Cosmic radiation exposure in air traffic grows with flight altitude, geographical latitude and flight time. For future high-speed intercontinental point-to-point travel, the trade-off between reduced flight time and enhanced dose rate at higher flight altitudes is investigated. Various representative (partly) hypersonic cruise missions are considered and in dependence on solar activity the integral route dose is calculated for envisaged flight profiles and trajectories. Our results are compared to those for corresponding air connections served by present day subsonic airliners. During solar maximum, we find a significant reduction in route dose for all considered high-speed missions compared to the subsonic reference. However, during solar minimum, comparable or somewhat larger doses result on transpolar trajectories with (partly) hypersonic cruise at Mach 5. Both solar activity and routing are hence found to determine, whether passengers can profit from shorter flight times in terms of radiation exposure, despite of altitude-induced higher dose rates. Yet, aircrews with fixed number of block hours are always subject to larger annual doses, which in the considered cases take values up to five times the reference. We comment on the implications of our results for route planning and aviation decision-making in the absence of radiation shielding solutions.

Purpose A reliable and safe operation of fuel cells (FCs) is imperative for their application in aviation, especially within the main powertrain. Moreover, performance and lifetime requirements for technical and economic viability are demanding compared to their stationary or road transportation counterparts, while the operating conditions are considered challenging. Prognostics and health management (PHM) could represent a powerful tool for enhancing reliability, durability and performance by detecting, predicting and/or mitigating relevant degradation and failure mechanisms. Against this backdrop, the authors consider it of high relevance to obtain an understanding of the effectiveness of PHM approaches for polymer electrolyte fuel cells (PEFCs) for future aircraft applications, which represents the aim of this paper. Design/methodology/approach In this study, the authors first discuss application relevant failure modes, review state-of-the-art PHM approaches and, consecutively, assess the potential of FC control strategies for aviation. Aiming for a tangible, comparable metric for this initial assessment, the authors apply a published remaining useful life prediction method to load profiles for a range of aviation-specific applications. Findings The authors’ analysis shows significant potentials for lifetime improvement by (partial) avoidance of high power operation and rapid load change through control strategies. Tapping into these theoretical potentials, however, requires significant developments in the field of PEFC PHM and a focus on aviation specific degradation and performance testing. Originality/value The novelty of this study lies in creating an understanding of the potential of avoiding or preventing certain degradation modes by means of PHM in the PEFC specifically in aviation applications.

Prescriptive Maintenance strategies are emerging as potential next level of reliability and maintenance best practice. Likely outcomes of maintenance alternatives and their effects on e.g. cost and safety are comparatively evaluated by exploiting various sources of data, knowledge and models. By this means, optimized courses of actions are recommended to quickly resolve problems and to automate Maintenance, Repair and Overhaul (MRO) decisions. In this work, the key question is pursued as to how their dependability and potential business advantage can be assessed and improved in the presence of uncertainty and variability of various decision-influencing factors such as degradation and maintenance model parameters and cost sources. For this purpose, a step-by-step procedure to optimal solution prescription and potential / risk assessment is developed based on a probabilistic approach to cost-benefit analysis and on the definition of relevant metrics. By the help of a Wiener process degradation model capable of implementing random effects of imperfect repairs and a Monte Carlo simulation, its value is illustrated by a use case example – repair / replacement decision support in the aeronautical context. The probabilistic approach not only allows to determine, which decision option promises the higher profit and is thus preferred, but also with which risk and potential cost disadvantage it is associated. Furthermore, it uncovers, where higher-quality data or information, can gainfully reduce result uncertainty and hence be assigned a monetary value. It is argued that the presented approach could give industry practitioners directions for identifying and optimizing business cases for Prescriptive Maintenance, by pointing at which sources of data or information are particularly valuable and hence justify dedicated investments for acquiring it. The relevance of the results is discussed specifically with reference to emerging digitized and automated repair processes as well as more generally in the context of future data-trading schemes.

Predictive maintenance approaches leveraging integrated knowledge, fleet-wide data and machine-learning techniques allow for earlier warnings on impeding failures and for higher accuracy in remaining useful life predictions compared with traditional prognostics. However, in case relative to correctly predicted maintenance needs, missed detections or false alarms occur too often, follow-up costs, e.g. due to cascading effects or unnecessary inspection effort, can outweigh the advantages. Here, we show that a general cost-benefit analysis based on the Receiver Operating Characteristics (ROC) curve of a failure prediction algorithm allows deducing application-specific requirements on the failure prediction quality for achieving a net benefit. Moreover, various prediction algorithms can be compared and optimized regarding cost-efficiency. The value of the approach is demonstrated by an application example in aircraft engine maintenance showing that for reducing unscheduled engine removals by (more) accurate prediction of turbine blade failures, maximal cost-saving potentials of up to 16 Mio $ emerge per mature-run, widebody engine and per Mean-Time Between Removals (MTBRs). Here, realistic, literature-based assumptions on various costs, failure probability and algorithm performance were incorporated and varied within sensible limits. As a further key result, the value of data for predictive maintenance purposes is impressively demonstrated. Compared with the net benefit achievable by failure prediction of a pure physics-based damage accumulation model and by turbine operating data, an up to 6 Mio. $ higher cost-saving potential per MTBRs was shown to emerge from a literature-based hybrid approach fusing physics and further sources of data, e.g. on manufacturing, geography and environment as well as customer and inspection information by means of machine learning techniques. Generalized applications of the presented cost-benefit analysis approach e.g. to optimize costs associated with engine workscope planning or other system maintenance are discussed.