Statistical approach to payload capability forecasting for large commercial aircraft operating payload range limited routes

• Main Author: Poprawa, Stefan
• Other Authors: Dala, Laurent
• Language: en
• Published: University of Pretoria 2020
• Subjects: UCTD; Aeronautical Engineering
• Online Access: http://hdl.handle.net/2263/72847; Poprawa, S 2019, Statistical approach to payload capability forecasting for large commercial aircraft operating payload range limited routes, PhD Thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/72847>

Large commercial aircraft by design typically are not capable of transporting maximum fuel capacity and maximum payload simultaneously. Maximum payload range remains less than maximum range. When an aircraft is operated on a route that may exceed its maximum payload range capability, environmental conditions can vary the payload capability by as much as 20%. An airline’s commercial department needs to know of such restrictions well in advance, to restrict booking levels accordingly. Current forecasting approaches use monthly average performance, at, typically, the 85% probability level, to determine such payload capability. Such an approach can be overly restrictive in an industry where yields are marginal, resulting in sellable seats remaining empty. The analysis of operational flight plans for a particular ultra-long routing revealed that trip fuel requirements are near exclusively predictable by the average wind component for a given route, at a correlation of over 98%. For this to hold, the route must be primarily influenced by global weather patterns rather than localised weather phenomena. To improve on the current monthly stepped approach the average wind components were modelled through a sinusoidal function, reflecting the annual repetitiveness of weather patterns. Long term changes in weather patterns were also considered. Monte Carlo simulation principles were then applied to model the variance around the mean predicted by the sinusoidal function. Monte Carlo simulation was also used to model expected payload demand. The resulting forecasting model thus combines supply with demand, allowing the risk of demand exceeding supply to be assessed on a daily basis. Payload restrictions can then be imposed accordingly, to reduce the risk of demand exceeding supply to a required risk level, if required. With payload demand varying from day of week to seasonally, restricting payload only became necessary in rare cases, except for one particular demand peak period where supply was also most restricted by adverse wind conditions. Repeated application of the forecasting model as the day of flight approaches minimises the risk of seats not sold, respectively of passengers denied boarding. === Thesis (PhD)--University of Pretoria, 2019. === Mechanical and Aeronautical Engineering === PhD === Unrestricted