Total Flight Time Research Articles

The Adaptability of Beet Webworm (Loxostege sticticalis) to Soybeans and Other Different Host Plants

The beet webworm Loxostege sticticalis has caused significant economic losses to the production of agriculture and animal husbandry in China. Preference testing of larva feeding and adult oviposition and flight capability tests were carried out on five host plants: soybean, pigweed, pea, broad bean, and alfalfa. An experimental population life table and population growth trends over the subsequent 200 days were constructed. The test results show that the larvae showed no significant preference among different plants. However, adults lay more eggs on soybeans. The pests failed to complete a generation when fed on broad beans and alfalfas. When fed on pigweed, they showed the shortest larvae development period, the heaviest pupal weight, the longest adult longevity, and the largest egg-laying amount. The net reproductive rate (R0), intrinsic rate of increase (r), finite rate of increase (λ), and simulated population growth trend of the pests fed on pigweed show the highest values, and those fed on peas showed the lowest values. The total flight distance and flight time of adults fed on pigweed and soybeans were significantly greater compared to those fed on peas. In summary, beet webworms fed on pigweed showed the fastest development and reproduction, the largest population, and the strongest flight capability.

Studies on Lygus pratensis' (Hemiptera: Miridae) Flight Ability.

Lygus pratensis (Linnaeus) is an important agricultural pest with a strong ability to move and spread between hosts. However, L. pratensis' flight potential and factors affecting its flight ability are unclear. We used the insect flight information system (flight mill) to determine the effects of temperature, humidity, age, sex, and mating on L. pratensis' flight ability in an artificial climate chamber. Temperature and relative humidity significantly affected L. pratensis' flight ability; however, low and high temperature, as well as low humidity, were unsuitable, and the optimal flight environment was 20-28 °C and 60-75% RH. Lygus pratensis' flying ability initially increased and then decreased with age and was highest at 10 days old (flight rate: 71.43%; total flight distance: 18.63 ± 1.89 km; total flight time: 6.84 ± 0.60 h). At 15 days old, flight speed was the highest (3.36 ± 0.18 km h-1). Sex had little effect on L. pratensis' flying ability; it was marginally stronger for females than males, but the difference was insignificant. Mating increased female flying ability but decreased that of males, but the difference was insignificant. Overall, L. pratensis had strong flight dispersal ability, was largely unaffected by sex and mating, and optimal flight conditions were mild temperature and humidity. This knowledge provides a scientific basis for L. pratensis outbreak prediction, prevention, and control.

Farm Monitoring System with Drones and Optical Camera Communication.

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On-Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10-3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture.

Collision-free trajectory planning for UAVs based on sequential convex programming

This paper proposes an optimization-based approach to solve the collision-free trajectory planning problem for unmanned aerial vehicles (UAVs) with nonlinear dynamical systems. The problem is modeled as a non-convex optimization problem, and then iteratively solved by sequential convex programming algorithm which approximates non-convex constraints by convexification methods. We linearize the dynamical equations of the UAV and penalize collision-free constraints with a hinge loss. Furthermore, a variable trust region method is employed to ensure the convergence and stability of the algorithm. We apply the algorithm to three-degree-of-freedom UAV case studies. Numerical results validate that our method can provide collision-free trajectories with a 27.19% reduction in total flight time and a 93.61% reduction in computation time on average compared with the genetic algorithm method.

Warm start for optimal transfer between close circular orbits with first generation E-sail

The Electric Solar Wind Sail (E-sail) is a propellantless propulsion system for deep space navigation that exploits the dynamic pressure of the solar wind to generate thrust using a web of long, conducting tethers. The heliocentric trajectory of an E-sail-based spacecraft in a classic transfer between two Keplerian orbits is usually analyzed, in an optimal framework, by minimizing the total flight time. This paper discusses an analytical, approximate procedure for solving the two-point boundary value problem associated with the trajectory optimization process in a heliocentric, two-dimensional scenario involving a first-generation E-sail. The procedure is applied in a typical transfer between two coplanar circular orbits whose (assigned) radii are sufficiently close to each other. Paralleling the approach proposed in the recent literature, the mathematical model presented in this paper discusses a set of analytical equations that give an accurate approximation of both the minimum flight time and the (unknowns) initial adjoint variables. The paper also analyzes a set of mission applications, which involve interplanetary transfers to some near-Earth asteroids whose orbits have a very small value of both eccentricity and inclination.

