Simulation of Propagating Acoustic Wavefronts with Random Sound Speed

2014 ◽  
Vol 16 (4) ◽  
pp. 1081-1101
Author(s):  
Sheri L. Martinelli
Keyword(s):  
Monte Carlo ◽  
Level Set Method ◽  
Level Set ◽  
High Frequency ◽  
Sound Speed ◽  
Input Data ◽  
Eikonal Equation ◽  
Random Variables ◽  
Full Field ◽  
Sources Of Uncertainty

AbstractA method for simulating acoustic wavefronts propagating under random sound speed conditions is presented. The approach applies a level set method to solve the Eikonal equation of high frequency acoustics for surfaces of constant phase, instead of tracing rays. The Lagrangian nature often makes full-field ray solutions difficult to reconstruct. The level set method captures multiple-valued solutions on a fixed grid. It is straightforward to represent other sources of uncertainty in the input data using this model, which has an advantage over Monte Carlo approaches in that it yields an expression for the solution as a function of random variables.

Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes

2017 ◽  
Vol 64 (7) ◽  
pp. 1579-1591 ◽  
Author(s):  
Thanh Minh Bui ◽  
Alain Coron ◽  
Jonathan Mamou ◽  
Emi Saegusa-Beecroft ◽  
Tadashi Yamaguchi ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Level Set Method ◽  
Level Set ◽  
High Frequency ◽  
Ultrasonic Backscatter ◽  
Transverse Slice

scholarly journals 2D and 3D simulation of grain growth in olivine aggregates using a full field model based on the level set method

2018 ◽  
Vol 283 ◽  
pp. 98-109 ◽  
Author(s):  
Jean Furstoss ◽  
Marc Bernacki ◽  
Clément Ganino ◽  
Carole Petit ◽  
Daniel Pino-Muñoz
Keyword(s):  
Grain Growth ◽  
Level Set Method ◽  
Level Set ◽  
Field Model ◽  
3D Simulation ◽  
Full Field ◽  
Model Based ◽  
2D And 3D

New finite element developments for the full field modeling of microstructural evolutions using the level-set method

2015 ◽  
Vol 109 ◽  
pp. 388-398 ◽  
Author(s):  
Benjamin Scholtes ◽  
Modesar Shakoor ◽  
Amico Settefrati ◽  
Pierre-Olivier Bouchard ◽  
Nathalie Bozzolo ◽  
...  
Keyword(s):  
Finite Element ◽  
Level Set Method ◽  
Level Set ◽  
Full Field ◽  
Field Modeling

scholarly journals Addressing Uncertainties Within Product Redesign for Sustainability: A Function Based Framework

2011 ◽  
Author(s):  
Devarajan Ramanujan ◽  
William Z. Bernstein ◽  
Fu Zhao ◽  
Karthik Ramani
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Design Methodology ◽  
Input Data ◽  
Variation Analysis ◽  
Function Structure ◽  
Sources Of Uncertainty ◽  
Pneumatic Impact ◽  
Input Uncertainties ◽  
Function Connectivity

The Function Impact Method (FIM) is a semi-quantitative eco-design methodology that is targeted specifically towards the early stages of the design process. The FIM allows a designer to predict the environmental impacts associated with a new functional embodiment by extrapolating knowledge from Life cycle assessment (LCA) of similar existing designs. LCA however, is associated with substantial sources of uncertainty. Furthermore, the FIM uses a subjective weighting scheme for representing function-structure affinities. In the authors’ previous work, a Monte-Carlo variation analysis was used to estimate sensitivity of the input data and select the preferred redesign strategy. This paper proposes a method to formalize the input uncertainties in the FIM by modeling the uncertainties present in the results of the LCA’s and the involved function-structure affinities using Info-gap decision theory. The desirability of redesigning a particular function based on the magnitude of its function-connectivity and eco-impact is estimated, and a decision making methodology based on robust satisficing is discussed. This method is applied for making robust redesign decisions with regards to re-designing a pneumatic impact wrench for sustainability.

scholarly journals Learning High-Order Geometric Flow Based on The Level Set Method

2021 ◽  
Author(s):  
Chun Li ◽  
Yunyun Yang ◽  
Hui Liang ◽  
Boying Wu
Keyword(s):  
Neural Networks ◽  
Level Set Method ◽  
Level Set ◽  
Input Data ◽  
Initial Conditions ◽  
High Order ◽  
Geometric Flow ◽  
Set Up ◽  
Almost All ◽  
Physical Phenomena

Abstract Recently, the development of deep learning (DL), which has accomplished unbelievable success in many fields, especially in scientific computational fields. And almost all computational problems and physical phenomena can be described by partial differential equations (PDEs). In this work, we proposed two potential high-order geometric flows. Motivation by the physical-information neural networks (PINNs) and the traditional level set method (LSM), we have integrated deep neural networks (DNNs) and LSM to make the proposed method more robust and efficient. Also, to test the sensitivity of the system to different input data, we set up three sets of initial conditions to test the model. Furthermore, numerical experiments on different input data are implemented to demonstrate the effectiveness and superiority of the proposed models compared to the state-of-the-art approach.

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