Engine Output Research Articles

AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics.

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.

Enhancing Sustainability of Water‐Emulsified Diesel–Hydrogen Carrier Biogas as Fuel in Dual‐Fuel Engines through the Parametric Optimization

This study investigates the usage of biodiesel‐based emulsified fuel as pilot fuel and waste‐derived biogas as prime fuel to run dual‐fuel engines at varying compression ratios (17, 17.5, 18), injection timing (23°, 26°, 29°, 32° BTDCs), and engine loads (20%, 40%, 60% 80%, 100%). Mahua biodiesel, surfactants, and water were used to produce two‐phase stable emulsification. The findings showed that using biodiesel‐based emulsified fuel as pilot fuel combined at the compression ratio of 18 and advanced fuel injection time of 29° BTDC increased performance and lowered emissions. Additionally, response surface methodology with variance analysis was used to create prediction models for all engine outputs with a robust R2 value (0.8927–0.9974). The engine operation was optimized employing the desirability approach to be 85.3% of engine load, compression ratio of 18, and injection timing of 29° BTDC. At optimal settings, the anticipated engine outputs were 17.46% for brake thermal efficiency, 318.5°C for exhaust gas temperature, 76.1% for liquid fuel replacement, 45.3 bar of peak in‐cylinder pressure, 3.97 vol.% carbon dioxide, 137.43 ppm carbon monoxide, 132 ppm hydrocarbon, and 42 ppm oxides of nitrogen. The lab‐based engine testing found that the results were well within 7.14% of the projected values at these optimal operating parameters.

Improving the characteristics of atomized fuel jets for automotive engines

The fuel supply system is one of the most critical components of a diesel engine. The processes of fuel injection, atomization, and mixing are crucial in determining the efficiency, power output, economic performance, and environmental impact of the diesel engine's operation. Furthermore, with the deteriorating environmental conditions in major cities, the toxicity levels of diesel exhaust emissions have become a priority concern. The mathematical model of the injected fuel jet's development and combustion enables the direct calculation of various indicators related to the engine's operating process. The value of these results lies in the fact that numerical modeling allows for the investigation of the influence of injection characteristics, fuel supply timing and duration, as well as combustion chamber design parameters on the indicator diagram and cycle efficiency across a wide range of engine speed and load conditions.

Machine learning predictions on the output parameters of CRDI engines fueled with ternary blend

This study aims to employ a machine learning algorithm (MLA) to predict CRDI engine emissions and performance using alternative feedstock. This study started with a diesel-SCOME- Methyl Acetate ternary mix. The engine was tested with fuel injection time (FIT) of 23?, 21?, and 19? bTDC with exhaust gas recirculation (EGR) levels of 10%, 15%, and 20% at estimated power productivity. Retard injection time and increasing EGR rates reduced in-cylinder peak pressure. Operating conditions with the maximum BTE were 21? bTDC and 10% EGR. Adjusting injection time and EGR reduced nitrogen oxide relative to the baseline. Smoke opacity was 1% lower at 21? bTDC and 10% exhaust gas recirculation than in conventional diesel operation. Retard injection time and exhaust gas recirculation increased HC and CO emissions. However, MLAs predict CI engine operation and discharge properties. The long short-term memory (LSTM) Model predicts engine output characteristics with a squared correlation (R2) of 0.92 to 0.961. At the same time, mean relative error (MRE) values ranged from 1.74 to 4.68%. These results show that the LSTM models provide superior predictive capabilities in this investigation, particularly when considering numerous variables to analyse engine responses.

Analysis of 38 kW class agricultural tractor engine load rate according to agricultural operation

This study was conducted to analyze the engine load ratio of a 38 kW agricultural tractor to calculate national emissions. Engine output was calculated using engine CAN (Controller area network) data measured through a load measurement system. In this study, plow tillage (PT) and rotary tillage (RT), which are the main agricultural tasks of small and medium horsepower tractors, were selected and performed three times for each stage. The engine load ratio was calculated as the ratio of the engine power generated during work and the engine rated power, and weights (25% each for PT M1, PT M2, RT L3, and RT L4) according to the agricultural work ratio were applied. The engine load ratio was found to be an average of 0.68 and 0.75 for the M1 and M2 stages of plow tillage work, respectively, and an average of 0.57 and 0.65 for the L3 and L4 stages of rotary tillage work, respectively. The weighted load ratio was 0.17, 0.19, 0.14, and 0.16 for plow tillage operation M1, M2, and rotary tillage operation L3 and L4, respectively. The integrated load factor was found to be 0.66, which is approximately 1.38 higher than the current domestic load factor of 0.48.

