scholarly journals A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7559
Author(s):  
Lisha Li ◽  
Shuming Yuan ◽  
Yue Teng ◽  
Jing Shao
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Influencing Factors ◽  
Carbon Emission ◽  
Estimation Method ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Fuel Price ◽  
Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

A Multidisciplinary Approach for the Comprehensive Assessment of Integrated Rotorcraft–Powerplant Systems at Mission Level

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Ioannis Goulos ◽  
Fakhre Ali ◽  
Konstantinos Tzanidakis ◽  
Vassilios Pachidis ◽  
Roberto d'Ippolito
Keyword(s):  
Environmental Impact ◽  
Fuel Consumption ◽  
Comprehensive Assessment ◽  
Pollutant Emissions ◽  
Operational Performance ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Nox Emissions ◽  
Reactor Model ◽  
Stirred Reactor

This paper presents an integrated methodology for the comprehensive assessment of combined rotorcraft–powerplant systems at mission level. Analytical evaluation of existing and conceptual designs is carried out in terms of operational performance and environmental impact. The proposed approach comprises a wide-range of individual modeling theories applicable to rotorcraft flight dynamics and gas turbine engine performance. A novel, physics-based, stirred reactor model is employed for the rapid estimation of nitrogen oxides (NOx) emissions. The individual mathematical models are implemented within an elaborate numerical procedure, solving for total mission fuel consumption and associated pollutant emissions. The combined approach is applied to the comprehensive analysis of a reference twin-engine light (TEL) aircraft modeled after the Eurocopter Bo 105 helicopter, operating on representative mission scenarios. Extensive comparisons with flight test data are carried out and presented in terms of main rotor trim control angles and power requirements, along with general flight performance charts including payload-range diagrams. Predictions of total mission fuel consumption and NOx emissions are compared with estimated values provided by the Swiss Federal Office of Civil Aviation (FOCA). Good agreement is exhibited between predictions made with the physics-based stirred reactor model and experimentally measured values of NOx emission indices. The obtained results suggest that the production rates of NOx pollutant emissions are predominantly influenced by the behavior of total air inlet pressure upstream of the combustion chamber, which is affected by the employed operational procedures and the time-dependent all-up mass (AUM) of the aircraft. It is demonstrated that accurate estimation of on-board fuel supplies ahead of flight is key to improving fuel economy as well as reducing environmental impact. The proposed methodology essentially constitutes an enabling technology for the comprehensive assessment of existing and conceptual rotorcraft–powerplant systems, in terms of operational performance and environmental impact.

scholarly journals Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Longlong Leng ◽  
Yanwei Zhao ◽  
Zheng Wang ◽  
Hongwei Wang ◽  
Jingling Zhang
Keyword(s):  
Fuel Consumption ◽  
Carbon Emission ◽  
Time Windows ◽  
Travel Distance ◽  
Low Carbon ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Total Cost ◽  
Location Routing ◽  
Self Adaptive

In this paper, we consider a variant of the location-routing problem (LRP), namely, the regional low-carbon LRP with reality constraint conditions (RLCLRPRCC), which is characterized by clients and depots that located in nested zones with different speed limits. The RLCLRPRCC aims at reducing the logistics total cost and carbon emission and improving clients satisfactory by replacing the travel distance/time with fuel consumption and carbon emission costs under considering heterogeneous fleet, simultaneous pickup and delivery, and hard time windows. Aiming at this project, a novel approach is proposed: hyperheuristic (HH), which manipulates the space, consisted of a fixed pool of simple operators such as “shift” and “swap” for directly modifying the space of solutions. In proposed framework of HH, a kind of shared mechanism-based self-adaptive selection strategy and self-adaptive acceptance criterion are developed to improve its performance, accelerate convergence, and improve algorithm accuracy. The results show that the proposed HH effectively solves LRP/LRPSPD/RLCLRPRCC within reasonable computing time and the proposed mathematical model can reduce 2.6% logistics total cost, 27.6% carbon emission/fuel consumption, and 13.6% travel distance. Additionally, several managerial insights are presented for logistics enterprises to plan and design the distribution network by extensively analyzing the effects of various problem parameters such as depot cost and location, clients’ distribution, heterogeneous vehicles, and time windows allowance, on the key performance indicators, including fuel consumption, carbon emissions, operational costs, travel distance, and time.

scholarly journals Estimation of Vehicle Carbon Emissions in China Accounting for Vertical Curve Effects

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yaping Dong ◽  
Jinliang Xu
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Vertical Profile ◽  
Carbon Emission ◽  
Emission Rate ◽  
Field Tests ◽  
Emission Model ◽  
Low Carbon ◽  
Conversion Model ◽  
Longitudinal Slope

