactivity fuels like biodiesel. Additionally, the compositional variability of biodiesels, reflected in cetane number variations based on different source feedstocks, poses another challenge. While various strategies have successfully extended the maximum load limit in diesel-HCCI engines, they have not been adequately explored in biodiesel-fueled HCCI engines. This study is the first to comprehensively investigate the interplay of biodiesel composition, cetane number, engine compression ratio, and charge dilution strategy for extending the operating envelope of a light-duty HCCI engine. Given the impracticality of controlling multiple variables experimentally, this work focuses on the potential of machine learning (ML) algorithms to predict the operational limits of an HCCI engine using neat biodiesels derived from diverse sources. Among the ML models explored, artificial neural networks were the most accurate in predicting the minimum stable load, with prediction errors of 3.5% (calibration) and 3.9% (validation). Support vector machine models predicted the maximum load (with and without dilution) with errors below 5%. Notably, biodiesel produced from a blend of linseed, karanja, coconut, and mustard oils in specific proportions (42%, 3%, 25%, and 30% mass, respectively) yielded a wide HCCI operating range from 0.4 to 3.25 bar (without dilution) and up to 3.67 bar BMEP with charge dilution. This study highlights the novelty and practicality of using ML to predict and extend the operating envelope for biodiesel-fueled HCCI engines, demonstrating their suitability for future applications.

Research on Dimension Reduction Method for Combustion Chamber Structure Parameters of Wankel Engine Based on Active Subspace

The combustion chamber structure of a rotary engine involves a combination of interacting parameters that are simultaneously constrained by engine size, compression ratio, machining, and strength. It is more difficult to study the weight of the effect of the combustion chamber structure on the engine performance using traditional linear methods, and it is not possible to find the combination of structural parameters that has the greatest effect on the engine performance under the constraints. This makes it impossible to optimize the combustion chamber structure of a rotary engine by focusing on important structural parameters; it can only be optimized based on all structural parameters. In order to solve the above problems, this paper proposes a method of dimensionality reduction for the structural parameters of a combustion chamber based on active subspace and combining a probability box and the EDF (Empirical Distribution Function). This method uses engine performance indexes such as explosion pressure, maximum cylinder temperature, and indicated average effective pressure as the influence proportion analysis targets and quantitatively analyzes the influence proportion of combustion chamber structure parameters on engine performance. Eight main structural parameters with an influence of more than 85% on the engine performance indexes were obtained, on the basis of which three important structural parameters with an influence of more than 45% on the engine performance indexes and three adjustable structural parameters with an influence of less than 15% on the engine performance indexes were determined. This quantitative analysis work provides an optimization direction for the further optimization of the combustion chamber structure in the future.

Studies on fuels and engine attributes powered by bio-diesel and bio-oil derived from stone apple seed (Aegle marmelos) for bioenergy

Biofuel is an alternative fuel for diesel engines which is necessary to reduce exhaust emissions and helps to balance the depletion of fossil fuels. This study details the synthesis of bio-diesel and bio-oil from stone apple seeds ( Aegle marmelos) through transesterification, direct oxygenate additive, and pyrolysis processes. The diesel engine is powered by the resulting stone apple methyl ester bio-diesel/diesel and bio-oil/diesel mixtures. In a test engine rig, the effectiveness and emission uniqueness of test fuel mixes were examined at various engine loads (EL = 3 kg–12 kg) and compression ratios (CR = 16–17.5) while maintaining a steady speed of 1500 r/min. Furthermore, engine performance and emissions for bio-diesel and bio-oil are measured and compared. The oxides of nitrogen (NOx) emission by bio-diesel and bio-oil opus were higher than neat diesel (FD). Also, carbon monoxide (CO) and hydrocarbon (HC) emissions were measured to be lower. The improvements of BTE by 4.87% and decrement of CO by 18.8%, HC by 13.26% were observed with the trans-esterified bio-diesel/diesel opus compared to diesel at a higher CR and rated load conditions. The engine analysis responses revealed that the FBD blend showed enhanced performance and lower emissions except NOx compared to diesel fuel. Hence, trans-esterified bio-diesel is a greater diesel alternative than FBDE and FBO.

