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The growing demand for environmentally sustainable technologies has intensified research into high-frequency motors and transformers, where Fe-Si soft magnetic alloys play a crucial role due to their exceptional magnetic properties. However traditional manufacturing methods limit geometric freedom and degrade alloy performance capabilities, hindering the development of advanced applications. To overcome these issues, laser powder bed fusion (L-PBF) offers a promising route by enabling complex shapes, yet mitigating eddy current losses remains challenging at elevated frequencies. As a preliminary study, this work investigates the effect of adjusting laser beam profiles (Gaussian versus ring) on the eddy current losses and magnetic properties of Fe-Si alloys produced by L-PBF. A microstructural analysis demonstrates that the ring beam produces a broader, shallower melt pool, fostering coarser grains and a pronounced < 100 > crystallographic texture along the build direction. These microstructural features significantly reduce the coercivity and core loss of the material, strongly highlighting their effectiveness in addressing the aforementioned challenges related to the eddy current. Additionally, the ring beam increased the build rate by approximately 108%, clearly illustrating enhanced productivity without compromising quality. This study confirms that modifying the laser beam profile effectively controls eddy current losses, enhancing the magnetic performance of Fe-Si soft magnetic alloys fabricated via additive manufacturing.

Because ammonia is easier to store and transport over long distances than hydrogen, it is a promising research direction as a potential carrier for hydrogen. However, its low ignition and combustion rates pose challenges for running conventional ignition engines solely on ammonia fuel over the entire operational range. In this study, we attempted to identify a stable engine combustion zone using a high-pressure direct injection of ammonia fuel into a 2.5 L spark ignition engine and examined the potential for extending the operational range by adding hydrogen. As it is difficult to secure combustion stability in a low-temperature atmosphere, the experiment was conducted in a sufficiently-warmed atmosphere (90 ± 2.5 °C), and the combustion, emission, and efficiency results under each operating condition were experimentally compared. At 1500 rpm, the addition of 10% hydrogen resulted in a notable 20.26% surge in the maximum torque, reaching 263.5 Nm, in contrast with the case where only ammonia fuel was used. Furthermore, combustion stability was ensured at a torque of 140 Nm by reducing the fuel and air flow rates.

Aerosol jet printing (AJP) is a promising printing technology in the printed electronics industry, offering significant advantages by enabling the creation of highly customized patterns with a wide range of materials. However, broader application of AJP technology still faces challenges due to issues with printed line quality. According to previous research, inconsistencies in line width could lead to functional failures in the printed circuits. While various process parameters can be manipulated to customize line widths, such approaches often overlook the impact of line thickness on overall quality. This can lead to sub-optimal printed line quality within the design space. Furthermore, external factors such as temperature fluctuations and solvent evaporation can affect the printing process, in turn impacting the reliability and performance of the final electronic components. Therefore, optimizing key parameters and enhancing print quality in the AJP process is essential for improving the electrical functionality of the produced devices. This paper presents an AI-driven framework for optimizing printing quality in AJP. The proposed approach begins with Latin Hypercube Sampling for the initial experimental design. Bayesian Neural Networks (BNNs) are then utilized to model the printed line morphology, taking advantage of their ability to provide uncertainty estimates in predictions. The BNN models subsequently are integrated with a multi-objective genetic algorithm, which systematically identifies optimal process parameters that balance line width customization and line thickness maximization in a cost-effective manner. Furthermore, a convolutional neural networks model is developed for real-time monitoring of the printing process, enabling early detection of anomalies and continuous evaluation of process performance. Finally, experimental results demonstrate the validity of the developed approach for enhancing printing performance and anomaly detection in AJP.

In this study, in order to convert a 2.4 L reciprocating gasoline engine into a hydrogen engine an experimental device for supplying hydrogen fuel was installed. Additionally, an injector that is capable of supplying the hydrogen fuel was installed. The basic combustion characteristics, including torque, were investigated by driving the engine with a universal engine control unit. To achieve stable combustion and maximize output, the intake and exhaust valve opening times were changed and the excess air ratio of the mixture was controlled. The changes in the torque, excess air ratio, hydrogen fuel, and intake airflow rate, were compared under low engine speed and high load (wide open throttle) operating conditions without throttling. As the intake valve opening time advanced at a certain excess air ratio, the intake air amount and torque increased. When the opening time of the exhaust valve was retarded, the intake airflow rate and torque decreased. The torque and thermal efficiency decreased when the opening time of the intake and exhaust valve advanced excessively. The change of the mixture condition’s excess air ratio did not influence the tendency of the torque variation when the exhaust valve opening time and torque increased, and when the mixture became richer and the intake valve opening time was fixed. Under a condition that was more retarded than the 332 CAD condition, the torque decreased by about 2 Nm with the 5 CAD of intake valve opening time retards. The maximum torque of 138.1 Nm was obtained at an optimized intake and the exhaust valve opening time was 327 crank angle degree (CAD) and 161 CAD, respectively, when the excess air ratio was 1.14 and the backfire was suppressed. Backfire occurred because of the temperature increase in the combustion chamber rather than because of the change in the fuel distribution under the rich mixture condition, where the other combustion control factors were constantly fixed from a three-dimensional (3D) computational fluid dynamics (CFD) code simulation.

