Research and development in the internal combustion engine (ICE) has been growing progressively. Issues such as air pollution, fuel cost, and market competitiveness have driven the automotive industry to develop and manufacture automobiles that meet new regulation and customers’ needs. The diesel engine has some advantages over the gasoline or spark ignition engine, including higher engine efficiency, greater power output, as well as reliability. Since the early stage of the diesel engine’s development phase, the quest to obtain better atomization, proper fuel supply, and accurate timing control, have triggered numerous innovations. In the last two decades, owing to the development of optical technology, the visualization of spray atomization has been made possible using visual diagnostics techniques. This advancement has greatly improved research in spray evolution. Yet, a more comprehensive understanding related to these aspects has not yet been agreed upon. Diesel spray, in particular, is considered a complicated phenomenon to observe because of its high-speed, high pressure, as well as its high temperature working condition. Nevertheless, several mechanisms have been successfully explained using fundamental studies, providing several suggestions in the area, such as liquid atomization and two-phase spray flow. There are still many aspects that have not yet been agreed upon. This paper comprehensively reviews the current status of theoretical diesel spray and modelling, including some important numerical and experimental aspects.

Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME) composition by developing an ANN model that was trained with 10 different algorithms. To the best of our knowledge, this is the first study to predict the CN of biodiesels using numerous ANN training algorithms utilizing sizeable datasets. Results revealed that the ANN model trained with Levenberg-Marquardt gave the highest prediction accuracy. LM algorithm successfully predicted the CN of biodiesels with the highest correlation and determination coefficient (R = 0.9615, R2 = 0.9245) as well as the lowest errors (MAD = 2.0804, RMSE = 3.1541, and MAPE = 4.2971). Hence, the Cascade neural network trained with the LM algorithm could be considered a promising alternative to the empirical correlations for predicting biodiesel”™s CN.

Conventional gasoline engines suffer from low performance and NOx emissions. Controlled auto-ignition (CAI), sometimes referred to as homogeneous charge compression ignition (HCCI), is a promising concept to solve such problems. CAI has the potential to improve spark ignition (SI) engine fuel economy while at the same time solving the trade-off of NOx-soot emissions found in compression ignition (CI) engines. The CAI engine can reach a fuel economy comparable to that of a conventional diesel engine with ultra-low NOx and negligible soot emissions. However, controlling auto-ignition remains the biggest difficulty that hinders the implementation of CAI as a commercial engine. Research towards a cleaner and more efficient engine is driven by the progressively stringent emission regulation imposed worldwide. Therefore, the CAI was developed to meet the emissions target while maintaining engine performance. CAI works on the principle of lean mixture and auto-ignition. To obtain CAI combustion, the temperatures in the cylinder must be sufficient to initiate auto-ignition. Without the use of a spark plug or injector, the CAI suffers from a direct control mechanism to start the combustion. The most practical approach to controlling the initiation of auto-ignition in CAI is diluting the intake charge by either trapping the residual gas or recirculating the exhaust gas. Both approaches enable the engine to achieve CAI combustion without requiring significant modifications to control the onset of CAI combustion phase.

This study investigates the mechanical properties and microstructure of a novel composite material designed for automatic clutch pads. The natural fiber composite (NFC) consists of coconut fiber and wood powder reinforced with aluminum powder, aiming to increase strength. A series of experimental analyses, including tensile testing, compressive testing, hardness measurement, and microstructural examination, were conducted to thoroughly evaluate the composite. The results indicate a significant improvement in mechanical strength compared to commercial clutch pad materials. Additionally, scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis shows the distribution and interaction of natural fibers and aluminum powder within the composite matrix, shedding light on the reinforcement mechanisms. The tensile strength, compressive strength, elastic modulus, and hardness values increase with aluminum composition from 0 to 15%. However, at 20% composition, a decrease was observed. This research provides valuable insights into the development of high-performance natural composite materials for automatic clutch pads.

Additive manufacturing (AM) has progressed from three-dimensional (3D) printing to four-dimensional (4D) printing, which incorporates the time dimension to enable adaptive responses to external stimuli. Nickel–titanium (NiTi) alloys, a shape memory alloy (SMA) distinguished by their shape memory effect (SME) and superelasticity, have emerged as key materials for 4D printing. This review examines AM techniques for NiTi alloys, focusing on Laser Directed Energy Deposition (LDED), Selective Laser Melting (SLM), and Electron Beam Melting (EBM). Each method is analyzed in terms of process parameters, manufacturing challenges, and medical and aerospace engineering applications. The discussion highlights that the thermomechanical and microstructural characteristics of NiTi require precise process control to ensure optimal functional performance. Demonstrated applications in medical implants and lightweight aerospace components underscore the potential of NiTi-based 4D printing for intelligent, adaptive devices. The remaining challenges include accurate prediction of final shapes, ensuring phase homogeneity, and the need for specialized design software. Overall, this study provides insights into the integration of NiTi alloys with 4D printing and identifies key directions for advancing next-generation AM systems. This review presents a unique process–structure–property–application relationship for NiTi-based 4D printing, demonstrating that control of energy input (150–800 W) and scan rate (>1000 mm s ⁻¹ ) directly influences transformation temperature (Af 10 °C–30 °C) and SME strain recovery (3%–6%).

The chemical composition of Fe-Mn-Si shape memory steel continues to be optimized by researchers since Sato and colleagues first reported its shape memory effect in 1982. This work aimed to elucidate the effect of carbon content on the shape memory effect of Fe-14Mn-4Si-8Ni-10Cr steel subjected to a large pre-strain. The shape memory steels were prepared through a process involving melting, hot rolling, cold drawing, and subsequent solution treatment. The recovery strain was measured in solution-treated steels after a large pre-strain of 36% applied via tensile testing, followed by recovery heating at 600 °C. The results indicated that adding carbon up to approximately 0.22 wt% has a beneficial effect on shape recovery strain; further carbon addition to 0.39 wt% degraded the shape memory effect. The maximum recovery strain of 5.72% was exhibited by the steel containing 0.22 wt% carbon. Notably, the recovery strain of the Fe-14Mn-4Si-8Ni-10Cr-xC shape memory steel tested at a large pre-strain of 36% showed values comparable to those tested at lower pre-strains up to 17% for similar steels reported in the literature.