Experimental Investigation of the Effects of E85 and Gasoline on NO Emission in a Spark Ignition Engine

Energy need is one of the indispensable needs of human beings. At this point, the important thing is to use the available resource that is sustainable, economical, and sensitive to the ecosystem. For this reason, alternative fuels will maintain their importance in the future as they do today. Internal combustion engines continue to be used as power plants for conventional and hybrid vehicles. The sustainability of internal combustion engines depends on their energy consumption and the emissions released. In this study, the effects of E85 and gasoline on NO emissions have been investigated experimentally by considering ignition timing and relative air/fuel ratio in a spark ignition engine. The experiments have been performed on the Ricardo Hydra research engine at 2000 rpm engine speed and a 10:1 compression ratio. The experimental results show that engine output power obtained using E85 was similar to or higher than that of E0. The exhaust gas temperatures for E85 decreased by an average of 22.6 °C, compared to E0. When all experimental data obtained with E0 and E85 were compared with each other, it was seen that gravimetric fuel consumption and brake specific fuel consumption were increased on average by 39.3% and 37.5%, respectively. The results of this study show that especially the improvement of E85 in NO emission is remarkable. Considering the maximum NO point at 1.05 or 1.1 relative air/fuel ratio, a 38.4% reduction in brake specific NO was obtained with E85.

Combined application of ANN prediction and RSM optimization of performance and emission parameters of a diesel engine using diesel-biodiesel-propanol fuel blends

In this study, an artificial neural network (ANN) was used to estimated the performance and exhaust emission parameters of a diesel engine running on diesel, biodiesel, and propanol fuel mixtures. In addition, the parameters estimated by ANN were tried determining the optimum operating parameter by using Response Surface Methodology (RSM). In the experimental study, propanol was added in 3 different ratios (5%, 10% and 20%) into 100% diesel, 80% diesel and 20% biodiesel fuel blends. In addition, engine tests, were made at 5 different engine speeds with 400 min-1 intervals between 1000 min-1 and 2600 min-1 revolutions at full load. In addition, HC (Hydrocarbon), CO (Carbon Monoxide), NOX (Nitrogen oxides) and Smoke emissions were measured during in the working. ANN model was developed for estimation of engine output parameters depending on fuel mixture ratios and engine speed. In the ANN results, the regression coefficients (R2) of the proposed model were found to be between 0.924 and 0.99. When the obtained ANN results were compared with the experimental results, it was seen that the maximum mean relative error (MRE) was 6.895%. It has been shown that the applied model can predict with a low error rate. The RSM results showed that the optimum operating parameters were 2034-min-1 engine speed, 74.667% diesel, 11.36% biodiesel and 15% propanol fuel mixture. In addition, in the validation tests of the model where the desirability was 0.7833%, the highest error rate was obtained as 7.37% as a result of NOX. As a result of the study, it was seen that RSM supported ANN is a good method for estimating diesel engine parameters working with diesel/biodiesel/propanol mixtures and determining optimum operating parameters.

Scientometric analysis of the publishing behaviour of EU + UK authors in engineering education and further afield

ABSTRACT The authors present a scientometric procedure comparing engineering education (EE) publication output in different countries. Selected European countries were compared using a snapshot of authors published in EE journals during a two-year period – 895 in all. The entire publication careers of these authors – 39,322 publications – were analysed to determine the breakdown of educational and non-educational publications. Spain and the UK produced the most publications, and we propose explanations based on relevant national policies. France, Germany, and Italy had notably fewer EE publications relative to their general engineering and science research outputs, whereas Portugal, Ireland, and three Nordic countries were the opposite. Countries varied widely in the ratio of educational to non-educational publications. Non-educational publications typically had a greater impact on authors’ h-index values. We believe this procedure can longitudinally map and compare EE publication output in and across Europe, providing a valuable resource for policy-makers and researchers in the region.

