Predictive Simulation
Predictive Simulation - Related Videos
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
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Cited by 79 •
2011
Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.
11:05
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Cited by 10 •
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.
08:45
Study of Siphon Breaker Experiment and Simulation for a Research Reactor
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Cited by 2 •
2017
The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.
10:34
Designing and Implementing Nervous System Simulations on LEGO Robots
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Cited by 7 •
2013
An approach to neural network modeling on the LEGO Mindstorms robotics platform is presented. The method provides a simulation tool for invertebrate neuroscience research in both the research lab and the classroom. This technique enables the investigation of biomimetic robot control principles.
02:27
Predicting Molecular Geometry
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2020
VSEPR Theory for Determination of Electron Pair Geometries The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures: Write the Lewis structure of the molecule or polyatomic ion. Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density. Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
FAQs
The governing physics determine which relationships the model uses to represent system behavior, while material properties, geometry, and boundary conditions determine how those relationships apply to a particular design. Changing any of these inputs can alter predicted stress, temperature, motion, or fluid flow. Careful representation of the physical system therefore directly affects whether simulation results support sound engineering decisions.
Boundary conditions specify how the modeled system interacts with its surroundings or operating environment. They help define the conditions under which the equations or numerical model are solved, such as the situation associated with a particular operating scenario. If those conditions do not match the intended use, predicted behavior may not provide a reliable basis for comparing designs or identifying risks.
Depending on the modeled system and selected conditions, predictive simulation can estimate quantities such as stress, temperature, motion, or fluid flow. Engineers can examine these results across operating scenarios to identify potential failure risks and compare alternatives before building them. This makes the approach useful for investigating how a design may perform without relying only on completed physical prototypes.
Engineers first represent the relevant physics, material properties, geometry, and boundary conditions in equations or a numerical model. They then solve the model over time or across selected operating scenarios and examine predicted quantities such as stress or temperature. The results can be used to compare design alternatives, identify risks, and guide later validation against experiments or field data.
Validation involves comparing simulation results with experiments or field data. Agreement provides evidence that the model represents the relevant physical behavior under the conditions being studied, while discrepancies indicate that the model or its inputs may need attention. Once validated, the simulations can support safer design choices and more efficient development with greater confidence.
Engineers use it when they need to evaluate design alternatives, investigate failure risks, or optimize performance before construction. By estimating system behavior across specified conditions, the method can reduce prototyping costs and shorten development efforts. Its applications include aerospace, automotive engineering, energy systems, and manufacturing, where predictions may address stress, temperature, motion, or fluid flow.