Use of physical model and 3D numerical models to design a selective withdrawal intake

2004 ◽  
pp. 283-288
Author(s):  
P Ryan ◽  
F Locher ◽  
S Tu
Keyword(s):  
Physical Model ◽  
Numerical Models ◽  
Selective Withdrawal

scholarly journals Two-Dimensional Numerical Modeling of Flow in Physical Models of Rock Vane and Bendway Weir Configurations

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 458
Author(s):  
Drew C. Baird ◽  
Benjamin Abban ◽  
S. Michael Scurlock ◽  
Steven B. Abt ◽  
Christopher I. Thornton
Keyword(s):  
Numerical Modeling ◽  
Physical Model ◽  
Numerical Models ◽  
Hydraulic Performance ◽  
Physical Models ◽  
Protection Measures ◽  
Design Engineers ◽  
Model Study ◽  
Study Results ◽  
Wide Range

While there are a wide range of design recommendations for using rock vanes and bendway weirs as streambank protection measures, no comprehensive, standard approach is currently available for design engineers to evaluate their hydraulic performance before construction. This study investigates using 2D numerical modeling as an option for predicting the hydraulic performance of rock vane and bendway weir structure designs for streambank protection. We used the Sedimentation and River Hydraulics (SRH)-2D depth-averaged numerical model to simulate flows around rock vane and bendway weir installations that were previously examined as part of a physical model study and that had water surface elevation and velocity observations. Overall, SRH-2D predicted the same general flow patterns as the physical model, but over- and underpredicted the flow velocity in some areas. These over- and underpredictions could be primarily attributed to the assumption of negligible vertical velocities. Nonetheless, the point differences between the predicted and observed velocities generally ranged from 15 to 25%, with some exceptions. The results showed that 2D numerical models could provide adequate insight into the hydraulic performance of rock vanes and bendway weirs. Accordingly, design guidance and implications of the study results are presented for design engineers.

Accelerating Climate Model Computation by Neural Networks: A Comparative Study

2021 ◽  
Author(s):  
Maha Mdini ◽  
Takemasa Miyoshi ◽  
Shigenori Otsuka
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Statistical Model ◽  
Physical Model ◽  
Climate Model ◽  
Numerical Models ◽  
Computation Time ◽  
High Accuracy ◽  
Physical Models ◽  
Computer Resources

<p>In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena based on fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources because of their computational complexity.  In recent years, new approaches to solve these models based on machine learning have been put forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon different ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical Process-Driven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model [SO3] show that HPSM reduces the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM, however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2 days. These first results are promising and imply the possibility of bringing complex physical models into real time systems with lower-cost computer resources in the future.</p>

scholarly journals Zonation of Positively Buoyant Jets Interacting with the Water-Free Surface Quantified by Physical and Numerical Modelling

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1324
Author(s):  
Javier García-Alba ◽  
Javier Bárcena ◽  
Andrés García
Keyword(s):  
Free Surface ◽  
Physical Model ◽  
Numerical Models ◽  
Longitudinal Axis ◽  
Laser Induce Fluorescence ◽  
Buoyant Jets ◽  
Physical Tests ◽  
Manipulation Model ◽  
Semiempirical Models ◽  
The Impact

The evolution of positively buoyant jets was studied with non-intrusive techniques—Particle Image Velocimetry (PIV) and Laser Induce Fluorescence (LIF)—by analyzing four physical tests in their four characteristic zones: momentum dominant zone (jet-like), momentum to buoyancy transition zone (jet to plume), buoyancy dominant zone (plume-like), and lateral dispersion dominant zone. Four configurations were tested modifying the momentum and the buoyancy of the effluent through variations of flow discharge and the thermal gradient with the receiving water body, respectively. The physical model results were used to evaluate the performance of numerical models to describe such flows. Furthermore, a new method to delimitate the four characteristic zones of positively buoyant jets interacting with the water-free surface was proposed using the angle (α) shaped by the tangent of the centerline trajectory and the longitudinal axis. Physical model results showed that the dispersion of mass (concentrations) was always greater than the dispersion of energy (velocity) during the evolution of positively buoyant jets. The semiempirical models (CORJET and VISJET) underestimated the trajectory and overestimated the dilution of positively buoyant jets close to the impact zone with the water-free surface. The computational fluid dynamics (CFD) model (Open Field Operation And Manipulation model (OpenFOAM)) is able to reproduce the behavior of positively buoyant jets for all the proposed zones according to the physical results.

scholarly journals May a Standard VOF Numerical Simulation Adequately Complete Spillway Laboratory Measurements in an Operational Context? The Case of Sa Stria Dam

