This study investigates the impact of biodiesel on jet engine performance and emissions, specifically focusing on the engine. With increasing pressure to reduce aviation's environmental footprint, alternative fuels such as biodiesel are being considered for their potential to mitigate NOX and CO2 emissions. The study employs a quantitative methodology to analyse the effects of biodiesel blends on engine performance and emission levels. Key research questions include how biodiesel influences NOX emission levels, the relationship between biodiesel blend ratios and CO2 emissions, and whether biodiesel can maintain acceptable engine performance. Additionally, the study examines the quadratic behaviour of NOX emissions in response to increasing biodiesel concentrations and evaluates the feasibility of biodiesel as a sustainable aviation fuel. Using simulation data from 2023, the research identifies key relationships between biodiesel blend ratios, emissions, and performance, demonstrating that biodiesel can effectively reduce NOX emissions, although it results in a proportional increase in CO2 emissions. The study concludes by emphasizing the importance of optimizing biodiesel blends for aviation, advocating for future research to explore real-world applications and long-term performance outcomes.
This article presents a novel approach utilizing qualimetry methods for selecting models and polymodel complexes to automate the process of calculating Earth remote sensing (ERS) data, particularly in the context of analyzing complex natural and technical systems. The proposed methodology is applied to the task of selecting calculation methods for forest sustainability indicators. In scenarios where multiple alternative methods and models can be applied at each stage of data processing, the approach employs multicriteria comparative analysis based on a set of key indicators. These indicators include cost, efficiency (calculation duration), and accuracy (the quality of the calculation result). The solution algorithm is demonstrated through the selection of a method to assess the consequences of forest fires. The results are presented in a table, allowing users to assess the trade-offs between different methods based on partial indicators. This algorithmic approach facilitates the automation of the selection process, simplifying the application of complex ERS data processing methods for end-users. Additionally, the approach expands the potential for scaling ERS data results from smaller to larger forest areas, offering greater flexibility and applicability.
Hybrid electric vehicle (HEV) performance and safety are critical areas of focus in modern automobile technology, with fault detection being a key challenge. Traditional methods often fall short when it comes to detecting complex faults in the HEV powertrain system, as these faults exhibit nonlinear behaviors. This paper introduces a novel data-driven approach for fault detection in HEV powertrains using Kernel Orthogonal Subspace Analysis (KOSA). The KOSA method addresses the limitations of linear Orthogonal Subspace Analysis (OSA) by mapping nonlinear problems to a higher-dimensional space through a kernel function, thereby enabling more effective fault separation. This transformation, combined with the dimensionality reduction capabilities of OSA, allows KOSA to detect complex faults in the powertrain system more efficiently. Experimental results from both a nonlinear model and real-world data from the HEV demonstrate that KOSA outperforms both OSA and Kernel Principal Component Analysis (KPCA) in terms of fault detection accuracy and robustness.
This study investigates the impact of atmospheric pressure on vehicle drivers in mountainous regions, focusing on the Bishkek-Naryn-Torugart international highway in Kyrgyzstan. The highway spans varying altitudes, with control points at Torugart pass (3752 m), At-Bashy (2046 m), and Kemin (1120 m). The study examines how altitude affects blood pressure in drivers, particularly in high-altitude conditions. Blood pressure measurements at these points showed a notable increase at higher elevations. At Torugart pass, 24% of drivers experienced elevated blood pressure readings (140-159/90-99 mmHg), compared to 19% at At-Bashy and 5% at Kemin. Moderate hypertension (160-179/100-109 mmHg) was observed in 7% of drivers at Torugart and 5% at At-Bashy, with none recorded at Kemin. The primary cause of the elevated blood pressure was identified as disruptions to the drivers' work-rest patterns due to the challenging conditions. Based on these findings, the study recommends implementing rest areas along the route and establishing guidelines for driver work schedules to reduce health risks. These measures aim to enhance safety for drivers navigating high-altitude roads in Kyrgyzstan.
This paper investigates the factors affecting human productivity when operating logging machinery, with a specific focus on the impact of training machines and simulators. It evaluates how these tools influence the results of training and examines the psychophysiological traits that affect the precision of guiding the logging machine both on the horizontal plane and through boom extension. The study presents a novel approach for testing these traits using author-developed methods, which were applied to a group of cadets. The results of the tests are compared with those obtained from final examinations required to complete logging machinery operation training. The findings suggest that the author-developed testing methods provide an effective measure of operator precision and productivity in comparison with traditional evaluation methods. Furthermore, the research highlights the role of simulators in improving training outcomes by enhancing operator skills and reducing errors. The paper concludes that using advanced testing techniques can better assess the psychophysiological factors influencing logging machinery operation and contribute to more efficient training programs. This research will be of particular interest to professionals in human-machine interaction and logging machine training, providing valuable insights into optimizing training processes and improving operator performance in the forestry industry.