Computational Simulation
Computational Simulation - Related Videos
12:39
Computational Fluid Dynamics Simulations of Blood Flow in a Cerebral Aneurysm
0 Views •
2023
Source: Joseph C. Muskat, Vitaliy L. Rayz, and Craig J. Goergen, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana The objective of this video is to describe recent advancements of computational fluid dynamic (CFD) simulations based on patient- or animal-specific vasculature. Here, subject-based vessel segmentations were created, and, using a combination of open-source and commercial tools, a high-resolution numerical solution was determined within a flow model.
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.
10:23
A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment
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Cited by 3 •
2023
The article describes a protocol to simulate the transient temperature profiles and the coupled spatiotemporal variation of the interstitial fluid pressure following the heating delivered by a dipolar radiofrequency hyperthermia system. The protocol can be used to assess the response of biophysical parameters characterizing the tumor microenvironment to interventional hyperthermia techniques.
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.
07:58
Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis
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Cited by 6 •
2022
In this protocol, the pancreatic islets are reconstructed and analyzed using computational algorithms implemented in a dedicated multiplatform application.
FAQs
The governing equations are first expressed as discrete calculations that a computer can evaluate. Variables such as position, velocity, temperature, or field strength are represented at selected points or states, then updated repeatedly according to a numerical algorithm. This stepwise structure makes time-dependent or strongly interacting systems tractable when solving the equations analytically is difficult.
Initial conditions specify the system’s starting state, while boundary conditions describe how the modeled region interacts with its surroundings or is constrained at its edges. Together, they determine which evolution the governing equations represent. Changing either can produce different predictions, so these conditions must match the physical situation being investigated.
Time stepping advances a model from one simulated moment to the next, allowing researchers to examine evolving behavior such as motion, flow, or changing fields. An iterative solver instead repeatedly updates variables toward a solution of the governing equations or constraints. The appropriate approach depends on whether the main goal is temporal evolution or solving a specified state.
Researchers can compare simulated predictions with measurements of the same physical quantities under corresponding conditions. Agreement provides evidence that the mathematical model and selected parameters represent the system adequately, while differences can motivate changes in the theory, conditions, or numerical treatment. This comparison also supports quantitative analysis and helps guide further experiments.
A typical setup identifies the physical system, selects governing equations, converts them into discrete calculations, and specifies initial and boundary conditions. The calculation then applies time stepping or an iterative solver to update the relevant variables. Researchers examine the resulting predictions, vary selected parameters when appropriate, and compare outcomes with measurements or theoretical expectations.
The approach can address fluid flow, particle motion, fields, thermodynamic behavior, and many-body interactions. These applications span situations in which numerous variables interact or behavior changes across time and scale. Simulations are especially useful when direct experiments are costly or when analytical solutions cannot adequately describe the system of interest.
By changing model parameters and examining the resulting predictions, researchers can investigate how a system responds to different conditions without repeating every physical experiment. This supports theory development, experimental design, and engineering decisions. The resulting comparisons can reveal which assumptions or parameter choices most strongly influence the modeled behavior.