Optimization strategies for enhancing diesel engine performance and emissions control with biofuel blends: A multi-objective approach

This study examines the effects of engine compression ratio, load, and biofuel composition on performance, combustion, and emissions. The experimental setup includes testing different compression ratios (16, 17, 18) and loads (25 %, 50 %, 75 % torque) at a fixed speed of 1500 rpm, using Thai standard diesel as the control fuel. Biofuel variations tested include neat biodiesel, neat biokerosene, and blends of 30 % biokerosene with 70 % biodiesel and 50 % biokerosene with 50 % biodiesel. The results show that an increased compression ratio enhances combustion efficiency, reduces brake specific fuel consumption, and improves brake thermal efficiency, although it also leads to higher nitrogen oxide emissions. Biofuels improve brake thermal efficiency due to their higher oxygen content. However, the increased viscosity of biodiesel can hinder fuel atomization, resulting in higher emissions. In contrast, biokerosene improves brake thermal efficiency by facilitating earlier combustion and reducing emissions. Performance is primarily affected by load, while emissions are influenced by both biokerosene content and load. Gradient boosting models and optimization techniques, such as the non-dominated sorting genetic algorithm III (NSGA-III) and adaptive geometry estimation-based multi-objective evolutionary algorithms (AGE-MOEA), produce consistent results, with AGE-MOEA identifying denser optimal points. The findings suggest that optimal biokerosene blends ranging from 20 % to 40 %, across all compression ratios and loads of 50 %–75 %, provide valuable insights for advancing sustainable fuel utilization practices in automotive vehicles.

Investigation of the operating characteristics of diesel engines with chromium oxide (Cr2O3) nanoparticles dispersed in Mesua ferrea biodiesel: an experimental and predictive approach using ANNs and RSM

Abstract This study investigates the effects of using biodiesel from Mesua ferrea (BD20) and chromium oxide (Cr2O3) nanoparticles in diesel engines. The Response Surface Methodology (RSM) model and artificial neural networks (ANNs) were developed to make precise predictions of the operating parameters. The amount of Cr2O3 nanoparticles was set at 80 mg/L, and surfactant and dispersant were applied to the nanoparticles in the same amounts. The study was carried out with different compression ratios and load conditions. The parameters evaluated were engine load, fuel samples and compression ratio as inputs and BTE, BSFC, CP, NHRR, CO, UHC, NO x and smoke opacity as outputs. The addition of the QPAN80 additive at the same dosage of 80 mg/L together with the BD20 fuel blend containing Cr2O3 at a concentration of 80 mg/L resulted in a significant increase in BTE by 16.58 % and a reduction in BSFC by 0.58 %. While the NHRR increased by 85.40 %, the CP increased sharply by 24.47 %. The CO concentration decreased by 31.85 %, the UHC concentration by 22.22 %, the NO x concentration by 6.16 % and the smoke emission by 62.61 %. For each output parameter, the correlation coefficient (R 2), calculated using ANNs and RSM was between 0.96 and 0.98. The observed range of values demonstrates a robust correlation between the experimental data and the predicted outcomes.

On the Influence of Engine Compression Ratio on Diesel Engine Performance and Emission Fueled with Biodiesel Extracted from Waste Cooking Oil

Despite the extensive research on biodiesels, further investigation is warranted on the impact of compression ratios on emissions and engine performance. This study addresses this gap by evaluating the effects of increasing the engine’s compression ratio on engine performance metrics—brake-specific fuel consumption (BSFC), power, torque, and exhaust gas temperature—and emissions—unburnt hydrocarbons (HCs), carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and oxygen (O2)—when fueled with a 20% blend of waste cooking oil biodiesel (WCB20) and petroleum diesel (PD) under various operating conditions. The viscosity of the prepared fuels was measured at 25 °C and 40 °C. Experiments were conducted on a single-cylinder diesel engine under wide-open throttle conditions at three different speeds (1400 rpm, 2000 rpm, and 2600 rpm) and two compression ratios (16:1 and 18:1). The results revealed that at a lower compression ratio, both WCB20 and petroleum diesel exhibited reduced BSFC compared to higher compression ratios. However, increasing the compression ratio from 16:1 to 18:1 significantly decreased HC emissions but increased CO2 and NOx emissions. Engine power increased with engine speed for both fuels and compression ratios, with WCB20 initially producing less power than diesel but surpassing it at higher compression ratios. WCB20 demonstrated improved combustion quality with lower unburnt hydrocarbons and carbon monoxide emissions due to its higher oxygen content, promoting complete combustion. This study provides critical insights into optimizing engine performance and emission characteristics by manipulating compression ratios and utilizing biodiesel blends, paving the way for more efficient and environmentally friendly diesel engine operations.

