Preliminary development of an onboard weight and balance estimator for commercial aircraft

Fuel management is an important issue in aviation for safety and efficiency reasons. This work develops an alternative solution based on an artificial intelligent technique to the fuel distribution management problem in commercial aircrafts. The fuel flow control amongst tanks during the flight is addressed. A fuzzy management system is implemented that decides the best fuel distribution based on safety criteria (keeping engines fed) and dynamic stability (placing the centre of gravity at the appropriate position), amongst other specifications. Expert knowledge is used to define the rules of the fuel fuzzy control, taking into account that the dynamics of the system changes, whilst fuel is being consumed. It has been simulated on a real long‐range‐type commercial aircraft with satisfactory results regarding stability, even in the case of internal malfunction (in pipes, pumps, or valves), and with external disturbances (engine failure). The knowledge‐based fuzzy control is able to maintain the centre of gravity position within the stability and manoeuvrability margins along the flight. Besides, the intelligent control strategy minimizes the action of the actuators, providing some advantages to the control solution.

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“…Dealing with the balance of an airplane, in Chaves et al (2018), a novel approach to on-board in-flight weight estimation system is presented.…”

Section: Related Workmentioning

confidence: 99%

“…Dealing with the balance of an airplane, in Chaves et al (2018), a novel approach to on‐board in‐flight weight estimation system is presented. Simple flight mechanics in combination with statistics allowed for the identification of the weight and the position of the CG, using available historical data of an Airbus A320 fleet.…”

Section: Related Workmentioning

confidence: 99%

Management and intelligent control of in‐flight fuel distribution in a commercial aircraft

Plaza

Santos

2022

Expert Systems

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“…In the work of Jumat and Abdullah (2018), a baggage load planning system for a Boeing 737-800 aircraft is considered to obtain a good CG position for improved fuel consumption[4]. Chaves, Silvestre, and Gamboa (2018) presented an approach to onboard in-flight weight and balance estimation systems for the identification of weight and CG position using cruise angle of attack, Mach number, and elevator deflection values[5]. Zhao et al (2021) constructed a mixed integer programming model to maximize the total payload and minimize the CG deviation and proposed a new set of CG envelope constraints[6].The position of the aircraft's center of gravity (CG) during operation is primarily influenced by factors such as the aircraft's empty weight CG; fuel quantity; and the distribution of passengers, cargo, baggage, and other relevant elements.…”

mentioning

confidence: 99%

Uncertainty Analysis of Aircraft Center of Gravity Deviation and Passenger Seat Allocation Optimization

Zhao,

Xiao

2024

Mathematics

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The traditional method of allocating passenger seats based on compartments does not effectively manage an aircraft’s center of gravity (CG), resulting in a notable divergence from the desired target CG (TCG). In this work, the Boeing B737-800 aircraft was employed as a case study, and row-based and compartment-based integer programming models for passenger allocation were examined and constructed with the aim of addressing the current situation. The accuracy of CG control was evaluated by comparing the row-based and compartment-based allocation techniques, taking into account different bodyweights and numbers of passengers. The key contribution of this research is to broaden the range of the mobilizable set for the aviation weight and balance (AWB) model, resulting in a significant reduction in the range of deviations in the center of gravity outcomes by a factor of around 6 to 16. The effectiveness of the row-based allocation approach and the impact of passenger weight randomness on the deviation of an airplane’s CG were also investigated in this study. The Monte Carlo method was utilized to quantify the uncertainty associated with passenger weight, resulting in the generation of the posterior distribution of the aircraft’s center of gravity (CG) deviation. The outcome of the row-based model test is the determination of the range of passenger numbers that can be effectively allocated under different TCG conditions.

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Weight Estimation of Aircraft in Flight by Sensor Fusion with Revised Forward–Backward Smoother

Ishikawa

Naruoka

Ninomiya

et al. 2022

Lecture Notes in Electrical Engineering

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Preliminary development of an onboard weight and balance estimator for commercial aircraft

Cited by 9 publications

References 13 publications

Management and intelligent control of in‐flight fuel distribution in a commercial aircraft

Management and intelligent control of in‐flight fuel distribution in a commercial aircraft

Uncertainty Analysis of Aircraft Center of Gravity Deviation and Passenger Seat Allocation Optimization

Weight Estimation of Aircraft in Flight by Sensor Fusion with Revised Forward–Backward Smoother

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