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Abstract The transportation sector’s growing focus on addressing environmental and sustainable energy concerns has led to a pursuit of the decarbonization path. In this context, hydrogen emerges as a promising zero-carbon fuel. The ability of hydrogen fuel to provide reliable performance while reducing environmental impact makes it crucial in the quest for net zero targets. This study compares gasoline and hydrogen combustion in a single-cylinder boosted direct injection (DI) spark ignition engine under various operating conditions. Initially, the engine was run over a wide range of lambda values to determine the optimal operating point for hydrogen and demonstrate lean hydrogen combustion’s benefits over gasoline combustion. Furthermore, a load sweep test was conducted at 2000 rpm, and the performance and emission results were compared between gasoline and optimized hydrogen combustion. An in-depth analysis was conducted by varying fuel injection time and pressure.

Abstract This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions.

Abstract In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite.

Abstract Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy.

Abstract The escalating demand for more efficient and sustainable working machines has pushed manufacturers toward adopting electric hybrid technology. Electric powertrains promise significant fuel savings, which are highly dependent on the nature of the duty cycle of the machine. In this study, experimental data measured from a wheel loader in a short-loading Y-cycle is used to exercise a developed mathematical model of a series electric hybrid wheel loader. The efficiency and energy consumption of the studied architecture are analyzed and compared to the consumption of the measured conventional machine that uses a diesel engine and a hydrostatic transmission. The results show at least 30% reduction in fuel consumption by using the proposed series electric hybrid powertrain, the diesel engine rotational speed is steady, and the transient loads are mitigated by the electric powertrain.

Abstract Measurements of air–fuel ratio (AFR) and λ (AFRactual/AFRstoich) are crucial for understanding internal combustion engine (ICE) performance. However, current λ sensors suffer from long light-off times (on the order of seconds following a cold start) and limited time resolution. In this study, a four-color mid-infrared laser absorption spectroscopy (LAS) sensor was developed to provide 5 kHz measurements of temperature, CO, CO2, and NO in engine-out exhaust. This LAS sensor was then combined with 1 kHz hydrocarbon (HC) measurements from a flame ionization detector (FID), and the Spindt exhaust gas analysis method to provide 1 kHz measurements of λ. To the authors’ knowledge, this is the first time-resolved measurement of λ during engine cold starts using the full Spindt method. Three tests with various engine AFR calibrations were conducted and analyzed: (1) 10% lean, (2) stoichiometric, and (3) 10% rich.

Abstract In this article, the effects of mixture dilution using EGR or excessive air on adiabatic flame temperature, laminar flame speed, and minimum ignition energy are studied to illustrate the fundamental benefits of lean combustion. An ignition system developing a new active pre-chamber (APC) design was assessed, aimed at improving the indicated thermal efficiency (ITE) of a 1.5 L four-cylinder gasoline direct injection (GDI) engine. The engine combustion process was simulated with the SAGE detailed chemistry model within the CONVERGE CFD tool, assuming the primary reference fuel (PRF) to be a volumetric mixture of 93% iso-octane and 7% n-heptane. The effects of design parameters, such as APC volume, nozzle diameter, and nozzle orientations, on ITE were studied. It was found that the ignition jet velocity from the pre-chamber to the main chamber had a significant impact on the boundary heat losses and combustion phasing.

Abstract This study aims to explore the wear characteristics of fused deposition modeling (FDM) printed automotive parts and techniques to improve wear performance. The surface roughness of the parts printed from this widely used additive manufacturing technology requires more attention to reduce surface roughness further and subsequently the mechanical strength of the printed geometries. The main aspect of this study is to examine the effect of process parameters and annealing on the surface roughness and the wear rate of FDM printed acrylonitrile butadiene styrene (ABS) parts to diminish the issue mentioned above. American Society for Testing and Materials (ASTM) G99 specified test specimens were fabricated for the investigations. The parameters considered in this study were nozzle temperature, infill density, printing velocity, and top/bottom pattern.

Abstract In today’s landscape, environmental protection and nature conservation have become paramount across industries, spurring the ever-increasing aspect of decarbonization. Regulatory measures in transportation have shifted focus away from combustion engines, making way for electric mobility, particularly in smaller engines. However, larger applications like ships and stationary power generation face limitations, not enabling an analogous shift to electrification. Instead, the emphasis shifted to zero-carbon fuel alternatives such as hydrogen and ammonia. In addition to minimal carbon-containing emissions due to incineration of lubricating oil, hydrogen combustion with air results in nitrogen oxide emissions, still necessitating quantification for engine operation compliance with legal regulations.

Abstract Biogas (60% methane–40% CO2 approximately) can be used in the reactivity-controlled compression ignition (RCCI) mode along with a high-reactivity fuel (HRF). In this work dimethyl ether (DME) that can also be produced from renewable sources was used as the HRF as a move toward sustainable power generation. The two-cylinder turbocharged diesel engine modified to work in the DME–biogas RCCI (DMB-RCCI) mode was studied under different proportions of methane (45–95%) in biogas since the quality of this fuel can vary depending on the feedstock and production method. Only a narrow range of biogas to DME ratios could be tolerated in this mode at each output without misfire or knock. Detailed experiments were conducted at brake mean effective pressures (BMEPs) of 3 and 5 bar at a speed of 1500 rpm and comparisons were made with the diesel–biogas dual-fuel and diesel–biogas RCCI modes under similar methane flow rates while the proportion of CO2 was varied.