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1606
Author(s):  
Maria Grazia Badas ◽  
Riccardo Rossi ◽  
Michela Garau
Keyword(s):  
Numerical Simulation ◽  
Physical Model ◽  
Numerical Models ◽  
Relevant Information ◽  
Mitigation Measures ◽  
Laboratory Measurements ◽  
Concrete Gravity Dam ◽  
Flood Discharge ◽  
Vof Model ◽  
Under Construction

The present work aims to assess whether a standard numerical simulation (RANS-VOF model with k − ϵ closure) can adequately model experimental measurements obtained in a dam physical model. The investigation is carried out on the Sa Stria Dam, a roller compacted concrete gravity dam currently under construction in Southern Sardinia (Italy). The original project, for which a physical model was simulated, included a downstream secondary dam. However, due to both economic and technical reasons, the secondary dam may not be built. Hence, it is important to assess the flood discharge routing and energy dissipation in the modified plan. Numerical validation is performed adopting the same laboratory configuration, in presence of the downstream dam, and results show a good agreement with mean experimental variables (i.e., pressure, water level). An alternative configuration without the downstream dam is here numerically tested to understand the conditions of flood discharge and assess whether its results can give relevant information for the design of mitigation measures. The topic is of interest also from a more general perspective. Indeed, the feasibility to integrate numerical models with existing laboratory measurements can be very useful not only for new constructions but also for existing dams, which may need either maintenance or upgrading works, such as in case of flood discharge increment.

scholarly journals A Physical Model Study of the North Shore Wastewater Treatment Plant Pump Station

2021 ◽  
Author(s):  
Maryam Shahab
Keyword(s):  
Wastewater Treatment ◽  
Water Level ◽  
Physical Model ◽  
Flow Condition ◽  
Numerical Models ◽  
Vortex Formation ◽  
Low Flow ◽  
Bell Mouth ◽  
Pump Station ◽  
Potential Vortex

<div>Large scale water pumps with bell mouth intakes have been broadly used by municipal wastewater services to move sewage to wastewater treatment plants. Swirling of the flow when entering the suction bell of the pump intake can cause free-surface and/or sub-surface vortices, resulting in poor pump operation. In order to properly design, the wet well of sewage pump station both physical and numerical models are used to analyze the flow condition entering the pump intake and the associated flow pattern and potential vortex formation. In cooperation with WSP Canada Ltd., a physical modelling study of the First Narrows Sewage Pumping Station was conducted by the Ryerson research team at Ryerson University’s Centre of Urban Innovation Laboratory. During the physical model testing, uneven flow distributions including vortices were observed at the intake chamber under three pump-working conditions. To achieve an even flow distribution with minimal vortices, alternative slot designs at the entrance of the chamber were analyzed.</div><div>Additionally, a tapered design of the suction bell intake was tested for potential vortex formation. The results showed that a reduced area of the entrance slot could distribute the inflow evenly in the chamber. Moreover, no vortex formation around the tapered suction bell was found under (a) a low flow condition of 10 l/s at a water level -5.10 m below datum and (b) a cleaning cycle scenario of 16 l/s at a water level -4.9 m below the datum. However, there was observed water rotations at the backwall side. For tight and intense water rotations, it might cause vortex formation. This study has provided design changes that can smooth the flow and reduce vortices at the bell mouth intakes of the pump intake chamber.</div>

scholarly journals Experimental and Numerical Analysis for Earth-Fill Dam Seepage

2020 ◽  
Vol 12 (6) ◽  
pp. 2490 ◽  
Author(s):  
Ahmed Mohammed Sami Al-Janabi ◽  
Abdul Halim Ghazali ◽  
Yousry Mahmoud Ghazaw ◽  
Haitham Abdulmohsin Afan ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Physical Model ◽  
Numerical Models ◽  
Seepage Flow ◽  
Water Levels ◽  
The Body ◽  
Silty Sand ◽  
Internal Erosion ◽  
Dam Failure ◽  
Seepage Analysis ◽  
Earth Fill

Earth-fill dams are the most common types of dam and the most economical choice. However, they are more vulnerable to internal erosion and piping due to seepage problems that are the main causes of dam failure. In this study, the seepage through earth-fill dams was investigated using physical, mathematical, and numerical models. Results from the three methods revealed that both mathematical calculations using L. Casagrande solutions and the SEEP/W numerical model have a plotted seepage line compatible with the observed seepage line in the physical model. However, when the seepage flow intersected the downstream slope and when piping took place, the use of SEEP/W to calculate the flow rate became useless as it was unable to calculate the volume of water flow in pipes. This was revealed by the big difference in results between physical and numerical models in the first physical model, while the results were compatible in the second physical model when the seepage line stayed within the body of the dam and low compacted soil was adopted. Seepage analysis for seven different configurations of an earth-fill dam was conducted using the SEEP/W model at normal and maximum water levels to find the most appropriate configuration among them. The seven dam configurations consisted of four homogenous dams and three zoned dams. Seepage analysis revealed that if sufficient quantity of silty sand soil is available around the proposed dam location, a homogenous earth-fill dam with a medium drain length of 0.5 m thickness is the best design configuration. Otherwise, a zoned earth-fill dam with a central core and 1:0.5 Horizontal to Vertical ratio (H:V) is preferred.

Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

2015 ◽  
Author(s):  
Niels Hørbye Christiansen ◽  
Per Erlend Torbergsen Voie ◽  
Jan Høgsberg
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Physical Model ◽  
Hybrid Method ◽  
Numerical Models ◽  
Computational Effort ◽  
Computational Power ◽  
Mooring Lines ◽  
Numerical Efficiency

As oil and gas exploration moves to deeper waters the need for methods to conduct reliable model experiments increases. It is difficult obtain useful data by putting a scaled model of an entire mooring line systems into an ocean basin test facility. A way to conduct more realistic experiments is by active truncated models. In these models only the very top part of the system is represented by a physical model whereas the behavior of the part below the truncation is calculated by numerical models and accounted for in the physical model by active actuators applying relevant forces to the physical model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybridmethod combining classical numerical models and artificial neural networks (ANN) can provide a dramatic reduction in computational effort when performing time domain simulation of mooring lines. The hybrid method uses a classical numerical model to generate simulation data, which are then subsequently used to train the ANN. After successful training the ANN is able to take over the simulation at a speed two orders of magnitude faster than conventional numerical methods. The AAN ability to learn and predict the nonlinear relation between a given input and the corresponding output makes the hybrid method tailor made for the active actuators used in the truncated experiments. All the ANN training can be done prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time — without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system where the demand for numerical efficiency and computational power is no longer an issue.

The Effects of Constructing a Barrage across the Tawe Estuary

1984 ◽  
Vol 16 (3-4) ◽  
pp. 463-475
Author(s):  
T W Broyd ◽  
A G Hooper ◽  
R S W M Kingsbury
Keyword(s):  
Physical Model ◽  
Model Calibration ◽  
Numerical Models ◽  
Physical Models ◽  
Biological Information ◽  
Flow Conditions ◽  
Field Surveys ◽  
Physical Chemical ◽  
South Wales ◽  
Remedial Actions

A proposed barrage across the tidal reaches of the R. Tawe, South Wales, was modelled to simulate its effect on the environment. Both computer and physical models were prepared, tested and run for a variety of tidal and fresh water flow conditions. A comprehensive data review identified further information essential for model calibration and information. Field surveys to gather physical, chemical and biological information were performed as part of the overall study. The numerical models predicted hydrodynamic and pollutant behaviour upstream of the barrage and sedimentation effects downstream. The physical model was used for navigational effects and for identifying problems during the construction phase. The study revealed that conditions would change but not significantly deteriorate provided that certain remedial actions were taken.

Data-driven parametrizations in numerical models using data assimilation and machine learning.

2020 ◽  
Author(s):  
Julien Brajard ◽  
Alberto Carrassi ◽  
Marc Bocquet ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Assimilation ◽  
Physical Model ◽  
Hybrid Model ◽  
Numerical Models ◽  
Coarse Graining ◽  
Model Error ◽  
Data Driven ◽  
Model Combining

&lt;p&gt;Can we build a machine learning parametrization in a numerical model using sparse and noisy observations?&lt;/p&gt;&lt;p&gt;In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most of the cases, ML is trained by coarse-graining high-resolution simulations to provide a dense, unnoisy target state (or even the tendency of the model).&lt;/p&gt;&lt;p&gt;Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrization using direct data. Furthermore, we intentionally place ourselves in the realistic scenario of noisy and sparse observations.&lt;/p&gt;&lt;p&gt;The algorithm proposed in this work derives from the algorithm presented by the same authors in https://arxiv.org/abs/2001.01520.The principle is to first apply data assimilation (DA) techniques to estimate the full state of the system from a non-parametrized model, referred hereafter as the physical model. The parametrization term to be estimated is viewed as a model error in the DA system. In a second step, ML is used to define the parametrization, e.g., a predictor of the model error given the state of the system. Finally, the ML system is incorporated within the physical model to produce a hybrid model, combining a physical core with a ML-based parametrization.&lt;/p&gt;&lt;p&gt;The approach is applied to dynamical systems from low to intermediate complexity. The DA component of the proposed approach relies on an ensemble Kalman filter/smoother while the parametrization is represented by a convolutional neural network. &amp;#160;&lt;/p&gt;&lt;p&gt;We show that the hybrid model yields better performance than the physical model in terms of both short-term (forecast skill) and long-term (power spectrum, Lyapunov exponents) properties. Sensitivity to the noise and density of observation is also assessed.&lt;/p&gt;

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