Hydrodynamic model of screen channel liquid acquisition devices for in-space cryogenic propellant management

2017 ◽  
Vol 29 (1) ◽  
pp. 017101 ◽  
Author(s):  
S. R. Darr ◽  
C. F. Camarotti ◽  
J. W. Hartwig ◽  
J. N. Chung
Keyword(s):  
Hydrodynamic Model ◽  
Propellant Management

Evaluation of Loosening and Soil Compaction with a Working Tool of Tillage Machines Using a Hydrodynamic Model

2020 ◽  
Vol 13 (6) ◽  
pp. 394
Author(s):  
Salavat Mudarisov ◽  
Ildar Farkhutdinov ◽  
Airat Mukhametdinov ◽  
Raushan Aminov ◽  
Rustam Bagautdinov ◽  
...  
Keyword(s):  
Hydrodynamic Model ◽  
Soil Compaction

UJI TARIK HIDRODINAMIKMODEL KAPAL BERSAYAP WiSE DENGAN LAMBUNG DASAR BERSTEP

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Iskendar Iskendar ◽  
Andi Jamaludin ◽  
Paulus Indiyono
Keyword(s):  
Comparative Study ◽  
Model Test ◽  
Hydrodynamic Model ◽  
Surface Effect ◽  
Center Of Gravity ◽  
Test Results ◽  
Flat Bottom ◽  
Towing Tank ◽  
Test Models ◽  
Wetted Surface Area

This paper describes hydrodynamic model tests of Wing in Surface Effect (WiSE) Craft. These craft  was fitted with  stephull  form in different location on longitudinal flat bottom (stepedhull planning craft) to determine the influences of sticking and porpoising motion performances. These motions are usually occured when the craft start to take-off from water surfaces. The test models with scale of 1 : 7 were comprised of 4 (four) stephull models and 1 (one) non-stephull model  as a comparative study. The hydrodynamic  tests were performed with craft speed of 16 – 32 knots (prototype values) in Towing Tank at UPT. Balai Pengkajian dan Penelitian Hidrodinamika (BPPH), BPPT, Surabaya. The resistance (drag) was measured by dynamo meter and the trim of model (draft changing at fore and aft  of model due to model speed) was measured by trim meter. By knowing the value of model trim, the wetted surface area can be determined. Then, the lift forces were calculated based on these measured values. The model test results were presented on tables and curves.  Test results show that models  with step located far away from center of gravity of the WiSE craft tend to porpoising and sticking condition, except if the step location on the below of these center of gravity. While model without step tends to sticking conditions.

Modeling the Snow Deposit Process on the Highways in the Flowvision Software

2020 ◽  
pp. 72-83
Author(s):  
Т. В. Самодурова ◽  
О. В. Гладышева ◽  
Н. Ю. Алимова ◽  
Е. А. Бончева
Keyword(s):  
Hydrodynamic Model ◽  
Geometric Model ◽  
Information Resources ◽  
Road Maintenance ◽  
Snow Removal ◽  
Experimental Section ◽  
Deposit Modeling ◽  
Winter Road ◽  
Highway Embankment ◽  
Winter Road Maintenance

Постановка задачи. Рассмотрена задача моделирования отложения снега во время метелей на автомагистралях с барьерными ограждениями в программе FlowVision . Результаты. В качестве опытного участка рассмотрен участок автомагистрали, проходящий в насыпи. Создана геометрическая модель участка автомагистрали. Обоснованы информационные ресурсы для создания гидродинамической модели обтекания насыпи автомагистрали с барьерными ограждениями снеговетровым потоком во время метелей. Проведено моделирование процесса снегонакопления на опытном участке с использованием программного комплекса FlowVision во время метелей с различными параметрами. Выводы. Сделан вывод о возможности применения программного комплекса FlowVision для совершенствования методики назначения снегозащитных устройств и определения параметров снегоочистки при зимнем содержании автомобильных дорог. Statement of the problem. The problems of snow deposit modeling on the highways with crash barriers during blizzards in the FlowVision was discussed. Results. The highway section passing in the embankment as an experimental section has been considered. The geometric model of the highway section was created. The information resources for designing a hydrodynamic model of a snowflow stream of highway embankment with barriers during blizzard were identified. The modeling of the snow deposit process in the experimental section using the FlowVision software during blizzards with different parameters was carried out. Conclusions. It was concluded that it is possible to use the FlowVision software to improve the methodology for snow protection designing and determining snow removal parameters for winter road maintenance.

scholarly journals Coupling of SWMM With 2D Hydrodynamic Model for Simulation of Sponge City Construction Scheme

2021 ◽  
Vol 643 ◽  
pp. 012002
Author(s):  
Yuyan Fan ◽  
Chengwen Wang ◽  
Haijun Yu ◽  
Junhao Pan ◽  
Zilu Ouyang
Keyword(s):  
Hydrodynamic Model ◽  
Sponge City ◽  
Construction Scheme ◽  
City Construction

scholarly journals A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

2021 ◽  
Vol 13 (10) ◽  
pp. 2003
Author(s):  
Daeyong Jin ◽  
Eojin Lee ◽  
Kyonghwan Kwon ◽  
Taeyun Kim
Keyword(s):  
Deep Learning ◽  
Chlorophyll A ◽  
Hydrodynamic Model ◽  
Ocean Color ◽  
Learning Model ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Temporal And Spatial Distribution ◽  
Deep Learning Model ◽  
Satellite Ocean Color

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

Sign in / Sign up

Export Citation Format

Share Document