Flight Sequences Research Articles

Resilience assessment and recovery of airport departure flights under severe weather

In order to ensure the overall performance of the airport under severe weather, scientifically evaluate the resilience of the airport's flight operations, improve flight recovery capabilities, and alleviate the impact of severe weather effectively. This article first gives the definition of airport departure flight operation. Starting from the performance of the airport departure flight operation system, it analyzes flight departure delay time, total departure delay time, departure flight normality rate and departure airport flight operation system comprehensive resilience index four indicators to evaluate the resilience changes of the system under severe weather conditions; put forward the concept of the performance recovery strategy of the airport departure flight operation system, and established the corresponding resilience model, and used the genetic algorithm to optimize the sequence of the delayed departure flights; finally, take the "721" heavy rain event in Beijing Capital International Airport in 2012 as an example to analyze the data, obtain the performance index and resilience index of the Capital Airport under the influence of heavy rain, and use the optimized departure flight schedule to compare and analyze the changes in airport departure flight operating system performance and resilience level. The results indicate that: under the influence of heavy rain, the comprehensive resilience index of the airport departure flight operation system decreased from 0.4573 to 0.0628 , and increased to 0.2223 after the rainstorm decreased; after the flight optimization and sequencing was carried out in the recovery stage of the airport departure flight operation performance, the total number of departure flights decreased. The delay time is reduced by 24.85%, the airport performance recovery speed is increased by 13.89%after optimization, and the minimum resilience index of the optimized airport departure flight operation system is increased by 13.38%, and under the same heavy rain, the system performance is given priority to restore to the initial state, indicating that the evaluation Indicators are valid.

Optimization of multi-path approach flight sequencing based on rolling horizon control

Arrival flight sequencing optimization is an effective method to improve the landing efficiency of incoming flights and reduce flight delays. Based on this, with the goal of maximizing landing efficiency, combined with multi-runway and multi-route selection, and considering the actual waypoint restrictions, a mixed integer programming model for multi-path and multi-runway integrated arrival flight sequencing optimization is proposed. In order to solve the real-time problem of large-scale flight sequencing calculation, a multi-waypoint rolling horizon control algorithm is proposed. The terminal area of Guangzhou Baiyun International Airport is used as an example for verification, and the actual arrival flight data is used to carry out calculation experiments. In terms of wake turbulence safety interval, the RECAT-CN operation standard is used. The calculation results show that when the number of small-scale flights (23 flights) is small, the maximum landing time of the proposed model is 55 s earlier than the first-come-first-served method and 271 s earlier than the non-optimized one; when the number of large-scale flights (104 flights) is large, the solver alone cannot find a feasible solution within 3600 s, and the proposed algorithm finds a solution within 128.65 s. The proposed model and algorithm are effective and can be applied to actual flight scheduling optimization.

Analysis of Approaches to Assessing Flight Delays Due to Technical Issues at Airline Network Airports Within the Operational Management Framework

ABSTRACT 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.

The Cloud Indicator: A novel algorithm for automatic detection and classification of clouds using airborne in situ observations

Airborne in situ observations of aerosols and clouds provide relevant data sets crucial to improving the understanding of cloud microphysical processes and reducing uncertainties connected to future climate predictions. However, the selection and classification of cloud sequences in such data sets can be very time-consuming if done manually, and criteria used by the community are numerous and prone to misclassification, especially when coarse aerosol particles (> 1 μm diameter) are present.In this study, we present the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The Cloud Indicator was developed using data from three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016–2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017), and FIREX-AQ (Fire Influence on Regional to Global Environments Experiment and Air Quality; 2019). The algorithm utilizes size distribution measurements, combined with measurements of relative humidity and temperature, to automatically detect flight sequences in clouds and classify the cloud type. As an additional criterion for the Cloud Indicator, we established the cloud-aerosol volume factor fCA to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning thereby reducing misclassifications. The Cloud Indicator algorithm was developed with data from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) which was calibrated with a novel calibration procedure – introduced in this study - that results in a refractive index independent calibration and works also in the field. However, the Cloud Indicator algorithm is not restricted specifically to the CAPS and thus allows its application to a variety of in situ instruments that measure coarse aerosol and cloud particle size.Case studies from ATom and A-LIFE demonstrate the ability of the Cloud Indicator to precisely screen (airborne) in situ data sets for clouds. The algorithm separates the data set into cloud-free periods, Aerosol-Cloud Transition Regime (ACTR), liquid clouds, clouds in the Mixed-Phase Temperature Regime (MPTR), and cirrus clouds. The unique ability of the Cloud Indicator to successfully differentiate between layers of enhanced coarse-mode aerosol concentrations and clouds is demonstrated by measurements in a complex mixture of clouds embedded into a mineral dust layer during the A-LIFE. The empirically derived parameter thresholds of the Cloud Indicator are in good agreement with values in the literature.