Enhancing air traffic operational efficiency by reducing network scale

The growing demand for air travel has led to the saturation of air traffic networks. Conventional methods of adding routes to alleviate congestion and reduce delays may not achieve the desired effect and even degrade system performance. In this paper, we explore the application of Braess’s Paradox in the reduction of air traffic networks. This counterintuitive phenomenon shows that adding new connections to a network can actually increase the overall network pressure. This study uses Hidden Markov methods and the Viterbi algorithm to match air traffic flow with routes, a machine learning approach and a mathematical method to construct cost functions for flight time and traffic volume, and finally uses genetic algorithm and the A* algorithm to detect Braess’s Paradox edges. We uses ADS-B data from the busy month of July 2019 for a case study of the air traffic network over the UK airspace. The results show that Braess’s Paradox is also applicable to multi-flight level air route networks. Removing such network edges can improve system performance. In one day’s case, the total flight time of the day’s traffic volume decreased from 11509.24 minutes to 10459.97 minutes. This equates to an average savings of 4.99 minutes of flight time per flight, which is significant in controlling delay performance.

Effects of high-gravity acceleration forces and anti-gravity maneuver on the cardiac function of fighter pilots.

The fighter pilots exposed to high gravitational (G) acceleration must perform anti-G maneuvers similar to the Valsalva maneuver. However, the effects of high-G acceleration and anti-G maneuvers on cardiac function have rarely been studied. This study aimed to investigate the effects of high-G forces on cardiac function of fighter pilots. Fighter pilots who underwent regular health check-ups and echocardiography were included (n = 29; 100% men, 41 ± 10years old; mean flight time, 1821 ± 1186h). Trainees who had not experienced any flights were included in the control group (n = 16; 100% men, 36 ± 17years old). Echocardiographic data included left ventricular chamber size, systolic and diastolic functions, right ventricular systolic pressure (RVSP), inferior vena cava (IVC) collapsibility, and tricuspid annular plane systolic excursion (TAPSE). No significant differences in left ventricular ejection fraction, RVSP, or IVC collapsibility were observed between two groups. In the multivariate linear regression analysis with total flight time as an independent continuous variable for fighter pilots, TAPSE was positively correlated with total flight time. The experience of fighter pilots who were exposed to high-G acceleration forces and anti-G maneuvers did not cause cardiac structural changes, but the exposure might be associated with right heart function changes.

Impact of Nutritional Supplements on the Fitness of the Parasitoid Binodoxys communis (Gahan).

Alterative nutritional foods consumed by adult parasitoids play an important role in their fitness and ability to control pests because of food scarcity in many crops. While adult parasitoids feed on various sugars, they vary in their nutritional value for parasitoids. We assessed the effects of seven sugars (fructose, glucose, sucrose, trehalose, maltose, melezitose, and sorbitol) on the longevity, parasitism ability, parasitism behavior, and flight ability of B. communis, an important parasitoid of cotton aphids. We found that access to glucose, sucrose, or fructose, increased B. communis adult longevity more than the other sugars offered. All sugars except trehalose increased the parasitism rate to more than 50% compared to the starved control (only provided with water). We then compared parasitoid behaviors of wasps fed glucose, sucrose, or fructose to that of the starved control (with access only to water) and found that those fed B. communis spent more time either examining or attacking aphids than parasitoids in the control group, which spent more time walking or resting. Also, consumption of glucose, sucrose, or fructose also significantly improved the flight ability (the total flight distance, flight time, and average flight speed) of B. communis.

Analysis of Failure States of Functional Systems of Aircraft Such as Boeing 737 in the Airline

Abstract The article presents the results of analysis of failures of the main functional systems units of aircraft Boeing 737 during the last 10 years of its operation in the national airline of Latvia ‘Air Baltic Corporation’. Total flight time was T∑ = 322,529 h and 184,538 cycles [1]. These data were obtained from daily reports of defects and unplanned consumption of spare parts for these systems. Failures of instrumental equipment of avionic systems were investigated in detail. Based on calculations of their failure probability and component replacement frequency, a comprehensive system including measures and their technical and instrumental support has been developed to improve maintenance productivity. Such a system requires relatively inexpensive components, is simple and can be used in the operation of this type of aircraft.

