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已同步 2025-08-07 15:10:13 +08:00
Updated references to line numbers.
Updated description of plots.
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@@ -107,9 +107,9 @@ The bowtie design features three vertical slots (y-direction) in each arm of the
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The first part of the input file (lines 1-7) contains the parameters to optimise, their initial ranges, and fitness function information for the optimisation process. Three parameters representing the resistor values are defined with ranges between 0.1 :math:`\Omega` and 1 :math:`k\Omega`. A pre-built fitness function called ``min_max_value`` is specified with a stopping criterion of 10V/m. Arguments for the ``min_max_value`` function are ``type`` given as ``absmax``, i.e. the maximum absolute values, and the output point in the model that will be used in the optimisation is specified as having the name ``Ex60mm``.
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The first part of the input file (lines 1-6) contains the parameters to optimise, their initial ranges, and fitness function information for the optimisation process. Three parameters representing the resistor values are defined with ranges between 0.1 :math:`\Omega` and 1 :math:`k\Omega`. A pre-built fitness function called ``min_max_value`` is specified with a stopping criterion of 10V/m. Arguments for the ``min_max_value`` function are ``type`` given as ``absmax``, i.e. the maximum absolute values, and the output point in the model that will be used in the optimisation is specified as having the name ``Ex60mm``.
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The next part of the input file (lines 9-93) contains the model. For the most part there is nothing special about the way the model is defined - a mixture of Python, NumPy and functional forms of the input commands (available by importing the module ``input_cmd_funcs``) are used. However, it is worth pointing out how the values of the parameters to optimise are accessed. On line 29 a NumPy array of the values of the resistors is created. The values are accessed using their names as keys to the ``optparams`` dictionary. On line 30 the values of the resistors are converted to conductivities, which are used to create new materials (line 34-35). The resistors are then built by applying the materials to cell edges (e.g. lines 55-62). The output point in the model in specifed with the name ``Ex60mm`` and as having only an ``Ex`` field output (line 42).
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The next part of the input file (lines 8-92) contains the model. For the most part there is nothing special about the way the model is defined - a mixture of Python, NumPy and functional forms of the input commands (available by importing the module ``input_cmd_funcs``) are used. However, it is worth pointing out how the values of the parameters to optimise are accessed. On line 29 a NumPy array of the values of the resistors is created. The values are accessed using their names as keys to the ``optparams`` dictionary. On line 30 the values of the resistors are converted to conductivities, which are used to create new materials (line 34-35). The resistors are then built by applying the materials to cell edges (e.g. lines 55-62). The output point in the model in specifed with the name ``Ex60mm`` and as having only an ``Ex`` field output (line 42).
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The optimisation process is run on the model using the ``--opt-taguchi`` command line flag.
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The optimisation process is run on the model using the ``--opt-taguchi`` command line flag.
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@@ -139,11 +139,11 @@ When the optimisation has completed a summary will be printed showing histories
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.. figure:: images/user_libs/taguchi_fitness_hist.png
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.. figure:: images/user_libs/taguchi_fitness_hist.png
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:width: 600 px
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:width: 600 px
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History of fitness metric (``maxabsvalue``) value.
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History of values of fitness metric (absolute maximum).
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.. figure:: images/user_libs/taguchi_parameter_hist.png
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.. figure:: images/user_libs/taguchi_parameter_hist.png
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:width: 600 px
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:width: 600 px
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History of values of parameters, ``resinner``, ``resmiddle``, and ``resouter``.
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History of values of parameters - ``resinner``, ``resmiddle``, and ``resouter`` (in this case they are all identical).
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The optimisation process terminated after 4 iterations because succcessive fitness values were within 0.1% of one another. A maximum absolute amplitude value of 5.7 V/m was achieved when the three resistors had values of 0.1 :math:`\Omega`.
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The optimisation process terminated after 4 iterations because succcessive fitness values were within 0.1% of one another. A maximum absolute amplitude value of 5.7 V/m was achieved when the three resistors had values of 0.1 :math:`\Omega`.
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