Physics-based Simulations
Physics-based Simulations - Related Videos
09:49
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
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Cited by 4 •
2015
This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes.
12:06
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Cited by 3 •
2023
This article explains how to use simulation-supervised machine learning for analyzing mitochondria morphology in fluorescence microscopy images of fixed cells.
11:05
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Cited by 11 •
2016
The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.
06:54
A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy
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2023
This work presents a three-dimensional virtual simulation experiment for material deformation and failure that provides visualized experimental processes. Through a set of experiments, users can become familiar with the equipment and learn the operations in an immersive and interactive learning environment.
10:52
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Cited by 13 •
2016
A protocol is presented for the characterization of the in-field pedestrian behavior and the simulation of the resulting structural response. Field-tests demonstrate that the in situ identified pacing rate and synchronization rate among the participants constitute an essential input for the simulation and verification of the human-induced loads.
FAQs
Numerical methods convert the governing physical equations into calculations that can be solved over space and time. Their role is to determine how the modeled system’s materials, geometry, forces, energy, and boundary conditions are represented and evaluated. In engineering, this enables analysis of changing conditions and supports predictions about performance before a physical prototype is built.
Boundary conditions specify how the modeled system interacts with its surroundings or with imposed constraints. Because the equations are solved under these conditions, changing them can alter predicted forces, energy behavior, temperatures, fluid responses, or electromagnetic effects. Careful specification therefore helps ensure that the simulated situation corresponds to the engineering design or operating condition being studied.
Each analysis emphasizes a different physical behavior: structural studies examine how designs respond to forces, fluid studies address fluid behavior, thermal studies examine energy and temperature effects, and electromagnetic studies address electromagnetic behavior. Multiphysics analysis considers more than one of these areas together, allowing engineers to investigate systems in which several physical effects influence performance.
The main influencing components are the material description, geometry, applied forces, energy conditions, and boundaries assigned to the model. These inputs establish what physical system is being examined and how it interacts with its environment. Altering them changes the equations’ evaluation and can affect predicted performance, identified failure risks, and comparisons among design alternatives.
An engineering workflow begins by representing the system’s materials and geometry, then assigning relevant forces, energy conditions, and boundaries. Numerical methods are applied to solve the governing equations across space and time. Engineers can then examine predicted performance, identify possible failure risks, and compare alternative designs before deciding which concepts warrant physical prototyping or testing.
Physical validation tests remain important when engineers need to assess how well a computational prediction represents the intended system. Simulations can reduce the number or cost of experiments and help prioritize designs, but they complement rather than eliminate testing. Using both approaches supports comparison between predicted and observed behavior while improving confidence in engineering decisions.