Multi-Objective Optimization of an Aircraft Position Indicator and Actuation System (PIAS) using GPSIA+DS

Gabriel, Okafor Ekene; Kole, Uhuegho O.; Youchao, Sun

doi:10.2174/1872212109666150917011547

Cited by 3 publications

(1 citation statement)

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“…A crossover probability of 0.75 was used in this work as the breeding operator. The choice of 0.75 as the crossover probability is in agreement with ref [17,18]. A random number [0-1] window of same size with the chromosome was generated based on the breeding operator.…”

Section: Crossovermentioning

confidence: 93%

A Study of Airline Fuel Planning Optimization Using R-Ga

Okafor

Uhuegho²,

Manshop³

et al. 2020

JERA

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In this study, airline planning optimization problem based on ferry strategy was considered. Cost was the study objective function subject to forty equality and inequality constraints. Regression analysis as well a genetic algorithm (GA) was used to solve the problem. The mathematical relationship between flight fuel consumption and flight time was established using regression analysis, while GA was used for the optimization. The established mathematical model was used to predict the fuel consumption for the twenty scheduled flight consider based on their respective flight time. The result was found to be satisfactory, as optimal fuel lift plan was achieved in approximately twenty seconds of program run time, as against the large time usually spend using human effort to solve the fuel planning problem. The optimized fuel lift plan was compared with the actual fuel lift plan executed by the airline for the twenty scheduled flight considered. The result revealed thirty percent savings using the optimized plan in comparison to the actual fuel lift plan executed by the airline.

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Section: Crossovermentioning

confidence: 93%

A Study of Airline Fuel Planning Optimization Using R-Ga

Okafor

Uhuegho²,

Manshop³

et al. 2020

JERA

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Failure Mechanisms Driven Reliability Models for Power Electronics: A Review

Okafor

Huitink

2022

Journal of Electronic Packaging

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Miniaturization as well as manufacturing processes that electronics devices are subjected to, often results to increase in operational parameters such as current density, temperature, mechanical load, with potential to induce stresses that may be detrimental to device reliability. Past studies have identified some failure mechanisms common to these devices. Examples of these failure mechanisms include fatigue, electromigration, stress induced voiding, corrosion, conduction filament formation and time dependent dielectric breakdown. While some review activities related to reliability model development based on these failure mechanisms can be easily found in literature, to the best of our knowledge, a single review paper, which captures the reliability model progresses made over the past four decades across these failure mechanisms in comparison with Standards such as Joint Electron Device Engineering Council (JEDEC) and Institute for Printed Circuits (IPC) is to the best of our knowledge lacking. To fill this gap, a detailed review of failure mechanism driven reliability models, with emphasis on physics of failure (PoF) for power electronics was carried out in this paper. Although, other failure mechanisms exist, our review is only limited to fatigue, electromigration, stress induced voiding, corrosion, conduction filament formation and time dependent dielectric breakdown. It was found that, most reliability research modelling efforts are yet to be fully integrated into Standards.

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Landing gear disassembly sequence planning using multi-level constraint matrix ant colony algorithm

Okafor¹,

Jemitola²,

Ubadike³

et al. 2021

Nig. J. Tech.

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The life cycle of most complex engineering systems is greatly a function of maintenance. Generally, most maintenance operation usually requires the removal of failed part. Disassembly sequence planning is an optimization program that seeks to identify the optimal sequence for the removal of the failed part. Most studies in this area usually, use single constraint matrix while implementing varied complex algorithm to identify the optimal sequence that saves time associated with carrying out maintenance operation. The used of single constraint matrix typically has the drawback of computer higher storage requirement as well as time consumption. To address this problem, this study proposes Multi-Level Constraint Matrix Ant Colony Algorithm (MLCMACA). MLCMACA efficiency was validated using complex aircraft landing gear systems in comparison with genetic algorithms. The result shows MLCMACA superior performance from the perspective of reduced search time and faster tracking of optimal disassembly sequence. Hence is recommended for handling of disassembly sequence planning problems.

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Multi-Objective Optimization of an Aircraft Position Indicator and Actuation System (PIAS) using GPSIA+DS

Cited by 3 publications

References 0 publications

A Study of Airline Fuel Planning Optimization Using R-Ga

A Study of Airline Fuel Planning Optimization Using R-Ga

Failure Mechanisms Driven Reliability Models for Power Electronics: A Review

Landing gear disassembly sequence planning using multi-level constraint matrix ant colony algorithm

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