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 prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
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 differences is also carried out in the research. Furthermore, it provides a granular breakdown of energy consumption data for fully loaded trucks exceeding 31 tons, as specified by national standards. This introduces a novel approach to precise carbon emission measurement in heavy-duty transportation in northwestern China. It also provides a method for establishing an emission mitigation policy that is region-specific on a scientific basis.
This paper demonstrates the sizing and optimization of a hybrid-electric multi-tilt rotor configuration of both conventional and vertical takeoff and landing capabilities. The study uses Parametric Energy-Based Aircraft Configuration Evaluator to design and optimize the aircraft. To explore the design space comprising both discrete and continuous design variables, a genetic algorithm is used for optimization. The design variables are not limited to conventional aero-propulsive parameters such as wing loading, aspect ratio, and disk loading. Battery-related parameters such as the maximum permissible depth of discharge, maximum permissible discharge rate, and the number of parallel strings in a battery pack are also considered in this work to study their impact on aircraft gross weight and fuel consumption. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization framework is used to solve the multi-objective optimization problem, with objectives to minimize the maximum take-off mass and fuel weight. The sensitivity studies showed that higher wing-loading and lower-aspect-ratio designs resulted in lower gross weight. A higher permissible depth of discharge led to lower fuel consumption, despite a slight increase in gross weight. But increasing the number of parallel strings in a battery pack increased gross weight with negligible change in fuel consumption.
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 integrity was compromised at non-optimal I/C ratios, exhibiting pore blockage or cracking. The CV test results confirmed that the electrochemical active surface area (ECSA) reaches a maximum of 40 m2gPt-1 when the I/C ratio is controlled at 0.6. And the EIS tests indicated that the lowest charge transfer impedance. Combined the physical and electrochemical characterization results with I-V curves, it was clear that the proper ratio of the low I/C region benefits the mass transfer and proton conductions. This study provides theoretical and technical support for performance enhancement and has the potential for the large-scale application of low-platinum MEA in fuel cells in the future.
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, will affect the engine oil viscosity. To demonstrate this whilst yielding insights, a precisely controlled engine test cell, running the cold start Worldwide Harmonized Light Duty Transient Cycle (WLTC) for both, a non-hybridized ICE only vehicle and a HEV in charge sustaining mode operation is described. This also has on-line viscosity sensing and oil sampling. Typical data is shared along with engine oil comparisons. For complimentary insights, the impact of the fuel dilution on engine friction was investigated using a novel, precise, fully transient engine friction test rig, which measures gasoline direct injection high pressure fuel-pump friction and engine oil viscosity accurately. The cycle is based on measured data from vehicles tested on a chassis dynamometer. On-line friction data, with oil comparisons is used to show real-time data of the effect of fuel dilution on the frictional energy required, thus CO2 over the full WLTC.
The U.S. auto industry might be getting “more than what they asked for” when it comes to slashing federal fuel economy regulations. That's a quote from Cox Automotive's director of industry insights, Stephanie Valdez Streaty, and reported by multiple outlets. This is a topic you'll find addressed in this month's Supplier Eye (page 6), but it's worth mentioning here just as one more marker in the sand of where things look like they are headed. On February 12th, the U.S. EPA rescinded the scientific finding that greenhouse gas emissions pose a threat to public health. As vehicles are a main source of GHGs, this finding allowed regulators to, well, regulate. The current White House discounts scientific findings it finds inconvenient, and has now weakened the EPA's power to regulate fuel economy standards. News reports say the administration is likely to reduce the current, agreed-to rules requiring 50.4 mpg (4.67 L/100km) new-vehicle fleet average by model year 2031 to just 34.5 mpg (6.82 L/100km).
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