Francisella salimarina infection in farmed cobia (Rachycentron canadum) and dusky grouper (Epinephelus marginatus) in Brazil: A case report.

This study aimed to identify and characterize isolates of Francisella salimarina associated with an outbreak on a marine fish farm in Brazil and to analyse their genetic variability and antimicrobial susceptibility. In 2021, diseased cobias (Rachycentron canadum, n = 10) and dusky groupers (Epinephelus marginatus, n = 10) were sampled and subjected to bacteriological and pathological examinations. The isolates obtained were morphologically and biochemically characterized and identified by matrix-assisted laser desorption/ionization-time of flight (MALDI-ToF) and 16S rRNA gene sequencing. The genetic diversity of these isolates was analysed using repetitive extragenic palindromic-polymerase chain reaction (REP-PCR). Antimicrobial susceptibility was assessed using the disk diffusion technique. Macroscopically, the fish presented skin ulcerations, ocular lesions, hepatomegaly and splenomegaly. A pleomorphic, gram-negative, catalase- and oxidase-positive bacterium was isolated from seven cobias and two groupers. The 16S rRNA gene sequences showed >99% coverage and identity with other deposited sequences of F. salimarina. The results of the biochemical analysis corresponded to these bacterial species. Histologically, granulomas were observed in the spleen, liver and heart of the cobias (n = 6), and necrotizing and fibrinous dermatitis and myositis were identified in some groupers (n = 2). The isolates exhibited the same banding pattern when REP-PCR was performed, indicating that they were clonally related. Finally, the antibiogram test, no inhibition halo was observed for amoxicillin and trimethoprim-sulfamethoxazole. To our knowledge, this is the first report of F. salimarina infection in cobias and dusky groupers.

Characterization of the airborne aerosol inlet and transport system used during the A-LIFE aircraft field experiment

Abstract. Atmospheric aerosol particles have a profound impact on Earth's climate by scattering and absorbing solar and terrestrial radiation and by impacting the properties of clouds. Research aircraft such as the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) Falcon are widely used to study aerosol particles in the troposphere and lower stratosphere. However, transporting a representative sample to the instrumentation inside the aircraft remains a challenge due to high airspeeds and changing ambient conditions. In particular, for high-quality coarse-mode aerosol measurements, knowledge about losses or enhancements in the aerosol sampling system is crucial. In this study, the sampling efficiency of the aerosol inlet aboard the Falcon research aircraft is characterized for the first time with state-of-the art in situ measurements including sizing instruments operated behind the Falcon aerosol inlet and mounted at the aircraft wing not affected by the aerosol inlet. Sampling efficiencies were derived for different true airspeed ranges by comparing the in-cabin and ”full”-size-range particle number size distributions during 174 flight sequences with a major contribution of mineral dust particles during the ”Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics” project (A-LIFE). Additionally, experimentally derived Stokes numbers were used to calculate the cutoff diameter of the aerosol sampling system for different particle densities as a function of true airspeed. As expected, the results show that the velocity of the research aircraft has a major impact on the sampling of coarse-mode aerosol particles with in-cabin instruments. For true airspeeds up to about 190 m s−1, aerosol particles larger than about 1 µm are depleted in the sampling system of the Falcon during the A-LIFE project. In contrast, for true airspeeds higher than 190 m s−1, an enhancement of particles up to a diameter of 4 µm is observed. For even larger particles, the enhancement effect at the inlet is still present, but inertial and gravitational particle losses in the transport system get more and more pronounced, which leads to a decreasing overall sampling efficiency. In summary, aerosol particles are either depleted or enhanced in the Falcon aerosol inlet, whereas transport in sampling lines always leads to a loss of particles. Here, we have considered both effects and determined the cutoff diameter for the A-LIFE transport system (i.e., the sampling lines only), the cutoff diameter of the Falcon aerosol inlet (i.e., the effect of the inlet only), and the combined effect of the inlet and sampling lines.

