Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis

Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis

Zhang, Ming;Huang, Qianwen;Liu, Sihan;Zhang, Yu;
sustainability 2019 Vol. 11 pp. 4362-
229
zhang2019fuelsustainability

Abstract

Accurate estimation of the fuel consumed during aircraft operation is key for determining the fuel load, reducing the airline operating cost, and mitigating environmental impacts. Aerodynamic parameters in current fuel consumption models are obtained from a static diagram extracted from the outcomes of wind tunnel experiments. Given that these experiments are performed in a lab setting, the parameters cannot be used to estimate additional fuel consumption caused by aircraft performance degradation. In addition, wind tunnel experiment results rarely involve the influence of crosswind on fuel consumption; thus, the results could be inaccurate when compared with field data. This study focuses on the departure climbing phase of aircraft operation and proposes a new fuel consumption model. In this model, the relationships between aerodynamic parameters are extracted by fitting quick access recorder (QAR) actual flight data, and the crosswind effect is also considered. Taking QAR data from two airports in China, the accuracy of the proposed model and its transferability are demonstrated. Applying the proposed model, the fuel saving of a continuous climb operation (CCO) compared with the traditional climb operation is further quantified. Finally, how aircraft mass, climbing angle, and different aircraft models could affect the fuel consumption of the climbing phase of aircraft operation is investigated. The proposed fuel consumption model fills gaps in the existing literature, and the method can be used for developing specific fuel consumption models for more aircraft types at other airports.

Citation

ID: 86462
Ref Key: zhang2019fuelsustainability
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
86462
Unique Identifier:
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet