The fuel system of a diesel engine is a crucial component that significantly impacts the economic and technical performance, as well as the quality of exhaust emissions released into the environment. Improving fuel injection pressure, combined with changes in the fuel injection rules, the number of fuel injections in a cycle, and the geometric dimensions of the injectors, helps improve efficiency, reduce harmful emissions, and lower fuel consumption. This study evaluated the effect of the shape of the fuel injection characteristic at injection pressures up to 350 MPa, with the intake air pressure maintained at 0.25 MPa, using a 3D AVL Fire model. The results showed that at ultra-high fuel injection pressures, the amount of particulate matter from the diesel engine was very small, ranging from 0.0012 to 0.0062 g/kWh. By changing the distribution rules, the minimum amount of NOx was formed, corresponding to the case where the injection velocity was highest in the initial phase (case 1 and a value of 9.51 g/kWh) and gradually decreased in the latter phase of the injection process (case 5 and a value of 21.01 g/kWh). In addition, engine power decreased by 1.9% and fuel consumption increased by 1.8%. Due to inadequate fuel mixing between zones and fuel being mixed leaner than the combustion limit when increased injection pressure with a large injection volume in the initial phase. As a result, the maximum HC amounts increase by 4.8 times and the maximum CO amounts increase by 1.6 times compared to other injection cases.
This article presents an analysis of the operation of the electric motor, generator, and internal combustion engine within the hybrid powertrain system of a Toyota Corolla. To compare data regarding energy utilization from these sources, a specially designed diagnostic scanner was employed in our laboratory, enabling the capture of data from the vehicle's Controller Area Network (CAN). The tests were conducted on a laboratory chassis dynamometer, taking into account the NEDC (New European Driving Cycle) and WLTP (Worldwide Harmonised Light Duty Vehicle Test Procedure) protocols. Additionally, experiments were performed under real urban traffic conditions. The analysis of the collected data provided detailed insights into the operation of the aforementioned components, which are integral to the powertrain during characteristic driving scenarios. For selected test fragments, an analysis of energy consumption and recovery by the electric motor and generator is presented.
Internal combustion engines constitute one of the two fundamental propulsion categories used in unmanned aerial vehicles (UAVs). This article analyzes the occurrence and application of individual types of internal combustion engines across unmanned platforms, with particular emphasis on their influence on aircraft performance and mission profiles. The advantages and limitations of specific propulsion system configurations are identified in the context of unmanned aviation, including factors such as energy efficiency, mass, structural complexity, endurance, and acoustic and thermal signatures. In addition, representative UAVs employing different types of internal combustion engines are presented, illustrating the practical implications of propulsion system selection in real-world operational applications.
Aircraft noise is one of the most significant environmental concerns around airports, generating both social conflicts and financial consequences. This study examined how the fleet composition influences daily exposure to aircraft noise in the vicinity of Poznań-Ławica Airport, focusing on two widely operated aircraft types: the Boeing 737-800 and the newer 737 MAX 8. Measurements were carried out at six monitoring points during 78 operations, and the analysis was based on the sound exposure level expressed on the physical scale and in decibels. The results show that the 737-800 consistently produces higher noise levels than the MAX 8, with mean LASel values of 0.89 Pa²·s (94.5 dB) compared to 0.57 Pa²·s (92.6 dB), a difference of 56%. The gap is particularly evident during take-off, where the 737-800 exceeds the MAX 8 by 137% (2.7 dB). Regression analysis confirmed a decreasing trend in SEL with increasing altitude; in the revised analysis, this dependence is modeled using a log-distance function in dB units, with altitude treated as a proxy for source–receiver distance. The simulation of nine fleet composition scenarios for a representative month showed that increasing the share of MAX 8 aircraft substantially reduces the number of days when the daily equivalent sound level (LAeqD) exceeds the legal threshold of 60 dB. Economic analysis using official Polish penalty tariffs for 2025 also showed that monthly environmental fines could be reduced by up to 69% if the fleet were fully modernised to MAX 8. Even partial renewal, with a 60–70% share of MAX 8, reduced costs by 60–70% relative to a fleet dominated by the 737-800. These findings highlight that fleet modernisation is a highly effective strategy for reducing community exposure to aircraft noise and mitigating the financial burden on airports, although the analysis was limited to daytime operations and one seasonal period.
