Simulation Model Build
Simulation Model Build - Related Videos
14:11
Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics
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Cited by 15 •
2016
Finite Element Analysis is a frequently used tool to investigate the mechanical performance of structures under load. Here we apply its use to modeling the biomechanics of the zebrafish jaw.
07:31
Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
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Cited by 1 •
2023
Membrane lipid diversity in structure and composition is an important contributor to cellular processes and can be a marker of disease. Molecular dynamics simulations allow us to study membranes and their interactions with biomolecules at atomistic resolution. Here, we provide a protocol to build, run, and analyze complex membrane systems.
09:04
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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2022
A simulation model specifically supporting the preliminary design of an electro-variable displacement pump (EVDP) is developed and partially verified by experiments. The control performance, life, reliability, etc., can all be evaluated using the proposed model, which covers the main performance requirements under the EVDP preliminary design task.
05:04
Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound
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2024
The present protocol describes an ideal solution to train novices in the use of point-of-care ultrasound devices for the practical clinical skill of visually assessing distinct individual anatomical vascular conditions prior to and during an intended venous vascular cannulation using point-of-care ultrasound in a patient.
09:54
A Model to Simulate Clinically Relevant Hypoxia in Humans
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Cited by 1 •
2016
Hypoxia simulation in humans has usually been performed by inhaling hypoxic gas mixtures. For this study, apneic divers were used to simulate dynamic hypoxia in humans. Additionally, physiological changes in desaturation and re-saturation kinetics were evaluated with non-invasive tools such as Near-Infrared-Spectroscopy (NIRS) and peripheral oxygenation saturation (SpO2).
FAQs
System boundaries determine which parts of an engineering problem the model represents and which influences remain outside it. Engineers also state assumptions about omitted behavior, idealized conditions, or simplified relationships. These choices control the model’s scope and interpretation: a result may be useful for one design question while being unsuitable for another.
Inputs and governing relationships connect the modeled system to its predicted outputs. Changing an input can alter the simulated response, while changing a relationship or assumption can alter how that response is produced. For this reason, engineers examine whether the selected inputs and relationships represent the behavior relevant to the intended analysis, rather than treating every output as universally applicable.
Calibration and validation serve different purposes in a Simulation Model Build. Calibration uses experimental or operational data to improve agreement between simulated and observed behavior. Validation then assesses whether the resulting model is suitable for its intended purpose. Separating these activities helps engineers avoid treating a better data fit as proof that the model answers every engineering question.
Mathematical, logical, and numerical methods provide different ways to implement the relationships within a Simulation Model Build. The appropriate choice depends on how the engineering system is described and what must be calculated or represented. Selecting a suitable method supports consistent translation from assumptions and inputs to outputs, which directly affects later verification and interpretation.
An effective workflow starts by defining the system boundary, assumptions, inputs, governing relationships, and desired outputs. Engineers then implement those elements computationally, apply verification, and compare results with experimental or operational data when calibration is needed. Validation follows in relation to the intended purpose, connecting the engineering question with the simulation result.
Verification examines whether the implemented computational representation follows the specified assumptions, relationships, inputs, and outputs. It is distinct from validation, which concerns suitability for the intended purpose. This distinction matters when interpreting discrepancies: an implementation may require correction before data-based calibration or purpose-based assessment can meaningfully support an engineering decision.
Engineers use completed models to evaluate designs, test operating conditions, identify performance limits, and compare alternatives before committing resources to physical prototypes or field trials. These comparisons support earlier assessment of possible behavior, while the model’s assumptions, verification status, calibration, and validation determine how confidently its results should guide a particular engineering decision.