[Structral 3.5a] How do you validate your models ? (2024)

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Hello everybody

First, I would suggest that we all start our subject line with a short summary of the application mode and the version we use or to which we relate our discussion, this would help us others to sort things and to give better replies.
Either by mentioning the specific application mode i.e [smsld 3.5a], or at least the global title in the subject line, knowing that the more precise we are more and better replies we could expect.

" Can we all survive with this ? "

Back to my specific question, (by the way Niklas I'm sure you are out here reading our comments, this could also be a subject for your Blog) :
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I'm interesting to know and to exchange means how you verify and validate your structural models, how are you sure that the model is set up correctly?

Aspects such as: no over-constrained BC's, you are asking for sufficient many modes, i.e. not overlooking something, then only you might "solve" and validate your model against your measurements.

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Update: following NAFEMS definition see:

www.nafems.org/downloads/working_groups/amwg/4pp_nafems_asme_vv.pdf

Definitions: for me "verification" of the model is checking that I'm coherent with the physics and I'm respecting the global hypothesis and limitations of the application mode. I start with this even before I solve the model. Thereafter, I "validate" my model that is: I compare my solved model results with my measurements to check that my "validated and solved model" is truly representative of my existing device being modelled.

The COMSOL documentation talks about how to do multiphysics for the Scientists, I'm still searching the specific chapter(s) on model verification methodology for us Engineers ;)

========
My first steps are systematically:

upd: 0) I import generally my CAD geometries from SolidWorks or another CAD tool to have independent volume, mass, centre of gravity (CoG) and inertia tensor calculations.

1) set up the model, the BC's, the material etc, mesh it and solve for "Get Initial Values" (just to fill the matrices).

2) check the mass against my CAD model values by: "Postprocessing Subdomain Integration" insert the density expression "rho_smsld" (or whatever application mode you use, but if you have several you must make a global variable i.e. "rho_all" and set it (Options Expressions Subdomain Expressions) and give the specific densities for the different subdomains, respectively shell, beam ... domains and do not forget any masses added to specific "points", if applicable). Then select all subdomains and "apply".
My tolerance here is typically < 5% difference, but mostly I'm around 1% or lower.

3) check the Inertia tensor against my CAD model values by "Postprocessing Geometric Properties", insert (again) the density expression "rho_smsld" (or whatever ... as above) ).
My tolerance here is typically < 5% difference, but mostly I'm around 1% or lower here too
Often you have to play with the coordinate system in the CAD, as COMSOL does not allow you to choose any coordinate system for the inertia, its all in the default coordinate system, w.r.t. the CoG (would be easy to update though in COMSOL).

4) Check the CoG, (and total Volume and Surfaces if applicable) here too I expect < 1% difference, otherwise I have misplaced a material or my meshing is so gross that I have lost significant items.

Note: the beam and shell modes do no allow you to calculate by COMSOL directly the inertia; you must go back to the subdomain integration and apply the full inertia formulas, just as if you want the results for a specific coordinate system).

5) I run a modal eigenfrequency analysis over the first >6 modes, none should be close to 0 Hz (typically none <1Hz if I expect my model to be fully constrained in 3D)
By the way, an additional question: how many modes to consider, how to, in COMSOL without any mass participation factors available, should one decide that most relevant modes stop at X Hz or after N modes ?

upd: 6) Static load check: by applying an acceleration of 1m/s^2 in x,y,z one should get reaction forces corresponding to the total mass of the model.

7) required for thermal analysis, but I mostly do it systematically: I go to the Subdomain Settings, I select all subdomains and set the thermal expansion to the same value i.e. 1.2E-5 [m/m/K], then in the "Load" tab I set include thermal expansion and put 100°C temperature difference between "Temp" and "Tempref". I solve and check the stress build-up, there should be none if my model is correctly constrained.
Typical tolerance: strain energy < 1e-3 [J].

8) ... more for specific needs, but let’s stop here, just now, and get your feedback.

Now I'm finally ready to solve, analyse my results and to validate my model.

========

What about your ways?

Expecting to read you soon
Ivar

[Structral 3.5a] How do you validate your models ? (2024)

FAQs

How do you validate your model? ›

Models can be validated by comparing output to independent field or experimental data sets that align with the simulated scenario.

How do you validate a project model? ›

The most straightforward way to do this is to exclude some data from the model-building process, and then use those to test the model's accuracy on data it hasn't seen before. This data is called validation data.

How do you validate a simulation model? ›

This validation method consists of (1) developing the model's as- sumptions on theory, observations, general knowledge, and function, (2) validating the model's assumptions where possible by empirically testing them, and (3) comparing (testing) the input-output relationships of the model to the real system.