Predicting vehicle carbon emissions on vertical curve sections can provide guidance for low-carbon vertical profile designs. Given that the influence of vertical curve design indicators on the fuel consumption and CO2 emissions of vehicles are underexplored, this study filled this research gap by establishing a theoretical carbon emission model of vehicles on vertical curve sections. The carbon emission model was established based on Xu’s vehicle energy conversion model, the conversion model of energy, fuel consumption, and CO2 emissions. The accuracy of the theoretical carbon emission model and the CO2 emission rules on vertical curve sections were verified by field test results. Field tests were carried out on flat sections, longitudinal slope sections, and various types of vertical curve sections, with five common types of vehicles maintaining cruising speed. The carbon emission rate effects on the vertical curve are closely related to the gradient and irrelevant of the radius. On the vertical profile composed with downhill/asymmetric/symmetrical vertical curve with a gradient greater than the balance gradient, the carbon emission rate is determined by the gradient and radius. The influence of the gradient on carbon emissions of vehicle on these vertical profiles was more significant than the radius. The radius is irrelevant to the carbon emission rate on the other forms of vertical profile. These results may benefit highway designers and engineers by providing guidelines regarding the environmental effects of highway vertical curve indexes.

scholarly journals Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Haiying Wang ◽  
Xinping Wang ◽  
Chao Wang ◽  
Jian Xu
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Compression Test ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Wavelet Neural Network ◽  
Accurate Estimation ◽  
Short Term ◽  
Data Estimation ◽  
Concrete Cube

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e1, and the root mean square error, e2, for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.

scholarly journals Association of Carbon Emissions and Circular Curve in Northwestern China

2019 ◽  
Vol 11 (4) ◽  
pp. 1156 ◽  
Author(s):  
Yaping Dong ◽  
Jinliang Xu ◽  
Menghui Li ◽  
Xingli Jia ◽  
Chao Sun
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Research Work ◽  
Field Tests ◽  
Quantitative Model ◽  
Northwestern China ◽  
Low Carbon ◽  
Emission Data ◽  
Circular Curve

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design.

Accounting and Characteristics Analysis of CO2 Emissions in Chinese Cities

2020 ◽  
Vol 08 (01) ◽  
pp. 2050004
Author(s):  
Xingmin WANG ◽  
Jing WU ◽  
Zheng WANG ◽  
Xiaoting JIA ◽  
Bing BAI
Keyword(s):  
Emission Intensity ◽  
Carbon Emission ◽  
North China ◽  
Spatial Separation ◽  
Yangtze River Delta ◽  
Policy Implications ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Carbon Policy ◽  
Per Capita

Accurate estimation of CO2 emissions is a prerequisite for scientific low-carbon emission policymaking. Based on 20 types of energy consumption data at the prefecture level in China, this paper re-estimates the CO2 emissions of 198 prefecture-level cities in 2016 by using the method of carbon emission coefficient. The spatial pattern and scale characteristics are analyzed, and the conclusions are as follows: (1) Overall, China’s urban CO2 emissions show a certain degree of spatial separation in terms of the total amount, per capita emissions, and emission intensity. Cities with the highest CO2 emissions in China are mainly concentrated in North China, East China and Chongqing, while cities with the highest per capita CO2 emissions and emission intensity are mainly concentrated in Northwest and North China. (2) Different types of cities have different CO2 emission characteristics. Resource-based cities have a higher total amount and emission intensity; tourism and underdeveloped cities both have lower values; while super-large-sized cities and many very-large-sized cities have higher CO2 emissions, but their emission intensities are usually lower; and no obvious rules are found in other cities. (3) Spatial analysis shows that cities with higher CO2 emissions are clustered. The Beijing–Tianjin–Hebei region, the Yangtze River Delta region, Shandong Province, and Shanxi–Henan–Anhui resource-producing areas are the agglomeration areas of high-emission cities. (4) Scale analysis shows that the characteristics of CO2 emissions at different scales are different. Provincial-level research can help to identify the environmental impact and total effect of carbon emissions, while urban-scale research is helpful to explore the diversity and phases of cities. Finally, based on the main conclusions of this study, the corresponding urban low-carbon policy implications are drawn.

scholarly journals Evaluation Model of Low-Carbon Circular Economy Coupling Development in Forest Area Based on Radial Basis Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chang Liu
Keyword(s):  
Neural Network ◽  
Circular Economy ◽  
Carbon Emission ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Structure ◽  
Low Carbon ◽  
Ecological Structure ◽  
Swarm Algorithm

In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. After an in-depth analysis of the particle swarm algorithm, an improved particle swarm algorithm is proposed to improve the search accuracy and capability of the algorithm by nonlinearly adjusting the inertia weights and introducing the average extreme value factor, in response to the problems of premature convergence and poor search capability that appear in the particle swarm algorithm. Through the analysis and evaluation of the interaction between industrial ecosystem and carbon emission, the main influencing factors of carbon emission are identified, and the size and magnitude of the influence of economic growth, industrial structure, energy intensity, and energy structure on carbon emission are determined; the current situation of the industrial ecological structure is evaluated, and the direction of optimization and adjustment of industrial economic structure, energy structure, and ecological structure is clarified. We construct a multidimensional multiconstraint multimodel industrial ecological structure optimization prediction model, set the development scenarios of economy and society, and optimize the prediction of low-carbon industrial ecological structure in forest areas; based on the simulation analysis of the prediction results, we propose the direction of industrial ecological structure adjustment and the path of industrial ecological system construction.

