Browse Topic: Fuel consumption
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance
In the field of measuring carbon emissions from road traffic, the carbon emission factor method has remarkable advantages in terms of standardization, operational simplicity, and adaptability. Backed by the IPCC international standard framework, this method offers convenient access to a dynamic factor database and incorporates an adaptive adjustment mechanism for real-world scenarios, such as technological advancements and regional disparities. Against this backdrop, this study employs the carbon emission factor method to establish refined measurement models based on load capacity and fuel consumption, respectively. These models are then applied to quantify carbon emissions from trucks on specific sections of the G30 highway in Xinjiang. The load-based model calculates emissions by integrating truck axle weight and driving distance, while the fuel-based model analyzes fuel consumption data in conjunction with driving mileage. A comparison of the two models in terms of measurement
Hybrid electric vehicles (HEVs) with an increasing level of electrification, are becoming a major part of the global energy transition. To achieve lower engine tailpipe exhaust emissions and improve total fuel consumption, typically the HEV control system expertly and frequently switches between the internal combustion engine and electric motor drive, with multiple stops and restarts of the internal combustion engine (ICE). As a consequential result of this switching, are typically slower or even incomplete engine warm-up times, depending on the engine speed, load pattern and run time of the vehicle drive cycle. Along with the speed and load transient control, the engine stop and start processes are also challenging to control, with respect to cold start fuel and combustion by-products entering the oil. Consequently, contamination enters the engine oil but may not completely leave. These effects are highly transient over the drive cycle. Contaminants and in particular, fuel dilution
With the growth of energy demand, fuel cells as efficient and clean energy devices, have attracted increasing attention. However, the high cost of membrane electrode assembly (MEA) restricts their large-scale application. Therefore, reducing the platinum usage and improving performance have become key research point. In this work, MEA was prepared and excellent performance of 1.52 W·cm-2 was achieved at a low platinum loading. The influence of different ionomer/carbon (I/C) ratio on the performance of fuel cells was systematically investigated. It was found that the performance of the MEA was the highest when the I/C ratio is 0.6. Quantifying hydrophilic and hydrophobic characteristics of catalyst layers with varying ionomer contents revealed that the proton conduction efficiency is optimal when the I/C ratio is 0.6. This balance established efficient proton conduction pathways, from the results of proton conduction impedance testing. SEM analysis demonstrated that pore structure
Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with
The Mahindra XUV 3XO is a compact SUV, the first-generation of which was introduced in 2018. This paper explores some of the challenges entailed in developing the subsequent generation of this successful product, maintaining exterior design cues while at the same time improving its aerodynamic efficiency. A development approach is outlined that made use of both CFD simulation and Coastdown testing at MSPT (Mahindra SUV proving track). Drag coefficient improvement of 40 counts (1 count = 0.001 Cd) can be obtained for the best vehicle exterior configuration by paying particular attention to: AGS development to limit the drag due to cooling airflow into the engine compartment Front wheel deflector optimization Mid underbody cover development (beside the LH & RH side skirting) Wheel Rim optimization In this paper we have analyzed the impact of these design changes on the aerodynamic flow field, Pressure plots and consequently drag development over the vehicle length is highlighted. An
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