scholarly journals TOWARDS MEETING THE IATA-AGREED 1.5% AVERAGE ANNUAL FUEL EFFICIENCY IMPROVEMENTS BETWEEN 2010 AND 2020: THE CURRENT PROGRESS BEING MADE BY U.S. AIR CARRIERS

Aviation ◽  
2020 ◽  
Vol 23 (4) ◽  
pp. 123-132
Author(s):  
Kit Sum Cho ◽  
Guanying Li ◽  
Nicholas Bardell
Keyword(s):  
Fuel Efficiency ◽  
Aircraft Engine ◽  
Efficiency Improvement ◽  
The World ◽  
Efficiency Performance ◽  
Fuel Efficiency Improvement ◽  
Overall Performance ◽  
Using Data ◽  
The One ◽  
Air Carriers

The purpose of this paper is to see if airlines in general, and U.S. air-carriers in particular, are meeting their IATA-agreed 1.5% average annual fuel efficiency improvements between 2010 and 2020. To assess the fuel efficiency performance, a quantitative analysis was performed using data provided by ICAO, IATA and the U.S. Bureau of Transportation Statistics (BTS) Form 41 Schedules P 12(a) and T-2. The metric used to assess fuel efficiency is the one advanced by ICAO, namely Litres per Revenue Tonne Kilometre performed. Trends are examined over an extended timeframe to establish annual fuel efficiency improvements. The findings show that the overall performance of U.S. air-carriers from 2010 to 2018 has just met IATA’s 1.5% target with a 1.52% year-upon-year annual fuel efficiency improvement, with domestic operations showing a greater level of improvement than international operations. Such performance suggests that the U.S.A, and by inference, the rest of the world, are just likely to meet their IATA target by 2020. This achievement has largely been made possible through industry’s tremendous efforts to enhance aircraft engine technologies, implement operational improvements, and reduce airframe weight through the extensive application of composite materials.

Fuel efficiency improvement on a business jet using a camber morphing winglet concept

2020 ◽  
Vol 96 ◽  
pp. 105542 ◽  
Author(s):  
João Paulo Eguea ◽  
Gabriel Pereira Gouveia da Silva ◽  
Fernando Martini Catalano
Keyword(s):  
Fuel Efficiency ◽  
Efficiency Improvement ◽  
Business Jet ◽  
Fuel Efficiency Improvement

The Kitchen Sink

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Multiple Regression ◽  
Historical Period ◽  
Scatter Plot ◽  
Consumer Spending ◽  
Explanatory Variables ◽  
The World ◽  
Scatter Plots ◽  
Using Data ◽  
The One ◽  
Over Time

Back in the 1980s, I talked to an economics professor who made forecasts for a large bank based on simple correlations like the one in Figure 1. If he wanted to forecast consumer spending, he made a scatter plot of income and spending and used a transparent ruler to draw a line that seemed to fit the data. If the scatter looked like Figure 1, then when income went up, he predicted that spending would go up. The problem with his simple scatter plots is that the world is not simple. Income affects spending, but so does wealth. What if this professor happened to draw his scatter plot using data from a historical period in which income rose (increasing spending) but the stock market crashed (reducing spending) and the wealth effect was more powerful than the income effect, so that spending declined, as in Figure 2? The professor’s scatter plot of spending and income will indicate that an increase in income reduces spending. Then, when he tries to forecast spending for a period when income and wealth both increase, his prediction of a decline in spending will be disastrously wrong. Multiple regression to the rescue. Multiple regression models have multiple explanatory variables. For example, a model of consumer spending might be: C = a + bY + cW where C is consumer spending, Y is household income, and W is wealth. The order in which the explanatory variables are listed does not matter. What does matter is which variables are included in the model and which are left out. A large part of the art of regression analysis is choosing explanatory variables that are important and ignoring those that are unimportant. The coefficient b measures the effect on spending of an increase in income, holding wealth constant, and c measures the effect on spending of an increase in wealth, holding income constant. The math for estimating these coefficients is complicated but the principle is simple: choose the estimates that give the best predictions of consumer spending for the data used to estimate the model. In Chapter 4, we saw that spurious correlations can appear when we compare variables like spending, income, and wealth that all tend to increase over time.

scholarly journals Methodology of Excavator System Energy Flow-Down

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 951 ◽  
Author(s):  
Kwangman An ◽  
Hyehyun Kang ◽  
Youngkuk An ◽  
Jinil Park ◽  
Jonghwa Lee
Keyword(s):  
Energy Consumption ◽  
Measurement System ◽  
Energy Flow ◽  
Fuel Efficiency ◽  
Experimental Methodology ◽  
Efficiency Improvement ◽  
Operating Modes ◽  
Actual Equipment ◽  
Fuel Efficiency Improvement ◽  
System Energy

Due to the strengthening of air-quality regulations, researchers have been investigating methods to improve excavator energy efficiency. Many researchers primarily conducted simulation studies employing mathematical models to analyze the energy consumption of excavator systems, which is necessary to examine the fuel efficiency improvement margin and the improvement effect. However, to effectively study the improvement of excavator efficiency, the real-time energy consumption characteristics must be examined through simulations and analyses of actual equipment-based energy consumption. Accordingly, this study establishes an energy flow-down model for the entire excavator system based on actual equipment tests. A measurement system is built to measure the required data, thereby establishing an experimental methodology for modeling each component. This paper presents an excavator system energy flow-down methodology that integrates the energy flow-down model, measurement system, and experimental methodology. This methodology was applied to dig and dump operations, and the energy consumption characteristics were analyzed. An analysis of the operating modes indicates that 59.8% of the total fuel energy was consumed in the engine system, 17% in the hydraulic system, and 23.2% in the hydraulic actuation systems. The methodology can be used to help analysis of the fuel efficiency improvement margin under various conditions.

Use of a PPS Sensor in Evaluating the Impact of Fuel Efficiency Improvement Technologies on the Particle Emissions of a Euro 5 Diesel Car

2014 ◽  
Author(s):  
Stavros Amanatidis ◽  
Leonidas Ntziachristos ◽  
Zissis Samaras ◽  
Chariton Kouridis ◽  
Kauko Janka ◽  
...  
Keyword(s):  
Fuel Efficiency ◽  
Efficiency Improvement ◽  
Particle Emissions ◽  
Fuel Efficiency Improvement ◽  
The Impact
Sign in / Sign up

Export Citation Format

Share Document