A comprehensive analysis of energy, exergy, performance, and emissions of a spark-ignition engine running on blends of gasoline, ethanol, and isoamyl alcohol

This present study showed a comprehensive analysis of energy, exergy, performance, and emission characteristics of a spark-ignition engine powered by blends of gasoline-isoamyl alcohol-ethanol at volume proportions (100_0_0)% (P0E0), (90_5_5)% (P5E5), (80_5_15)% (P5E15), and (70_5_25)% (P5E25) at various engine speeds and compression ratios (CR). The outcomes of thermodynamic analyses revealed that the highest exergy and energy efficiency were 28.92 % (P0E0, 2600 rpm, CR of 9:1) and 31.01 % (P0E0, 2600 rpm, CR of 9:1), respectively. In addition, the average brake power and brake thermal efficiency for P0E0, P5E5, P5E15, and P5E25 increased by (30.36 %, 27 %, 26.74 %, 27.84 %) and (0.34 %, 0.07 %, 0.35 %, 0.33 %) respectively, while brake specific fuel consumption decreased by (2.5 %, 0.64 %, 3.62 %, 6.33 %) in the engine speed range of 2600–3200 rpm. Compared to gasoline, the maximum reduction of carbon monoxide emissions was 6.13 %, 8.81 %, and 9.96 %, while unburnt hydrocarbon emissions exhibited the maximum decrement by 5.8 %, 10.75 %, 12.58 % for P5E5, P5E15, and P5E25, respectively. However, nitrogen oxide (NOx) emissions for P5E25 tended to increase significantly compared to P0E0, while the difference in NOx emissions for P5E15 and P0E0 was marginal. Overall, P5E15 could be considered as the potential alternative fuel for spark-ignition engines towards the goal of sustainable energy conversion.

Exploring the Combustion Performance of a Non-Road Air-Cooled Two-Cylinder Turbocharged Diesel Engine

In order to explore the combustion performance of a non-road air-cooled two-cylinder turbocharged diesel engine, an experiment on the effects of engine compression ratio, combustion chamber shape and injection timing were systematically conducted in this study. Moreover, the effects of intake air conditions on combustion performance were numerically investigated using the one-dimensional simulation platform. The findings of this study could help provide new insights for promoting the sustainable development of diesel engines used in generator sets. The results show that the increase in intake air temperature can delay the combustion center of gravity and improve the combustion performance and the sustainability of diesel engines. The decrease in intake air pressure leads to a reduction in oxygen amount during the combustion process, thus causing the deterioration of cylinder pressure and combustion performance. By modifying the combustion chamber, the ignition delay and combustion duration are each extended by 1.6 degrees and 4.2 degrees under 100% engine load. The ignition delay and combustion duration are not obviously affected by modifying the combustion chamber shape under 25% and 50% loads. By increasing the compression ratio from 19.5 to 20.5, the ignition delay and combustion duration are shortened, which could enhance the cylinder pressure and heat release rate. However, reducing the compression ratio from 19.5 to 18.5 could significantly decrease the heat release rate. Under middle and low loads, combustion duration is less affected by injection timing. Under 100% load, the peak cylinder pressure increases to 11.4 MPa, and the ignition delay is shortened by advancing injection timing from −17 °CA to −20 °CA.

A numerical investigation on the effect of altering compression ratio, injection timing, and injection duration on the performance of a diesel engine fuelled with diesel-biodiesel-butanol blend

