scholarly journals Applying Numerical Models for Water Environments in Watersheds – Case Studies of Tai Lake, Middle and Lower Han River and East Lake in China

10.5772/60119 ◽  
2015 ◽  
Author(s):  
Wanshun Zhang
Keyword(s):  
Case Studies ◽  
Numerical Models ◽  
Han River ◽  
Tai Lake ◽  
East Lake

Droughts, floods and freshwater ecosystems: evaluating climate change impacts and developing adaptation strategies

2011 ◽  
Vol 62 (3) ◽  
pp. 223 ◽  
Author(s):  
Allison Aldous ◽  
James Fitzsimons ◽  
Brian Richter ◽  
Leslie Bach
Keyword(s):  
Climate Change ◽  
Case Studies ◽  
Management Practices ◽  
Climate Adaptation ◽  
Numerical Models ◽  
Climate Change Impacts ◽  
Freshwater Ecosystems ◽  
Adaptation Strategies ◽  
South Eastern ◽  
Eastern Australia

Climate change is expected to have significant impacts on hydrologic regimes and freshwater ecosystems, and yet few basins have adequate numerical models to guide the development of freshwater climate adaptation strategies. Such strategies can build on existing freshwater conservation activities, and incorporate predicted climate change impacts. We illustrate this concept with three case studies. In the Upper Klamath Basin of the western USA, a shift in land management practices would buffer this landscape from a declining snowpack. In the Murray–Darling Basin of south-eastern Australia, identifying the requirements of flood-dependent natural values would better inform the delivery of environmental water in response to reduced runoff and less water. In the Savannah Basin of the south-eastern USA, dam managers are considering technological and engineering upgrades in response to more severe floods and droughts, which would also improve the implementation of recommended environmental flows. Even though the three case studies are in different landscapes, they all contain significant freshwater biodiversity values. These values are threatened by water allocation problems that will be exacerbated by climate change, and yet all provide opportunities for the development of effective climate adaptation strategies.

scholarly journals Recent advances in understanding Antarctic climate evolution

2008 ◽  
Vol 20 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin J. Siegert ◽  
Peter Barrett ◽  
Robert DeConto ◽  
Robert Dunbar ◽  
Colm Ó Cofaigh ◽  
...  
Keyword(s):  
Case Studies ◽  
Numerical Models ◽  
Full Range ◽  
Ice Sheet ◽  
Geological Evidence ◽  
Last Glacial ◽  
Recent Advances ◽  
Climate Evolution

AbstractGeological evidence shows that the ice sheet and climate in Antarctica has changed considerably since the onset of glaciation around 34 million years ago. By analysing this evidence, important information concerning processes responsible for ice sheet growth and decay can be determined, which is vital for appreciating future changes in Antarctica. Geological records are diverse and their analyses require a variety of techniques. They are, however, essential for the establishment of hypotheses regarding past Antarctic changes. Numerical models of ice and climate are useful for testing such hypotheses, and in recent years there have been several advances in our knowledge relating to ice sheet history gained from these tests. This paper documents five case studies, employing a full range of techniques, to exemplify recent insights into Antarctic climate evolution from modelling ice sheet inception in the earliest Oligocene to quantifying Neogene ice sheet fluctuations and process-led investigations of recent (last glacial) changes.

Analytical and Low-Order Numerical Modeling of Ball-to-Ball Contact Friction in Linear Ball Bearings and Ball Screws

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Bo Lin ◽  
Molong Duan ◽  
Chinedum E. Okwudire
Keyword(s):  
Case Studies ◽  
Numerical Models ◽  
Experimental Studies ◽  
Point Contact ◽  
Ball Screw ◽  
Contact Friction ◽  
Ball Bearings ◽  
Friction Behavior ◽  
Ball Contact ◽  
Low Order

Analytical and low-order numerical models are very useful for studying friction behavior of rolling element machine components like ball bearings and ball screws. This is because they provide generalizable insights into friction behavior at much lower computational costs compared with high-order numerical models like finite element analysis (FEA). While analytical and low-order numerical models in the literature are mainly focused on ball-to-groove contact friction, experimental studies have shown that ball-to-ball contact friction is also very important. This is especially true for linear ball bearings/guideways and ball screws which, unlike rotary ball bearings, do not typically make use of caged balls to prevent ball-to-ball contact. Therefore, in this paper, low-order numerical models for ball-to-ball contact friction in linear ball bearings and ball screws are developed. Furthermore, an analytical model for ball-to-ball contact friction in four-point contact linear ball bearing is derived by making simplifications to its low-order numerical model. Compared with ball-to-ball friction predictions from FEA models developed in ansys, the proposed numerical models are shown in case studies to be accurate within 7%, while computing at least three orders of magnitude faster. Moreover, case studies are used to demonstrate how the developed models can be used in practice, e.g., for the mitigation of ball-to-ball contact friction in linear ball bearings and the prediction of friction variation during the operation of a ball screw.

scholarly journals STABILITY CONSIDERATIONS AND CASE STUDIES OF SUBMERGED STRUCTURES CONSTRUCTED FROM LARGE, SAND FILLED, GEOTEXTILE CONTAINERS

2011 ◽  
Vol 1 (32) ◽  
pp. 60
Author(s):  
Jose Carlos Borrero ◽  
Shaw T Mead ◽  
Andrew Moores
Keyword(s):  
Case Studies ◽  
Wave Attenuation ◽  
Numerical Models ◽  
Cost Effective ◽  
Offshore Structures ◽  
Lessons Learned ◽  
Shore Protection ◽  
Submerged Breakwater ◽  
Cost Effective Method ◽  
Design Guidance

The use of large, sand filled geotextile containers for the construction of offshore structures is gaining acceptance as a cost effective method of submerged breakwater or reef construction. This method of construction is partuicularly well suited for multipurpose structures where the intent is to provide breakwater-like wave attenuation and shore protection while at the same time providing recreational amenities such as ecological enhancment or surfing. Because the materials and methods used in these structures is relatively new, design guidance is lacking. This paper discusses the general stability considerations for submerged structures constructed from sand filled geotextile containers (SFC’s) and describes a method of assessing container stability through the use of numerical models and empirically derived stability formulae. The paper also describes lessons learned from case studies of four very differnt examples of this type of construction.

scholarly journals Performance improvement with parallel numerical model simulations in the field of urban water management

2013 ◽  
Vol 16 (2) ◽  
pp. 477-486 ◽  
Author(s):  
Michael Mair ◽  
Robert Sitzenfrei ◽  
Manfred Kleidorfer ◽  
Wolfgang Rauch
Keyword(s):  
Water Management ◽  
Case Studies ◽  
Performance Improvement ◽  
Real World ◽  
Numerical Models ◽  
Model Analysis ◽  
Urban Water ◽  
Urban Water Management ◽  
Analysis Software ◽  
Modeling Software

Numerical models are used to enhance the understanding of the behavior of real world systems. With increasing complexity of numerical models and their applications, there is a need of more computational power. State of the art processors contain many cores on one single chip. As such, new programming techniques are required if all these cores are to be utilized during model simulation runs. This manuscript reviews the runtime and speedup behavior of parallel model analysis software (e.g. Calimero and Achilles) applied to simulation tools for urban water management (e.g. CityDrain3, EPANET2, SWMM5, par-SWMM). The potential of using a parallel programming environment for ‘coordinating’ tasks of multiple runs of commonly used modeling software is analyzed. This is especially interesting as the modeling software itself can be implanted sequentially or parallel. Performance tests are performed on a set of real-world case studies. Additionally, a benchmark set of 2,280 virtual case studies is used to investigate performance improvement in relation to the size of the system. It was found that speedup depends on the system size and the time spent in critical code sections with increasing number of used cores. Applying parallelism only at the level of the model analysis software performs best.

scholarly journals Probabilistic-Based Assessment of Liquefaction-Induced Damage with Analytical Fragility Curves

Geosciences ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 315
Author(s):  
Davide Forcellini
Keyword(s):  
Case Studies ◽  
Numerical Models ◽  
Fragility Curves ◽  
Free Field ◽  
Historical Earthquakes ◽  
Soil Liquefaction ◽  
Lateral Spread ◽  
Shear Behaviour ◽  
Cyclic Shear ◽  
Lp Estimation

Soil liquefaction may cause severe damages to structures mainly in terms of lateral spread and settlements, as documented during historical earthquakes. Liquefaction-potential (LP) estimation has become an important issue in seismic assessment, and this paper aims to propose a new methodology based on fragility curves. LP curves were developed and applied to two case studies performed with 3D numerical models applying Opensees. Nonlinear hysteretic materials and advanced plasticity models were used to reproduce the high nonlinear mechanisms of liquefaction, such as strong dilation tendency and cyclic shear behaviour. LP curves were applied to compare the results of the performed free field (FF) and soil–structure interaction (SSI) case studies.

scholarly journals Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

2021 ◽  
Author(s):  
Roberto Bentivoglio ◽  
Elvin Isufi ◽  
Sebastian Nicolaas Jonkman ◽  
Riccardo Taormina
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Case Studies ◽  
Flood Risk ◽  
Numerical Models ◽  
Flood Mapping ◽  
Future Research ◽  
Learning Models ◽  
Research Directions ◽  
Future Research Directions

Abstract. Deep Learning techniques have been increasingly used in flood risk management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 45 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Traditional models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies and sites. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Neural operators can also speed up numerical models while preserving the underlying physical equations and could thus be used for reliable real-time warning. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes.

Engaging the Community in the Development of Physics for NWP Models

2020 ◽  
Author(s):  
Ligia Bernardet ◽  
Grant Firl ◽  
Dom Heinzeller ◽  
Laurie Carson ◽  
Xia Sun ◽  
...  
Keyword(s):  
Case Studies ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast System ◽  
Linear Processes ◽  
Column Model ◽  
Model Code ◽  
Single Column ◽  
Single Column Model ◽  
The U.S

<p>Contributions from the community (national laboratories, universities, and private companies) have the potential to improve operational numerical models and translate to better forecasts. However, researchers often have difficulty learning about the most pressing forecast biases that need to be addressed, running operational models, and funneling their developments onto the research-to-operations process. Common impediments are lack of access to current and portable model code, insufficient documentation and support, difficulty in finding information about forecast shortcomings and systematic errors, and unclear processes to contribute code back to operational centers. </p><p>The U.S. Developmental Testbed Center (DTC) has the mission of connecting the research and operational Numerical Weather Prediction (NWP) communities. Specifically in the field of model physics, the DTC works on several fronts to foster the engagement of community developers with the Unified Forecast System (UFS) employed by the U.S. National Oceanic and Atmospheric Administration (NOAA).  As a foundational step, the UFS’ operational and developmental physical parameterizations and suites are now publicly distributed through the Common Community Physics Package (CCPP), a library of physics schemes and associated framework that enables their use with various models. The CCPP can be used for physics experimentation and development in a hierarchical fashion, with hosts ranging in complexity from a single-column model driven by experimental case studies to fully coupled Earth system models. This hierarchical capability facilitates the isolation of non-linear processes prior to their integration in complex systems. </p><p>The first public release of a NOAA Unified Forecast System (UFS) application is expected for February 2020, with a focus on the Medium-Range Weather Application. This global configuration uses the CCPP and will be documented and supported to the community. To accompany future public releases, the DTC is creating a catalog of case studies to exemplify the most prominent model biases identified by the US National Weather Service. The case studies will be made available to the community, who will be able to rerun the cases, to test their innovations and document model improvements. </p><p>In this poster we will summarize how we are using the UFS public release, the single-column model, the CCPP, and the incipient catalog of code studies to create stronger connections among the groups that diagnose, develop, and produce predictions using physics suites.</p>

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