Despite efforts to use ammonia as a fuel, there remain problems with low combustion speeds and high unburned ammonia (NH3) emissions. Therefore, methods to compensate for slow combustion speeds and stabilize combustion have been studied. This study aims to analyze how increasing the compression ratio affects engine performance to enhance thermal efficiency and reduce unburned emissions in a high-pressure ammonia direct injection spark-ignited engine. In addition, by applying a high-flow-rate (HFR) injector, an improvement in the combustion of ammonia fuel and exhaust gas emissions is observed through changes in the air–fuel mixture formation of high-pressure directly injected ammonia fuel. Compared with the existing compression ratio, the incomplete combustion loss due to unburned NH3 increases significantly, and the thermal efficiency does not increase under an increased compression ratio. When HFR injectors are applied with an increase in the compression ratio, the net work increases by 4.7%, as incomplete combustion and energy losses of fuel are reduced by reducing the amount of unburned NH3.

Directed energy deposition (DED) is a metal additive manufacturing (AM) process where material is deposited layer by layer using a focused energy source. This study investigates the effects of hexagonal boron nitride (hBN) particles as solid lubricant additives in the DED process. AISI P21 tool steel was applied as the base material, with hBN added at 0.2 wt.% and 0.4 wt.% fractions. Geometrical analysis showed that P21/hBN composites had better shape accuracy and reduced over-deposition compared to pure P21, due to improved heat distribution. Tribological testing confirmed that hBN improved lubrication, reducing the coefficient of friction, though wear track depth remained consistent across samples. Electron Backscattered Diffraction (EBSD) analysis revealed grain growth with increasing hBN content, enhancing thermal stability. Overall, hBN improved part quality and lubricating performance.

This report details our experimental study investigating particulate matter (PM) emissions from a diesel generator fueled with wood pyrolysis oil (WPO)–butanol blended fuel for electricity generation. Particle number-size distributions and PM mass concentrations from diesel, n-butanol, and WPO-butanol blended fuels were investigated via aerosol measurements using a fast mobility particle sizer and an aerosol monitor with three generator outputs (0, 3.3, and 6.6 kWe). For the n-butanol and WPO-blended fuels, the total number concentrations of exhaust particles were higher than that of conventional diesel combustion; however, the PM mass was observed to be nearly zero for all the engine operating conditions due to the higher number concentration in the nuclei mode. The morphology of the exhaust particles was investigated by analyzing transmission electron microscopy (TEM) micrographs. The morphology of the particles was drastically changed according to the test fuels and engine loads. Two types of particles were observed, including soot and coke shaped particles. These results were directly related to the immaturity of incipient soot particles due to the different physical properties and chemical compositions of the fuels.

Gasoline compression ignition (GCI) engines have been considered a promising technology for achieving diesel-like efficiency with less NOx and soot emissions than diesel engines. In recent research, naphtha has emerged as a suitable fuel for GCI owing to its octane number and its potential to reduce CO2 and production costs. The present work develops a multicomponent surrogate fuel model for heavy and light naphtha fuels by matching their distillation profiles and covering the measured concentrations of the various hydrocarbon classes and properties. The developed naphtha multicomponent surrogate model shows better prediction results than the primary reference fuel (PRF) surrogate model and exhibits good agreement with the measured data. The performance of naphtha in compression ignition (CI) combustion modes is investigated using the surrogate model and engine experiments. Naphtha fuels under CI combustion modes show better soot, HC, and CO emissions than diesel owing to the longer ignition delay and higher burned gas temperature. Moreover, as naphtha fuels are less affected by the injection pressure, naphtha CI engines appear to operate at a lower injection pressure than diesel engines.

The Civilian Access Control Zone (CACZ), south of the Demilitarized Zone (DMZ) separating North and South Korea, has functioned as a unique bio-reserve owing to restrictions on human use. However, it is now increasingly threatened by damaged land and slope failures. In this study, a machine-learning-based method was used to assess slope stability by introducing the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) approaches. These classification models were trained and evaluated on 393 slope stability cases from 2009 to 2019 to assess slope stability in the northern area of the Civilian Control Line, South Korea. For comparison, the performance of these classification models was measured by considering the accuracy, Cohen’s kappa, F1-score, recall rate, precision, and area under the ROC curve (AUC). Furthermore, 14 influencing factors (slope, vegetation, structure conditions, etc.) were considered to explore feature importance. The evaluation and comparison of the results showed that the performance of all classifier models was satisfactory for assessing the stability of the slope, the ability of LR was validated (accuracy = 0.847; AUC = 0.838), and XGBoost proved to be the most efficient method for predicting slope stability (accuracy = 0.903; AUC = 0.900). Among the 14 influencing factors, the external condition was the most important. The proposed supervised learning method offers a promising method for assessing slope status, may be beneficial for government agencies in early-stage risk mitigation, and provides a database for efficient restoration management.

Aerosol Jet Printing (AJP) offers high resolution and flexible working distances, making it a promising technology for the customization of complex electronic devices. However, devices fabricated through the AJP process often suffer from reduced electrical performance due to limited control over printed line width, which constrains its applicability in advanced electronic manufacturing. Consequently, achieving high precision in line width control is of paramount importance for optimizing AJP technology. In this research, a machine learning framework is proposed to enable rapid modeling of printed line width. The framework considers sheath gas flow rate, carrier gas flow rate, and print speed as input variables, with line width as the target output. Three representative machine learning algorithms – support vector regression, artificial neural networks, and XGBoost – were employed to develop predictive models. The modeling performance of these algorithms was systematically compared using four conventional evaluation metrics. Ultimately, the optimal machine learning model identified through this process was selected for the rapid modeling of printed line width in the AJP process.