What are the most important steps to validate numerical models of materials and structures?

Validating numerical models necessitates a methodical methodology. First, confirm that the model adequately describes the physics. The numbers should next be validated by benchmarking them against analytical solutions. Then, compare material properties to physical test data. Finally, validate the entire model by comparing predictions to non-calibrated physical experiments. Model fidelity is validated by quantitative indicators such as error and uncertainty estimates. However, because models are approximations, engineering judgement is required. Validation is a continuous process in which models are refined as new data becomes available. The ultimate goal is to acquire enough trust in the model's correctness for its intended function.

Validating numerical models of materials and structures is crucial to ensure their accuracy and reliability. Take your time to define the scope and do your research. Look for experimental data for the material or structure you are studying and gather real-world data for similar models. Make sure your model's assumptions aren't just floating in space—they need to make sense in the real world.

Dentro de la definicion del alcance es importante saber definir las condiciones específicas del proyecto, es decir, las condiciones intrinsecas del mismo: altitud, temperatura, clima, sismologia, etc.

Validation of Numerical methods is a very sufficient way to decrease the experimental test number which leading to the decreasing of the costs.for exact numerical validation we must enter property model and the boundary condition as close as the experimental situation.also the number of the tests must be convincing

- Experimental Data Comparison: Compare model predictions with experimental results to ensure accuracy and reliability. - Sensitivity Analysis: Assess how changes in parameters affect model outputs to gauge robustness. - Verification of Assumptions: Validate underlying assumptions against real-world behaviors for model fidelity. - Peer Review and Benchmarking: Seek input from experts and compare results against established benchmarks to confirm model credibility and performance.

There are various modeling approaches which varies in accuracy and computational speed. Choosing the right method depends on the physics included within the engineering application, and the reason for modeling (can we wait the simulation for days and hours or we want quick results).

Play around with the settings, kind of like turning knobs to see what happens. You want to know which factors really shake things up in your model. Take your time to identify critical parameters and assess their impact on the model's accuracy. Test your model in all sorts of scenarios—different loads, temperatures, material properties, shapes, you name it. You want your model to be like a superhero, handling everything that comes its way.

Pit your model against others. Validating the numerical model against a variety of test cases helps establish the versatility and reliability of the model. Compare the results of your numerical model with those obtained using different simulation software or numerical methods. Consistency across different platforms enhances confidence in the model's predictions. This is like a friendly face-off between different simulation tools. Make sure your model isn't just a one-hit wonder by validating its ability to predict the long-term behavior of materials and structures, especially if it involves time-dependent phenomena. Document the model development process, including assumptions, boundary conditions, and input parameters.

Share your work with others. A good way to do this is by engaging in peer reviews and collaborating with experts in the field. A second opinion is like gold—sometimes they see things you might've missed. Regularly monitor the performance of the numerical model against new experimental data or real-world observations. Update the model as needed to reflect any improvements or changes in understanding. Remember, validation is an ongoing process, and it's essential to continually refine and improve numerical models as more information becomes available or as the understanding of the materials and structures evolves.