Effect of high-gravity acceleration forces and anti-gravity maneuver for cardiac function in fighter pilots

Abstract Background Environmental factors such as hypoxia and gravity affect to the human cardiovascular system. It’s more important for fighter pilots who are exposed to high altitude, harsh gravitational (G) acceleration, and perform anti-G maneuvers similar to the Valsalva maneuver. Experimental studies examining how high gravitational acceleration and anti-gravity maneuver affect cardiac function are rare. Purpose This study aimed to investigate the effect on cardiac function of fighter pilots exposed to high G acceleration forces. Methods From July 2019 to June 2020, fighter pilots underwent regular health check-up and echocardiogram. Data from trainees who did not experience a flight were used for control group. Echocardiographic data included left ventricle (LV) chamber size, systolic function, and diastolic function, as well as right ventricle systolic pressure (RVSP), inferior vena cava (IVC) collapsibility, and the tricuspid annular plane systolic excursion (TAPSE) to evaluate right heart function. The multiple linear regression analysis for TAPSE was performed with total flight time as an independent continuous variable in fighter pilots. Results We enrolled 29 fighter pilots (41±10years-old; male, 100%; mean flight time 1821±1186 hours) and 16 trainees (36±17years-old; male, 100%). There was no significant difference in age, blood pressure, LVEF, RVSP, and the IVC collapsibility between fight pilots and trainees. In multivariate linear regression analysis for fighter pilots, TAPSE was affected by total flight time with positive correlation. Conclusion The experience of fighter pilots exposed to the high G acceleration forces and anti-G maneuver might be associated with right heart function change, but not associated with the prominent cardiac structural changes.

An integrated vertiport placement model considering vehicle sizing and queuing: A case study in London

The increasing level of congestion and infrastructure costs in cities have created a need for more intelligent and flexible transport systems. Urban Air Mobility (UAM) introduces the third dimension to intra-urban transport at a minimal infrastructure cost, bypassing congestion and providing reliable travel times to users through the provision of air passenger transportation. The performance of UAM systems is highly dependent on vertiport locations, vehicle sizing and infrastructure specifications, which themselves are intrinsically linked. This study takes a holistic approach to UAM network optimisation by considering the inter-relatedness of these decisions in a three-stage algorithm. In the first stage, a linear vertiport placement model with vehicle sizing constraints is developed to determine the optimal vertiport configuration while considering eVTOL performance. The vertiport configuration serves to determine the operational requirements of the aircraft and are incorporated in the vehicle sizing models. The resulting network configuration and vehicle sizing constraints are used as inputs in the infrastructure model, which utilises open network theory to determine the service rate requirements and the allowed loiter and waiting times based on the number of take-off, landing, and charging pads at each vertiport. These stages are executed sequentially through a feedback mechanism, which balances the infrastructure, operational costs as well as passenger waiting times. The algorithm optimises the profitability of the UAM network ensuring all operational constraints are satisfied. A case study based on the hypothetical implementation of UAM in the city of London is presented using Rolling Origin and Destination Survey data to estimate demand patterns. Our results suggest that ignoring vehicle sizing and infrastructure modelling in the network optimisation stage results in infeasible UAM networks. Furthermore, reducing waiting times and loiter times are critical to reduce operational costs of the UAM network, with all optimal configurations yielding waiting times below 5% of the total flight time. The proposed method can be used to plan future UAM developments whilst ensuring operational viability.

Solar Sail Orbit Raising with Electro-Optically Controlled Diffractive Film

The aim of this paper is to analyze the transfer performance of a spacecraft whose primary propulsion system is a diffractive solar sail with active, switchable panels. The spacecraft uses a propellantless thruster that converts the solar radiation pressure into propulsive acceleration by taking advantage of the diffractive property of an electro-optically controlled (binary) metamaterial. The proposed analysis considers a heliocentric mission scenario where the spacecraft is required to perform a two-dimensional transfer between two concentric and coplanar circular orbits. The sail attitude is assumed to be Sun-facing, that is, with its sail nominal plane perpendicular to the incoming sunlight. This is possible since, unlike a more conventional solar sail concept that uses metalized highly reflective thin films to reflect the photons, a diffractive sail is theoretically able to generate a component of the thrust vector along the sail nominal plane also in a Sun-facing configuration. The electro-optically controlled sail film is used to change the in-plane component of the thrust vector to accomplish the transfer by minimizing the total flight time without changing the sail attitude with respect to an orbital reference frame. This work extends the mathematical model recently proposed by the authors by including the potential offered by an active control of the diffractive sail film. The paper also thoroughly analyzes the diffractive sail-based spacecraft performance in a set of classical circle-to-circle heliocentric trajectories that model transfers from Earth to Mars, Venus and Jupiter.

