Commercial Simulation Software
Commercial Simulation Software - Related Videos
08:22
Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software
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Cited by 4 •
2018
This protocol describes a calibration-free approach for quantifying protein homo-oligomerization in vitro based on fluorescence fluctuation spectroscopy using commercial light scanning microscopy. The correct acquisition settings and analysis methods are shown.
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.
11:03
Pouring and Running a Protein Gel by reusing Commercial Cassettes
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Cited by 2 •
2012
Our protocol demonstrates how to pour multiple protein gels at a time by recycling Invitrogen Nupage Novex minigel cassettes, and inexpensive materials purchased at a home improvement store. This economical and streamlined method includes a way to store the gels at 4°C for a few weeks. By re-using the plastic gel cassettes from commercially available gels, labs that run frequent protein gels can save significant costs and help the environment.
09:46
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Cited by 48 •
2012
An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.
13:44
Simulation, Fabrication and Characterization of THz Metamaterial Absorbers
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Cited by 6 •
2012
This protocol outlines the simulation, fabrication and characterization of THz metamaterial absorbers. Such absorbers, when coupled with an appropriate sensor, have applications in THz imaging and spectroscopy.
FAQs
Numerical solvers discretize the governing equations represented by an engineering model, transforming continuous physical behavior into calculations that a computer can evaluate. The resulting solution estimates quantities such as stress, temperature, flow, motion, or electromagnetic fields throughout the modeled system. This computational step allows engineers to examine internal behavior that may be difficult to observe directly during early design work.
Mesh quality and model assumptions determine how faithfully the computational model represents the real system. A poorly resolved mesh or an unsuitable simplifying assumption can change predicted stresses, temperatures, flows, or other outputs. Engineers therefore interpret results in relation to the model’s limitations rather than treating numerical values as automatically accurate, particularly when making design or failure-risk decisions.
Materials, loads, and boundary conditions provide the physical inputs that constrain a computational model. Material descriptions influence how a product or system responds, while loads represent applied influences and boundary conditions describe how the modeled domain interacts with its surroundings. If these inputs do not reflect the intended operating situation, the calculated behavior may not provide a reliable basis for comparing designs.
Simulation extends engineering analysis before or alongside physical testing, but it does not replace independent evaluation. Engineers can compare computational predictions with experiments and use analytical checks to examine whether the model behaves plausibly. Agreement supports confidence in the analysis, while discrepancies may reveal inaccurate inputs, assumptions, or numerical representation. This combined approach strengthens decisions about performance and failure risk.
A typical setup begins by representing the product, system, or process with geometry, material information, loads, and boundary conditions. The software then forms a computational model, discretizes the governing equations, and applies a numerical solver. Engineers review outputs such as stress, temperature, flow, motion, or electromagnetic fields, then use those results to compare alternatives or investigate possible failure.
Engineers can use these tools before physical testing or deployment to evaluate candidate designs and identify potential risks earlier. The approach is valuable when teams need to compare performance, explore alternatives, reduce prototype iterations, or examine behavior across mechanical, civil, aerospace, and chemical engineering work. Its conclusions are most useful when the computational predictions remain connected to experiments and analytical reasoning.
Calculated results can help engineers compare designs, identify failure risks, and assess performance before committing to deployment or repeated prototyping. Field values such as stress, temperature, flow, motion, or electromagnetic behavior provide evidence for evaluating how alternatives respond under specified conditions. Because predictions depend on assumptions and input quality, results support decisions most effectively when interpreted with validation and analytical checks.