This study presents a combined experimental and numerical investigation of jet–crossflow interaction relevant to gas turbine blade film cooling applications. Four staggered rows of cylindrical cooling jets inclined at 30° are examined on a flat plate subjected to an incompressible crossflow with a mainstream velocity of 2 m·s⁻¹. Experiments were conducted at a fixed blowing ratio of M = 2 using Particle Image Velocimetry (PIV) to obtain time-averaged velocity fields and turbulence quantities across six measurement planes. Corresponding Reynolds-Averaged Navier–Stokes (RANS) simulations were performed using the standard k–ε and Shear Stress Transport (SST) turbulence models under identical boundary conditions. Quantitative comparisons of normalized axial and vertical velocity profiles (U/Ue and V/Ue) reveal that both turbulence models overpredict near-wall axial velocities in the jet exit region; however, the SST model shows consistently closer agreement with PIV measurements across streamwise locations ranging from X/D = −2 to X/D = 30. In the near-field region, the SST model captures jet penetration and mixing trends more accurately, whereas the k–ε model underestimates jet diffusion and lateral spreading downstream. Turbulent kinetic energy distributions further demonstrate that the SST model provides improved prediction of shear-layer turbulence intensity, although both models underestimate the spatial extent of turbulence compared to experimental data. The results indicate that, under the investigated conditions (M = 2, α = 30°), the SST model offers superior predictive capability for jet–crossflow interaction compared to the standard k–ε model, particularly in regions dominated by strong shear and recirculation. These findings guide RANS turbulence model selection.
This article presents an uncertainty analysis of several of the most commonly used methods for calculating motor vehicle collision speeds. Based on a real-world crash test, a simulation-based collision model and an energy-based post-impact vehicle motion were developed in MATLAB. The calculation results were subjected to optimization using the Monte Carlo method. A custom-designed script was employed to search the full range of feasible solutions, and the results were compared with those obtained from analytical reconstruction methods and simulations conducted in two commercially available vehicle dynamics software packages. The study demonstrated that analytical calculations are highly sensitive to uncertainties in estimating the base post-impact trajectory angles and, depending on the reconstruction type (I, II, III, IV), to pre-impact approach angles. This sensitivity is not observed in simulation-based calculations, regardless of whether the post-impact motion is modeled using energy methods or vehicle dynamics models integrated into accident reconstruction software. Additionally, the performance of optimization algorithms built into these programs was analyzed. In this particular test, the uncertainties for different methods—at measured speeds of 42.1 and 30.0 km/h—did not exceed a maximum of ±13%. In contrast, the randomization of the friction coefficient (µ) resulted in uncertainties of up to ±28% and varied between the individual vehicles. These values are consistent with the theoretical analyses conducted by other researchers.
Motor vehicles undergo plastic deformation as a result of colliding with each other or with another obstacle, which leads to a loss of the system's kinetic energy. The work of permanent deformation of a vehicle involved in a collision is equal to the loss of kinetic energy and is often represented as the value EES, i.e., Energy Equivalent Speed. This article describes three well-known and commonly used methods for estimating the value of deformation work: comparative, analytical, and graphical methods. The later section presents the calculation of the EES parameter using a comparative approach for a hybrid passenger vehicle struck by a motorcycle. Possible differences in estimating the value of the EES parameter have been indicated using this method for standard and hybrid vehicles.
A model of an Internal Combustion Engine (ICE) was created using Simulink, which could be used as part of a drive component of the propulsion system during the development stage of an Unmanned Aerial Vehicle (UAV)/Aircraft. The model can predict the engine power, engine torque, Engine brake specific fuel consumption, and engine fuel flow of an ICE for a given engine throttle, engine atmospheric pressure, engine rotational speed, temperature, and altitude at which the UAV is flying. Results obtained from the model were compared with results obtained by previous researchers from the literature. The engine power, torque, fuel flow, and brake specific fuel consumption curves from simulations were found to be in good agreement with those obtained by previous researchers experimentally.