Study on the Effect of Parameter Sensitivity on Engine Optimization Results

The effects of six control parameters, intake valve opening timing (IVO), exhaust valve opening timing (EVO), compression ratio (CR), engine speed, intake temperature, and intake pressure on engine output power, indicated specific fuel consumption (ISFC), and nitrogen oxides (NOx) emissions, are analyzed through engine simulation. The six parameters were categorized into two groups based on the degree of influence: high influence (EVO, speed and intake pressure) and low influence (CR, IVO and intake temperature). The relationship between these two groups of parameters and power, ISFC and NOx emissions was explored. Optimization was carried out for each of the two groups of parameters, and the optimization of the high impact parameters resulted in a higher diversity and wider distribution of the solution set. On the other hand, the optimization of the low-impact parameters resulted in a more concentrated distribution of the solution set, while better reflecting the trade-off between the optimization objectives. For the optimal solutions for both sets of parameters, the high-impact parameters provided significant optimization performance compared to the standard operating conditions. Although power and ISFC were optimized, the optimal solution for the low-impact parameter performed poorly with a significant increase in NOx emissions. Therefore, the parameters should be evaluated for optimization using high impact parameters to improve engine performance.

Integrated modeling and coupling characteristics analysis of helicopter/engine/infrared suppressor

Abstract In order to explore the influence of the infrared stealth technology on the performance of the integrated helicopter/engine system, an integrated modeling method of helicopter/engine/infrared suppressor is proposed. Firstly, based on the power calculation model of the helicopter, combined with the high-precision turboshaft engine component-level model, an integrated simulation platform is built, which takes into account the nonlinear characteristics. Then, the aerodynamic characteristics of infrared suppressors under different engine operation states are studied by CFD numerical computation method, and the infrared radiation characteristics are obtained through combining the positive and negative ray tracing method and narrow band model method. Ultimately, the utilization of the power turbine outlet stagnation pressure is employed as the pivotal interface linking the turboshaft engine and the infrared suppressor in order to formulate an integrated model encompassing the helicopter, engine, and infrared suppressor subsystems. The simulation results demonstrate that compared with the conventional exhaust system, the application of the infrared suppressor greatly enhances the stealth performance of the helicopter, but also results in the unexpected decrease in engine output power. Moreover, the specific fuel consumption of the turboshaft engine increase and the compressor surge margin decreases in case of the consistent flight condition.

Coordination Control Algorithm for Wheel Torque of Parallel Hybrid Vehicle

When the Parallel Hybrid Vehicle (PHV) wheel’s torque is coordinated and controlled, the issue of coordination control of wheel torque under mode switching has some limitations. Therefore, the experiment proposed a novel wheel torque coordination control technique for parallel hybrid vehicles. The feedforward and feedback joint control technique of the Wheel Drive Torque (WDT) control algorithm may be used to implement the wheel torque drive control. The technique for the wheel braking Torque Control (TC) is applied to obtain the wheel braking torque through the Fuzzy PID controller (F-PID). A Dynamic Coordination Controller (DCC) is created and the engine output torque is adjusted using the PID control technique of wheel angular velocity difference. The motor provides the compensation torque. The deviation of the intended angular velocity from the actual angular velocity is adjusted intime to realize the wheel's dynamic coordination while being controlled by a mode switch. The results demonstrates that the reaction time of the proposed algorithm can be reduced to 0.08s; The overall output TC precision is improved by 11.1%. The precision of the predicted velocity of vehicle speed tracking has risen by 8.0%. The vehicle dynamics during the transition procedure is improved by 4.4%, which enhances the ride comfort and the PHV’s power.