An Integer Programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed

Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone’s flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.

Processing time reduction for UAV optimal altitude and investigating its effect on flight time and energy consumption

ABSTRACT In this article, the time to calculate the optimal height of the UAV is investigated as an important factor in determining the total flight time, energy consumption and total delay. In particular, in this paper, the calculation of the optimal height of the UAV, reducing the time of calculating the optimal height, reducing the energy consumption, reducing the total flight time and reducing the total delay is done. First, using the average path loss and UAV transmitted power functions, we present the optimal height of the UAV in the form of an optimization problem with a convex altitude range. Then, using the golden section search (GSS) algorithm and based on the condition of the function being unimodal, we calculate the optimization problem and obtain the optimal height value, which is the minimum of the average functions of the path loss and the transmitted power of the UAV. Also, using the convexity principle, we show that the average path loss function is convex in the mentioned interval. Next, using the relationship between the time to calculate the optimal height of the drone and the total flight time, we calculate the amount of energy consumed and the total delay. The simulation results using MATLAB show that the time to calculate the optimal height with the proposed algorithm is much faster than other methods. The time to calculate the optimal height in the proposed method is 0.03 s. The energy consumption using the proposed method is 53 kJ, and the flight time is 37 s, considering the stop on the way, which is the lowest value compared to other methods. Also, the total delay in the proposed method is less than in other methods.

A Multiagent Mission Coordination System for Continuous Situational Awareness of Bushfires

This paper devises a multi-agent Mission Coordinating Architecture (MCA) to achieve continuous situational awareness (SA) in bushfires, which can help with quick detection and accurate response to the hazards. In this paper, we use Unmanned Aerial Vehicles (UAVs) as instantiations of physical agents. MCA is a scalable architecture and aims to provide the UAVs with parallel mission plans, adopting a fire spread probability map and the Fuzzy C-means method to avoid mission overlap and duplicate information. The architecture is then enhanced by integrating a synchronized communication framework to facilitate UAVs’ adaptive cooperation and total flight time optimization. Furthermore, integrating the communication framework minimizes the number of deployed UAVs to fully cover the same area, saving considerable cost and energy compared to the Parallel Mode. The scalability challenge of the Parallel Mode, determining the required number of UAVs to cover the entire area, and the efficiency of the mission planning algorithm are thoroughly investigated and compared to the performance of the Communication Mode. Finally, the simulation results prove the MCA’s effectiveness in enhancing the UAVs’ exploration capability, resulting in comprehensive monitoring of all affected areas. Note to Practitioners—The proposed architecture in this research could offer flexibility to increase the number of UAVs on demand without requiring a change and adjustment of the parameters. Furthermore, a synchronized communication framework enhances MCA, enabling all UAVs to share their resources and exploit residual battery time to assist each other, manage the overall operation time, and reduce the total operation cost.

Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases

A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators.

Optimal Interplanetary Transfer of Solar Wind Ion Focusing Thruster-Based Spacecraft

The Solar Wind Ion Focusing Thruster (SWIFT) is a highly-innovative propellantless propulsion concept, recently proposed by Gemmer and Mazzoleni. In its nominal configuration, a SWIFT consists of a conically-shaped mesh of positively-charged conducting tethers, with its vertex linked to the spacecraft and its axis oriented towards the Sun. The SWIFT collects and filters the solar wind plasma and suitably directs the positive ions, which are then accelerated by an ion thruster. Such a device is theoretically able to generate a deep-space propulsive acceleration that comes, in part, from the solar wind dynamic pressure impinging on the conical grid and, in part, from the positive ion beam. In particular, the orientation of the ion beam may be chosen in such a way as to set the resultant propulsive acceleration and steer the spacecraft. The aim of this paper is to analyze the performance of a SWIFT-propelled spacecraft in an orbit-to-orbit two-dimensional interplanetary transfer. To that end, some mission scenarios are studied, in an optimal framework, by minimizing the total flight time necessary for the spacecraft to complete the transfer as a function of the propulsion system performance parameters. Numerical simulations are used to compare the optimal flight times calculated in simplified Earth–Venus and Earth–Mars transfers with those obtained by considering other propellantless propulsion systems.

