Simulation-based Validation
Simulation-based Validation - Related Videos
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.
05:37
An R-Based Landscape Validation of a Competing Risk Model
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Cited by 2 •
2022
The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of it.
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.
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.
09:17
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Cited by 2 •
2022
The goal of this protocol is to reveal structural dynamics of one-dimensional diffusion of protein along DNA, using a plant transcription factor WRKY domain protein as an exemplary system. To do this, both atomistic and coarse-grained molecular dynamics simulations along with extensive computational samplings have been implemented.
FAQs
Validation becomes meaningful when the simulated response is compared with a defined reference, such as an engineering requirement, an experimental result, or observed real-world performance. Agreement supports confidence in the design or model, while disagreement identifies behavior that needs investigation. This comparison also makes the assessment traceable to a specific expected outcome.
Accurate assumptions, calibration, and independent verification provide complementary safeguards. Assumptions affect how operating conditions and system behavior are represented; calibration helps align the model with available evidence; and independent verification strengthens confidence that the computational implementation has been checked separately. Together, they reduce the risk of treating an unexamined model result as dependable engineering evidence.
Controlled inputs and selected operating conditions determine what the model actually tests. Engineers can examine behavior across operating ranges rather than relying on one scenario, then inspect outputs such as stress, temperature, motion, reliability, or overall system response. Choosing outputs that match the requirement helps reveal whether a design performs acceptably under the conditions being assessed.
A practical workflow starts by identifying the requirement or expected behavior, then using a computational model to reproduce the relevant operating conditions and apply controlled inputs. Engineers evaluate the resulting outputs, compare them with the chosen reference, and investigate discrepancies. Repeating this process across operating ranges can expose design flaws before physical deployment or prototype construction.
Simulation-based validation is especially useful early in development, when engineers need to test alternatives before committing to physical deployment or extensive prototype work. It can reveal potential flaws, evaluate performance over multiple operating ranges, and reduce development time and cost. The approach is therefore valuable when early computational assessment can guide later design and testing decisions.
In engineering, the results can support decisions about whether a design meets safety, performance, and regulatory requirements. The method can also organize evidence around measurable behaviors, including stress, temperature, motion, reliability, and system response. Its value depends on the credibility of the assumptions, calibration, and independent verification accompanying those results.