Search

This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions. [ABSTRACT FROM AUTHOR]

Motivated by a critical issue of airline planning process, this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions. Following the route network scheme and generated flight timetables, aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management. This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints, rules, and regulations. Considering multiple locations for airline maintenance and crew bases, we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering (AMRCR) to achieve the minimum airline cost. One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights. Due to the fact that disruption scenarios are expressed discretely with a specified probability, and we provide adjustable decisions under disruption to deal with this disruption risk, we provide a Two-Stage Scenario-Based Robust Optimization (TSRO) model. In this model, here-and-now or first-stage variables are the initial resource assignment. Furthermore, to adapt itself to different disruption scenarios, the model considers some adjustable variables, such as the decision to cancel the flight in case of disruption, as wait-and-see or second-stage variables. Considering the complexity of integrated models, and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance, we apply the column and row generation (CRG) method that iteratively considers the disruption scenarios. The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability. To evaluate the proposed TSRO model, which solves the AMRCR problem in an integrated and robust manner, five Key Performance Indicators (KPIs) like Number of delayed/canceled flights, Average delay time, and Average profit are taken into account. As key results driven by conducting a case study, we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models. The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems. However, for large-scale instances the proposed TSRO model falls short in terms of computational efficiency. Conversely, the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level. [ABSTRACT FROM AUTHOR]

Preventive maintenance is performed to sustain the safe and reliable operation of industrial equipments. In order to plan preventive maintenance or evaluate the existing maintenance plan, the failure behavior of the system must be modeled. The failure behavior of a repairable system is modeled utilizing counting processes. In this study failure behavior of propellers belonging to a small aircraft fleet is modeled. First non-parametric estimate of population mean cumulative function (MCF) is obtained. MCF helps discovering the special features of the maintenance data. The parametric model selection depends on the result of the trend analysis of the time between failures. In the second part of the study trend analysis is performed on propeller maintenance data. Based on the trend analysis two prospect parametric models are selected. Reliability measures are estimated using both models and results are compared to evaluate the existing preventive maintenance plan. [ABSTRACT FROM AUTHOR]

This paper analyzes approaches to developing models for the technical exploitation (TE) of aircraft within operational management framework. The operational management contour of the TE of the airline's AC fleet is considered as a TE process consisting of a sequence of flight routes, which, in turn, comprise a sequence of flights between airports in the airline network during the calendar interval when the aircraft are in this contour. We present a model that evaluates the capacity for aircraft recovery at the network airports of a hypothetical airline (dubbed "RAF") if the need so arises, utilizing the probabilistic and temporal characteristics of the airports used for aircraft recovery. [ABSTRACT FROM AUTHOR]

In the past, the discourse on State subsidies involving commercial aircraft was dominated by allegations of the support granted to aircraft manufacturers Airbus and Boeing, and the respective disputes brought before the Dispute Settlement Body of the World Trade Organization (WTO). While the focus has until now been narrowly confined to Airbus and Boeing and, thus, the European Union (EU) and the USA and the competition between the two, new large commercial aircraft manufacturers have entered the market—notably, those based in China, Russia, and Japan. This article examines the semi-recent 'truce' between Airbus and Boeing and what has followed—namely, the introduction of State investment mechanisms and strategies in the EU, USA, and China for commercial airlines seeking to 'green' their aircraft fleets. Against the backdrop of airlines purchasing aircraft and operating services on a global market, this article exposes the lack of harmonization in approach to State subsidies globally and questions the extent to which the State and the market—and, indeed, States or groups of States—perform a balancing act in aircraft financing, an area fraught with complex political and commercial realities. [ABSTRACT FROM AUTHOR]

The main task of operators is to maintain the airworthiness of aircraft by implementing the technologies outlined in the sets of documents attached to standard products. Engines, the main source of energy for flight, usually work in conjunction with many systems, each of which, in turn, is accompanied by instructions and manuals to ensure operability. In accordance with accepted practice, most of the disaggregated information is provided in text form. These forces you to remember the necessary data or constantly search for it in dozens of updated and replaced pages of the provided technologies, or create your own convenient techniques that allow you to conduct parametric analyzes and develop decisions on control actions to correct the technical condition. Using examples of power plants with turboprop engines, approaches to the development and application of step-bystep unified graphical generalizations of processes and control parameter values, combined with indications of characteristics setting elements, are proposed. For the full cycle of work, the article proposes the most convenient, applicable to different types of power plants, unified method for constructing compact representations of all information in a generalized form. Recommendations have been developed for monitoring, adjusting characteristics and making decisions on the prospects for further operation. The methods have been tested under operating conditions, having confirmed their convenience and effectiveness at different stages of operation, and can be recommended for inclusion in Flight Operations Manuals created by airlines in relation to the range of operating conditions of their aircraft fleet. [ABSTRACT FROM AUTHOR]