Abundance estimates of southern right whales (Eubalaena australis) in Bahía San Antonio, Patagonia, Argentina

The abundance of southern right whales (Eubalaena australis) was estimated by the means of aerial line-transect surveys for the area of Bahía San Antonio, a bay located in the north-western region of the San Matías Gulf (40°50’S 64°50’W), Rio Negro, Patagonia Argentina. In total, seven aerial surveys were conducted in the first week of August and September 2009, September, October and November 2010, and August, September 2011. Survey effort equalled a total flight time of 12.4h, during which 200 whales were counted in 119 whale groups. Half of the encounters were solitary animals and 17% were mating groups. Corrected abundance estimates showed the highest amount of whales present in the bay during the month of September, with 85±71, 207±108 and 117±55 animals in 2009, 2010 and 2011 respectively. In adjacent months, less than half the amount of whales seemed to be present. The correction factor g(0)availability resulted 0.392±0.456. Perception bias was not accounted for. These aerial surveys resulted in the first estimates of southern right whale abundance in this north Patagonian bay and indicated a rather abrupt peak during the month of September. This being the peak month for right whale presence is consistent with data from other regions in the Southwest Atlantic, but data obtained in the other months remained scarce and thus results should be interpreted carefully. The complete absence of whales in the area during November 2010 and August 2011 raises further questions on the predictability of the whale’s presence in the area. Overall, more consistent aerial surveys should be conducted to accurately determine the annual and interannual evolution of southern right whale abundance in the study area.

Trajectory optimization of laser-charged UAV to minimize the average age of information for wireless rechargeable sensor network

This paper considers the laser-charged Unmanned Aerial Vehicle (UAV) assisted Wireless Rechargeable Sensor Network (WRSN), where rechargeable sensors are deployed in surveillance environment to monitor information, a UAV is not only used as aerial wireless mobile collector for gathering data from sensors but also used as mobile charger to replenish energy for sensors, Laser Beam Directors (LBDs) are uniformly deployed in the monitoring environment to charge UAV by emitting laser beams. In such network, we study the average Age of Information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors such that all data of the network are transported to the base station and the remaining energy of any sensor exceeds a certain threshold. We prove that the AoIO problem is NP-hard. To solve the AoIO problem, we first study the Total Flight Time Minimizing of UAV (TFTM) problem, which aims at finding an optimal charging solution of UAV to minimize the flight time of UAV based on the order of sensors visited by UAV. Then we prove that the TFTM problem is also NP-hard. Afterwards, we propose a heuristic algorithm to solve the TFTM problem by optimizing flight path, data collection, energy power transfer and laser charging of UAV. Based on the solution for the TFTM problem, we propose an approximation algorithm to solve the AoIO problem. Finally, we conduct extensive simulation experiments to verify the effectiveness of the proposed algorithm.

Optimal aircraft arrival scheduling with continuous descent operations in busy terminal maneuvering areas

Continuous Descent Operation (CDO), an aircraft arrival procedure in which an aircraft descends from an optimal position with minimum engine thrust, can significantly reduce aircraft noise, fuel burn and emissions. The implementation of CDOs, however, requires more separation buffers among flights than traditional stepwise descents, resulting in loss of airspace and runway capacity. It is therefore challenging to maximize the number of aircraft performing CDOs for an airport, especially in busy terminal maneuvering areas. In this paper, we tackle the difficulty by developing an aircraft arrival scheduling model to sequence arrival aircraft before their merge point and generate conflict-free trajectories by means of a lateral path stretching method. The contributions of our study to the research community are twofold: firstly, we investigate a new aircraft arrival scheduling problem that incorporates both the CDO trajectory optimization and aircraft scheduling, and develop the first model simultaneously considering these two procedures; secondly, we conduct extensive computational experiments based on historical data in Guangzhou Baiyun International Airport, which would provide an important reference for future research in the area. Our numerical results demonstrate the benefit of the proposed CDO-based aircraft arrival scheduling approach in terms of reducing total flight time and saving fuel consumption when applied in this congested airport.