Quasi-Stochastic Departure Metering Model Considering Non-Deterministic Taxi Time, Standard Instrument Departure Track Time, and a New Delay–Conflict Relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

Fleet & tail assignment under uncertainty

Airlines solve many different optimization problems and combine the resulting solutions to ensure smooth, minimum-cost operations. Crucial problems are the Fleet Assignment, which assigns aircraft types to flights of a given schedule, and the Tail Assignment, which determines individual flight sequences to be performed by single aircraft. In order to find a cost-optimal solution, many airlines use mathematical optimization models. For these to be effective, the available data and forecasts must reflect the situation as accurately as possible. However, especially in times of a pandemic, the underlying plan is subject to severe uncertainties: Staff and demand uncertainties can even lead to flight cancellations or result in entire aircraft having to be grounded. Therefore, it is advantageous for airlines to protect their mathematical models against uncertainties in the input parameters. In this work, two computational tractable and cost-efficient robust models and solution approaches are developed: First, we set up a novel mixed integer model for the integrated fleet and tail assignment, which ensures that as few subsequent flights as possible have to be canceled in the event of initial flight cancellations. We then solve this model using a procedure that ensures that the costs of the solution remain considerably low. Our second model is an extended fleet assignment model that allows us to compensate for entire aircraft cancellations in the best possible way, taking into account rescheduling options. We demonstrate the effectiveness of both approaches by conducting an extensive computational study based on real flight schedules of a major German airline. It turns out that both models deliver stable, cost-efficient solutions within less than ten minutes, which significantly reduce follow-up costs in the case uncertainties arise.

AI-Embedded UAV System for Detecting and Pursuing Unwanted UAVs

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather.
 Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications.
 This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

Optimization of terminal area arrival flight sorting based on an improved sparrow search algorithm.

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whale optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas.

Actinobacillus ureae may be a critical pathogen in patients with predispositions: A case report and review of the literature.

Actinobacillus ureae (A. ureae) is an unusual commensal of human respiratory flora, rarely causing human infection. The predisposing factors, identification, clinical features, and antibiotic therapy of A. ureae are seldomly reported. Herein, we present a case of 64-year-old man affected by A. ureae pneumonia after intracranial surgery. A 64-year-old male was admitted with vomiting, drowsiness, and a severe disturbance of consciousness and was later diagnosed with cerebral hemorrhage by computed tomography images. After a craniocerebral surgery, the patient suffered from intractable pneumonia, experiencing treatment failure with multiple anti-bacterial agents. Sputum culture yield pure colonies of A. ureae, confirmed by matrix-assisted laser desorption/ionization time of flight and 16S rRNA gene sequencing. Minocycline (100 mg p.o. per 12 hours) with a course of 15 days was administrated for this patient. The respiratory symptoms, presenting as intermittent coughing with purulent and yellowish sputum, were gone. A 3-month follow-up examination showed a complete resolution of radiological findings. Clinically, the actual incidence of A. ureae pneumonia may be higher than that we generally recognized, and clinicians should consider A. ureae as a possible etiologic agent in patients with predispositions. Currently, A. ureae may be susceptible to penicillin, ampicillin, and third-generation cephalosporins. Other antibacterial agents, such as tetracycline, amoxicillin/clavulanic acid, and aminoglycosides also respond well and can be a choice in the treatment of A. ureae infections.