Maintaining and deploying a fleet of aircraft with limited resources and various mission requirements is both immensely challenging and of primary importance. Traditional preventive maintenance methods are static and inflexible and are not equipped to consider the complex dynamics of aircraft (e.g., wear and age), which may lead to low fleet availability and high maintenance costs. In this paper, we propose an integrated learn-then-optimize framework for conditionbased predictive maintenance scheduling to support daily flight and maintenance planning by fusing data from multiple onboard sensors. The paradigm first predicts the remaining useful life for components of aircraft by using deep learning techniques, then models the fleetlevel optimization as a constrained mixed-integer programming problem that captures different failure modes of aircraft and the available maintenance facilities. We also propose valid inequalities to improve the computational efficiency of the optimization model. Finally, we conduct a series of simulated experiments to validate the performance of the proposed predictive maintenance model. The numerical results show that the predictive maintenance model outperforms the traditional preventive maintenance model with respect to the mission accomplishment rate, aircraft availability rate, and cost effectiveness. [ABSTRACT FROM AUTHOR]

This national research study examined the effects operationally limited environments have on primary flight training at the collegiate level. The study utilized the operationally limited environment of the COVID-19 pandemic to quantify the amount of training that was done pre, post, and during the pandemic to evaluate the effects operationally limited environments have on primary flight training at the collegiate level to help aid in planning for future possible disturbances and successful self-mitigation measures. Data was collected from 10 FAA Part 141 collegiate flight schools that offered a four-year bachelor's degree and operated their own fleet of aircraft were analyzed for the findings and conclusions in this study. [ABSTRACT FROM AUTHOR]

Health monitoring and fault detection of complex aircraft systems are paramount for ensuring reliable and efficient operation. The availability of monitoring data from modern aircraft onboard sensors provides a wealth of big data for developing deep learning-based fault detection methods. However, aircraft onboard systems typically have limited labeled fault samples and large amounts of unlabeled data. To better utilize the information contained in limited labeled fault samples, a deep learning-based semi-supervised fault detection method is proposed, which leverages a small number of labeled fault samples to enhance its performance. A novel sample pairing strategy is introduced to improve algorithm performance by iteratively utilizing fault samples. A comprehensive loss function is employed to accurately reconstruct normal samples and effectively separate fault samples. The results of a case study using real data from a commercial aircraft fleet demonstrate the superiority of the proposed method over existing techniques, with improvements of approximately 16.7% in AP, 9.5% in AUC, and 19.2% in F1 score. Ablation studies confirm that performance can be further improved by incorporating additional labeled fault samples during training. Furthermore, the algorithm demonstrates good generalization ability. [ABSTRACT FROM AUTHOR]

The modern civil aircraft engine is a type of highly complex engineering system in design, manufacturing, and life-cycle management. They are constantly operated under extreme and critical conditions, and yet, high reliability and safety are top priorities in the civil aviation industry. To ensure top performance and efficiency in operations, engines follow a modular design. This article intends to apply the data-driven cluster analysis to real-life operation data for aircraft engine fleets, which provides a module maintenance priority recommendation solution to increase the efficiency of operations and best use of the engine values. [ABSTRACT FROM AUTHOR]

Reliability prediction is crucial for aircraft maintenance and spare part inventory decisions. These predictions are made based on operational data collected by fleet operators or design life estimates provided by aircraft suppliers. Purely data-driven predictions have limited use especially when the fleet is young, hence the data is scarce. In this case, design life estimates are used for predicting reliability often by assuming a constant failure rate. This strong assumption is not necessarily valid for all components. This paper proposes a Bayesian Network (BN) modelling framework that systematically combines design life estimates, operational data, and expert judgement for reliability prediction of aircraft subsystems. The proposed BN adjusts the design life estimates based on expert judgement regarding supplier and manufacturing quality and revises it based on operational data. We used the BN to predict the reliability of a large aircraft fleet by using failure and maintenance data provided by a large fleet operator. We compared the predictive performance of the BN to using only data-driven approaches and to using only design life estimates provided by the aircraft supplier. The BN model provides consistently accurate reliability predictions compared to design-life estimates and purely data-driven approaches especially when the available data is scarce. [ABSTRACT FROM AUTHOR]

