Laboratory tests on surges created by ice jam releases

1985 ◽  
Vol 12 (4) ◽  
pp. 930-933 ◽  
Author(s):  
J. Wong ◽  
S. Beltaos ◽  
B. G. Krishnappan
Keyword(s):  
Unsteady Flow ◽  
Laboratory Tests ◽  
Water Levels ◽  
Flow Conditions ◽  
Ice Jam ◽  
Discernible Effect

Laboratory tests were conducted to investigate the unsteady-flow conditions after the release of an ice jam. The tests revealed that the presence of the moving ice has no discernible effect on the characteristics of the surge that follows the release of the jam and that the model of S. Beltaos and B. G. Krishnappan gives good predictions of the water levels and fair predictions of the water speeds.

Seepage flow through simulated grounded ice jam

1985 ◽  
Vol 12 (4) ◽  
pp. 926-929 ◽  
Author(s):  
J. Wong ◽  
S. Beltaos ◽  
B. G. Krishnappan
Keyword(s):  
Unsteady Flow ◽  
Laboratory Tests ◽  
Seepage Flow ◽  
Flow Conditions ◽  
Local Flow ◽  
Ice Jam ◽  
Flow Through ◽  
Water Depths

Laboratory tests were conducted to study the seepage flow conditions through a simulated grounded jam. Results indicated that nonlaminar seepage predominates and that the resulting relationship between the local flow and the upstream and downstream water depths may be applied to both steady and unsteady flow conditions.

The Estimation of Discharge in an Experimental Open Channel

2014 ◽  
Vol 905 ◽  
pp. 369-373
Author(s):  
Choo Tai Ho ◽  
Yoon Hyeon Cheol ◽  
Yun Gwan Seon ◽  
Noh Hyun Suk ◽  
Bae Chang Yeon
Keyword(s):  
Unsteady Flow ◽  
River Discharge ◽  
Open Channel ◽  
Flow Conditions ◽  
Mean Velocity ◽  
Velocity Equation ◽  
The Mean ◽  
Loop Form

The estimation of a river discharge by using a mean velocity equation is very convenient and rational. Nevertheless, a research on an equation calculating a mean velocity in a river was not entirely satisfactory after the development of Chezy and Mannings formulas which are uniform equations. In this paper, accordingly, the mean velocity in unsteady flow conditions which are shown loop form properties was estimated by using a new mean velocity formula derived from Chius 2-D velocity formula. The results showed that the proposed method was more accurate in estimating discharge, when compared with the conventional formulas.

Computational Study of the Risø-B1-18 Airfoil with a Hinged Flap Providing Variable Trailing Edge Geometry

2005 ◽  
Vol 29 (2) ◽  
pp. 89-113 ◽  
Author(s):  
Niels Troldborg
Keyword(s):  
Reynolds Number ◽  
Unsteady Flow ◽  
Computational Study ◽  
Turbine Blades ◽  
Aerodynamic Characteristics ◽  
Flow Conditions ◽  
Wind Turbine Blades ◽  
Trailing Edge ◽  
Edge Geometry ◽  
Fully Developed Turbulent Flow

A comprehensive computational study, in both steady and unsteady flow conditions, has been carried out to investigate the aerodynamic characteristics of the Risø-B1-18 airfoil equipped with variable trailing edge geometry as produced by a hinged flap. The function of such flaps should be to decrease fatigue-inducing oscillations on the blades. The computations were conducted using a 2D incompressible RANS solver with a k-w turbulence model under the assumption of a fully developed turbulent flow. The investigations were conducted at a Reynolds number of Re = 1.6 · 106. Calculations conducted on the baseline airfoil showed excellent agreement with measurements on the same airfoil with the same specified conditions. Furthermore, a more widespread comparison with an advanced potential theory code is presented. The influence of various key parameters, such as flap shape, flap size and oscillating frequencies, was investigated so that an optimum design can be suggested for application with wind turbine blades. It is concluded that a moderately curved flap with flap chord to airfoil curve ratio between 0.05 and 0.10 would be an optimum choice.

