Physically Based Method for Cloth Deformation

2011 ◽  
Author(s):  
Shi Min ◽  
Wang Zhaoqi ◽  
Mao Tianlu
Keyword(s):  
Physically Based

A physically based model for the isothermal martensitic transformation in a maraging steel

2003 ◽  
Vol 112 ◽  
pp. 437-440 ◽  
Author(s):  
S. O. Kruijver ◽  
H. S. Blaauw ◽  
J. Beyer ◽  
J. Post
Keyword(s):  
Martensitic Transformation ◽  
Maraging Steel ◽  
Physically Based Model ◽  
Physically Based ◽  
Isothermal Martensitic Transformation

DETERMINISTIC APPROACH TO WATERSHED MODELING

1971 ◽  
Vol 2 (3) ◽  
pp. 146-166 ◽  
Author(s):  
DAVID A. WOOLHISER
Keyword(s):  
Boundary Conditions ◽  
Watershed Modeling ◽  
Deterministic Approach ◽  
Mass Energy ◽  
Deterministic Models ◽  
Conservation Of Mass ◽  
Hydrologic Process ◽  
Theoretical Structure ◽  
Physically Based ◽  
Initial And Boundary Conditions

Physically-based, deterministic models, are considered in this paper. Physically-based, in that the models have a theoretical structure based primarily on the laws of conservation of mass, energy, or momentum; deterministic in the sense that when initial and boundary conditions and inputs are specified, the output is known with certainty. This type of model attempts to describe the structure of a particular hydrologic process and is therefore helpful in predicting what will happen when some change occurs in the system.

scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Joint Probability ◽  
North Cascades ◽  
Physically Based Model ◽  
New Approach ◽  
The North ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

scholarly journals Blowing and drifting snow in Alpine terrain: numerical simulation and related field measurements

1998 ◽  
Vol 26 ◽  
pp. 174-178 ◽  
Author(s):  
Peter Gauer
Keyword(s):  
Numerical Simulation ◽  
Numerical Model ◽  
Three Dimensional ◽  
Field Measurements ◽  
Blowing Snow ◽  
Related Field ◽  
Drifting Snow ◽  
Physically Based ◽  
Snow Transport

A physically based numerical model of drifting and blowing snow in three-dimensional terrain is developed. The model includes snow transport by saltation and suspension. As an example, a numerical simulation for an Alpine ridge is presented and compared with field measurements.

Multi-Criteria and Time Dependent Sensitivity Analysis of an Event-Oriented and Physically-Based Distributed Sediment and Runoff Model

2021 ◽  
pp. 126268
Author(s):  
Menberu B. Meles ◽  
Dave C. Goodrich ◽  
Hoshin V. Gupta ◽  
I. Shea Burns ◽  
Carl L. Unkrich ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Time Dependent ◽  
Physically Based ◽  
Runoff Model

scholarly journals Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Luca Schilirò ◽  
José Cepeda ◽  
Graziella Devoli ◽  
Luca Piciullo
Keyword(s):  
Numerical Simulations ◽  
Regional Scale ◽  
Shallow Landslides ◽  
South Eastern ◽  
Physically Based ◽  
Triggering Conditions ◽  
Cover Thickness ◽  
Regional Analyses ◽  
Factor Maps ◽  
Eastern Norway

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

Physically‐based Book Simulation with Freeform Developable Surfaces

2021 ◽  
Vol 40 (2) ◽  
pp. 449-460
Author(s):  
Thomas Wolf ◽  
Victor Cornillère ◽  
Olga Sorkine-Hornung
Keyword(s):  
Developable Surfaces ◽  
Physically Based

scholarly journals Physically Based Soap Bubble Synthesis for VR

2021 ◽  
Vol 11 (7) ◽  
pp. 3090
Author(s):  
Sangwook Yoo ◽  
Cheongho Lee ◽  
Seongah Chin
Keyword(s):  
Virtual Reality ◽  
Virtual Space ◽  
Soap Bubble ◽  
Temporal Constraints ◽  
High Expectations ◽  
Soap Bubbles ◽  
Physical Motion ◽  
Head Mounted Display ◽  
Interesting Topic ◽  
Physically Based

To experience a real soap bubble show, materials and tools are required, as are skilled performers who produce the show. However, in a virtual space where spatial and temporal constraints do not exist, bubble art can be performed without real materials and tools to give a sense of immersion. For this, the realistic expression of soap bubbles is an interesting topic for virtual reality (VR). However, the current performance of VR soap bubbles is not satisfying the high expectations of users. Therefore, in this study, we propose a physically based approach for reproducing the shape of the bubble by calculating the measured parameters required for bubble modeling and the physical motion of bubbles. In addition, we applied the change in the flow of the surface of the soap bubble measured in practice to the VR rendering. To improve users’ VR experience, we propose that they should experience a bubble show in a VR HMD (Head Mounted Display) environment.

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