How do you validate a decision model? ›

Validating a Decision Model
  1. Syntactical Correctness - ensuring the rules comply with the syntax of the specification and the expression language.
  2. Completeness - ensuring that gaps do not exist between the rules.
  3. Overlaps - ensuring that rules do not overlap.

How to validate a model after training? ›

How does cross-validation work?
  1. Split the dataset into two parts: one for training and one for testing.
  2. Train the model on the training set.
  3. Validate the model on the test set.
  4. Repeat steps 1-3 a couple of times. ...
  5. The scores from the different cross-validation techniques are used to measure the efficacy of the model.

Which is used to validate the model? ›

Accuracy, precision, recall, F1-score for classification problems, and Mean Squared Error (MSE) for regression problems are some common metrics used in model validation.

Why is it important to validate a model? ›

Primarily, validation gives a simple sanity check to the modeling team by addressing oversights, providing additional insights, and verifying that performance is as reported. Validation also intents the modeling team to extensively document the building protocol so that the validating team can accurately recreate it.

How do you use model validation? ›

model_validate() : this is very similar to the __init__ method of the model, except it takes a dict or an object rather than keyword arguments. If the object passed cannot be validated, or if it's not a dictionary or instance of the model in question, a ValidationError will be raised.

How you would verify and validate requirements of models? ›

Here are six verification approaches that can be incorporated to identify requirements defects before they are significantly more expensive and time-consuming to fix.
  • 1) Explore Innovative Tests. ...
  • 2) Ensure Appropriate Characteristics. ...
  • 3) Use a Checklist. ...
  • 4) Use the Proper Tools. ...
  • 5) Involve the Entire Team.

How do you validate a conceptual model? ›

The best way to validate a conceptual model is to do good empirical research with your framework and competing frameworks to see which fit reality better. Structural Equation Modeling is great for this if your data fits. This is known as the scientific method.

How to validate a computational model? ›

This is done through a multi-step validation process and two sets of real data (see Figure 3). First the computational model is calibrated against detailed data and then verified against the first set of real data. Then the model is re- verified against a second set of real data.

How do you perform validation? ›

What validation looks like
  1. Mindful listening. Pay attention to what the other person is saying, avoid distractions (including your own emotions), and don't judge. ...
  2. Be aware of your facial expressions. Are you making eye contact? ...
  3. Think about your physical gestures. ...
  4. Demonstrate validating actions.
Sep 6, 2022

How do you test and validate a model? ›

Model Development, Validation and Testing: Step-by-Step
  1. Create the Development, Validation and Testing Data Sets. ...
  2. Use the Training Data Set to Develop Your Model. ...
  3. Compute Statistical Values Identifying the Model Development Performance. ...
  4. Calculate the Model Results to the Data Points in the Validation Data Set.
Dec 15, 2021

How do you validate a process model? ›

How do you validate and test your business process model before implementation?
  1. Identify validation criteria. ...
  2. Review the model with stakeholders. ...
  3. Perform walkthroughs and simulations. ...
  4. Apply formal methods and tools. ...
  5. Document and communicate the results. ...
  6. Repeat and update as needed. ...
  7. Here's what else to consider.
Apr 18, 2023

How do you validate model performance? ›

Best Practices in Model Validation
  1. Choose the right validation technique based on your data and task. ...
  2. Use a diverse set of metrics to evaluate performance. ...
  3. Incorporate interpretability and explainability into your validation process.
  4. Split your data carefully into training, validation, and test sets.
Jan 10, 2024

What does validation mean in Modelling? ›

Model validation is the process that is carried out after Model Training where the trained model is evaluated with a testing data set. The testing data may or may not be a chunk of the same data set from which the training set is procured.

How do you evaluate your model? ›

What are the model evaluation methods?
  1. Accuracy - percentage of the total variables that were correctly classified. ...
  2. False positive rate - how often the model predicts a positive for a value that is actually negative. ...
  3. Precision - percentage of positive cases that were true positives as opposed to false positives.

How you would verify and validate requirements of model? ›

6 Ways to Verify Requirements Specifications
  • 1) Explore Innovative Tests. ...
  • 2) Ensure Appropriate Characteristics. ...
  • 3) Use a Checklist. ...
  • 4) Use the Proper Tools. ...
  • 5) Involve the Entire Team. ...
  • 6) Optimize Your Requirements.

How do you verify a normal model? ›

Graphical methods

An informal approach to testing normality is to compare a histogram of the sample data to a normal probability curve. The empirical distribution of the data (the histogram) should be bell-shaped and resemble the normal distribution. This might be difficult to see if the sample is small.

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