Modakafusp Alfa Demonstrates Type I Interferon-Mediated Innate and Adaptive Immune Enhancement in a Phase 1/2 Study in Patients with Relapsed and/or Refractory Multiple Myeloma (RRMM)

Modakafusp alfa (moda), a novel immunocytokine, is an innate immunity enhancer comprising two attenuated interferon (IFN) α2b molecules fused to an anti-CD38 IgG4 monoclonal antibody backbone, driving preferential IFNα signalling in CD38-expressing innate and adaptive immune cells as well as myeloma cells. Here, we present pharmacodynamic (PD) data from the first-in-human Phase 1/2 study of moda as a monotherapy in patients with RRMM (iinnovate-1; NCT03215030). A total of 37 patients treated with moda at 1.5 or 3 mg/kg every 4 weeks were included in the analysis. Peripheral blood (PB) and bone marrow (BM) samples were collected during screening or pre-dose on cycle 1 day 1 (C1D1) and at multiple timepoints after treatment. BM and PB samples were evaluated by cytometry time of flight (CyTOF) and bulk RNA sequencing (RNAseq) to assess pharmacodynamic changes in immune cell populations, cell activation, and gene expression. Administration of moda led to enhanced innate immune cell activation and cytotoxic function as demonstrated by increased proportions of CD69+ and granzyme B+ in peripheral NK cells analyzed by CyTOF. This was accompanied by a decrease in the proportion of TIGIT+ NK cells, indicating reduced inhibitory signaling. Furthermore, enhanced proliferation (%Ki67+) of NK cells was observed in both BM and PB. Moda also impacted myeloid cell populations in BM and PB. Dendritic cells (DCs) and monocytes in PB showed increased surface expression of the co-stimulatory molecule CD86, suggesting a potential for enhanced antigen presentation and co-stimulation ability. Increased proliferation (%Ki67) of DCs and monocytes was detected in both PB and BM, accompanied by a decrease in numbers of peripheral DCs which may indicate recruitment or homing to secondary lymphoid organs. Additionally, RNAseq analysis revealed that treatment with moda led to upregulation of CD68 expression in both PB and BM, indicating a proinflammatory M1 macrophage type response. Finally, CyTOF analysis also indicated that moda significantly impacted adaptive immunity. We observed enhanced activation (%CD69, %PD1 and %CD40L) and cytotoxic function (%granzyme B) of peripheral CD8 T cells, and increased CD8 T cell proliferation (%Ki67 CD8) in both PB and BM samples. Enhanced activation of peripheral CD4 T cells (%CD69, %CD40L) was also detected. Consistent with activation of the adaptive immune system, there was a reduction in naïve CD8 T cells accompanied by an increase in CD8 T effector memory cells in PB. Analysis of exhaustion markers (TIGIT, PD-1) in cycle 1 (C1D8, C1D15, and C2D1 predose) showed that moda did not induce CD8 T cell exhaustion in either PB or BM, and there was no change in FoxP3+ Treg cells. Moda-induced innate and adaptive immune cell phenotypic changes were evaluated for correlation with clinical response at timepoints with sufficient number of evaluable samples. While none of the associations were statistically significant after correcting for multiplicity testing (using the false discovery rate method), we observed trends warranting further investigation. There was a greater increase of CD40L+ NK cells and PD1+ CD8 cells in responders vs. non-responders at C1D2. Additionally, at C1D15 responders showed a lower increase in proliferation (%Ki67+) of classical monocytes, NK cells, and myeloid DCs in PBas compared to non-responders. A similar trend was also observed in the BM classical monocytes. Even though the peak of Ki67 expression was observed at C1D8, diminished increases of proliferating cells at C1D15 in responders was a surprising observation that needs further consideration. The PD biomarker data from this first-in-human clinical trial demonstrated that treatment with moda enhances both innate and adaptive immune cell activation in PB and BM. Importantly, moda-mediated immune activation did not result in T cell exhaustion during cycle 1, as evaluated in both PB and BM. We have observed interesting trends of immune phenotypic changes that may correlate with response. Follow-up analyses of the correlation of biomarkers with clinical response are in progress and will be presented. Further clinical trials are underway (iinnovate-2, NCT05556616; iinnovate-3, NCT05590377) to evaluate moda's novel immune activating mechanism in combination with standard of care anti-myeloma therapies.

