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The combination of configuration parameters which makes the fundamental frequency reach the maximum value is provided.
These indices are highly sensitive to the geometrical design parameters which makes designing cyclones a challenging problem.
Furthermore, the designed RMPT shows excellent structural tunability by optimizing the structural parameters, which makes it user-friendly.
Similar to PID and TID, MFOPID is also linear in its parameters which makes it possible to study all of them in a same framework.
This is especially true when a model has a large number of parameters, which makes a full range of calibration expensive in terms of computing time.
Conventional BEADS makes use of trial-and-error strategies to set up the working parameters, which makes the process slow and inconsistent.
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However, ROP depends on many parameters which make its prediction a complex problem.
The ICE-ORC system is influenced by several unsteady parameters which make it difficult to determine the optimal design parameters.
The results indicate that the new model is much more robust and reliable with less model parameters, which make it useful for industrial applications.
Therefore, artificial neural network as compared to other empirical models is capable to deal with many input meteorological parameters, which make it more accurate and reliable.
In addition, the designed splitter has a tolerance of ±10 nm in its all structure parameters, which make the design not sensitive to the perturbation during the fabrication process.
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