Abstract
The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.
Address
C.C. Hung: Faculty of National Hsin Hua Senior High School, Tainan, Taiwan
T. Nguyen: Ha Tinh University, Dai Nai Ward, Ha Tinh City, Vietnam
C.Y. Hsieh: National Pingtung University Education School, No.4-18, Minsheng Rd., Pingtung City, Pingtung County 900391, Taiwan
Abstract
Reynolds-averaged Navier-Stokes (RANS) models are extensively employed in industrial settings for the purpose of simulating intricate fluid flows. However, these models are subject to certain limitations. Notably, disparities persist in the Reynolds stresses when comparing the RANS model with high-fidelity data obtained from Direct Numerical Simulation (DNS) or experimental measurements. In this work we propose an approach to mitigate these discrepancies while retaining the favorable attributes of the Menter Shear Stress Transport (SST) model, such as its significantly lower computational expense compared to DNS simulations. This strategy entails incorporating an explicit algebraic model and employing a neural network to correct the turbulent characteristic time. The imposition of realizability constraints is investigated through the introduction of penalization terms. The assimilated Reynolds stress model demonstrates good predictive performance in both in-sample and out-of-sample flow configurations. This suggests that the model can effectively capture the turbulent characteristics of the flow and produce physically realistic predictions.
Address
Thomas Philibert: Institut National de Recherche en Informatique et en Automatique, 200 Av. de la Vieille Tour, Talence, France; Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy; Institut de Mathématiques de Bordeaux, Université Bordeaux,
Abstract
The widespread introduction of unmanned aircrafts has led to the understanding of the need to organize a common information space for manned and unmanned aircrafts, which is reflected in the Russian Unmanned aircraft system Traffic Management (RUTM) project. The present article deals with the issues of spatial information database (DB) organization, which is the core of RUTM and provides storage of various data types (spatial, aeronautical, topographical, meteorological, vector, etc.) required for flight safety management. Based on the analysis of functional capabilities and types of work which it needs to ensure, the architecture of spatial information DB, including the base of source information, base of display settings, base of vector objects, base of tile packages and also a number of special software packages was proposed. The issues of organization of these DB, types and formats of data and ways of their display are considered in detail. Based on the analysis it was concluded that the optimal construction of the spatial DB for RUTM system requires a combination of different model variants and ways of organizing data structures.
Key Words
aeronautical information; integration of unmanned aircraft systems; Russian Unmanned aircraft system Traffic Management (RUTM); spatial information database; unmanned aerial vehicle (UAV); vector object
Address
Maksim Kalyagin and Yuri Bukharev: Moscow Aviation Institute, Volokolamskoe Highway 4, 125993 Moscow, Russia
Abstract
Airplanes in flight collide with raindrops, and the leading edges of the airframe can be damaged when colliding with raindrops. A single waterjet testing platform was created to study rain erosion damage. Carbon fiber samples with three types of skins were studied and the mechanical properties were measured using a nanoindentation instrument. The research results show that the impact force on the sample increases with the continuous increase in the impact speed of raindrops, which leads to an increase in the damage area. Sheathing with low surface roughness is more damaged than other sheathings due to its rougher surface, and the result proves that surface roughness has a more significant effect on rain erosion damage to sheathings compared to their hardness.
Key Words
coating; composite material; impact dynamics; rain erosion damage; single jet
Address
Minggong Sha: School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; Shaanxi Impact Dynamics and Engineering Application Laboratory, Xi'an 710072, Shaanxi, China; NPU Yangtze River Delta Research Institute, 215400 Suzhou, China
Ying Sun: Moscow Aviation Institute, Volokolamskoe Highway 4, 125993 Moscow, Russia
Li Yulong: School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; Shaanxi Impact Dynamics and Engineering Application Laboratory, Xi'an 710072, Shaanxi, China; NPU Yangtze River Delta Research Institute, 215400 Suzhou, China
Vladimir I. Goncharenko, Vladimir S. Oleshko, Anatoly V. Ryapukhin: Moscow Aviation Institute, Volokolamskoe Highway 4, 125993 Moscow, Russia
Victor M. Yurov: Karaganda Technical University, Nazarbaev Street 56, 100056 Karaganda, Kazakhstan