Balancing the demand for high thermal efficiency with strict NOx emission limits remains a persistent dilemma in modern diesel engine development. This study explores a potential solution by investigating how hydrogen enrichment interacts with EGR in a diesel dual fuel engine with constant load. Instead of treating these variables in isolation, we employed a randomized full factorial design to map their simultaneous effects, testing hydrogen flow rates up to 7.5 LPM and EGR levels up to 15%. Through rigorous statistical analysis (ANOVA and Tukey’s HSD), we uncovered a distinct "sweet spot" in engine operation. While hydrogen injection successfully boosted thermal efficiency and cut fuel consumption due to its rapid combustion, it expectedly triggered a spike in NOx emissions. However, our analysis revealed a crucial interaction: adding 10% EGR effectively neutralized this emission penalty without severely crippling the engine's power. Interestingly, we also identified a saturation point at 7.5 LPM of hydrogen, where the engine ceased to gain further efficiency due to volumetric losses. Ultimately, the data suggests that pairing 5.0 LPM of hydrogen with 10% EGR offers the most balanced configuration restoring power to baseline levels while keeping emissions in check.
This study examines the fuel performance characteristics of a wheeled tractor transporting a trailer tank under varying load conditions within standardized driving cycles (ELR, EPA, and NRTC). A mathematical model of the tractor–trailer system was developed in MATLAB/Simulink, incorporating the dynamics of the FPT NEF 67 internal combustion engine, the transmission and wheel drives, as well as the effects of fluid redistribution in the tank. The model enables evaluation of instantaneous fuel consumption, specific fuel consumption, effective power, and engine efficiency under dynamic operating modes. Particular attention is given to the impact of fluid sloshing on energy expenditure, which is most pronounced at a tank fill height of 1.3 m (out of 1.6 m), corresponding to resonance conditions. The results demonstrate that the dynamic characteristics of the driving cycles have a significant impact on additional fuel consumption: NRTC yields the highest relative increase (9.22%), EPA the highest absolute increase (6.76%), and ELR the lowest (4.72%). These findings provide a basis for optimizing tractor operation during liquid cargo transport and for assessing the potential benefits of hybridizing tractor transmission systems in transport applications.
In the context of global efforts to reduce greenhouse gas emissions and air pollution, increasing importance is being attached to the accurate monitoring and analysis of emissions generated by means of transport, including commercial vehicles used in specialized applications. This article presents the results of research conducted on the emissions of harmful compounds from concrete mixers in real traffic conditions. The measurements were performed using the Axion R/S+PM portable emission measurement system from Global MRV, which enabled the mass measurement of emissions of pollutants such as CO, CO2, NOx and HC under real truck driving conditions. Based on the collected data, the time density characteristics of the tested compounds were determined as a function of crankshaft speed and engine load, and their emission intensity was determined as a function of vehicle speed. On this basis, the impact of changes in load weight on the obtained emission intensity values of the tested pollutants was demonstrated. The analysis of pollutant emissions from a heavy vehicle designed for transporting concrete made it possible to determine the impact of various road conditions and engine operation on the amount and type of compounds emitted. By monitoring operating parameters such as load and engine crankshaft speed, it is possible to gain a more accurate understanding of the mechanisms of exhaust emissions under actual vehicle operating conditions. In this way, it is possible to more effectively identify situations in which pollutant emissions are particularly high and to determine the optimal operating conditions for reducing emissions.