Abstract Using renewable fuels for diesel engines can reduce both air pollution and dependence on fossil fuels. A computer simulation was constructed to predict the performance, combustion characteristics, and NOx emission of a diesel engine fuelled with diesel-biodiesel-butanol blends. The simulation was validated by comparing the modeling results against experimental data and a good agreement between the results was found. The fuels used for the validation were diesel (B0), biodiesel (B100), diesel-biodiesel blend (B50), and two diesel-biodiesel-butanol blends with 45% diesel-45% biodiesel-10% butanol (Bu10), and 40% diesel-40% biodiesel-20% butanol (Bu20) by volume. Experimental results showed that the addition of butanol reduced NOx emissions but deteriorated the engine performance. The aim of the current work was the numerical optimization of the different parameters to enhance the engine performance while using butanol to decrease NOx emissions. The engine compression ratio (CR) varied from 14 to 24, in increments of 2. Fuel injection timing (IT) was retarded from 30° before top dead center (bTDC) to 5° bTDC in increments of 5o. Also, the fuel injection duration (IDur) was extended from 20° to 50° in increments of 10°. Results showed that the increase in CR improved engine performance for the two investigated fuels, Bu10 and Bu20. The maximum engine brake power (BP), thermal efficiency (BTE), and minimum brake-specific fuel consumption (BSFC) of 1.46 kW, 32.3%, and 0.273 kg/kWh respectively, were obtained when the Bu10 fuel was injected at the optimum conditions of 24 CR, 15°bTDC IT, and 40° injection duration (IDur). Under these optimum conditions, the BP, BTE, and BSFC improved by 3% to 3.5% for Bu10 and Bu20 fuel blends compared to the base engine conditions of CR of 22, 30° IDur, and 10° bTDC IT. The heat release rate during the premixed phase increased when the IT was advanced, while the mixing-controlled combustion phase was enhanced when the IT was retarded. NOx emissions increased with increasing the CR, while both the increase in IDur at constant IT and the retard of IT decreased engine NOx emissions. At the optimum conditions, the NOx emissions for Bu10 and Bu20 were further decreased by 2.2% and 0.9% respectively compared to the experimental results at base engine conditions. Retarding the injection timing from 15° bTDC to 5° bTDC at a CR of 24 and IDur of 40° caused the NOx emissions for Bu10 and Bu20 to decrease by 16%. When the injection duration was increased from 20° to 50° at a CR of 24 and an IT of 15°bTDC, the NOx emissions for Bu10 and Bu20 decreased by 12.3% and 11.8% respectively. The addition of butanol to the diesel-biodiesel blend at optimum conditions showed results comparable to pure diesel, with a decrease in NOx emissions.

Thermal characteristics of gasoline engine powered by petrol and LPG under variable speed, compression ratio

ABSTRACT This study involves the evaluation of the thermal characteristics of gasoline engines fuelled with LPG instead of gasoline fuel using the simulation software Diesel-RK. The feasibility of using LPG is investigated via parameters (combustion, performance, and emissions) at different engine speeds and compression ratios. A two-zone combustion model is used. The results revealed that the maximum pressure obtained was 82 bar for gasoline compared to 78 bar for LPG at 5000 rpm. There is a noticeable reduction in BSFC with the use of LPG relative to gasoline for all ranges of compression ratio and engine speed used. The NOx emissions were significantly reduced by 8.60% using LPG rather than gasoline. Studies conducted by different researchers validate the obtained results.

A machine learning-response surface optimization approach to enhance the performance of diesel engine using novel blends of Aloe vera biodiesel with MWCNT nanoparticles and hydrogen

This study aims to optimize the performance and exhaust emission characteristics of a dual-fuel diesel engine using wastewater-derived oxyhydrogen and nanoparticle-dispersed biodiesel/diesel blends. A Box Behnken Design (BBD) with L46 orthogonal arrays is employed to experiment with five factors and three levels of parameters: engine load, compression ratio, blend ratio, nanoparticle concentration, and oxyhydrogen (HHO) flow rate. In addition, six different machine learning algorithms (DTR, ABR, ETR, GBR, LGBM, and XGBR) are used to develop a prognostic model based on the acquired experimental data. These models estimate key performance indicators such as BTE, BSFC, CO, HC, and NOx. Based on the evaluation of performance indicators, including R2 (0.9729–0.9991), RMSE (0.0021–4.5765), MAE (0.0014–3.0015), and MAPE (2.37–4.83), the XGBR model demonstrates the most accurate predictions. Furthermore, the RSM-based desirability approach evaluates the trade-off between performance and emissions, thereby identifying the optimal engine operating condition. This condition includes a load of 7.17 kg, a compression ratio of 18:1, a blend ratio of 10 %, a nanoparticle concentration of 143 ppm, and an HHO flow rate of 5.57 L/min. The findings from the RSM analysis are validated through experimental investigation, demonstrating an error margin of 7 %. Overall, the introduction of oxyhydrogen improves combustion in a nanoparticle-dispersed biodiesel-diesel blend-powered diesel engine, resulting in increased engine performance and reduced carbon-based emissions, except for nitrogen oxide emissions.