An experimental study on optimum temperature of B20 POBD for higher efficiency and lower emissions of a DI diesel engine in lean and ultra‐lean combustion conditions

AbstractThe impact of inlet fuel temperature on the performance and emission characteristics of a 406 cc, single‐cylinder, air‐cooled, 4‐stroke, direct injection (DI) diesel engine was investigated to determine the suitable temperature of fuel for the best conditions of the engine in lean and ultra‐lean mixture combustion mode. B20 palm oil biodiesel‐diesel blend fuel (B20 POBD), as common renewable fuel and favorite biodiesel blend ratio used in the research, was provided and injected in the engine at varying temperatures in the range of 10–40°C and the engine parameters including the output power, specific fuel consumption (SFC), thermal efficiency, flue gas heat recovery efficiency and CO and NOx pollutant emissions were measured at different air–fuel ratios corresponded to lean and ultra‐lean mixtures. The results showed that there is an optimum temperature for fuel (about 20°C) in which maximum thermal efficiency and minimum SFC and CO emission are obtained. The minimum SFC and CO emission were 0.45 Kg/kWh and 610 ppm at the optimum temperature and output power of 1.5 kW, respectively. The effect of fuel temperature on SFC and CO increases as the output power decreases and for all fuel temperatures, the output power is inversely related to the air–fuel ratio. In addition, the elevated output power reduces the CO emission in all inlet fuel temperatures. Furthermore, an ultra‐lean mixture (very high A/F ratio) can dramatically decrease the thermal efficiency of diesel engines at all fuel temperatures, such that at the inlet fuel temperature of 17°C, increase in A/F ratio from 51 to 108 decreases the thermal efficiency from 16.8% to 0.12%. Finally, low engine output power leads to low levels of NOx emission, such that decrease in output power from 1.5 to 0.5 kW reduces the NOx emission from 522 to 204 ppm, respectively.

A comprehensive study on the performance and emission analysis in diesel engine via optimization of novel ternary fuel blends: Diesel, manganese, and diethyl ether

Ecosystem degradation and fossil fuel depletion are the two foremost concerns to look for alternative fuels. Rapid population growth is primarily accountable for higher consumption of fossil fuel sources, although engine technology is achieving milestones in terms of fuel efficiency and lower exhaust emissions in order to contribute towards a sustainable environment. The main root cause of global warming is carbon dioxide emissions; therefore, it is imperative to assess the impact of alternative fuels in diesel engines with an aim to minimize carbon emissions. A current study deals with the reduction of carbon emissions and improvement of efficiency through addition of manganese nano-additive to di-ethyl ether and diesel fuel blend in particulate form. Fuel blends were formed by adding various proportions of manganese to high-speed diesel fuel and stirring the mixture while heating it for 10 min. The blends were then tested in diesel engines at two distinct loads and five engine speed ranges. Emission analyzer was used to ascertain the CO2 output of engine. At higher loads for 10 % diethyl ether in diesel, the increase in brake thermal efficiency was 24.19, 28.17 and 26.86 % when the manganese amount in blend was changed as 250 mg, 375 mg and 500 mg respectively. On the other side CO2 emissions increase by 11.57, 30.52 and 20.33 % for manganese concentrations of 250 mg, 375 mg and 500 mg respectively. Analysis performed with Design Expert 13 showed that the desirability was 0.796 for a blend of 375 mg manganese at 1300 rpm and 4500 W load with 33.0611 % BTE, 334.011kg/kWh BSFC, 67.8821Nm torque, and 6.072 % CO2. Therefore, it can be deduced that manganese nanoparticle blends improved engine performance but CO2 emissions also increase which can be responsible for global warming and it should be reduced through catalytic converters.

Predicting the performance and emissions of an HCCI-DI engine powered by waste cooking oil biodiesel with Al2O3 and FeCl3 nano additives and gasoline injection – A random forest machine learning approach

