Computer Simulation of Unsteady Flows in Waterways

Baltzer, Robert A.; Lai, Chintu

doi:10.1061/jyceaj.0001842

Cited by 37 publications

(4 citation statements)

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“…The spatial derivatives are expressed by a finite difference relation weighted in time,Equation 8 β is usually assigned in the range 0.5≤ β ≤1.0 for the unconditional stable situation. A value of 1.0 yields the fully forward scheme used by Baltzer and Lai (1968), whereas a value of 0.5 yields the fully centered scheme employed by Amein (1966).…”

Section: Finite Difference Schemesmentioning

confidence: 99%

Simulation of unsteady flow and solute transport in a tidal river network

Zhan

2003

Engineering Computations

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A mathematical model and numerical method for water flow and solute transport in a tidal river network is presented. The tidal river network is defined as a system of open channels or rivers with junctions and cross sections. As an example, the Pearl River in China is represented by a network of 104 channels, 62 nodes, and a total of 330 cross sections with 11 boundary sections for one of the applications. The simulations are performed with a supercomputer for seven scenarios of water flow and/or solute transport in the Pearl River, China, with different hydrological and weather conditions. Comparisons with available data are shown. The intention of this study is to summarize previous works and to provide a useful tool for water environmental management in a tidal river network, particularly for the Pearl River, China.

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Section: Finite Difference Schemesmentioning

confidence: 99%

Simulation of unsteady flow and solute transport in a tidal river network

Zhan

2003

Engineering Computations

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“…Modern ships are equipped with various navigation tools, including the Global Positioning System (GPS), radar, sonar, electronic charts, and autonomous navigation systems, aiding them in accurately ascertaining their positions within complex surroundings. These technologies enable crew members to undertake appropriate actions, such as adjusting course or speed, to avert potential collisions [2]. Low-light conditions impose constraints on the field of vision for crew members, particularly making it challenging to detect smaller vessels promptly.…”

Section: Introductionmentioning

confidence: 99%

S-D HFFM: A Network Shallow-Deep Improvement Mechanism for Ship Detection in Low-light Channel Scenarios

Chen,

Ren,

et al. 2024

Preprint

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Detecting ships with high precision in low-light conditions poses a critical challenge to maritime safety. Under such conditions, ship boundaries become blurry, and overall contrast diminishes, resulting in missed and false detections in detection algorithms. Many existing solutions rely on applying deep learning networks for low-light enhancement. However, these methods often lack adequate mechanisms to integrate shallow spatial and deep semantic information within the network. This paper proposes two improvement strategies for video ship detection. In the first scheme, for the shallow layer of the backbone network, we design a block to enhance the model's attention to boundary information(SABlock). SABlock enhances the model's attention to boundary information, allowing it to determine the ship's position and shape accurately. In the second scheme, for the deep layers of the backbone network, we design a block to preserve detailed features by increasing contrast (DPBlock). DPBlock aims to preserve detailed features by increasing contrast, introducing differences that retain deep-level details. This enables the model to identify the ship's category better. We present a shallow-deep hybrid feature fusion mechanism (S-D HFFM) to integrate shallow spatial and deep semantic information effectively. SABlock and DPBlock are strategically embedded in the mainstream network to form a feature extraction chain. This chain hierarchically incorporates two types of information. We validate these strategies through experiments conducted on YOLOv5s and YOLOv8s, demonstrating the feasibility of our approach. Experimental results show that S-D HFFM significantly enhances the model's ability to recognize ship positions, shapes, and categories under low-light conditions.

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“…A large number of old and new problems, which hitherto were unapproachable, became solvable by means of numerical simulations and experiments in both hydraulic and hydrologic specializations. In deterministic modeling, numerical techniques were developed for the solution of transient flow of estuaries (Baltzer and Shen, 1961;Lai, 1965;and Baltzer and Lai, 1968), for solutions of solute transport in upland streams (Yotsukura and Fiering, 1964), and for the simulation of rainfall-runoff processes on small watersheds (Dawdy and O'Donnell, 1965). In stochastic modeling, probabilistic numerical experiments became important tools for advancing synthetic hydrology and systems-analysis research (Matalas, 1967(Matalas, , 1968Matalas and Gilroy, 1968;Kirby, 1969).…”

Section: Introductionmentioning

confidence: 99%

Status of surface-water modeling in the U.S. Geological Survey

Jennings¹,

Yotsukura²

1979

Circular

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The U.S. Geological Survey is active in the development and use of models for the analysis of various types of surfacewater problems. Types of problems for which models have been, or are being developed, include categories such as the following: (1) specialized hydraulics, (2) flow routing in streams, estuaries, lakes, and reservoirs, (3) sedimentation, (4) transport of physical, chemical, and biological constituents, (5) surface exchange of heat and mass, (6) coupled stream-aquifer flow systems, (7) physical hydrology for rainfall-runoff relations, stream-system simulations, channel geometry, and water quality, (8) statistical hydrology for synthetic streamflows, floods, droughts, storage, and water quality, (9) management and operation problems, and (10) miscellaneous hydrologic problems. Following a brief review of activities prior to 1970, the current status of surfacewater modeling is given as being in a developmental, verification, operational, or continued improvement phase. A list of recently published selected references, provides useful details on the characteristics of models.

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Computer Simulation of Unsteady Flows in Waterways

Cited by 37 publications

References 0 publications

Simulation of unsteady flow and solute transport in a tidal river network

Simulation of unsteady flow and solute transport in a tidal river network

S-D HFFM: A Network Shallow-Deep Improvement Mechanism for Ship Detection in Low-light Channel Scenarios

Status of surface-water modeling in the U.S. Geological Survey

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