Environmental protection is currently receiving significant attention, particularly in the context of transport's impact on the environment. Limited fossil resources, climate change, and global warming are driving the automotive industry toward more efficient and sustainable solutions. These challenges are driving car manufacturers to adopt new technologies and alternative drive systems. Examples of such vehicles include electric vehicles (EVs) and hybrid vehicles (HEVs or PHEVs). The impact of operating these modes of transport on the emission of pollutants other than exhaust gases is crucial. Examples of such emissions include particulate matter generated by brake and tire wear. All vehicles, whether conventionally or alternatively powered, generate such emissions during operation, regardless of their drive type. These particulate matter enter the air and can pose a threat to the environment and human health. While it may seem that electric cars may emit less particulate matter from their braking systems due to the frequent use of recuperation, tires remain a significant source of emissions. The article included measurements of dust emissions during the operation of an electric vehicle and a conventionally driven vehicle, as well as studies of the elemental composition of particles using scanning electron microscopy, analyzing dust collected from the vehicle's surroundings, braking system and tires
The urgent need for environmentally friendly alternative fuels arises from concerns over fossil fuel depletion and the harmful effects of exhaust gas emissions. One promising solution involves the use of biodiesel blended with natural additives like essential oils to improve combustion efficiency in diesel engines. However, conventional engine performance testing is often time-consuming and repetitive, requiring a more efficient approach such as predictive modeling using Machine Learning (ML). This study investigates the effect of biodiesel and essential oil blend ratios on engine performance and develops predictive models to estimate engine power and torque. A total of 1045 experimental data points were collected from tests using varying compositions of biosolar, dexlite, and essential oil additives at different RPMs. The research applied a quantitative method, utilizing Linear Regression and Support Vector Machine (SVM) algorithms implemented in RStudio. Model accuracy was evaluated using MAE, RMSE, and R² metrics. The results indicate that variations in biodiesel blend ratios and essential oil additives had no statistically significant effect on engine performance. However, ML-based predictive modeling proved highly effective. The SVM model achieved superior accuracy, with R² values of 0.993 for power and 0.973 for torque. In contrast, the Linear Regression model yielded much lower R² values—0.671 for power and 0.093 for torque. These findings demonstrate the potential of Machine Learning, particularly SVM, as a reliable tool for predicting diesel engine performance using biodiesel and additive blends, offering a faster and more accurate alternative to conventional testing methods.
The maritime sector is undergoing a critical transition driven by increasingly stringent emissions regulations from the International Maritime Organization (IMO) and the European Commission (EC). Among the leading alternative fuels for marine diesel engines, methanol and liquefied natural gas (LNG) have gained significant attention. This review synthesizes recent research and industry data to compare the two fuels across physicochemical properties, combustion performance, emission behavior, safety, and economic feasibility. Methanol, a liquid under ambient conditions, enables easier storage and refueling using existing liquid-fuel infrastructure while providing substantial reductions in sulfur oxides (SOx), nitrogen oxides (NOx), and particulate matter (PM) emissions. However, its low energy density and the formation of formaldehyde remain drawbacks. In contrast, LNG offers higher volumetric energy density and immediate reductions in carbon dioxide (CO₂), SOx, and NOx emissions but requires expensive cryogenic storage systems and faces the persistent challenge of methane slip. Economically, LNG engines entail higher capital investment yet support short-term regulatory compliance, whereas renewable methanol offers a scalable pathway toward long-term carbon neutrality. Overall, the optimal choice between methanol and LNG depends on operational profiles and strategic objectives, with LNG serving as a transitional solution and methanol representing a flexible, future-proof option for sustainable marine propulsion.
This paper discusses the potential use of stochastic processes as road vehicle velocity models for road transport emissions inventory purposes. Empirical studies have presented stochastic passenger-car velocity models, each modeling traffic conditions: in traffic congestion, in cities outside traffic congestion, outside cities, and on highways and expressways. Zero-dimensional characteristics of the model velocity processes have been examined. The characteristics of passenger car emissions for 2020 have been determined using simulation methods. Road pollutant emissions from passenger cars under specific velocity process implementations have been determined and analyzed. The research results have been assessed, among other things, for their variability. Based on the results, the feasibility of using stochastic processes as road vehicle velocity models for road transport emissions inventory purposes has been assessed.