Analysis of maintenance data for a repairable system provides information about the failure behavior of the system. Such information is needed for determining preventive maintenance and retirement policy for the system. Parametric and non-parametric models can be used for analysis. Parametric models require more assumptions about the failure process of the systems under consideration compared to non-parametric models. To verify these assumptions statistical expertise needed. The purpose of this paper is to show that in practice non-parametric estimator of mean cumulative function can be utilized easily to model the failure behavior of a fleet. Mean cumulative function estimates the mean number of failures as function of operating hours. The method is exemplified on the attitude indicator units of a commercial aircraft fleet. Sampling uncertainty of the estimates is quantified by normal approximation confidence intervals. [ABSTRACT FROM AUTHOR]

The KC-46A Pegasus, a Boeing 767 (B767) commercial derivative aircraft (CDA), is a key part of the United States Air Force's (USAF) efforts to modernize their aging tanker fleet. The Department of Defense (DoD) and the USAF have heavily emphasized the desire and need for Condition-Based Maintenance Plus (CBM+) to improve aircraft maintenance programs such as the KC-46A. This study reviews existing CBM+ practices on B767 and related aircraft fleets at Delta Air Lines to identify initial steps for implementing CBM+ in the KC-46 maintenance program. Specifically, comparative vacuum monitoring (CVM) sensors are proposed for KC-46A structural health monitoring (SHM) as a Federal Aviation Administration (FAA)-certified CBM+ technology. As demonstrated at Delta Air Lines Technical Operations (Delta TechOps), CVM sensors satisfy the technological, procedural, financial, and regulatory requirements to advance KC-46A SHM and serve as a template for future CBM+ initiatives. [ABSTRACT FROM AUTHOR]

The data measured by the pitot tube (PT) is related to airspeed indication and flight safety. However, there are few methods to solve the problem of failure prediction and predictive maintenance of PT. This study proposes a method for predicting the remaining useful life (RUL) of the PT based on dynamic operating data. First, an exposure index (EI) characterizing the severity level of operating conditions is proposed based on multiple dynamic operating and environmental parameters, then a cumulative exposure model (CEM) is developed to calculate the cumulative exposure. Dynamic covariate information is incorporated into the time‐to‐failure distribution of PT through the CEM, the RUL distribution of the individual PT is then obtained by Monte Carlo simulation. A case study is carried out based on actual data of a commercial aircraft fleet and shows that the proposed CEM can effectively use historical aircraft environmental information and truncated failure data of the components. The relative error of the remaining life prediction is substantially improved compared to the traditional reliability analysis methods, which means that the proposed model can provide more reliable RUL results based on historical operating conditions, providing better support for PT risk assessment and condition‐based maintenance decisions. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Disciplines Economics & Administrative Scienves Studies is the property of International Journal of Disciplines in Economics & Administrative Sciences Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Range anxiety of electric vehicles (EVs) still poses a major barrier in their adoption in the logistics operations despite the advancements in the battery technology. The need for recharging the battery during the day brings additional complexities to the operational planning of commercial EVs in last mile deliveries. The driving range of an EV may vary according to different factors including ambient temperature, weight, speed, acceleration/deceleration, and the road profile. In this study, we revisit the well-known electric vehicle routing problem with time windows by taking into account the weight of the load carried. Cargo weight may play a crucial role in the operational efficiency of the EVs since it may affect the energy consumption significantly. We first present two alternative mathematical formulations of the problem and test their performances on small-size instances that can be solved using a commercial solver. Next, we develop a matheuristic approach that integrates an optimal repair procedure in the large neighbourhood search method and validate its performance. Then, we present an extensive numerical study to investigate the influence of load on the routing decisions. Our results show that cargo weight may create substantial changes in the route plans and fleet size, and neglecting it may cause severe disruptions in service and increase the costs. [ABSTRACT FROM AUTHOR]