This study presents a novel method for predicting the performance and emissions of a Homogeneous Charge Compression Ignition-Direct Injection (HCCI-DI) engine fuelled by waste cooking oil biodiesel (WCOB) blended with Aluminum oxide (Al2O3) and Ferric chloride (FeCl3) nano additives and premixed with gasoline. The test fuels considered were pure diesel, WCOB, B50 (a 50–50 blend of diesel and WCOB), and the nano-additives at concentrations of 50 ppm and 100 ppm. All experiments were performed on a 4.4 kW HCCI-DI engine operating at 1500 rpm. The results revealed that, utilization of WCOB showed a substantial reduction in emissions. Hydrocarbon (HC), carbon monoxide (CO) and smoke emissions decreased by 54.17 %, 50 %, and 22.69 %. Nevertheless, oxides of nitrogen (NOx) emissions increased by 18.32 %. When introducing gasoline (20 %) as a premix in HCCI-DI engines, a favourable shift in emission and efficiency metrics was observed. The brake thermal efficiency (BTE) increased 4.23 %. Moreover, NOx and smoke emissions decreased by 4.3 % and 39.21 %. Furthermore, compared to conventional diesel-fueled combustion, the integration of the nano additives manifested promising results. After introducing Al2O3 into neat fuel, The BTE improved by 11.27 % for direct injection (DI) combustion and 18.31 % for HCCI-DI combustion. Additionally, there was a marked decrease in exhaust emissions. FeCl3 nano additive into the test fuel significantly reduced HC, CO, and smoke emissions. Furthermore, the random forest machine learning approach demonstrated an extensive accuracy in forecasting both engine performance and emissions for the HCCI-DI engine. The innovative combination of cutting-edge machine learning techniques and combustion technology used. This model to understand the complicated correlations between input parameters and engine outputs and account for the system's intrinsic non-linearities and interactions. This dramatically improves standard prediction approaches, often assuming linear correlations or disregarding variable interdependencies.

Power performance and emissions analysis of outboard diesel engines by use of waste cooking oil biodiesel

Analytically determining the fuel consumption and power output of outboard engines of a higher speed little planned boat is difficult. The goal of this study is to see how waste cooking oil (WCO) affects engine efficiency and emissions when used in varying amounts in an outboard fuel engine. Use of waste cooking oil biofuel culminated in lower CO emissions but increased NOx emissions. In addition, waste cooking oil biodiesel indicates a slight increase in specific fuel consumption which may be tolerable considering the decreasing in exhaust emissions.

Fuzzy-based optimal energy management strategy of series hybrid-electric propulsion system for UAVs

Hybrid-electric propulsion system (HEPS) has gained significant attention in unmanned aerial vehicles (UAVs) due to its potential to significantly reduce fuel consumption and pollutant emissions. Effective energy management strategies are crucial in ensuring the efficient operation of HEPS in UAVs, as they involve multiple power and energy sources. While conventional equivalent consumption minimum strategy (ECMS) is effective in distributing engine and battery output power, it struggles to maintain the battery’s state of charge (SOC) within desired levels in case of engine failure. The present study introduced the fuzzy logic control-equivalent consumption minimum strategy (FLC-ECMS), a fuzzy-based composite energy management strategy that incorporates the robustness and adaptability of fuzzy logic control. Through simulation tests under general aviation cruise flight and cargo transport flight scenarios, the results indicated that hybrid UAV equipped with the HEPS can decrease fuel consumption and CO2 emissions by at least 18.6% and NOx emissions by 10.1% in the first profile and by 23.6% and 11.2% in the second profile, compared to conventional oil-powered UAV. The FLC-ECMS method not only reduces fuel consumption and emissions, but also maintains the battery SOC within permitted range and minimizes the difference between front and back SOC. This study provides new insights into the optimal energy management of HEPS for complex profiles in UAVs, highlighting the significance of UAVs in reducing environmental impacts, and has the potential for broader application in other electric propulsion systems.

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles.

Road parameter identification is of great significance for the active safety control of tracked vehicles and the improvement of vehicle driving safety. In this study, a method for establishing a prediction model of the engine output torques in tracked vehicles based on vehicle driving data was proposed, and the road rolling resistance coefficient f was further estimated using the model. First, the driving data from the tracked vehicle were collected and then screened by setting the driving conditions of the tracked vehicle. Then, the mapping relationship between the engine torque Te, the engine speed ne, and the accelerator pedal position β was obtained by a genetic algorithm-backpropagation (GA-BP) neural network algorithm, and an engine output torque prediction model was established. Finally, based on the vehicle longitudinal dynamics model, the recursive least squares (RLS) algorithm was used to estimate the f. The experimental results showed that when the driving state of the tracked vehicle satisfied the set driving conditions, the engine output torque prediction model could predict the engine output torque T^e in real time based on the changes in the ne and β, and then the RLS algorithm was used to estimate the road rolling resistance coefficient f^. The average coefficient of determination R of the T^e was 0.91, and the estimation accuracy of the f^ was 98.421%. This method could adequately meet the requirements for engine output torque prediction and real-time estimation of the road rolling resistance coefficient during tracked vehicle driving.