Abstract This study demonstrates the defossilized operation of a heavy-duty port-fuel-injected dual-fuel engine and highlights its potential benefits with minimal retrofitting effort. The investigation focuses on the optical characterization of the in-cylinder processes, ranging from mixture formation, ignition, and combustion, on a fully optically accessible single-cylinder research engine. The article revisits selected operating conditions in a thermodynamic configuration combined with Fourier transform infrared spectroscopy. One approach is to quickly diminish fossil fuel use by retrofitting present engines with decarbonized or defossilized alternatives. As both fuels are oxygenated, a considerable change in the overall ignition limits, air–fuel equivalence ratio, burning rate, and resistance against undesired pre-ignition or knocking is expected, with dire need of characterization.

Abstract A detailed investigation was carried out on the performance, combustion, and emissions of a single-cylinder direct injection hydrogen spark ignition (SI) engine with either a side-mounted direct injection (SDI) or a centrally installed direct injection (CDI) injector. The first part of the study analyzed the performance and emissions characteristics of CDI and SDI engine operations with different injection timings and pressures. This was followed by comparing the engine’s performance and emissions of the CDI and SDI operations at different engine speeds and relative air-to-fuel ratios (lambda) with the optimized injection pressure and timings. Furthermore, the performance and emission attributes of the hydrogen engine with the CDI and SDI setups were conducted at a fixed λ value of 2.75 across a broad spectrum of engine loads. The study’s main outcome demonstrates that both direct injection systems produced near-zero CO2, CO, and HC emissions.

Abstract Using ammonia as a carbon-free fuel is a promising way to reduce greenhouse gas emissions in the maritime sector. Due to the challenging fuel properties, like high autoignition temperature, high latent heat of vaporization, and low laminar flame speeds, a dual-fuel combustion process is the most promising way to use ammonia as a fuel in medium-speed engines. Currently, many experimental investigations regarding premixed and diffusive combustion are carried out. A numerical approach has been employed to simulate the complex dual-fuel combustion process to better understand the influences on the diffusive combustion of ammonia ignited by a diesel pilot. The simulation results are validated based on optical investigations conducted in a rapid compression–expansion machine (RCEM). The present work compares a tabulated chemistry simulation approach to complex chemistry-based simulations.

Abstract Fossil fuel reserves are swiftly depleting when consumer demand for these fuels continues to rise. In order to meet the demand and diminish the pollution derived through conventional fuels, it is crucial to employ cleaner fuels made from substitutes such as waste biomass. Also, converting waste biomass to fuel can lower usage of landfills. There are many biomass resources that are suitable for fuel production, out of which groundnut is also a potential feedstock. Groundnut shell biomass was chosen for this study, as it is a waste leftover during shelling of groundnuts for various commercial applications. The procured groundnut shells were converted to oil using pyrolysis process and was distilled. Both the pyrolysis oil and the distilled oil were analyzed using Fourier transform infrared instrument wherein the presence of functional groups such as alcohols, amines, and carboxylic acids were identified.

Abstract The expansion of the internet has made everyone’s personal and professional lives more transparent. There are network security issues because people like sharing resources under the right conditions. Academics have demonstrated significant interest in situation awareness, which includes situation prediction, situation appraisal, and event detection, rather than focusing on the security of a single device in the network. Multi-stage attack forecasting and security situation awareness are two significant issues for network supervisors because the future usually is unknown. Hence, this study suggests combined intuitionistic fuzzy sets and deep neural network (CIFS-DNN) for network security situation prediction. The goal is to provide network administrators with a resource they can use as a point of reference while they formulate and carry out preventive actions in the event of a network assault.

Abstract TOC

Abstract Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value.

Abstract Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions.

Abstract Increasing engine efficiency is essential to reducing emissions, which is a priority for automakers. Unconventional modes such as boosted and highly dilute operation have the potential to increase engine efficiency but suffer from stability concerns and cyclic variability. To aid engineers in designing ignition systems that reduce cyclic variability in such engine operation modes, reliable and accurate spark-ignition models are necessary. In this article, a Lagrangian–Eulerian spark-ignition (LESI) model is used to simulate electrical discharge, spark channel elongation, and ignition in inert or reacting crossflow within a combustion vessel, at different pressures, flow speeds, and dilution rates. First the model formulation is briefly revisited. Then, the experimental and simulations setups are presented.

Abstract The water droplet erosion (WDE) on high-speed rotating wheels appears in several engineering fields such as wind turbines, stationary steam turbines, fuel cell turbines, and turbochargers. The main reasons for this phenomenon are the high relative velocity difference between the colliding particles and the rotor, as well as the presence of inadequate material structure and surface parameters. One of the latest challenges in this area is the compressor wheels used in turbochargers, which has a speed up to 300,000 rpm and have typically been made of aluminum alloy for decades, to achieve the lowest possible rotor inertia. However, while in the past this component was only encountered with filtered air, nowadays, due to developments in compliance with tightening emission standards, various fluids also collide with the spinning blades, which can cause mechanical damage.