There has been renewed interest in developing commercial supersonic transport aircraft due to the increased overall demands by the public for air travel, the aspiration for more intercontinental travel, and the desire for shorter flight times. Various companies and academic institutions have been actively considering the designs of such supersonic aircraft. As these new designs are developed, the environmental impact on ozone and climate of these fleets need to be explored. This study examines one such proposed commercial supersonic fleet of 55‐seater that is projected to fly at Mach 2.2, corresponding to cruise altitudes of 17–20 km, and which would burn 122.32 Tg of fuel and emit 1.78 Tg of NOx each year. Our analyses indicate this proposed fleet would cause a 0.74% reduction in global column ozone (∼2 Dobson Units), which is mainly attributed to the large amounts of nitrogen oxides released in the atmosphere from the supersonic aircraft. The maximum ozone loss occurs at the tropics in the fall season, with a reduction of −1.4% in the total column ozone regionally. The stratospheric‐adjusted radiative forcing on climate from this fleet was derived based on changes in atmospheric concentrations of ozone (59.5 mW/m2), water vapor (10.1 mW/m2), black carbon (−3.9 mW/m2) and sulfate aerosols (−20.3 mW/m2), resulting in a net non‐CO2, non‐contrail forcing of 45.4 mW/m2, indicating an overall warming effect. Plain Language Summary: With the general public's increased demand for air travel, a desire for more intercontinental travel with shorter flight times, there has been renewed interest in developing commercial supersonic transport aircraft. Various companies and academic institutions have been actively considering the design of such a supersonic aircraft. As these new designs are developed, the environmental impact of these realistic fleets on ozone and climate needs to be explored. This study looked at one such supersonic fleet, expected to fly at Mach 2.2, corresponding to a cruising altitude of 17–20 km, that would burn 122.32 Tg of fuel and emit 1.78 Tg of NOx per year. Our analysis shows that this proposed fleet would result in a 0.74% reduction in global columnar ozone (approximately 2 Dobson units), mainly due to the large atmospheric release of nitrogen oxides by supersonic aircraft. The impact on climate from this fleet was derived to have a net forcing of 45.4 mW/m2, indicating an overall warming effect. Key Points: A proposed fleet of 55‐seater supersonic aircraft is projected to fly at Mach 2.2 (17–21 km altitude) and burn 122.32 Tg of fuel each year and emit 1.78 Tg of NOx per yearThis proposed fleet would cause a 0.74% reduction in global column ozone (∼2 Dobson Units), mainly attributed to the nitrogen oxides emissionsClimate impact from changes in atmospheric ozone, water vapor and aerosols from this fleet result in a net non‐CO2 and non‐contrail climate forcing of 45.4 mW/m2 [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame. [ABSTRACT FROM AUTHOR]

Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of resource-constrained scheduling problems under uncertainty and is often solved using a mixed integer linear programming (MILP) formulation. While a MILP framework is very promising, the problem size can scale exponentially with the number of considered aircraft and considered tasks, leading to significantly high computational costs. The most recent advances in artificial intelligence have demonstrated the capability of deep reinforcement learning (DRL) algorithms to alleviate this curse of dimensionality, as once the DRL agent is trained, it can achieve real-time optimization of the maintenance schedule. However, there is no guarantee of optimality. These comparative merits of a MILP and a DRL formulation for the aircraft fleet maintenance scheduling problem have not been discussed in the literature. This study is a response to this research gap. We conduct a comparison of a MILP and a DRL scheduling model, which are used to derive the optimal maintenance schedule for various maintenance scenarios for aircraft fleets of different sizes in a disruptive environment, while considering health prognostics and the available resources for the execution of each task. The quality of solutions is evaluated on the basis of four planning objectives, defined according to real airline practice. The results show that the DRL approach achieves better results with respect to scheduling of prognostics-driven tasks and requires less computational time, whereas the MILP model produces more stable maintenance schedules and induces less maintenance ground time. Overall, the comparison provides valuable insights for the integration of health prognostics in airline maintenance practice. [ABSTRACT FROM AUTHOR]