Estimating Carbon Emission of Tourism Transportation and Accommodation in China

2014 ◽  
Vol 962-965 ◽  
pp. 1327-1331
Author(s):  
Zhong Hong Yang ◽  
Yang Yang ◽  
Xiao Hui Chen
Keyword(s):  
Emission Reduction ◽  
Carbon Emission ◽  
Fossil Fuels ◽  
Ghg Emissions ◽  
Civil Aviation ◽  
Low Carbon ◽  
Great Demand

The tourism’s growing consumption of energy, especially in transportation and accommodation, and its great demand on fossil fuels has important implications for GHG emissions. From the perspective of low carbon and emission reduction, the paper discussed the carbon emission of China’s tourism. Based on the previous studies, the paper calculated carbon emission of 31 Chinese provinces from tourism transportation and accommodation. The results show that: 1) Tourism transportation contributes most to carbon emission, accounting for 89.4% of the total, while tourism accommodation accounts for 10.6%. 2) In terms of transportation, civil aviation contributes most, accounting for 44.71% of the carbon emission of tourism transportation. 3) In terms of accommodation, three-star hotels contribute most, accounting for 45.3% of the carbon emission of tourism accommodation. 4) Cities of Guangzhou, Beijing and Shanghai make the largest contribution to the carbon emission among 31 provinces. Based on the above, it is reasonable that Tourism Sectors should make targeted policies to promote the development of low-carbon tourism according to the structure of tourism transportation and accommodation.

scholarly journals A Model for Estimating Passenger-Car Carbon Emissions that Accounts for Uphill, Downhill and Flat Roads

2020 ◽  
Vol 12 (5) ◽  
pp. 2028
Author(s):  
Jinliang Xu ◽  
Yaping Dong ◽  
Menghua Yan
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Mechanical Energy ◽  
Maximum Error ◽  
Low Carbon ◽  
Passenger Cars ◽  
Proposed Model ◽  
Longitudinal Slope ◽  
Main Factor

The geometric longitudinal slope line of a given road significantly effects the carbon emissions of vehicles traversing it. This study was conducted to explore the carbon emission rules of passenger cars on various highway slopes. The law of conservation of mechanical energy, the first law of thermodynamics and the vehicle longitudinal dynamics theory were utilized to determine the influence of slope design indicators on fuel consumption. The energy conversion, fuel consumption, and carbon emission models of passenger cars on a flat straight road, uphill road, and downhill road sections were derived accordingly. Two types of passenger cars were selected for analysis. A field test was carried out to verify the proposed model where the vehicle maintained a cruise speed on flat straight road, uphill road and downhill road with equal gradient and mileage, and continuous longitudinal slope to gather fuel consumption data. The proposed model showed strong accuracy and a maximum error of 9.97%. The main factor affecting the vehicle’s carbon emissions on the continuous longitudinal slope was found to be the average gradient. For a round-trip longitudinal slope with a small gradient, the main factor affecting the vehicle’s carbon emissions is speed: higher speed results in higher carbon emissions. The results of this study are likely to provide the data for support and a workable reference for the low-carbon highway design and operation.

A Novel Particle Swarm Neural Network Model to Optimize Aircraft Fuel Consumption

2013 ◽  
Vol 694-697 ◽  
pp. 3370-3374 ◽  
Author(s):  
Ying Jie Zhang ◽  
Jian Xing Xu
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Particle Swarm Optimization Algorithm ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Learning Ability ◽  
Wind Tunnel Testing ◽  
The Neural Network ◽  
Aircraft Fuel ◽  
Neural Network System

Due to the complex relations among the various factors, the nonlinear calculation of aircraft fuel consumption is very difficult. The purpose of this paper is to present a simplified method to estimate aircraft fuel consumption using a novel particle swarm neural network. Fuel consumption information obtained directly from QAR recorded flight data is trained by the neural network. The method can avoid the high cost of flight testing and wind tunnel testing. An improved particle swarm optimization algorithm embeds neural network topology to replace the network BP learning algorithm. The experimental results demonstrate that the proposed method integrates a new particle swarm neural network system, and significantly improves the system's learning ability and prediction of evolutionary effects.

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