Prefetch and Push Method of Flight Information Based on Migration Workflow

As the architecture of aircraft cockpit panels becomes more complicated and more flight data are placed onto the panels, trainee pilots require more time during flight training to learn and comprehend flight information. This problem lengthens flight training time and raises costs. This paper proposes a mechanism for prefetching and pushing flight information to facilitate flight training for trainee pilots. This paper addresses the challenges of a high quantity of data and the chaotic time-series relationship between distinct data in flight sequence data by building a migration workflow model in the aircraft cockpit environment and getting flight data with shorter time intervals. Then the flight data are input into the Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) prediction algorithm, which generates the prompt operation information and prediction information by analyzing the current flight data and predicting flight data of next stage. A case study of the takeoff stage is given. The experimental results of the prediction algorithm are given, which prove that the time-series flight data refined by the migration workflow model and MLP-LSTM algorithm have a better prediction effect compared with the LSTM algorithm.

Learning structural stress virtual sensors from on-board instrumentation of a commercial aircraft

This work aims to predict the mechanical stress on the structure of a business jet in service phase from flight instrument only. A significant database obtained from test flights using aircraft instrumented with strain gauges has been provided as part of an Artificial Intelligence challenge organized by the French Ile-de-France region and the aircraft manufacturer Dassault Aviation. Learning techniques are considered to train a prediction model of the aircraft structural stress. The proposed baseline includes a clustering step for phase identification in time series and an ensemble model with two stacked regressors. The model is trained on a dataset of 117 flights and its overall performance is evaluated on a validation set of flight sequences from 186 flights. The main advantages of the proposed learning approach are its prediction accuracy, its training frugality and its interpretability. This paper presents a global data science workflow applied to a problem of structural stress prediction. Despite a development in a constrained time period with no direct access to the data, the proposed approach demonstrates the feasibility of the concept of learned virtual sensor of aircraft structural stress and paves the way for applications to other structures.

Application of raycast method for person geolocalization and distance determination using UAV images in Real-World land search and rescue scenarios

People enjoy spending time in the wilderness for numerous reasons. However, they occasionally get lost or injured, and their survival depends on being efficiently found and rescued in the shortest possible time. A search and rescue operation (SAR) is launched after the accident is reported, and all possible resources are activated. The inclusion of drones in SAR operations has enabled the use of computer vision methods to detect persons in aerial imagery automatically. When searching by drone, preference is given to oblique photographs that cover a larger area within a single image, reducing the search time. Unlike vertical photographs, oblique photographs include a significant scale change, making it challenging to locate a person in the real world and determine their distance from the drone. In order to solve this problem, encouraged by our previous successful simulations, we explored the possibility of applying the raycast method for person geolocalization and distance determination for use in real-world scenarios. In this paper, we propose a system able to precisely geolocate persons automatically detected in offline processed images recorded during the SAR mission. After a series of experiments on terrains of different configurations and complexity, using a custom-made 3D terrain generator and raycaster, along with a deep neural network-based person detector trained on our custom dataset, we defined a method for geolocating detected person based on raycast, which allows using low-cost commercial drones with a monocular camera and no Real-Time Kinematic module while enabling laser rangefinder emulation during offline image analysis. Our person geolocating method overcomes the problems faced by previous methods and, using a single flight sequence with only 4 consecutive detections, significantly outperforms the previous best results, with reliability of 42,85% (geolocating error of 0.7 m on recording from a 30 m height). Also, a short time of only 247 s enables offline processing of data recorded during a 21-minute drone flight covering approximately an area of 10 ha, proving that the proposed method can be effectively used in actual SAR missions. We also proposed a new evaluation metric (ErrDist) for person geolocalization and provided recommendations for using the proposed system for person detection and geolocation in real-world scenarios.

Intelligent temporal detection network for boundary-sensitive flight regime recognition

Flight regime recognition is critical in guaranteeing flight safety, making informed maintenance decisions for key components, and evaluating flight quality. However, previous methods take flight data points/segments as inputs to make point-wise/segment-wise prediction, which results in imprecise regime boundary localization, poor recognition accuracy or low efficiency. To this end, an intelligent temporal detection network is proposed from a brand-new perspective of regime detection, which directly handles long flight sequences and concurrently generates multiple regime boxes with corresponding categories. Moreover, specific model structures are designed for flight data, including an adaptive graph embedding to explore spatial relations of multi-modal flight parameters, a multi-scale Transformer encoder to recognize regimes of different durations, and a balanced joint loss to mitigate negative impact on imbalanced flight regimes. To validate the superiority of the proposed method, various parameters of real-world flight sorties are collected. Then flight regime dataset is constructed via manual annotation. Extensive experiments and ablation studies show that our method can achieve accurate and boundary-sensitive regime recognition simultaneously.