The Effect of Curve Cylinder Liner on in-Cylinder Tumble Flow Motion Characteristic using CFD Port Flow Simulation

One crucial task in determining maximum engine output performance is evaluating its respiration capacity. While the steady state flow bench is a commonly used tool in the automotive industry, computational fluid dynamics (CFD) provides a more detailed and comprehensive analysis of flow characteristics within the cylinder compared to experiments. The objective of this paper is to investigate the impact of curve cylinder liners on flow motion characteristics. Using both experimental and CFD methods, this study examines the effect of curve liners on tumble flow motion at different valve lifts and pressure differences. Scalar maps were analysed to understand the behaviour of flow, and the findings indicate that the air velocity measured at the intake port was consistent between both methods, permitting further CFD analysis. Results show that air velocity measured at the intake port was consistent between both methods, allowing for further CFD analysis. Differences in average velocities between liners were insignificant at lower valve lifts, but noticeable at higher valve lifts, with up to a 15.67% difference recorded when the curve wall assisted air flow, resulting in an increase in air rotational strength. In addition, curve liners produced 11.44% and 10.09% more turbulent kinetic energy (TKE) than straight liners for 150mmH2O and 600mmH2O, respectively, with Plane 2 exhibiting the most significant difference in average TKE. Finally, the tumble ratio produced within the curve cylinder was significantly higher than the slider cylinder liner, with differences of up to 17.03% and 11.04% found at higher valve lift (5.4mm) for 150mmH2O and 600mmH2O, respectively.

Effective utilization of waste plastic derived fuel in CI engine using multi objective optimization through RSM

This study provides the prediction that plastic pyrolysis oil (PPO) is equivalent to petroleum diesel in CI engines. The four input parameters have been considered to enhance performance and reduced emissions. The input parameters of engine trials, like fuel blend (PPO and diesel), compression ratio, nanoparticle concentration, and injection timing, were studied with the response surface methodology (RSM) using the central composite rotating design (CCRD) matrix. The main goal is to identify the ideal values of input parameters that will provide the highest achievable brake thermal efficiency (BTE), the lowest possible brake-specific fuel consumption (BSFC), and the lowest possible emission like carbon monoxide (CO), unburnt hydrocarbons (HC) and nitrogen oxides (NOx). The optimal engine output responses for BTE, BSFC, in-cylinder pressure, heat release rate (HRR), and ignition delay, respectively. Along witha composite desirability of 0.89 under optimum operating conditions. After the regression analysis, 16.56% PPO blend ratio, 53.53 ppm Al2O3 nanoparticle concentration, 18.06 compression ratio, and 20.95 °bTDC injection timing were received the optimized engine settings. Optimal engine input parameters were validated through actual engine trials and compared with optimized responses and found satisfactory and acceptable errors (less than 5%). The current study establishes vital input parameters providing the best engine performance, combustion characteristics, and lower emissions during engine trials. So current investigation declared PPO with Al2O3 nanoparticles is potential fuel for the CI engine.

Trade-off study and the influence of different fuel injection strategies to enhance the engine characteristics powered with ternary fuel in CRDI diesel engine.

The effects of a diesel engine combustion chamber geometries (PB1, PB2, PB3) and fuel injection pressure (FIP) (500, 750, and 1000 bar) with ternary fuel are discussed in this paper. It is observed that at maximum load as the FIP increased, the PB3 surpasses the PB1 and PB2 in brake thermal efficiency (4.86%) and hydrocarbon (7.2% decrease). However, a minor reduction in NOx (3%) is seen with increased FIP in the PB2. The NHRR (net heat release rate), peak pressure (in-cylinder), and ROPR (rate of pressure increases) all considerably increase with an enhancement in FIP in the case of PB3 by 2.96%, 3.86%, and 1.98%, respectively. It is discovered that altering the fuel injection pressure and piston shape concurrently offers a viable substitute for raising engine output and reducing emissions.