Performance and emission characteristics of a diesel engine fuelled with Mesua ferrea biodiesel with chromium oxide (Cr2O3) nanoparticles: Experimental approach and response surface methodology

This study investigates the effects of using nano-fuelled diesel engines, including Mesua ferrea biodiesel and chromium oxide (Cr2O3) nanoparticles. The RSM model was created to make accurate predictions of the operating parameters. The amount of Cr2O3 nanoparticles was set at 60, 80, and 100 mg/L and an equal amount of surfactant and dispersant was added to the nanoparticles. The test was carried out under different compression ratios and load conditions. The parameters evaluated were the engine loads (3, 6, 9, and 12 kgf), fuel samples, and compression ratios (16.5:1, 17.5:1, and 18.5:1) as inputs and the parameters BTE, BSFC, CO, UHC, NOx, and smoke opacity as outputs. The BD20 + Cr2O3 80 mg/L + DSP 80 mg/L fuel sample resulted in a significant improvement in BTE by 16.58 % and a reduction in BSFC by 0.58 % compared to the other samples, while CO concentration decreased by 31.85 %, UHC concentration by 22.23 %, NOx concentration by 6.16 % and smoke opacity by 62.61 % at CR 18.5:1 and full load. The correlation coefficient (R2), which was determined using response surface methods and artificial neural networks, was between 0.96 and 0.98 for all output parameters. The observed range of values demonstrates a strong correlation between the experimental data and the predicted results.

Effect of Compression Ratio on the Performance of Direct-Injection Hydrogen Engines

Direct injection of hydrogen into the cylinder can avoid abnormal combustion such as backfire, and the hydrogen engine can operate in a wider range of excess air coefficient. However, direct injection hydrogen engines still have problems such as high NOx emissions under high load conditions, reduced power output due to lean combustion, and low thermal efficiency. This paper adopts a variable compression ratio structural design to study the impact of compression ratio changes on the comprehensive performance of direct injection hydrogen engines. The results show that under the same working conditions, as the engine compression ratio increases, the turbulence in the engine cylinder becomes more intense, increasing the back pressure in the cylinder, inhibiting the diffusion of hydrogen, making the hydrogen distribution more concentrated and the combustion conditions in the cylinder better. The overall performance of the engine is significantly improved.

Low-carbon fuels for spark-ignited engines: A comparative study of compressed natural gas and liquefied petroleum gas on a CFR engine with exhaust gas recirculation

Decades of work on low-carbon fuels have established their potential for substantial emissions reductions; however, their adoption is still limited by infrastructure concerns and engine efficiency deficits. As infrastructures have begun to evolve, research on strategies that maximize engine efficiency through interactions with fuel properties must also now take center place. This paper compares the performance, emissions, and combustion characteristics of two forefront low-carbon fuels: compressed natural gas (CNG) and liquefied petroleum gas (LPG) in a cooperative fuel research (CFR) engine over a range of compression ratios and engine loads. The effects of exhaust gas recirculation (EGR), end-gas auto-ignition, and a novel combustion control tool, the combustion intensity metric (CIM), were also evaluated at different stoichiometric engine operating conditions. In comparison to LPG, CNG operation demonstrated an extended knock-free regime, allowing engine operation at higher engine loads and compression ratios, but LPG operation exhibited enhanced combustion characteristics with higher peak pressures and faster apparent heat release rates (AHRR). LPG operation achieved higher brake thermal efficiencies and lower equivalent CO2 emissions compared to CNG operation at the tested engine loads and compression ratios. LPG demonstrated significantly higher EGR tolerance limits compared to CNG, with a maximum of 28% EGR rate, compared to 23% for CNG. This improved EGR dilution tolerance was responsible for a 90% reduction in NOx emissions for LPG compared to a maximum of 70% with CNG. EGR dilution also exhibited more effective knock mitigation potential with LPG, suppressing knock intensity values by up to 98% and transitioning the engine operation towards normal combustion from heavy knocking conditions. The CIM was found to decrease burn durations and improve the quality of combustion by controlling the desired fraction of end-gas auto-ignition.