Sequence optimization of departure flights considering ferry scheduling

Due to uncertain factors such as weather, large-scale flight delays will occur on the ground at the airport. A reasonable and effective flight sequence can make the airport resume regular operation as soon as possible. However, limited shuttle bus resources will limit the execution of the flight sequence plan. In order to provide decision support for airport authorities, we establish an integrated optimization model for flight sequences that consider shuttle bus resources. The objective function comprehensively considers the operational losses, airline profit losses, and loss of passenger time value. Then we design a multi-chromosome genetic algorithm to solve the model according to the characteristics of the integration problem. In addition, a simulation experiment is conducted based on the data from Yunnan Kunming Changshui International Airport. The simulation experiment results show that compared with the current first-come-first-served (FCFS) strategy, the algorithm reduces the overall loss caused by delays by 7.03%. The results of the simulation example verify the effectiveness of the proposed model and algorithm.

Adaptive time multiscale algorithms for fluid–structure interaction with impacts - Application to a row of PWR fuel assemblies under seismic loading

The present article is dedicated to numerical methods for the simulation of the response of PWR fuel assemblies under external mechanical loading such as earthquakes. It proposes new algorithms to manage impact sequences within the time multiscale resolution of a strongly coupled fluid–structure partitioned problem. Preserving the computational efficiency imposes an adaptive strategy to adjust the time scale to force the solution of the costly pressure problem for the fluid only when it is necessary to account for the brutal variations in the structure acceleration resulting from significant impacts. The adaptation criteria must be built upon a thorough monitoring and characterization of all impacts, including the duration of the flight sequence before it occurs and the relative velocity between impact entities. Finally, solutions to mitigate the consequences of missed and unresolved impacts in time are provided to avoid spurious amplification of the variation of the acceleration under impact in the fluid velocity field through the time integration scheme. This leads to two classes of adaptive time multiscale algorithms, extensively tested and qualified through two chosen cases of growing complexity. The paper is completed by a full experimental validation of the proposed fluid–structure framework with contact and impacts, using the results available for a row of six fuel scale assembly immersed in water and submitted to a seismic excitation on a shaking table.

Cost-Effective Flight Sequencing and Gate Assignment Considering Transfer Time under Pandemic Conditions

An efficient hub-and-spoke network (HSN) can reduce operating costs and passenger delays at hubs through proper sequencing of flights and assignment of gates. Typically, batches of flights are scheduled to arrive within brief time windows. By considering aircraft sizes/loads and locations of the available gates, there is a considerable potential for reducing the total cost. During pandemic conditions, passenger transfer time and aircraft dwell time significantly increased because of outbreak controls (e.g., COVID-19 and its variant). In addition to walking time between connecting flights, there is a significant delay for the airport staff to validate passengers’ proper travel documents. The objective of this study is to minimize the total cost by optimizing the flight sequencing and gate assignment and by considering the realistic transfer delay under pandemic situations. A genetic algorithm with an elite selection strategy is developed to search for the optimal solution, which significantly reduces the total cost by 25% compared with that under existing operations, and the relations between optimized solutions and various model parameters are explored.

How to Improve the Efficiency of Check-In Counter: A Counter-Sharing Method

Improving the efficiency of check-in counters is a significant concern for airport operation management departments and airline companies. However, the existing studies mainly optimize the counter allocation based on the number of counters and airport demands, which needs more exploration on improving the utilization rate of check-in counters under the condition of unchanged resource allocation. In this paper, we propose a counter-sharing method to improve check-in efficiency by sharing the idle check-in counters in the adjacent check-in area. In the proposed counter-sharing method, we first define the passenger’s total walking distance and waiting time for queues as the metrics, then reassign the check-in areas and adjust the internal departure sequence of flights to maximize the counter-sharing rate. The numerical results indicate that the proposed counter-sharing method can improve the efficiency of check-in counters and reduce the passenger’s total walking distance and waiting time in the queue during the check-in process, which can enhance the airport’s operation efficiency and competitiveness.