Modern production management patterns, in which multi-unit (e.g. an aircraft fleet) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision-making. To schedule a good maintenance plan, not only does the individual aircraft maintenance have to be considered, but also the maintenance of the other aircraft in fleet have to be taken into account. Condition-based maintenance (CBM) is a maintenance scheme which recommends maintenance decisions according to equipment status collected by condition monitor over a period of time. Evaluating risk is necessary for scheduling appropriate maintenance, avoiding aircraft losses and maintaining the repairable components at a high-reliable state. In this paper, a novel two-models-fusion framework is proposed to predict the reliability of aircraft structures subjected to fatigue loads. Furthermore, we established a fleet maintenance decision-making model based on CBM for the maintenance of fatigue structures. The model concentrates on both minimising fleet maintenance cost and maximising fleet availability, overcoming the shortcomings of traditional fleet CBM research, which has simply focused on one or the other of these parameters. Finally, a case study regarding a fleet of 10 aircraft is conducted, and the results indicated that the proposed model efficiently generates outcomes that meet the schedule requirements. [ABSTRACT FROM AUTHOR]

In this paper, we discuss fleet size decisions of an equipment rental firm. The availability of the rental equipment depends on the fleet size of the firm and has a direct impact on its profitability. In our setting, we allow for partial backordering, reneging, and finite commitment capacity. Moreover, we explicitly consider the breakdown possibility of the available equipment fleet. We develop an efficient recursive algorithm to solve the underlying two‐dimensional stochastic single‐player model. Our algorithm determines the global optimal fleet size of the firm for the same reneging and equipment return rates. Our extensive managerial insights quantify the behavior of various performance measures in the single‐player model with regard to repair performance of the firm, customer impatience level, traffic intensity, and equipment rental revenue. We demonstrate that by applying our model to a real case, there is a potential of more than a 4% (400,000 USD) increase in total daily profits. Extending our model to a two‐player game, we propose an approximation heuristic to derive closed‐form solutions to estimate equilibrium fleet sizes under complete information. Using our heuristic as the initial solution, we develop a simulation model to determine the exact equilibrium fleet sizes and draw a detailed comparison between the two‐player and single‐player models. [ABSTRACT FROM AUTHOR]

The maintenance of carrier-based aircraft is a critical factor restricting the availability of aircraft fleets and their capacity to sortie and operate. In this study, an aeronautical maintenance and repair task scheduling problem for carrier-based aircraft fleets in hangar bays is investigated to improve the maintenance efficiency of aircraft carrier hangar bays. First, the operational process of scheduling aeronautical maintenance tasks is systematically analyzed. Based on maintenance resource constraints and actual maintenance task requirements, a wave availability index and load balance index for the maintenance personnel are proposed for optimization. An aeronautical maintenance task scheduling model is formulated for carrier-based aircraft fleets. Second, model abstraction is performed to simulate a multi-skill resource-constrained project scheduling problem, and an improved teaching-learning-based optimization algorithm is proposed. The algorithm utilizes a serial scheduling generation scheme based on resource constraint advancement. Finally, the feasibility and effectiveness of the modeling and algorithm are verified by using simulation cases and algorithm comparisons. The improved teaching-learning-based optimization algorithm exhibits improved solution stability and optimization performance. This method provides theoretical support for deterministic aeronautical maintenance scheduling planning and reduces the burden associated with manual scheduling and planning. [ABSTRACT FROM AUTHOR]

Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado's San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China has gradually shifted its geopolitical focus from land-based control to seabased developments out of both security and economic considerations. Marked by the official launch of its aircraft carriers a decade ago, China has dedicated resources to building up its navy as a blue water force rather than the offshore defensive force that it had primarily been until that point. Meanwhile, by serving as the pillar of sea power, these aircraft carriers will play more important roles in China's naval development, ultimately reshaping the global strategic picture of sea power. This article aims to explore the influence of Chinese aircraft carriers to maritime order of the South China Sea and strategic pattern of global sea power by analyzing the Chinese aircraft carrier fleet's operational design and the reconstruction of China's naval strategy. The presence of Chinese aircraft carriers in the South China Sea and their inherent combat capabilities has inevitably casted a subversive light on the region and brought the attention of the United States' and other regional actors' own activities and strategies for the region. This research has great significance for understanding China's grand strategic conception and practice on the South China Sea issue as well as the construction of a new maritime order in the context of China-U.S. competition. [ABSTRACT FROM AUTHOR]