Experimental analysis of the effect of hydrogen as the main fuel on the performance and emissions of a modified compression ignition engine with water injection and compression ratio reduction

This study aimed to investigate the combined effects of compression ratio (CR), start of injection angle (SOI) and water injection (WI) on the performance, including NOx emissions, of a modified turbocharged compression ignition engine using H2 as the primary fuel and diesel solely as a pilot ignition source. WI and H2 were both injected into the intake manifold just after the charge heat exchanger. Test conditions varied the engine speed from 1500 rpm to 2750 rpm, covering a wide range of the original engine's operating conditions, water mass flow varied from 0 to 0.795 g/cycle and SOI for pilot ignition was studied in the range 10°, 5°, 0° before top dead center. Subsequently, the study examined the combined effect of reducing the engine's CR from 17.5:1 (base) to 13.5:1, in one-point increments, reducing SOI and water addition to further enhance the hydrogen energy share (HES).HES varied from 0 % to 85 % under different engine conditions, and WI and SOI were employed to prevent knocking and ensure that the maximum combustion pressure did not exceed 160 bar. The test results indicate that NOx emissions increase with HES, but reducing the CR results in a reduction in NOx emissions of approximately 50 %. Additionally, WI further reduces NOx emissions by up to 50 %. The experimental evidence demonstrates that combining WI with CR and SOI reduction can effectively lower NOx emissions, achieving an efficiency greater than 35 % and therefore approaching the base diesel mode's efficiency. This also reduces the maximum in-cylinder pressure, allowing for an increase in turbocharger pressure and consequently enhancing the engine's specific power. The maximum engine power achieved was 31.6 kW at 2500 rpm (mean effective pressure of 10.1 bar), with a CR of 15.5, SOI at 10°, 1.5 kg/kWh of water, a HES of 81 %, and a minimum NOx level of approximately 480 ppm, with a maximum combustion pressure below 120 bar.

Performance evaluation of a diesel engine fueled with Chlorella Protothecoides microalgal biodiesel

The main objective of the current study is to determine the engine performance, combustion, and emission analysis of compression ignition engines fuelled with Chlorella Protothecoides microalgal biodiesel (CPMB). The biodiesel was derived from Chlorella Protothecoides microalgal oil (CPMO) through transesterification. Under the specified conditions, which included a methanol-to-oil molar ratio of 7.41 (vol/vol), a reaction time of 105.6 min, a reaction temperature of 65 °C, and a catalyst concentration of 1.024 (% wt/vol) and achieved a biodiesel yield of 98.1%. The fuel properties were determined using standard methods, revealing that the density and kinematic viscosity of CPMB were significantly higher than those of diesel. However, CPMB still exhibits satisfactory fuel properties that meet most biodiesel specifications. CPMB boasts a low cloud point (CP) and pour point (PP) of 0 and -3 °C, respectively, indicating its effective usability in cold climates. The oxidation stability of CPMB, determined by the modified Rancimat method, was found to be 4.6 h, satisfying ASTM specifications. The fuel tested in this study was 20% biodiesel with diesel, 100% biodiesel, and neat diesel. These fuels had been tested on a CI engine in which the engine speed and compression ratio were fixed at 1500 rpm and 18:1, respectively. Based on the results, it is found that the indicated thermal efficiency decreases up to 3.68% and 8.22% with B20 and B100 as compared to diesel. However, mechanical efficiency, volumetric efficiency, and indicated power were estimated to be higher at peak engine load conditions when correlated with neat diesel. Furthermore, the mean effective pressure for B20 and B100 was estimated to be decreasing while the mass fraction of fuel burnt and the rate of pressure rise increased compared to diesel at peak load conditions. For exhaust emission analysis, a significant reduction was recorded in CO and UHC emissions, while NOX emission increases at peak engine loads. From the above results, it is accomplished that the performance of B20 blends is nearly identical to diesel, and it can be recommended to be used in engines without any modification.