A one-dimensional model of a three-way catalyst (TWC) was developed in MATLAB/Simulink, consisting of a monolith pressure-drop module and a reaction block for CO/HC/NOx. The model was validated against a reference GT-SUITE solution, with agreement in levels and trends. Kinetic parameters were tuned and verified using emission tests in the NEDC cycle. The model reproduces pressure drop, instantaneous profiles, and cumulative emissions upstream and downstream of the catalyst. Satisfactory agreement between predicted reductions and NEDC measurements confirms the model’s suitability for assessing TWC effectiveness and for calibration under test conditions. The approach is computationally lightweight and ready to be extended to other driving cycles, additional physical effects, and exhaust aftertreatment systems such as a GPF.
The application of 3D-printed metal pistons in internal combustion engines (ICE) presents significant advantages, including enhanced design flexibility, weight reduction, and improved thermal management. This innovative manufacturing technique enables the creation of complex geometries and tailored surface textures, contributing to better fuel efficiency and reduced emissions. Moreover, it opens new possibilities for customised piston design in advanced combustion strategies. However, challenges such as material anisotropy, surface roughness, and long-term reliability must be addressed to ensure consistent and safe performance under demanding engine conditions. Ongoing research in material science, process optimisation, and post-processing techniques is essential for overcoming these hurdles and realising the full industrial potential of 3D-printed pistons in modern ICE technology.
Reliable modeling of emissions and dispersion of pollutants emitted in exhaust gases from motor vehicles is highly challenging due to the presence of multiple variables and inconsistencies in input data quality across different stages of the process. This article focuses on the vehicle fleet. It has been demonstrated that the structure of the vehicle fleet in a given area varies depending on the data collection methods and sources, which ultimately determines the dispersion results. This issue becomes particularly significant in urban environments, where the intensity of road traffic is high, and ventilation conditions are often poor, partly due to the formation of street canyons by dense urban development. The modeling was conducted at a selected intersection in Wrocław for various fleet structure scenarios. The results were compared with the results of pollutant concentrations from a nearby air quality monitoring station. The Copert emission model and emission factors from the European EMEP/Corinair database were used. In contrast, the GRAL model, a CFD model (suitable for urban dispersion modeling), was used to simulate pollution dispersion.
The thermal balance of vehicle cabins when only an internal combustion engine was used was an insignificant element of the vehicle design. The excess thermal energy on board did not require energy saving. Only the cold start was slightly problematic. However, it could still use a combustion heater that quickly warmed up the cabin. In purely electric cars, each use of electricity stored in the battery shortens the vehicle's range. Three types of heating are used in this case: a) an electric air heater; b) an electric heater for the liquid cooling the drive components; c) a heat pump receiving heat from the drive components, also temporarily supported by an electric air or liquid heater. The use of a heat pump is the most promising and is also used in cheap city cars (e.g. Renault ZOE). The source of thermal energy for a heat pump is: a) vehicle drive engine(s); b) AC/DC converters – charger, DC/DC – charger, DC/AC – motor inverter; c) battery. In cheaper vehicles, air cooling is partially used; currently, a mixture of antifreeze fluids is used. The article presents a calculation example for a city bus cabin.
The present study reviews the state-of-art regarding the application of H2 in the automotive industry. This part focuses on dual-fuel (DF) internal combustion engines (ICEs) supplied with fuel and H2. This is a continuation of the earlier part, which focused on out-of-engine studies on the effect of H2 combustion processes, ICEs supplied with H2, and vehicles powered by fuel cells (FCs). Using H2 in diesel engines via the DF strategy enables a decrease in diesel fuel consumption and a reduction in harmful exhaust emissions. Using H2 in diesel engines powered by diesel/biodiesel mixtures also allows for reducing reliance on fossil fuels. Also, incorporating nanomaterials or oxygen-containing compounds into diesel fuel formulations may enhance the combustion efficiency of H2-powered diesel engines, thereby increasing thermal efficiency and reducing fuel consumption. However, additional studies are required on this subject.