Competitive flow and anastomosis angle influence on bypass hemodynamics in unsteady flow conditions

2017 ◽  
Author(s):  
A. F. Totorean ◽  
S. I. Bernad ◽  
I. C. Hudrea ◽  
R. F. Susan-Resiga
Keyword(s):  
Unsteady Flow ◽  
Flow Conditions

Ice jam mitigation

1990 ◽  
Vol 17 (5) ◽  
pp. 675-685 ◽  
Author(s):  
Harold S. Belore ◽  
Brian C. Burrell ◽  
Spyros Beltaos
Keyword(s):  
Key Words ◽  
Carrying Capacity ◽  
Flood Control ◽  
Human Life ◽  
Water Levels ◽  
Economic Losses ◽  
River Ice ◽  
Ice Jams ◽  
Ice Jam ◽  
Ice Breakup

In Canada, flooding due to the rise in water levels upstream of an ice jam, or the temporary exceedance of the flow and ice-carrying capacity of a channel upon release of an ice jam, has resulted in the loss of human life and extensive economic losses. Ice jam mitigation is a component of river ice management which includes all activities carried out to prevent or remove ice jams, or to reduce the damages that may result from an ice jam event. This paper presents a brief overview of measures to mitigate the damaging effects of ice jams and contains a discussion on their application to Canadian rivers. Key words: controlled ice breakup, flood control, ice jams, ice management, river ice.

scholarly journals The geometric parameters of plants on the spur dykes in a selected section of the Odra River

2018 ◽  
Vol 45 ◽  
pp. 00110
Author(s):  
Magda Hudak
Keyword(s):  
Water Surface ◽  
Flow Resistance ◽  
Water Levels ◽  
Flow Conditions ◽  
Plant Structure ◽  
Central Section ◽  
Quantitative Characteristics ◽  
Odra River ◽  
The Impact ◽  
Resistance Coefficients

Spur dykes are structures for regulating rivers. They are designed for medium water levels, when spur dyke tops are above the water surface. In the central section of the Odra River the water level is changeable, and the spur dykes work in different hydrological conditions: as non-submerged and submerged. Correct recognition of the plant structure growing on the spur dykes is of great importance in the context of the subsequent allocation of its measure related to the hydraulic action, among others coefficients of resistance of plant zones and refers mainly to grasses. In hydraulic calculations, it is required to determine the value of flow resistance coefficients. In such a departure, the flow is omitted in the area occupied by vegetation. Therefore, it is necessary to know the quantitative characteristics of overgrowth. Vegetation should be presented in the form of a model reflecting the impact of plants growing on the spur dykes and their impact on the water flow conditions in the river. Literature data are not very numerous and are still awake unsatisfied. The paper presents the results of research on the density of vegetation on the Odra River in the Nowa Sól region.

scholarly journals Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

2021 ◽  
Author(s):  
Fatemehalsadat Madaeni ◽  
Karem Chokmani ◽  
Rachid Lhissou ◽  
Saeid Homayuni ◽  
Yves Gauthier ◽  
...  
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Weather Forecasting ◽  
Multivariate Time Series ◽  
Water Levels ◽  
Time Series Classification ◽  
Ice Jams ◽  
Ice Jam ◽  
Learning Techniques ◽  
Long Short Term Memory

Abstract. In cold regions, ice-jam events result in severe flooding due to a rapid rise in water levels upstream of the jam. These floods threaten human safety and damage properties and infrastructures as the floods resulting from ice-jams are sudden. Hence, the ice-jam prediction tools can give an early warning to increase response time and minimize the possible corresponding damages. However, the ice-jam prediction has always been a challenging problem as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. The ice-jam prediction problem can be considered as a binary multivariate time-series classification. Deep learning techniques have been successfully applied for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied CNN, LSTM, and combined CN-LSTM networks for ice-jam prediction for all the rivers in Quebec. The results show that the CN-LSTM model yields the best results in the validation and generalization with F1 scores of 0.82 and 0.91, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of them further improves classification.

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