You're a funny dude, thanks for the video. Learned about mesh independence. Thanks.

that is a great explanation!!! Congrats/ and how could you deal with stress singularities in complex models?

Very informative.thanks sir👍

Amazing channel

Great videos! On another matter, it would be interesting to hear the pros and cons of the different bow designs currently used in marine architecture.

thank you sir❤

Thanks.

you are a diamond in the forest of Indian guys :))

You should first explain what "mesh" and FEA are.

this is pretty entertaining.

What if we keep getting result value lesser & lesser even beyond the accurate real value with further mesh size decrement. For e.g. 6mm mesh size give the result equal to real result but any lesser than 6mm mesh size give result way different from the real result.

@DatawaveMarineSolutions

3 жыл бұрын

Then it isn't strictly an issue with mesh size. It could be that your mesh restructures below 6 mm, resulting in poor element quality. Or possibly part connectivity issues.

With the "right" size of meshing you can "adjust" the result if you want to :-)

Question is, how exactly do you find this convergence mesh size?

@DatawaveMarineSolutions

4 жыл бұрын

There are various techniques for a mesh convergence study. I still need to create a video about mesh convergence.

I'm really awesome. 😎

Wrong! below 50mm results change a lot. they actually change more than above 50mm. It continuously goes down!

@DatawaveMarineSolutions

5 жыл бұрын

Not what my data shows. I would love to see your data that supports that argument.

@arionfar

5 жыл бұрын

@@DatawaveMarineSolutions Your data is constantly decreasing. how can you judge based on that?

@DatawaveMarineSolutions

5 жыл бұрын

@@arionfar I'm not sure what you mean by constantly decreasing. Can you please refer to a specific graph or part of the video.

@arionfar

5 жыл бұрын

@@DatawaveMarineSolutions I mean the element size verses stress. how did you conclude about the mesh?

@DatawaveMarineSolutions

5 жыл бұрын

@@arionfar As stated in the video, that graph showed that below 50 mm, the data showed very little variation in the Y-axis, which was the Von-Mises stress. I don't normally use statistics to analyse numerical methods, because I don't like to apply numerical methods include any random variables. But for a comparison in this case, the coefficient of variation (COV) for the bottom five data points was only 0.0051. Where the top four data points had a COV of 0.0715, 14x larger. Or if you compare the bottom four data points to the entire data set, the entire data set had a COV of 0.0616, 12x larger than the bottom five data points.

Color correction does matter. Either that, or you were suffering from some severe case of Venus flu.

@DatawaveMarineSolutions

3 жыл бұрын

Yes. That was one of my older videos when I was very new to video editing. (Still new to video editing, but at least I know about color correction now.)