Parametric optimisation and performance evaluation of gasifier-CI engine on dual fuel and dual feed material gasification

Gasification-based energy derived is one of the appealing resources for producing heat and power generation. The objective of this paper is to conduct an experimental study on downdraft air co-gasification of Madhuca longifolia wood and Indian low-grade coal (50:50 wt %) to generate producer gas and its application in dual-fuelled mode CI engine. Besides, the optimisation using the response surface methodology (RSM) has been applied to Gasifier-Engine to determine the best engine performance and emission response by optimising the input variables, i.e. gasifier equivalence ratio, engine compression ratio, and engine load. The RSM-based analysis predicted that optimum input operating parameters could be attained for co-gasification for Equivalence ratio (ER), compression ratio (CR), and engine load (EL) at 0.43, 16, and 100%, respectively. Based on optimal input variables, the performance response values of the gasifier-engine are 29.52% Brake thermal efficiency (BTE), 3.30 kW Brake power (BP), 0.3851 kg/kWh Brake specific fuel consumption (BSFC), 0.0186 vol% CO, 0.958 ppm UHC, 0.9768 vol % CO2, and 109.415 ppm NOx. The results show that the projected regression model has a high accuracy of R2 > 90% and a high correlation coefficient of 0.8927.

Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning

Large-bore natural gas reciprocating generator engines will play crucial roles in future low-carbon energy systems. Knock, as an abnormal combustion phenomenon, is a bottleneck for long time to restrict the engine performance improvement. Development and application of the reliable knock model can contribute the predictive engine control and optimization to further improve the engine efficiency and performance. The biggest challenge of developing the predictive knock model is the stochastic nature of the knock phenomenon. To bridge the technological gap, this study aims to develop a novel predictive knock model for a large-bore natural gas engine and showcase its application for co-optimization of the engine design and control parameters, together with the machine learning. The knock model, taking the effects of hot spots heat release rate and energy density into account, is calibrated by the statistical phenomenological knock factor. The 1-D engine simulation model embedded with the knock model is built and calibrated. The data-driven model based on machine learning is developed and used to predict the relation between the key parameters and the engine performance. The genetic algorithms are employed to achieve the multi-objectives global optimization of geometric compression ratio, Miller degree and other control parameters of the engine to reduce the exhaust gas temperature and improve the engine performance. The co-optimized results show that the engine exhaust gas temperature can be remarkably lowered and the indicated thermal efficiency is increased by 1–3% depending on the operational conditions, without degrading the engine power output and increasing the exhaust NOx emissions.

Effect of first-stage ignition on the onset of knocking in SI engines

Knocking limits the performance and efficiency of highly compressed, downsized spark ignition combustion engines. In this paper, the effect of the first-stage ignition on knock is analyzed using a quasi-dimensional engine model. The standard coherent flame model is used for turbulent combustion, and five available reduced chemical kinetics mechanisms are applied to thermal ignition of the end-gas. Measurements in a modified CFR engine operating with two PRF mixtures are used to test the predictive ability of the models and to identify the conditions that lead to knocking. Although the chemical kinetics models used predict similar autoignition delay curves, they do not result in the same knock predictions. The results show that the chemistry must correctly capture the NTC range to predict the observed onset of knocking. In addition, the chemistry models are used to determine the first- and second-stage ignition delay times and their dependence on engine speed and compression ratio. Results show that the autoignition stages occur at relatively fixed temperatures, independent of engine speed and compression ratio. A sensitivity analysis shows that only one set of reactions in the low-temperature chemistry contributes to first-stage ignition, which in turn determines the onset of knocking. This suggests that fuel additives that inhibit or delay the inflection in the NTC region from low- to high-temperature could prevent knock in SI engines.

Study on Compression Ratio Optimization of M100 Methanol Engine

A turbocharged and intercooled diesel engine was transformed into an ignited M100 methanol engine by adding a spark plug to the cylinder head. A one-dimensional model of the methanol fuel engine was established using the bench test data as the boundary conditions, and the accuracy and accuracy of the model were verified. Methanol has better octane number and explosion resistance than traditional fuels, increasing the compression ratio of the engine will improve the power and fuel efficiency of the methanol engine. Based on the original compression ratio of 12, 8 compression ratio simulation schemes were selected, including 11.4, 11.7, 12, 12.3, 12.6, 12.9, 13.2, 13.5, etc., and the engine performance and the combustion state in the cylinder changed with the compression ratio were analyzed by simulation calculation, and the engine was optimized and improved. The results show that when the engine compression ratio reaches 12.9, the engine performance reaches the optimal value.